diff --git a/.gitkeep b/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/.ipynb_checkpoints/model fine-tunning-checkpoint.ipynb b/.ipynb_checkpoints/model fine-tunning-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e4a7799c3f1ce7d43640ccb6852e3949d852c70c --- /dev/null +++ b/.ipynb_checkpoints/model fine-tunning-checkpoint.ipynb @@ -0,0 +1,923 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "96d10e25-bc25-4058-baaa-a74ab15af266", + "metadata": {}, + "source": [ + "# Kaggle API Setup for TensorFlow and Keras NLP Project\n", + "Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models.\n", + "\n", + "## Introduction\n", + "In this notebook, we will prepare the environment to download and utilize the Gemma model in a TensorFlow and Keras NLP project. We will begin by importing the necessary libraries, followed by configuring the Kaggle API credentials to enable seamless access to the required datasets.\n", + "\n", + "## 1. Importing Required Libraries\n", + "To start, we will import the essential libraries for this project, including TensorFlow, Keras, and Keras NLP, which are crucial for building and deploying NLP models.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "996572a0-edb7-49c9-89cc-27ad9b5a42d2", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import required libraries\n", + "\n", + "import os\n", + "import tensorflow as tf\n", + "import keras_nlp\n", + "import keras\n", + "import json\n", + "\n", + "# Ignore Warnings\n", + "from silence_tensorflow import silence_tensorflow\n", + "silence_tensorflow()\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "29ec8457-c4b5-4017-b190-812e0b2428f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Set Kaggle API credentials\n", + "\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"rogerkorantenng\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"9a33b6e88bcb6058b1281d777fa6808d\"" + ] + }, + { + "cell_type": "markdown", + "id": "0145d6ca-9424-4c32-9e87-7cbac86cf65f", + "metadata": {}, + "source": [ + "## 2. Building and Compiling the Gemma Model\n", + "\n", + "In this section, we will build and compile the Gemma model, a language model designed for natural language processing (NLP) tasks. The process involves several key steps: loading the pre-trained model, enabling fine-tuning with LoRA (Low-Rank Adaptation), configuring the input sequence length, setting up the optimizer, and compiling the model for training.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e8045579-6e77-4879-b630-d1a9d2550a4d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_compiled_model():\n", + " gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\n", + " gemma_lm.summary()\n", + "\n", + " gemma_lm.backbone.enable_lora(rank=4)\n", + " gemma_lm.summary()\n", + " \n", + " # Set the sequence length to 128 before using the model.\n", + " gemma_lm.preprocessor.sequence_length = 256\n", + " \n", + " # Use AdamW (a common optimizer for transformer models).\n", + " optimizer = keras.optimizers.AdamW(\n", + " learning_rate=5e-5,\n", + " weight_decay=0.01,\n", + " )\n", + " \n", + " # Exclude layernorm and bias terms from decay.\n", + " optimizer.exclude_from_weight_decay(var_names=[\"bias\", \"scale\"])\n", + " \n", + " gemma_lm.compile(\n", + " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " optimizer=optimizer,\n", + " weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\n", + " )\n", + "\n", + " \n", + " return gemma_lm" + ] + }, + { + "cell_type": "markdown", + "id": "6475baba-ce54-40ec-a484-22629936c845", + "metadata": {}, + "source": [ + "## 3. Loading and Processing the Dataset\n", + "\n", + "In this section, we will define a function to load and process a JSON dataset. The dataset is read line by line, and each line is parsed and formatted according to the required structure. The function returns a list of formatted examples that can be used for training or analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "238f65b1-75c2-42a1-abd3-b4d8c7c36799", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dataset():\n", + " # Initialize an empty list to hold the processed data.\n", + " data = []\n", + " \n", + " # Open and read the JSON file line by line.\n", + " with open('/project/data/combined_dataset.json') as file:\n", + " for line in file:\n", + " features = json.loads(line)\n", + " \n", + " # Filter out examples without \"Context\".\n", + " if not features.get(\"Context\"):\n", + " continue\n", + " \n", + " # Format the example as a string.\n", + " template = \"Instruction:\\n{Context}\\n\\nResponse:\\n{Response}\"\n", + " formatted_example = template.format(**features)\n", + " \n", + " # Append the formatted example to the data list.\n", + " data.append(formatted_example)\n", + " \n", + " return data " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d589e374-cc82-4132-9613-23ee064aa10e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-30 16:13:39.953235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13775 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:18:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.954747: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13775 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:19:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.956103: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13775 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:35:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.957459: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13775 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:36:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.958812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:4 with 13775 MB memory: -> device: 4, name: Tesla T4, pci bus id: 0000:e7:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.960166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:5 with 13775 MB memory: -> device: 5, name: Tesla T4, pci bus id: 0000:e8:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.961521: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:6 with 13775 MB memory: -> device: 6, name: Tesla T4, pci bus id: 0000:f4:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.962850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:7 with 13775 MB memory: -> device: 7, name: Tesla T4, pci bus id: 0000:f5:00.0, compute capability: 7.5\n", + "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n" + ] + }, + { + "data": { + "text/html": [ + "
Preprocessor: \"gemma_causal_lm_preprocessor\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Tokenizer (type)                                   ┃                                             Vocab # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │\n",
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+       "┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │\n",
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+       "│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │\n",
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+       "│ gemma_backbone                │ (None, None, 2048)        │   2,506,172,416 │ padding_mask[0][0],        │\n",
+       "│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_embedding               │ (None, None, 256000)      │     524,288,000 │ gemma_backbone[0][0]       │\n",
+       "│ (ReversibleEmbedding)         │                           │                 │                            │\n",
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Preprocessor: \"gemma_causal_lm_preprocessor\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Tokenizer (type)                                   ┃                                             Vocab # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │\n",
+       "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
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Model: \"gemma_causal_lm\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ gemma_backbone                │ (None, None, 2048)        │   2,507,536,384 │ padding_mask[0][0],        │\n",
+       "│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_embedding               │ (None, None, 256000)      │     524,288,000 │ gemma_backbone[0][0]       │\n",
+       "│ (ReversibleEmbedding)         │                           │                 │                            │\n",
+       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+       "
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 Total params: 2,507,536,384 (9.34 GB)\n",
+       "
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 Trainable params: 1,363,968 (5.20 MB)\n",
+       "
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\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,506,172,416\u001b[0m (9.34 GB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get Model and Compile\n", + "model = get_compiled_model()\n", + "\n", + "# Get the dataset outside the strategy scope.\n", + "data = get_dataset()" + ] + }, + { + "cell_type": "markdown", + "id": "50106081-cd9e-4246-974e-a4e7db99be97", + "metadata": {}, + "source": [ + "## 4. Defining the Prompt Template and Generating Responses\n", + "\n", + "In this section, we define a template for creating prompts that the language model will use to generate responses. The template includes placeholders for an 'instruction' and a 'response'. We then format this template with actual data to create a complete prompt. Finally, the prompt is passed to the language model to generate a response.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "47a8c8da-b73a-4684-9199-176b4fa9bd3d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-30 16:13:57.828298: E tensorflow/core/util/util.cc:131] oneDNN supports DT_INT64 only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725034446.797146 18073 service.cc:146] XLA service 0x5a7be0cb31d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1725034446.797176 18073 service.cc:154] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797180 18073 service.cc:154] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797182 18073 service.cc:154] StreamExecutor device (2): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797185 18073 service.cc:154] StreamExecutor device (3): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797188 18073 service.cc:154] StreamExecutor device (4): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797190 18073 service.cc:154] StreamExecutor device (5): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797193 18073 service.cc:154] StreamExecutor device (6): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797196 18073 service.cc:154] StreamExecutor device (7): Tesla T4, Compute Capability 7.5\n", + "2024-08-30 16:14:07.351952: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-08-30 16:14:09.094695: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8905\n", + "I0000 00:00:1725034456.429832 18073 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instruction:\n", + "I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\n", + " I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\n", + " How can I change my feeling of being worthless to everyone?\n", + "\n", + "Response:\n", + "I'm sorry to hear that you're going through some things. I'm not sure what you mean by \"I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\" I'm not sure what you mean by \"I've never tried or contemplated suicide.\" I'm not sure what you mean by \"I've always wanted to fix my issues, but I never get around to it.\" I'm not sure what you mean by \"How can I change my feeling of being worthless to everyone?\"\n", + "\n", + "I'm not sure what you mean by \"I'm sorry to hear that you're going through some things.\"\n", + "\n", + "I'm not sure what you mean by \"I'm not sure what you mean by 'I barely sleep\n" + ] + } + ], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(model.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "markdown", + "id": "9784a448-b7a2-40e7-8162-3caa3ac64d22", + "metadata": {}, + "source": [ + "## 5. Model Fine Tunning\n", + "\n", + "In this section, we compile the model, prepare the dataset, and then train the model using the data. We will walk through the steps of obtaining the compiled model, loading the dataset, and fitting the model to the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9355a347-5893-4f94-bff8-09cb126d818d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/40\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1725034506.217331 18546 assert_op.cc:38] Ignoring Assert operator compile_loss/sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert\n", + "2024-08-30 16:15:23.759806: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'loop_add_subtract_fusion_2', 220 bytes spill stores, 220 bytes spill loads\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2455s\u001b[0m 682ms/step - loss: 2.0513 - sparse_categorical_accuracy: 0.4572\n", + "Epoch 2/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.9421 - sparse_categorical_accuracy: 0.4760\n", + "Epoch 3/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.8639 - sparse_categorical_accuracy: 0.4929\n", + "Epoch 4/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.7974 - sparse_categorical_accuracy: 0.5083\n", + "Epoch 5/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 683ms/step - loss: 1.7301 - sparse_categorical_accuracy: 0.5239\n", + "Epoch 6/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.6699 - sparse_categorical_accuracy: 0.5380\n", + "Epoch 7/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.6151 - sparse_categorical_accuracy: 0.5512\n", + "Epoch 8/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.5641 - sparse_categorical_accuracy: 0.5633\n", + "Epoch 9/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2393s\u001b[0m 681ms/step - loss: 1.5194 - sparse_categorical_accuracy: 0.5776\n", + "Epoch 10/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2392s\u001b[0m 681ms/step - loss: 1.4795 - sparse_categorical_accuracy: 0.5869\n", + "Epoch 11/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2393s\u001b[0m 681ms/step - loss: 1.4477 - sparse_categorical_accuracy: 0.5949\n", + "Epoch 12/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2395s\u001b[0m 682ms/step - loss: 1.4191 - sparse_categorical_accuracy: 0.6025\n", + "Epoch 13/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 683ms/step - loss: 1.3948 - sparse_categorical_accuracy: 0.6080\n", + "Epoch 14/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.3707 - sparse_categorical_accuracy: 0.6142\n", + "Epoch 15/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.3508 - sparse_categorical_accuracy: 0.6195\n", + "Epoch 16/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.3308 - sparse_categorical_accuracy: 0.6236\n", + "Epoch 17/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.3068 - sparse_categorical_accuracy: 0.6303\n", + "Epoch 18/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.2879 - sparse_categorical_accuracy: 0.6350\n", + "Epoch 19/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2401s\u001b[0m 683ms/step - loss: 1.2676 - sparse_categorical_accuracy: 0.6395\n", + "Epoch 20/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.2474 - sparse_categorical_accuracy: 0.6444\n", + "Epoch 21/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2395s\u001b[0m 682ms/step - loss: 1.2283 - sparse_categorical_accuracy: 0.6491\n", + "Epoch 22/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.2086 - sparse_categorical_accuracy: 0.6543\n", + "Epoch 23/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.1896 - sparse_categorical_accuracy: 0.6593\n", + "Epoch 24/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.1706 - sparse_categorical_accuracy: 0.6644\n", + "Epoch 25/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.1508 - sparse_categorical_accuracy: 0.6695\n", + "Epoch 26/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2401s\u001b[0m 683ms/step - loss: 1.1322 - sparse_categorical_accuracy: 0.6744\n", + "Epoch 27/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.1152 - sparse_categorical_accuracy: 0.6789\n", + "Epoch 28/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.0921 - sparse_categorical_accuracy: 0.6851\n", + "Epoch 29/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.0791 - sparse_categorical_accuracy: 0.6881\n", + "Epoch 30/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.0581 - sparse_categorical_accuracy: 0.6941\n", + "Epoch 31/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.0382 - sparse_categorical_accuracy: 0.6994\n", + "Epoch 32/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2401s\u001b[0m 684ms/step - loss: 1.0208 - sparse_categorical_accuracy: 0.7045\n", + "Epoch 33/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 1.0037 - sparse_categorical_accuracy: 0.7089\n", + "Epoch 34/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 0.9862 - sparse_categorical_accuracy: 0.7137\n", + "Epoch 35/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2405s\u001b[0m 685ms/step - loss: 0.9688 - sparse_categorical_accuracy: 0.7183\n", + "Epoch 36/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 0.9554 - sparse_categorical_accuracy: 0.7219\n", + "Epoch 37/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 0.9479 - sparse_categorical_accuracy: 0.7239\n", + "Epoch 38/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2404s\u001b[0m 685ms/step - loss: 0.9224 - sparse_categorical_accuracy: 0.7313\n", + "Epoch 39/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2408s\u001b[0m 686ms/step - loss: 0.9132 - sparse_categorical_accuracy: 0.7335\n", + "Epoch 40/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2410s\u001b[0m 686ms/step - loss: 0.8930 - sparse_categorical_accuracy: 0.7399\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit the model using the data.\n", + "model.fit(data, epochs=40, batch_size=1, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8ca51424-9aa2-4933-9f97-106eec5347c5", + "metadata": {}, + "source": [ + "## 6. Generating a Response from the Fine-Tuned Language Model\n", + "\n", + "In this section, we will define a template for generating prompts and use the language model `gemma_lm` to generate a response based on the provided instruction. This process involves creating a formatted prompt and then using the model to produce a response.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9199e8bd-3269-4b4a-bb8b-78399965883f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instruction:\n", + "I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\n", + " I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\n", + " How can I change my feeling of being worthless to everyone?\n", + "\n", + "Response:\n", + "It sounds like you are having a really tough time with feeling this way.  Feeling this way is not normal and it is important to talk about these feelings with someone.  You are not worthless, you are a wonderful person who is going through some tough times.  Therapy can help you to work through these issues and come to a place of self-love and acceptance.  You can do this!  \n" + ] + } + ], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(model.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b418c2e7-af66-4625-887d-0c1e5d1f4464", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2388s\u001b[0m 680ms/step - loss: 0.8785 - sparse_categorical_accuracy: 0.7438\n", + "Epoch 2/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2388s\u001b[0m 680ms/step - loss: 0.8661 - sparse_categorical_accuracy: 0.7473\n", + "Epoch 3/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2384s\u001b[0m 679ms/step - loss: 0.8522 - sparse_categorical_accuracy: 0.7514\n", + "Epoch 4/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2380s\u001b[0m 678ms/step - loss: 0.8408 - sparse_categorical_accuracy: 0.7547\n", + "Epoch 5/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2379s\u001b[0m 677ms/step - loss: 0.8266 - sparse_categorical_accuracy: 0.7585\n", + "Epoch 6/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2380s\u001b[0m 678ms/step - loss: 0.8117 - sparse_categorical_accuracy: 0.7635\n", + "Epoch 7/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2379s\u001b[0m 677ms/step - loss: 0.8048 - sparse_categorical_accuracy: 0.7653\n", + "Epoch 8/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2382s\u001b[0m 678ms/step - 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loss: 0.4772 - sparse_categorical_accuracy: 0.8611\n", + "Epoch 54/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 683ms/step - loss: 0.4749 - sparse_categorical_accuracy: 0.8617\n", + "Epoch 55/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2393s\u001b[0m 681ms/step - loss: 0.4785 - sparse_categorical_accuracy: 0.8603\n", + "Epoch 56/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2389s\u001b[0m 680ms/step - loss: 0.4587 - sparse_categorical_accuracy: 0.8680\n", + "Epoch 57/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2389s\u001b[0m 680ms/step - loss: 0.4649 - sparse_categorical_accuracy: 0.8655\n", + "Epoch 58/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2385s\u001b[0m 679ms/step - loss: 0.4573 - sparse_categorical_accuracy: 0.8675\n", + "Epoch 59/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2382s\u001b[0m 678ms/step - loss: 0.4545 - sparse_categorical_accuracy: 0.8689\n", + "Epoch 60/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2376s\u001b[0m 676ms/step - loss: 0.4499 - sparse_categorical_accuracy: 0.8697\n", + "Epoch 61/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2377s\u001b[0m 677ms/step - loss: 0.4482 - sparse_categorical_accuracy: 0.8698\n", + "Epoch 62/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2370s\u001b[0m 675ms/step - loss: 0.4421 - sparse_categorical_accuracy: 0.8722\n", + "Epoch 63/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2360s\u001b[0m 672ms/step - loss: 0.4370 - sparse_categorical_accuracy: 0.8739\n", + "Epoch 64/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2352s\u001b[0m 670ms/step - loss: 0.4315 - sparse_categorical_accuracy: 0.8753\n", + "Epoch 65/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2351s\u001b[0m 669ms/step - loss: 0.4342 - sparse_categorical_accuracy: 0.8747\n", + "Epoch 66/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2355s\u001b[0m 670ms/step - loss: 0.4305 - sparse_categorical_accuracy: 0.8758\n", + "Epoch 67/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2354s\u001b[0m 670ms/step - loss: 0.4266 - sparse_categorical_accuracy: 0.8771\n", + "Epoch 68/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2351s\u001b[0m 669ms/step - loss: 0.4204 - sparse_categorical_accuracy: 0.8790\n", + "Epoch 69/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2350s\u001b[0m 669ms/step - loss: 0.4176 - sparse_categorical_accuracy: 0.8795\n", + "Epoch 70/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2346s\u001b[0m 668ms/step - loss: 0.4132 - sparse_categorical_accuracy: 0.8811\n", + "Epoch 71/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2344s\u001b[0m 667ms/step - loss: 0.4143 - sparse_categorical_accuracy: 0.8804\n", + "Epoch 72/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2342s\u001b[0m 667ms/step - loss: 0.4078 - sparse_categorical_accuracy: 0.8828\n", + "Epoch 73/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2343s\u001b[0m 667ms/step - loss: 0.4041 - sparse_categorical_accuracy: 0.8837\n", + "Epoch 74/90\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2341s\u001b[0m 666ms/step - loss: 0.4060 - sparse_categorical_accuracy: 0.8832\n", + "Epoch 75/90\n", + "\u001b[1m2888/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m6:55\u001b[0m 667ms/step - loss: 0.4078 - sparse_categorical_accuracy: 0.8826" + ] + } + ], + "source": [ + "# Fit the model using the data.\n", + "model.fit(data, epochs=90, batch_size=1, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "f5d19bd9-72c8-4519-933b-c94a25b1ee3d", + "metadata": {}, + "outputs": [], + "source": [ + "model.backbone.save_lora_weights(\"model2.lora.h5\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1fda3d9d-488d-46a2-8f4e-b0b83f175426", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instruction:\n", + "I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\n", + " I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\n", + " How can I change my feeling of being worthless to everyone?\n", + "\n", + "Response:\n", + "I'm glad you're willing to slow down, but it's a complicated feeling that can be challenging to identify and work through. I've heard it described as feeling like a heavy cloud or rain cloud, and it's good to know where you are when you want to know what to do about it.It is possible to know more about this feeling and why it's arising from time to time like this. It helps to know that what you're experiencing is likely to do with your sense of who you are and your sense of how important you are in people's lives. It's also helpful to know that this feeling is something that's happening deep within you, not something outside of you.It can be valuable to look at the relationships in your life and consider your place in them.\n" + ] + } + ], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(model.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "252c7c12-e763-4524-8cd4-7582431efb02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m3136/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m4:11\u001b[0m 668ms/step - loss: 0.3683 - sparse_categorical_accuracy: 0.8940" + ] + } + ], + "source": [ + "model.fit(data, epochs=1, batch_size=1, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11bda333-9e55-4a62-9992-3d595d27ff54", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/README.md b/README.md index d70a34a186fb70864d5e81c5683cc1022ee2e056..2bd6bf916ef7586881c96115f278827080f4378e 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,6 @@ --- -title: Mental Sage -emoji: 🦀 -colorFrom: red -colorTo: yellow +title: Mental-Sage +app_file: main.py sdk: gradio sdk_version: 5.5.0 -app_file: app.py -pinned: false --- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7d335efbbf698d1e3bf30f99b7e0acc4ecfe63f8 --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "fac5022c-df54-4824-8c57-98fe045372fd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-02 15:52:58.146880: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 692 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:18:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.148409: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13673 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:19:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.149801: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13673 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:35:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.151206: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13673 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:36:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.152545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:4 with 13673 MB memory: -> device: 4, name: Tesla T4, pci bus id: 0000:e7:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.153906: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:5 with 13673 MB memory: -> device: 5, name: Tesla T4, pci bus id: 0000:e8:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.155220: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:6 with 13673 MB memory: -> device: 6, name: Tesla T4, pci bus id: 0000:f4:00.0, compute capability: 7.5\n", + "2024-09-02 15:52:58.156627: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:7 with 13673 MB memory: -> device: 7, name: Tesla T4, pci bus id: 0000:f5:00.0, compute capability: 7.5\n", + "2024-09-02 15:53:08.871520: W external/local_tsl/tsl/framework/bfc_allocator.cc:482] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.95GiB (rounded to 2097152000)requested by op StatelessRandomNormalV2\n", + "If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \n", + "Current allocation summary follows.\n", + "Current allocation summary follows.\n", + "2024-09-02 15:53:08.871548: I external/local_tsl/tsl/framework/bfc_allocator.cc:1039] BFCAllocator dump for GPU_0_bfc\n", + "2024-09-02 15:53:08.871559: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (256): \tTotal Chunks: 6, Chunks in use: 6. 1.5KiB allocated for chunks. 1.5KiB in use in bin. 48B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871566: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (512): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871572: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (1024): \tTotal Chunks: 1, Chunks in use: 1. 1.2KiB allocated for chunks. 1.2KiB in use in bin. 1.0KiB client-requested in use in bin.\n", + "2024-09-02 15:53:08.871577: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (2048): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871582: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (4096): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + 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"2024-09-02 15:53:08.871627: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (4194304): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871631: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (8388608): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871636: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (16777216): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871640: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (33554432): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871645: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (67108864): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871650: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (134217728): \tTotal Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871656: I external/local_tsl/tsl/framework/bfc_allocator.cc:1046] Bin (268435456): \tTotal Chunks: 1, Chunks in use: 0. 692.62MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.\n", + "2024-09-02 15:53:08.871662: I external/local_tsl/tsl/framework/bfc_allocator.cc:1062] Bin for 1.95GiB was 256.00MiB, Chunk State: \n", + "2024-09-02 15:53:08.871670: I external/local_tsl/tsl/framework/bfc_allocator.cc:1068] Size: 692.62MiB | Requested Size: 0B | in_use: 0 | bin_num: 20, prev: Size: 256B | Requested Size: 16B | in_use: 1 | bin_num: -1\n", + "2024-09-02 15:53:08.871674: I external/local_tsl/tsl/framework/bfc_allocator.cc:1075] Next region of size 726269952\n", + "2024-09-02 15:53:08.871681: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000000 of size 256 next 1\n", + "2024-09-02 15:53:08.871685: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000100 of size 1280 next 2\n", + "2024-09-02 15:53:08.871689: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000600 of size 256 next 3\n", + "2024-09-02 15:53:08.871692: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000700 of size 256 next 4\n", + "2024-09-02 15:53:08.871695: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000800 of size 256 next 5\n", + "2024-09-02 15:53:08.871700: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000900 of size 256 next 6\n", + "2024-09-02 15:53:08.871703: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] InUse at 7f7fac000a00 of size 256 next 7\n", + "2024-09-02 15:53:08.871708: I external/local_tsl/tsl/framework/bfc_allocator.cc:1095] Free at 7f7fac000b00 of size 726267136 next 18446744073709551615\n", + "2024-09-02 15:53:08.871713: I external/local_tsl/tsl/framework/bfc_allocator.cc:1100] Summary of in-use Chunks by size: \n", + "2024-09-02 15:53:08.871718: I external/local_tsl/tsl/framework/bfc_allocator.cc:1103] 6 Chunks of size 256 totalling 1.5KiB\n", + "2024-09-02 15:53:08.871723: I external/local_tsl/tsl/framework/bfc_allocator.cc:1103] 1 Chunks of size 1280 totalling 1.2KiB\n", + "2024-09-02 15:53:08.871727: I external/local_tsl/tsl/framework/bfc_allocator.cc:1107] Sum Total of in-use chunks: 2.8KiB\n", + "2024-09-02 15:53:08.871732: I external/local_tsl/tsl/framework/bfc_allocator.cc:1109] Total bytes in pool: 726269952 memory_limit_: 726269952 available bytes: 0 curr_region_allocation_bytes_: 1452539904\n", + "2024-09-02 15:53:08.871740: I external/local_tsl/tsl/framework/bfc_allocator.cc:1114] Stats: \n", + "Limit: 726269952\n", + "InUse: 2816\n", + "MaxInUse: 2816\n", + "NumAllocs: 9\n", + "MaxAllocSize: 1280\n", + "Reserved: 0\n", + "PeakReserved: 0\n", + "LargestFreeBlock: 0\n", + "\n", + "2024-09-02 15:53:08.871746: W external/local_tsl/tsl/framework/bfc_allocator.cc:494] *___________________________________________________________________________________________________\n", + "2024-09-02 15:53:08.871766: W tensorflow/core/framework/op_kernel.cc:1840] OP_REQUIRES failed at stateless_random_ops_v2.cc:64 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[256000,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n", + "2024-09-02 15:53:08.871780: I tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[256000,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n" + ] + }, + { + "ename": "ResourceExhaustedError", + "evalue": "{{function_node __wrapped__StatelessRandomNormalV2_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[256000,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:StatelessRandomNormalV2]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m os\u001b[38;5;241m.\u001b[39menviron[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKAGGLE_KEY\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m9a33b6e88bcb6058b1281d777fa6808d\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mkeras_nlp\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m gemma_lm \u001b[38;5;241m=\u001b[39m \u001b[43mkeras_nlp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGemmaCausalLM\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_preset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mgemma_2b_en\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m gemma_lm\u001b[38;5;241m.\u001b[39mgenerate(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mKeras is a\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_nlp/src/models/task.py:264\u001b[0m, in \u001b[0;36mTask.from_preset\u001b[0;34m(cls, preset, load_weights, **kwargs)\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m 263\u001b[0m config_overrides[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 264\u001b[0m backbone \u001b[38;5;241m=\u001b[39m \u001b[43mbackbone_preset_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_preset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 265\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 266\u001b[0m \u001b[43m \u001b[49m\u001b[43mload_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 267\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig_overrides\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfig_overrides\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 268\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpreprocessor\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m 270\u001b[0m preprocessor \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpreprocessor\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_nlp/src/models/backbone.py:190\u001b[0m, in \u001b[0;36mBackbone.from_preset\u001b[0;34m(cls, preset, load_weights, **kwargs)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(preset_cls, \u001b[38;5;28mcls\u001b[39m):\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 185\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPreset has type `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpreset_cls\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` which is not a \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma subclass of calling class `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m`. Call \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 187\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`from_preset` directly on `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpreset_cls\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 188\u001b[0m )\n\u001b[0;32m--> 190\u001b[0m backbone \u001b[38;5;241m=\u001b[39m \u001b[43mload_serialized_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCONFIG_FILE\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m load_weights:\n\u001b[1;32m 192\u001b[0m jax_memory_cleanup(backbone)\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_nlp/src/utils/preset_utils.py:569\u001b[0m, in \u001b[0;36mload_serialized_object\u001b[0;34m(preset, config_file, config_overrides)\u001b[0m\n\u001b[1;32m 567\u001b[0m config \u001b[38;5;241m=\u001b[39m load_config(preset, config_file)\n\u001b[1;32m 568\u001b[0m config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig_overrides}\n\u001b[0;32m--> 569\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msaving\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeserialize_keras_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/src/saving/serialization_lib.py:718\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(config, custom_objects, safe_mode, **kwargs)\u001b[0m\n\u001b[1;32m 716\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m custom_obj_scope, safe_mode_scope:\n\u001b[1;32m 717\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 718\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43minner_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 719\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 720\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m 721\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m could not be deserialized properly. Please\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 722\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m ensure that components that are Python object\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mconfig=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mException encountered: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 727\u001b[0m )\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_nlp/src/models/backbone.py:119\u001b[0m, in \u001b[0;36mBackbone.from_config\u001b[0;34m(cls, config)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_config\u001b[39m(\u001b[38;5;28mcls\u001b[39m, config):\n\u001b[1;32m 117\u001b[0m \u001b[38;5;66;03m# The default `from_config()` for functional models will return a\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;66;03m# vanilla `keras.Model`. We override it to get a subclass instance back.\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_nlp/src/models/gemma/gemma_backbone.py:171\u001b[0m, in \u001b[0;36mGemmaBackbone.__init__\u001b[0;34m(self, vocabulary_size, num_layers, num_query_heads, num_key_value_heads, hidden_dim, intermediate_dim, head_dim, query_head_dim_normalize, use_post_ffw_norm, use_post_attention_norm, attention_logit_soft_cap, final_logit_soft_cap, use_sliding_window_attention, sliding_window_size, layer_norm_epsilon, dropout, dtype, **kwargs)\u001b[0m\n\u001b[1;32m 165\u001b[0m token_id_input \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mInput(\n\u001b[1;32m 166\u001b[0m shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m,), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m\"\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 167\u001b[0m )\n\u001b[1;32m 168\u001b[0m padding_mask_input \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mInput(\n\u001b[1;32m 169\u001b[0m shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28;01mNone\u001b[39;00m,), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m\"\u001b[39m, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpadding_mask\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 170\u001b[0m )\n\u001b[0;32m--> 171\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtoken_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtoken_id_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 172\u001b[0m x \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m*\u001b[39m ops\u001b[38;5;241m.\u001b[39mcast(ops\u001b[38;5;241m.\u001b[39msqrt(hidden_dim), x\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m 173\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m transformer_layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransformer_layers:\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n", + "File \u001b[0;32m~/.local/lib/python3.10/site-packages/keras_nlp/src/layers/modeling/reversible_embedding.py:115\u001b[0m, in \u001b[0;36mReversibleEmbedding.build\u001b[0;34m(self, inputs_shape)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbuild\u001b[39m(\u001b[38;5;28mself\u001b[39m, inputs_shape\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs_shape\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtie_weights:\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreverse_embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_weight(\n\u001b[1;32m 119\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreverse_embeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 120\u001b[0m shape\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_dim, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_dim),\n\u001b[1;32m 121\u001b[0m initializer\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings_initializer,\n\u001b[1;32m 122\u001b[0m dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdtype,\n\u001b[1;32m 123\u001b[0m )\n", + "\u001b[0;31mResourceExhaustedError\u001b[0m: {{function_node __wrapped__StatelessRandomNormalV2_device_/job:localhost/replica:0/task:0/device:GPU:0}} OOM when allocating tensor with shape[256000,2048] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:StatelessRandomNormalV2]" + ] + } + ], + "source": [ + "# Set Kaggle API credentials\n", + "import os\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"rogerkorantenng\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"9a33b6e88bcb6058b1281d777fa6808d\"\n", + "import keras_nlp\n", + "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\n", + "gemma_lm.generate(\"Keras is a\", max_length=30)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5488db1-48b2-4d0c-a4cd-1329a333b3f9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Untitled1.ipynb b/Untitled1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..64984f91d3bb970a9980dcf06d3c1af90c1dbcff --- /dev/null +++ b/Untitled1.ipynb @@ -0,0 +1,94 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "be79942c-2080-452a-a349-ea19b7611c85", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-09-03 17:04:36.485116: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-09-03 17:04:36.500874: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-09-03 17:04:36.519822: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-09-03 17:04:36.525533: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-09-03 17:04:36.539736: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "data": { + "text/plain": [ + "'3.5.0'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import keras\n", + "keras.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "00e538b8-91c1-401b-9cc9-320fc496a828", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: keras\n", + "Version: 3.5.0\n", + "Summary: Multi-backend Keras.\n", + "Home-page: https://github.com/keras-team/keras\n", + "Author: Keras team\n", + "Author-email: keras-users@googlegroups.com\n", + "License: Apache License 2.0\n", + "Location: /home/workbench/.local/lib/python3.10/site-packages\n", + "Requires: absl-py, h5py, ml-dtypes, namex, numpy, optree, packaging, rich\n", + "Required-by: tensorflow\n" + ] + } + ], + "source": [ + "!pip show keras\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85cff0f4-2cb7-4c20-ba0c-a1c6198bf8dd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/chatbot/.env b/chatbot/.env new file mode 100644 index 0000000000000000000000000000000000000000..6b5f7b17114aaf7bb1d41135f75bac969475d1dd --- /dev/null +++ b/chatbot/.env @@ -0,0 +1,4 @@ +GPT4_API_KEY=XTtd4wdC0mlEdbQqp0KWOe9aWrlg4z4HurxXlgBf5ASUvrB9iPkwJQQJ99AKACHYHv6XJ3w3AAABACOGaeSg +GPT4O_API_KEY=XTtd4wdC0mlEdbQqp0KWOe9aWrlg4z4HurxXlgBf5ASUvrB9iPkwJQQJ99AKACHYHv6XJ3w3AAABACOGaeSg +GPT35_TURBO_API_KEY=cfe75d17d47c40318db6230d2677d84f +GPT4_32K_API_KEY=AebO97c0w0lnrppFNpbuPVueKth7HfGOdMbKeaX5R2Uql6wvJA5qJQQJ99AKACL93NaXJ3w3AAABACOGh8MD diff --git a/chatbot/.gradio/certificate.pem b/chatbot/.gradio/certificate.pem new file mode 100644 index 0000000000000000000000000000000000000000..b85c8037f6b60976b2546fdbae88312c5246d9a3 --- /dev/null +++ b/chatbot/.gradio/certificate.pem @@ -0,0 +1,31 @@ +-----BEGIN CERTIFICATE----- +MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw +TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh +cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4 +WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu +ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY +MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc +h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+ +0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U 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load_dotenv + +# Load environment variables from .env file +load_dotenv() + +# Define endpoints and securely load API keys from environment variables +model_config = { + "gpt-4": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_API_KEY") + }, + "gpt-4o": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4O_API_KEY") + }, + "gpt-35-turbo": { + "endpoint": "https://rogerkoranteng.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT35_TURBO_API_KEY") + }, + "gpt-4-32k": { + "endpoint": "https://roger-m38orjxq-australiaeast.openai.azure.com/openai/deployments/gpt-4-32k/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_32K_API_KEY") + } +} + + +# Function to generate a response from Azure OpenAI +def generate_response(model_choice, user_message, chat_history): + model_info = model_config.get(model_choice) + + if not model_info: + return "Invalid model selection. Please choose a valid model.", chat_history + + chat_history.append({"role": "user", "content": user_message}) + + headers = { + "Content-Type": "application/json", + "api-key": model_info['api_key'], + } + + data = { + "messages": chat_history, + "max_tokens": 150, + "temperature": 0.7, + } + + try: + response = requests.post( + model_info["endpoint"], + headers=headers, + json=data + ) + + response.raise_for_status() + response_data = response.json() + + assistant_message = response_data['choices'][0]['message']['content'] + chat_history.append({"role": "assistant", "content": assistant_message}) + + return assistant_message, chat_history + + except requests.exceptions.RequestException as e: + return f"Error: {e}", chat_history + + +# Function to format chat history with improved readability +def format_chat_history(history): + formatted_history = "" + for message in history: + role = "User:" if message["role"] == "user" else "Sage:" + formatted_history += f"{role} {message['content']}\n\n" # Message content with a line break + return formatted_history.strip() # Remove any trailing whitespace + + +# List of available models +azure_models = [ + "gpt-4", + "gpt-4o", + "gpt-35-turbo", + "gpt-4-32k" +] + + +# Function to handle model change status update +def change_model(model_choice): + return f"Selected model: {model_choice}" + + +# Create the Gradio interface +with gr.Blocks() as interface: + gr.Markdown("## Sage - Your Mental Health Advisor") + + with gr.Tab("Model Selection"): + gr.Markdown("### Select Model for Chat") + model_dropdown = gr.Dropdown( + choices=azure_models, + label="Choose a Model", + value=azure_models[0], # Default model + interactive=True + ) + + # Add status update on model change + status_textbox = gr.Textbox(label="Model Selection Status", value="Selected model: gpt-4", interactive=False) + model_dropdown.change(change_model, inputs=model_dropdown, outputs=status_textbox) + + gr.Markdown("The selected model will be used for chat interaction.") + + with gr.Tab("Chat Interface"): + gr.Markdown("### Chat with Sage - Your Mental Health Advisor") + chat_history_state = gr.State([]) # Store chat history + model_choice_state = gr.State(azure_models[0]) # Default model + user_message = gr.Textbox( + placeholder="Hello, I am Sage. How can I assist you today?", + label="Your Message", + lines=2, + scale=7 + ) + + model_dropdown.change( + lambda x: x, inputs=model_dropdown, outputs=model_choice_state + ) + + assistant_response = gr.Textbox(label="Assistant's Response") + submit_button = gr.Button("Send Message") + + submit_button.click( + generate_response, + inputs=[model_choice_state, user_message, chat_history_state], + outputs=[assistant_response, chat_history_state] + ) + + # Chat History Tab + with gr.Tab("Chat History"): + gr.Markdown("### Chat History") + + # Add the fetch history button inside the "Chat History" tab + fetch_history_button = gr.Button("Fetch Chat History") + chat_history_display = gr.Textbox(label="Chat History", interactive=False, lines=10) + + + # Function to fetch and display chat history + def fetch_chat_history(chat_history): + return format_chat_history(chat_history) + + + # Bind the fetch history button to fetch the chat history + fetch_history_button.click( + fetch_chat_history, + inputs=chat_history_state, + outputs=chat_history_display + ) + + # Launch the Gradio app + interface.launch(server_name="0.0.0.0", server_port=8080, share=True) diff --git a/chatbot/app2.pyn b/chatbot/app2.pyn new file mode 100644 index 0000000000000000000000000000000000000000..687509d76eca7122c93603971b6a397106f482da --- /dev/null +++ b/chatbot/app2.pyn @@ -0,0 +1,226 @@ +import os +import gradio as gr +import pandas as pd +import numpy as np +import chromadb +from chromadb.config import Settings +from io import StringIO +from sentence_transformers import SentenceTransformer +import plotly.express as px +from sklearn.manifold import TSNE + + +# Constants for Model Configuration +MODEL_CONFIG = { + "gpt-4": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_API_KEY") + }, + "gpt-4o": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4O_API_KEY") + }, + "gpt-35-turbo": { + "endpoint": "https://rogerkoranteng.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT35_TURBO_API_KEY") + }, + "gpt-4-32k": { + "endpoint": "https://roger-m38orjxq-australiaeast.openai.azure.com/openai/deployments/gpt-4-32k/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_32K_API_KEY") + } +} + +# Initialize Chroma client with DuckDB and Parquet for persistence +chroma_client = chromadb.Client() + + +# Functions for Data Processing and Embedding + +def process_csv_text(temp_file): + """Process the uploaded CSV and return the dataframe and column options.""" + if isinstance(temp_file, str): + df = pd.read_csv(StringIO(temp_file)) + else: + df = pd.read_csv(temp_file.name, header='infer', sep=',') + return df, gr.Dropdown.update(choices=list(df.columns)) + + +def insert_or_update_chroma(col, table, model_name, similarity_metric): + """Insert or update embeddings in ChromaDB.""" + try: + collection = chroma_client.create_collection( + name="my_collection", + embedding_function=SentenceTransformer(model_name), + metadata={"hnsw:space": similarity_metric} + ) + except Exception: + print("Collection exists, deleting it") + chroma_client.delete_collection(name='my_collection') + collection = chroma_client.create_collection( + name="my_collection", + embedding_function=SentenceTransformer(model_name), + metadata={"hnsw:space": similarity_metric} + ) + + if collection: + try: + collection.add( + documents=list(table[col]), + metadatas=[{"source": i} for i in range(len(table))], + ids=[str(i + 1) for i in range(len(table))] + ) + return "Embedding calculations and insertions successful" + except Exception as e: + return f"Error in embedding calculations: {e}" + + +def show_fig(): + """Show t-SNE 2D plot for embeddings.""" + collection = chroma_client.get_collection(name="my_collection") + embeddings = collection.get(include=['embeddings', 'documents']) + + df = pd.DataFrame({ + 'text': embeddings['documents'], + 'embedding': embeddings['embeddings'] + }) + + embeddings_np = np.array(df['embedding'].tolist()) + tsne = TSNE(n_components=2, random_state=42) + transformed = tsne.fit_transform(embeddings_np) + + df['tsne_x'] = transformed[:, 0] + df['tsne_y'] = transformed[:, 1] + + fig = px.scatter(df, x='tsne_x', y='tsne_y', hover_name='text') + return fig, transformed + + +def show_test_string_fig(test_string, tsne, model_name, similarity_metric): + """Show t-SNE plot with test string to compare embeddings.""" + collection = chroma_client.get_collection(name="my_collection", + embedding_function=SentenceTransformer(model_name)) + + collection.add( + documents=[test_string], + metadatas=[{"source": 'test'}], + ids=['test_sample'] + ) + + embeddings = collection.get(include=['embeddings', 'documents']) + df = pd.DataFrame({ + 'text': embeddings['documents'], + 'embedding': embeddings['embeddings'], + 'set': ['orig' if document != test_string else 'test_string' for document in embeddings["documents"]] + }) + + embeddings_np = np.array(df['embedding'].tolist()) + transformed = tsne.transform(embeddings_np) + + df['tsne_x'] = transformed[:, 0] + df['tsne_y'] = transformed[:, 1] + + fig = px.scatter(df, x='tsne_x', y='tsne_y', hover_name='text', color='set') + return fig, tsne + + +def ask_gpt(message, messages_history, embedding_model, system_prompt, temperature, max_tokens, chatgpt_model): + """Interacts with the OpenAI API using Azure endpoint.""" + if len(messages_history) < 1: + messages_history = [{"role": "system", "content": system_prompt}] + + model_info = MODEL_CONFIG[chatgpt_model] + headers = {"Content-Type": "application/json", "api-key": model_info["api_key"]} + + message = retrieve_similar(message, embedding_model) + messages_history += [{"role": "user", "content": message}] + + response = openai.ChatCompletion.create( + model=chatgpt_model, + messages=messages_history, + temperature=temperature, + max_tokens=max_tokens + ) + + return response['choices'][0]['message']['content'], messages_history + + +def retrieve_similar(prompt, embedding_model): + """Retrieve similar documents from ChromaDB to enhance context.""" + # Initialize SentenceTransformer correctly + embedding_function = SentenceTransformer(embedding_model) + + collection = chroma_client.get_collection( + name="my_collection", + embedding_function=embedding_function + ) + + results = collection.query(query_texts=prompt, n_results=10) + additional_context = '' + for i, document in enumerate(results['documents'][0]): + additional_context += f'{i + 1}. {document}\n' + + return additional_context + f'Question: {prompt}' + + + +# Gradio Interface Setup + +def build_gradio_ui(): + """Setup the complete Gradio UI.""" + with gr.Blocks() as demo: + # Tab 1: Upload CSV and Display Data + with gr.Tab("Upload data"): + upload_button = gr.File(label="Upload CSV", file_types=['.csv'], file_count="single") + table = gr.Dataframe(type="pandas", interactive=True) + cols = gr.Dropdown(choices=[], label='Dataframe columns') + + upload_button.change(fn=process_csv_text, inputs=upload_button, outputs=[table, cols]) + + # Tab 2: ChromaDB, Embeddings, and Plotting + with gr.Tab("ChromaDB and Embeddings"): + cols = gr.Dropdown(choices=[], label='Dataframe columns') + embedding_model = gr.Dropdown(value='all-MiniLM-L6-v2', + choices=['all-MiniLM-L6-v2', 'intfloat/e5-small-v2', 'intfloat/e5-base-v2', + 'intfloat/e5-large-v2', 'paraphrase-multilingual-MiniLM-L12-v2'], + label='Embedding Model') + similarity_metric = gr.Dropdown(value='cosine', choices=['cosine', 'l2'], label='Similarity Metric') + embedding_button = gr.Button(value="Insert or Update Embeddings") + text = gr.Textbox(label='Process Status') + + show_embeddings_button = gr.Button(value="Show Embeddings") + embeddings_plot = gr.Plot() + + tsne = gr.State(value=None) # Using gr.State for intermediate results (tsne) + test_string = gr.Textbox(label='Test String') + + calculate_2d_repr_button = gr.Button(value="Calculate 2D Representation") + embeddings_plot_with_text_string = gr.Plot() + + embedding_button.click(insert_or_update_chroma, inputs=[cols, table, embedding_model, similarity_metric], outputs=[text]) + show_embeddings_button.click(show_fig, inputs=[], outputs=[embeddings_plot, tsne]) + calculate_2d_repr_button.click(show_test_string_fig, inputs=[test_string, tsne, embedding_model, similarity_metric], outputs=[embeddings_plot_with_text_string, tsne]) + + # Tab 3: Chat with GPT + with gr.Tab("Chat"): + system_prompt = gr.Textbox(value="You are a helpful assistant.", label="System Message") + chatgpt_model = gr.Dropdown(value="gpt-4", choices=list(MODEL_CONFIG.keys()), label="ChatGPT Model") + temperature = gr.Slider(minimum=0, maximum=2, step=0.1, value=0.7, label="Temperature") + max_tokens = gr.Slider(minimum=50, maximum=2000, step=50, value=300, label="Max Tokens") + chatbot = gr.Chatbot(label="ChatGPT Chat") + clear_button = gr.Button("Clear Chat History") + msg = gr.Textbox() + msg_log = gr.Textbox("Message history", label="History") + + # Replacing `.submit()` with `.change()` to trigger callback when user enters a message + msg.submit(fn=ask_gpt, inputs=[msg, chatbot, system_prompt, embedding_model, temperature, max_tokens, chatgpt_model], outputs=[msg, chatbot]) + clear_button.click(fn=lambda: None, inputs=None, outputs=[chatbot]) + + return demo + +# Launch the Gradio interface +demo = build_gradio_ui() +demo.launch(server_name="0.0.0.0", server_port=8080, share=True) + + + +# Launch the Gradio interf diff --git a/chatbot/app2.pynnn b/chatbot/app2.pynnn new file mode 100644 index 0000000000000000000000000000000000000000..2a10eb25ab2ad3141852948527f67223e8ae31ad --- /dev/null +++ b/chatbot/app2.pynnn @@ -0,0 +1,213 @@ +import os +import gradio as gr +import pandas as pd +import numpy as np +import chromadb +from chromadb.config import Settings +from io import StringIO +from sentence_transformers import SentenceTransformer +import openai +import plotly.express as px +from sklearn.manifold import TSNE + +# Initialize Chroma client with DuckDB and Parquet for persistence +chroma_client = chromadb.Client(Settings( + chroma_db_impl="duckdb+parquet", + persist_directory="./chroma_db" +)) + +# Model Configuration for Dynamic Dropdown +model_config = { + "gpt-4": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_API_KEY") + }, + "gpt-4o": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4O_API_KEY") + }, + "gpt-35-turbo": { + "endpoint": "https://rogerkoranteng.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT35_TURBO_API_KEY") + }, + "gpt-4-32k": { + "endpoint": "https://roger-m38orjxq-australiaeast.openai.azure.com/openai/deployments/gpt-4-32k/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_32K_API_KEY") + } +} + +# Function to process uploaded CSV +def process_csv_text(temp_file): + if isinstance(temp_file, str): + df = pd.read_csv(StringIO(temp_file)) + else: + df = pd.read_csv(temp_file.name, header='infer', sep=',') + return df, gr.Dropdown.update(choices=list(df.columns)) + +# Insert or update ChromaDB with embeddings +def insert_or_update_chroma(col, table, model_name, similarity_metric, client=chroma_client): + try: + collection = client.create_collection(name="my_collection", + embedding_function=SentenceTransformer(model_name), + metadata={"hnsw:space": similarity_metric}) + except Exception as e: + print("Collection exists, deleting it") + client.delete_collection(name='my_collection') + collection = client.create_collection(name="my_collection", + embedding_function=SentenceTransformer(model_name), + metadata={"hnsw:space": similarity_metric}) + if collection: + try: + collection.add( + documents=list(table[col]), + metadatas=[{"source": i} for i in range(len(table))], + ids=[str(i + 1) for i in range(len(table))] + ) + return "Embedding calculations and insertions successful" + except Exception as e: + return "Error in embedding calculations" + +# Show plot with embeddings using t-SNE +def show_fig(): + collection = chroma_client.get_collection(name="my_collection") + embeddings = collection.get(include=['embeddings', 'documents']) + + df = pd.DataFrame({ + 'text': embeddings['documents'], + 'embedding': embeddings['embeddings'] + }) + + embeddings_np = np.array(df['embedding'].tolist()) + tsne = TSNE(n_components=2, random_state=42) + transformed = tsne.fit_transform(embeddings_np) + + df['tsne_x'] = transformed[:, 0] + df['tsne_y'] = transformed[:, 1] + + fig = px.scatter(df, x='tsne_x', y='tsne_y', hover_name='text') + return fig, transformed + +# Show test string figure +def show_test_string_fig(test_string, tsne, model_name, similarity_metric): + collection = chroma_client.get_collection(name="my_collection", + embedding_function=SentenceTransformer(model_name)) + + collection.add( + documents=[test_string], + metadatas=[{"source": 'test'}], + ids=['test_sample'] + ) + + embeddings = collection.get(include=['embeddings', 'documents']) + + df = pd.DataFrame({ + 'text': embeddings['documents'], + 'embedding': embeddings['embeddings'], + 'set': ['orig' if document != test_string else 'test_string' for document in embeddings["documents"]] + }) + + embeddings_np = np.array(df['embedding'].tolist()) + transformed = tsne.transform(embeddings_np) + + df['tsne_x'] = transformed[:, 0] + df['tsne_y'] = transformed[:, 1] + + fig = px.scatter(df, x='tsne_x', y='tsne_y', hover_name='text', color='set') + return fig, tsne + +# Function to interact with OpenAI's Azure API +def ask_gpt(message, messages_history, embedding_model, system_prompt, temperature, max_tokens, chatgpt_model): + if len(messages_history) < 1: + messages_history = [{"role": "system", "content": system_prompt}] + model_info = model_config[chatgpt_model] + headers = { + "Content-Type": "application/json", + "api-key": model_info["api_key"] + } + + message = retrieve_similar(message, embedding_model) + + messages_history += [{"role": "user", "content": message}] + response = openai.ChatCompletion.create( + model=chatgpt_model, + messages=messages_history, + temperature=temperature, + max_tokens=max_tokens + ) + + return response['choices'][0]['message']['content'], messages_history + +# Function to retrieve similar questions from ChromaDB +def retrieve_similar(prompt, embedding_model, client=chroma_client): + collection = client.get_collection(name="my_collection", embedding_function=SentenceTransformer(model_name=embedding_model)) + + results = collection.query(query_texts=prompt, n_results=10) + additional_context = '' + for i, document in enumerate(results['documents'][0]): + if i == 0: + additional_context = 'Information: \n' + str(i+1) + '. ' + document + else: + additional_context += '\n' + str(i+1) + '. ' + document + + prompt_with_context = additional_context + '\nQuestion: ' + prompt + return prompt_with_context + +# Gradio App Setup +with gr.Blocks() as demo: + # Tab 1: Upload CSV and Display Data + with gr.Tab("Upload data"): + upload_button = gr.UploadButton(label="Upload csv", file_types=['.csv'], file_count="single") + table = gr.Dataframe(type="pandas", max_rows='20', overflow_row_behaviour='paginate', interactive=True) + cols = gr.Dropdown(choices=[], label='Dataframe columns') + + upload_button.upload(fn=process_csv_text, inputs=upload_button, outputs=[table, cols], api_name="upload_csv") + + # Tab 2: ChromaDB, Embeddings, and Plotting + with gr.Tab("Select Column and insert embeddings to ChromaDb"): + with gr.Row(): + gr.Markdown("
") + with gr.Row(): + cols = gr.Dropdown(choices=['text_column_1_placeholder'], label='Dataframe columns') + + with gr.Row(): + embedding_model = gr.Dropdown(value='all-MiniLM-L6-v2', choices=['all-MiniLM-L6-v2', 'intfloat/e5-small-v2', 'intfloat/e5-base-v2', 'intfloat/e5-large-v2','paraphrase-multilingual-MiniLM-L12-v2'], label='Embedding model to use') + similarity_metric = gr.Dropdown(value='cosine', choices=['cosine', 'l2'], label='Similarity metric to use') + + with gr.Row(): + embedding_button = gr.Button(value="Insert or update rows from selected column to embeddings db") + text = gr.Textbox(label='Process status for Chroma', placeholder='This will be updated once you click "Process status for Chroma"') + + with gr.Row(): + show_embeddings_button = gr.Button(value="Calculate 2d values from embeddings and show scatter plot") + embeddings_plot = gr.Plot() + + with gr.Row(): + tsne = gr.State(value=None) + test_string = gr.Textbox(label='test string to try to embed', value="Insert test string here") + + with gr.Row(): + calculate_2d_repr_button = gr.Button(value="See where text string is in 2d") + embeddings_plot_with_text_string = gr.Plot() + + embedding_button.click(insert_or_update_chroma, inputs=[cols, table, embedding_model, similarity_metric], outputs=[text]) + show_embeddings_button.click(show_fig, inputs=[], outputs=[embeddings_plot, tsne]) + calculate_2d_repr_button.click(show_test_string_fig, inputs=[test_string, tsne, embedding_model, similarity_metric], outputs=[embeddings_plot_with_text_string, tsne]) + + # Tab 3: Chat with GPT Models + with gr.Tab("Chat"): + system_prompt = gr.Textbox(value="You are a helpful assistant.", label="System Message") + chatgpt_model = gr.Dropdown(value="gpt-4", choices=list(model_config.keys()), label="ChatGPT Model to Use") + temperature = gr.Slider(minimum=0, maximum=2, step=0.1, value=0.7, label="Temperature") + max_tokens = gr.Slider(minimum=50, maximum=2000, step=50, value=300, label="Max Tokens") + chatbot = gr.Chatbot(label="ChatGPT Chat") + clear_button = gr.Button("Clear Chat History") + + msg = gr.Textbox() + msg_log = gr.Textbox("Message history will be visible here", label='Message history') + + msg.submit(ask_gpt, [msg, chatbot], [msg, chatbot]) + chatbot.submit(ask_gpt, [chatbot, system_prompt, embedding_model, temperature, max_tokens, chatgpt_model], [chatbot, system_prompt]) + clear_button.click(fn=lambda: None, inputs=None, outputs=[chatbot]) + +# Launch Gradio interface +demo.launch() diff --git a/chatbot/bar_chart.png b/chatbot/bar_chart.png new file mode 100644 index 0000000000000000000000000000000000000000..bd25ad03973e89331968cd5c56cd1237b5eb638f Binary files /dev/null and b/chatbot/bar_chart.png differ diff --git a/chatbot/category_breakdown.png b/chatbot/category_breakdown.png new file mode 100644 index 0000000000000000000000000000000000000000..a68bd1551b672b05a30f18ecb9030ceb4ef0954c Binary files /dev/null and b/chatbot/category_breakdown.png differ diff --git a/chatbot/chat_history.json b/chatbot/chat_history.json new file mode 100644 index 0000000000000000000000000000000000000000..4aaad061770b7c53a5812aea49378cdef591844a --- /dev/null +++ b/chatbot/chat_history.json @@ -0,0 +1,62 @@ +[ + { + "role": "assistant", + "content": "Hello, I am Sage. How can I assist you today?" + }, + { + "role": "user", + "content": "Hi Sage, my boyfriend broke up with me. I'm feeling so sad. What should I do?" + }, + { + "role": "assistant", + "content": "I'm sorry to hear that you're going through a tough time. Breakups can be incredibly painful, but there are ways to help you cope with the emotions and eventually move forward. Here are a few suggestions:\n\n1. **Allow Yourself to Grieve**: It's okay to feel sad, angry, or confused. Allow yourself time to process these emotions. It's an important part of healing.\n\n2. **Reach Out for Support**: Talk to friends and family who care about you. Sharing your feelings can be very therapeutic and remind you that you're not alone.\n\n3. **Stay Active**: Engaging in physical activities can help boost your mood and take your mind off the pain. Consider trying out exercises like walking, yoga, or anything you enjoy.\n\n4. **Pursue Interests**: Reconnect with hobbies and interests that you may have neglected. This can help you find joy and fulfillment in other areas of your life.\n\n5. **Avoid Contact**: It might be helpful to take a break from seeing or communicating with your ex while you heal. Constant contact can often prolong the pain.\n\n6. **Seek Professional Help**: If you find it very difficult to cope, speaking with a therapist or counselor can provide professional support and strategies to manage your feelings.\n\nRemember, it's okay to take things one day at a time. Healing is a process, and it's different for everyone." + }, + { + "role": "user", + "content": "Hello, Sage. I feel like nobody likes me. What should I do?" + }, + { + "role": "assistant", + "content": "Feeling like nobody likes you can be really tough, but remember, these feelings don't define your worth or your ability to be liked. Here are some steps you can take to address these feelings:\n\n1. **Reflect on Your Feelings**: Sometimes, these feelings might not reflect reality but rather our own insecurities or past experiences. Try to assess if there's any specific reason you feel this way.\n\n2. **Communicate**: Talk to people you trust about how you feel. They might provide reassurance and help you see that people do care about you.\n\n3. **Build Self-Esteem**: Engage in activities that make you feel good about yourself. This could be hobbies, sports, learning new skills, or volunteering. Accomplishments can boost your self-esteem and change how you perceive yourself.\n\n4. **Social Skills**: Sometimes, improving social skills can boost your confidence in interactions. This might include practicing small talk, being a good listener, or showing interest in others' activities.\n\n5. **Meet New People**: Sometimes the feeling of not being liked can stem from not having found the right group of friends. Try joining clubs, groups, or online communities where you can meet people with similar interests.\n\n6. **Consider Professional Help**: If these feelings are overwhelming and persistent, talking to a therapist or counselor can be very beneficial. They can help you work through these feelings and develop strategies to improve your social interactions.\n\nRemember, many people feel this way at some point in their lives. It's brave to confront these feelings and seek ways to improve your situation. You're not alone in this." + }, + { + "role": "user", + "content": "Hello, I am feeling sad today, what should I do?" + }, + { + "role": "assistant", + "content": "I'm sorry to hear that you're feeling sad. It's important to take care of yourself and address your emotions. Here are a few suggestions that might help improve your mood:\n\n1. **Acknowledge Your Feelings**: It's okay to feel sad. Acknowledging your emotions is the first step toward feeling better. Allow yourself some time to just feel what you're feeling without judgment.\n\n2. **Engage in a Favorite Activity**: Doing something you enjoy, such as reading a book, watching a favorite movie, or engaging in a hobby, can provide a comforting distraction and bring some joy.\n\n3. **Connect with Others**: Reach out to friends or family members who can offer support. Sometimes just talking about how you feel can lighten the burden.\n\n4. **Get Some Exercise**: Physical activity can boost endorphins, which are chemicals in the brain that can lighten your mood. Even a short walk can make a difference.\n\n5. **Listen to Music**: Music can have a powerful effect on mood. Choose songs that lift your spirits or calm and soothe you, depending on what you feel you need.\n\n6. **Practice Mindfulness or Meditation**: These practices can help you stay present and reduce negative thoughts. Even a few minutes can have a significant impact.\n\n7. **Consider Professional Help**: If sadness persists and starts to affect your daily life, it might be helpful to talk to a mental health professional who can provide guidance and support.\n\nRemember, it's perfectly okay to have days when you feel down. Taking steps to care for yourself during these times is important." + }, + { + "role": "user", + "content": "okay thanks how do i contact mental health persons" + }, + { + "role": "assistant", + "content": "Seeking help from a mental health professional is a positive step towards taking care of your well-being. Here\u2019s how you can get started:\n\n1. **Primary Care Doctor**: You can start by talking to your primary care doctor. They can assess your situation and may provide a referral to a mental health specialist such as a psychologist or psychiatrist.\n\n2. **Insurance Provider**: If you have health insurance, check your provider\u2019s website or call their customer service for a list of covered mental health professionals. They can guide you on how to proceed based on your plan.\n\n3. **Online Directories**: Use reputable online directories or websites that list mental health professionals. Websites like Psychology Today, GoodTherapy, or even Google can help you find therapists in your area.\n\n4. **Local Mental Health Clinics**: Look for community mental health clinics in your area. These often offer services on a sliding scale fee based on income, making them accessible to more people.\n\n5. **Telehealth Services**: Consider online therapy options, which can be more convenient and sometimes more affordable. Platforms like BetterHelp, Talkspace, or Amwell offer various online therapy services.\n\n6. **Employee Assistance Programs (EAP)**: If you are employed, check if your employer offers an Employee Assistance Program, which often includes a few free therapy sessions.\n\n7. **School Counselors**: If you are a student, your school or university likely has counselors available to help students at no extra cost.\n\nRemember, seeking help is a sign of strength, and it\u2019s important to find a professional you feel comfortable with. Don\u2019t hesitate to reach out and take that first step." + }, + { + "role": "user", + "content": "Hello, I am feeling sad today, what should I do?" + }, + { + "role": "assistant", + "content": "I'm sorry to hear that you're feeling sad. It's important to take care of yourself and address your emotions. Here are a few suggestions that might help improve your mood:\n\n1. **Acknowledge Your Feelings**: It's okay to feel sad. Acknowledging your emotions is the first step toward feeling better. Allow yourself some time to just feel what you're feeling without judgment.\n\n2. **Engage in a Favorite Activity**: Doing something you enjoy, such as reading a book, watching a favorite movie, or engaging in a hobby, can provide a comforting distraction and bring some joy.\n\n3. **Connect with Others**: Reach out to friends or family members who can offer support. Sometimes just talking about how you feel can lighten the burden.\n\n4. **Get Some Exercise**: Physical activity can boost endorphins, which are chemicals in the brain that can lighten your mood. Even a short walk can make a difference.\n\n5. **Listen to Music**: Music can have a powerful effect on mood. Choose songs that lift your spirits or calm and soothe you, depending on what you feel you need.\n\n6. **Practice Mindfulness or Meditation**: These practices can help you stay present and reduce negative thoughts. Even a few minutes can have a significant impact.\n\n7. **Consider Professional Help**: If sadness persists and starts to affect your daily life, it might be helpful to talk to a mental health professional who can provide guidance and support.\n\nRemember, it's perfectly okay to have days when you feel down. Taking steps to care for yourself during these times is important." + }, + { + "role": "user", + "content": "I'M SORRY TO HEAR THAT YOUR FEELING SAD ITS IMPORTANT TO TAKE CARE OF YOURSELF AND ADDRESS YOUREMOTIONS ERE ARE A FU SUGGESTIONS THAT MIGHT HELP IMPROVE YOUR MOOD ONE ACKNOWLEDGE YOUR FEELINS ITS OCADE A FEEL SAD ACKNOWLEDGING YOUR EMOTIONS AS THE FIRST STEP TOWARD FEELING BETTER ALLOW YOUR SELF SOME TIME TO JUST FEEL WHAT YOUR FEELING WITHOUT JUDGMAN TWO ENGAGIN A FAVORITE ACTIVITY DOING SOMETHING YOU IN JOY SUCH AS READING A BOOK WATCHING A FVORITE MOVY OR ENGAGING IN A HOBB CAN PROVIDE A COMFORTING DISTRACTION AND BRING SOME JOY THREE CONNECT WITH OTHERS REACH OUT TO FRIENDS OUR FAMLY MEMBERS WHO CAN OFFER SUPPORT SOMETIMES JUST TALTING ABOUT HOW YOU FEEL CAN LIGHTEN THE BURDN FOUR GET SOME EXERCISE FISICAL ACTIVITY CAN BOOSED AN DORPHANS WICH OURE CHEMICULS IN THE BRAIN THAT CAN LIGHTEN YOUR MOOD EVEN A SHORT WALK CAN MAKE A DIFFERENE FIVE LISEN TO MUSIC MUSIC CAN HAVE A POWERFUL AFFECT ON MOOD CHOOSE SONGS THAT LIFT YOUR PIRITS ARE COMMAN SOOTHYOU DEPENDING ON WHAT YOU FEEL YOU NEED SIX PRACTICE MINDFULNESS OR MEDITATION VUSE PRACTICES CAN HELP YOU STAKE PRESON AND REDUCE NEGATIVE THOUGHTS EVEN A FEW MINUTES CAN HAVE US O NIFICANT IMPACT SEVEN CAN SIDER PROFESSIONAL HELP IT SADNESS PERSISTS AND STARTS TO EFFECT YOUR DAILY LIFE IT MIGHT BE HELPFUL TO TALK TO A MENTAL HELTH PROFESSIONAL WHO CAN PROVIDE GUIDANCE AND SUPORT REMEMBER ITS PERFECALY OCADE TO HAVE DAYS WHEN YOU FEEL DOWN TACING STEPS TO CARE FOR YOURSELF DURING THESE TIMES AS IMPORDANT" + }, + { + "role": "assistant", + "content": "Thank you for your concern, but I am an AI language model and do not have feelings. My purpose is to provide assistance and support to those who need it. If you have any other questions or concerns, please feel free to ask." + }, + { + "role": "user", + "content": "what AI model are you GPT or what" + }, + { + "role": "assistant", + "content": "Yes, I am a GPT (Generative Pre-trained Transformer) AI language model developed by OpenAI. I am designed to understand natural language and generate human-like responses to various queries and prompts. My purpose is to assist and provide helpful responses to users like you." + } +] \ No newline at end of file diff --git a/chatbot/chroma_migrated/chroma.sqlite3 b/chatbot/chroma_migrated/chroma.sqlite3 new file mode 100644 index 0000000000000000000000000000000000000000..c2b60eb2e05148f031a21852f1baad7abbf402e0 Binary files /dev/null and b/chatbot/chroma_migrated/chroma.sqlite3 differ diff --git a/chatbot/fine_tuned_model/config.json b/chatbot/fine_tuned_model/config.json new file mode 100644 index 0000000000000000000000000000000000000000..bb4d6017a5101c73754210f69fa831ea678bd28e --- /dev/null +++ b/chatbot/fine_tuned_model/config.json @@ -0,0 +1,2026 @@ +{ + "_name_or_path": "google/vit-base-patch16-224", + "architectures": [ + "ViTForImageClassification" + ], + "attention_probs_dropout_prob": 0.0, + "encoder_stride": 16, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 768, + "id2label": { + "0": "tench, Tinca tinca", + "1": "goldfish, Carassius auratus", + "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", + "3": "tiger shark, Galeocerdo cuvieri", + "4": "hammerhead, hammerhead shark", + "5": "electric ray, crampfish, numbfish, torpedo", + "6": "stingray", + "7": "cock", + "8": "hen", + "9": "ostrich, Struthio camelus", + "10": "brambling, Fringilla montifringilla", + "11": "goldfinch, Carduelis carduelis", + "12": "house finch, linnet, Carpodacus mexicanus", + "13": "junco, snowbird", + "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", + "15": "robin, American robin, Turdus migratorius", + "16": "bulbul", + "17": "jay", + "18": "magpie", + "19": "chickadee", + "20": "water ouzel, dipper", + "21": "kite", + "22": "bald eagle, American eagle, Haliaeetus leucocephalus", + "23": "vulture", + "24": "great grey owl, great gray owl, Strix nebulosa", + "25": "European fire salamander, Salamandra salamandra", + "26": "common newt, Triturus vulgaris", + "27": "eft", + "28": "spotted salamander, Ambystoma maculatum", + "29": "axolotl, mud puppy, Ambystoma mexicanum", + "30": "bullfrog, Rana catesbeiana", + "31": "tree frog, tree-frog", + "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", + "33": "loggerhead, loggerhead turtle, Caretta caretta", + "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", + "35": "mud turtle", + "36": "terrapin", + "37": "box turtle, box tortoise", + "38": "banded gecko", + "39": "common iguana, iguana, Iguana iguana", + "40": "American chameleon, anole, Anolis carolinensis", + "41": "whiptail, whiptail lizard", + "42": "agama", + "43": "frilled lizard, Chlamydosaurus kingi", + "44": "alligator lizard", + "45": "Gila monster, Heloderma suspectum", + "46": "green lizard, Lacerta viridis", + "47": "African chameleon, Chamaeleo chamaeleon", + "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", + "49": "African crocodile, Nile crocodile, Crocodylus niloticus", + "50": "American alligator, Alligator mississipiensis", + "51": "triceratops", + "52": "thunder snake, worm snake, Carphophis amoenus", + "53": "ringneck snake, ring-necked snake, ring snake", + "54": "hognose snake, puff adder, sand viper", + "55": "green snake, grass snake", + "56": "king snake, kingsnake", + "57": "garter snake, grass snake", + "58": "water snake", + "59": "vine snake", + "60": "night snake, Hypsiglena torquata", + "61": "boa constrictor, Constrictor constrictor", + "62": "rock python, rock snake, Python sebae", + "63": "Indian cobra, Naja naja", + "64": "green mamba", + "65": "sea snake", + "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", + "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", + "68": "sidewinder, horned rattlesnake, Crotalus cerastes", + "69": "trilobite", + "70": "harvestman, daddy longlegs, Phalangium opilio", + "71": "scorpion", + "72": "black and gold garden spider, Argiope aurantia", + "73": "barn spider, Araneus cavaticus", + "74": "garden spider, Aranea diademata", + "75": "black widow, Latrodectus mactans", + "76": "tarantula", + "77": "wolf spider, hunting spider", + "78": "tick", + "79": "centipede", + "80": "black grouse", + "81": "ptarmigan", + "82": "ruffed grouse, partridge, Bonasa umbellus", + "83": "prairie chicken, prairie grouse, prairie fowl", + "84": "peacock", + "85": "quail", + "86": "partridge", + "87": "African grey, African gray, Psittacus erithacus", + "88": "macaw", + "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", + "90": "lorikeet", + "91": "coucal", + "92": "bee eater", + "93": "hornbill", + "94": "hummingbird", + "95": "jacamar", + "96": "toucan", + "97": "drake", + "98": "red-breasted merganser, Mergus serrator", + "99": "goose", + "100": "black swan, Cygnus atratus", + "101": "tusker", + "102": "echidna, spiny anteater, anteater", + "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", + "104": "wallaby, brush kangaroo", + "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", + "106": "wombat", + "107": "jellyfish", + "108": "sea anemone, anemone", + "109": "brain coral", + "110": "flatworm, platyhelminth", + "111": "nematode, nematode worm, roundworm", + "112": "conch", + "113": "snail", + "114": "slug", + "115": "sea slug, nudibranch", + "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", + "117": "chambered nautilus, pearly nautilus, nautilus", + "118": "Dungeness crab, Cancer magister", + "119": "rock crab, Cancer irroratus", + "120": "fiddler crab", + "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", + "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", + "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", + "124": "crayfish, crawfish, crawdad, crawdaddy", + "125": "hermit crab", + "126": "isopod", + "127": "white stork, Ciconia ciconia", + "128": "black stork, Ciconia nigra", + "129": "spoonbill", + "130": "flamingo", + "131": "little blue heron, Egretta caerulea", + "132": "American egret, great white heron, Egretta albus", + "133": "bittern", + "134": "crane", + "135": "limpkin, Aramus pictus", + "136": "European gallinule, Porphyrio porphyrio", + "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", + "138": "bustard", + "139": "ruddy turnstone, Arenaria interpres", + "140": "red-backed sandpiper, dunlin, Erolia alpina", + "141": "redshank, Tringa totanus", + "142": "dowitcher", + "143": "oystercatcher, oyster catcher", + "144": "pelican", + "145": "king penguin, Aptenodytes patagonica", + "146": "albatross, mollymawk", + "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", + "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", + "149": "dugong, Dugong dugon", + "150": "sea lion", + "151": "Chihuahua", + "152": "Japanese spaniel", + "153": "Maltese dog, Maltese terrier, Maltese", + "154": "Pekinese, Pekingese, Peke", + "155": "Shih-Tzu", + "156": "Blenheim spaniel", + "157": "papillon", + "158": "toy terrier", + "159": "Rhodesian ridgeback", + "160": "Afghan hound, Afghan", + "161": "basset, basset hound", + "162": "beagle", + "163": "bloodhound, sleuthhound", + "164": "bluetick", + "165": "black-and-tan coonhound", + "166": "Walker hound, Walker foxhound", + "167": "English foxhound", + "168": "redbone", + "169": "borzoi, Russian wolfhound", + "170": "Irish wolfhound", + "171": "Italian greyhound", + "172": "whippet", + "173": "Ibizan hound, Ibizan Podenco", + "174": "Norwegian elkhound, elkhound", + "175": "otterhound, otter hound", + "176": "Saluki, gazelle hound", + "177": "Scottish deerhound, deerhound", + "178": "Weimaraner", + "179": "Staffordshire bullterrier, Staffordshire bull terrier", + "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", + "181": "Bedlington terrier", + "182": "Border terrier", + "183": "Kerry blue terrier", + "184": "Irish terrier", + "185": "Norfolk terrier", + "186": "Norwich terrier", + "187": "Yorkshire terrier", + "188": "wire-haired fox terrier", + "189": "Lakeland terrier", + "190": "Sealyham terrier, Sealyham", + "191": "Airedale, Airedale terrier", + "192": "cairn, cairn terrier", + "193": "Australian terrier", + "194": "Dandie Dinmont, Dandie Dinmont terrier", + "195": "Boston bull, Boston terrier", + "196": "miniature schnauzer", + "197": "giant schnauzer", + "198": "standard schnauzer", + "199": "Scotch terrier, Scottish terrier, Scottie", + "200": "Tibetan terrier, chrysanthemum dog", + "201": "silky terrier, Sydney silky", + "202": "soft-coated wheaten terrier", + "203": "West Highland white terrier", + "204": "Lhasa, Lhasa apso", + "205": "flat-coated retriever", + "206": "curly-coated retriever", + "207": "golden retriever", + "208": "Labrador retriever", + "209": "Chesapeake Bay retriever", + "210": "German short-haired pointer", + "211": "vizsla, Hungarian pointer", + "212": "English setter", + "213": "Irish setter, red setter", + "214": "Gordon setter", + "215": "Brittany spaniel", + "216": "clumber, clumber spaniel", + "217": "English springer, English springer spaniel", + "218": "Welsh springer spaniel", + "219": "cocker spaniel, English cocker spaniel, cocker", + "220": "Sussex spaniel", + "221": "Irish water spaniel", + "222": "kuvasz", + "223": "schipperke", + "224": "groenendael", + "225": "malinois", + "226": "briard", + "227": "kelpie", + "228": "komondor", + "229": "Old English sheepdog, bobtail", + "230": "Shetland sheepdog, Shetland sheep dog, Shetland", + "231": "collie", + "232": "Border collie", + "233": "Bouvier des Flandres, Bouviers des Flandres", + "234": "Rottweiler", + "235": "German shepherd, German shepherd dog, German police dog, alsatian", + "236": "Doberman, Doberman pinscher", + "237": "miniature pinscher", + "238": "Greater Swiss Mountain dog", + "239": "Bernese mountain dog", + "240": "Appenzeller", + "241": "EntleBucher", + "242": "boxer", + "243": "bull mastiff", + "244": "Tibetan mastiff", + "245": "French bulldog", + "246": "Great Dane", + "247": "Saint Bernard, St Bernard", + "248": "Eskimo dog, husky", + "249": "malamute, malemute, Alaskan malamute", + "250": "Siberian husky", + "251": "dalmatian, coach dog, carriage dog", + "252": "affenpinscher, monkey pinscher, monkey dog", + "253": "basenji", + "254": "pug, pug-dog", + "255": "Leonberg", + "256": "Newfoundland, Newfoundland dog", + "257": "Great Pyrenees", + "258": "Samoyed, Samoyede", + "259": "Pomeranian", + "260": "chow, chow chow", + "261": "keeshond", + "262": "Brabancon griffon", + "263": "Pembroke, Pembroke Welsh corgi", + "264": "Cardigan, Cardigan Welsh corgi", + "265": "toy poodle", + "266": "miniature poodle", + "267": "standard poodle", + "268": "Mexican hairless", + "269": "timber wolf, grey wolf, gray wolf, Canis lupus", + "270": "white wolf, Arctic wolf, Canis lupus tundrarum", + "271": "red wolf, maned wolf, Canis rufus, Canis niger", + "272": "coyote, prairie wolf, brush wolf, Canis latrans", + "273": "dingo, warrigal, warragal, Canis dingo", + "274": "dhole, Cuon alpinus", + "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", + "276": "hyena, hyaena", + "277": "red fox, Vulpes vulpes", + "278": "kit fox, Vulpes macrotis", + "279": "Arctic fox, white fox, Alopex lagopus", + "280": "grey fox, gray fox, Urocyon cinereoargenteus", + "281": "tabby, tabby cat", + "282": "tiger cat", + "283": "Persian cat", + "284": "Siamese cat, Siamese", + "285": "Egyptian cat", + "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", + "287": "lynx, catamount", + "288": "leopard, Panthera pardus", + "289": "snow leopard, ounce, Panthera uncia", + "290": "jaguar, panther, Panthera onca, Felis onca", + "291": "lion, king of beasts, Panthera leo", + "292": "tiger, Panthera tigris", + "293": "cheetah, chetah, Acinonyx jubatus", + "294": "brown bear, bruin, Ursus arctos", + "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", + "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", + "297": "sloth bear, Melursus ursinus, Ursus ursinus", + "298": "mongoose", + "299": "meerkat, mierkat", + "300": "tiger beetle", + "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", + "302": "ground beetle, carabid beetle", + "303": "long-horned beetle, longicorn, longicorn beetle", + "304": "leaf beetle, chrysomelid", + "305": "dung beetle", + "306": "rhinoceros beetle", + "307": "weevil", + "308": "fly", + "309": "bee", + "310": "ant, emmet, pismire", + "311": "grasshopper, hopper", + "312": "cricket", + "313": "walking stick, walkingstick, stick insect", + "314": "cockroach, roach", + "315": "mantis, mantid", + "316": "cicada, cicala", + "317": "leafhopper", + "318": "lacewing, lacewing fly", + "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + "320": "damselfly", + "321": "admiral", + "322": "ringlet, ringlet butterfly", + "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", + "324": "cabbage butterfly", + "325": "sulphur butterfly, sulfur butterfly", + "326": "lycaenid, lycaenid butterfly", + "327": "starfish, sea star", + "328": "sea urchin", + "329": "sea cucumber, holothurian", + "330": "wood rabbit, cottontail, cottontail rabbit", + "331": "hare", + "332": "Angora, Angora rabbit", + "333": "hamster", + "334": "porcupine, hedgehog", + "335": "fox squirrel, eastern fox squirrel, Sciurus niger", + "336": "marmot", + "337": "beaver", + "338": "guinea pig, Cavia cobaya", + "339": "sorrel", + "340": "zebra", + "341": "hog, pig, grunter, squealer, Sus scrofa", + "342": "wild boar, boar, Sus scrofa", + "343": "warthog", + "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "345": "ox", + "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", + "347": "bison", + "348": "ram, tup", + "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", + "350": "ibex, Capra ibex", + "351": "hartebeest", + "352": "impala, Aepyceros melampus", + "353": "gazelle", + "354": "Arabian camel, dromedary, Camelus dromedarius", + "355": "llama", + "356": "weasel", + "357": "mink", + "358": "polecat, fitch, foulmart, foumart, Mustela putorius", + "359": "black-footed ferret, ferret, Mustela nigripes", + "360": "otter", + "361": "skunk, polecat, wood pussy", + "362": "badger", + "363": "armadillo", + "364": "three-toed sloth, ai, Bradypus tridactylus", + "365": "orangutan, orang, orangutang, Pongo pygmaeus", + "366": "gorilla, Gorilla gorilla", + "367": "chimpanzee, chimp, Pan troglodytes", + "368": "gibbon, Hylobates lar", + "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", + "370": "guenon, guenon monkey", + "371": "patas, hussar monkey, Erythrocebus patas", + "372": "baboon", + "373": "macaque", + "374": "langur", + "375": "colobus, colobus monkey", + "376": "proboscis monkey, Nasalis larvatus", + "377": "marmoset", + "378": "capuchin, ringtail, Cebus capucinus", + "379": "howler monkey, howler", + "380": "titi, titi monkey", + "381": "spider monkey, Ateles geoffroyi", + "382": "squirrel monkey, Saimiri sciureus", + "383": "Madagascar cat, ring-tailed lemur, Lemur catta", + "384": "indri, indris, Indri indri, Indri brevicaudatus", + "385": "Indian elephant, Elephas maximus", + "386": "African elephant, Loxodonta africana", + "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", + "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", + "389": "barracouta, snoek", + "390": "eel", + "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", + "392": "rock beauty, Holocanthus tricolor", + "393": "anemone fish", + "394": "sturgeon", + "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", + "396": "lionfish", + "397": "puffer, pufferfish, blowfish, globefish", + "398": "abacus", + "399": "abaya", + "400": "academic gown, academic robe, judge's robe", + "401": "accordion, piano accordion, squeeze box", + "402": "acoustic guitar", + "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", + "404": "airliner", + "405": "airship, dirigible", + "406": "altar", + "407": "ambulance", + "408": "amphibian, amphibious vehicle", + "409": "analog clock", + "410": "apiary, bee house", + "411": "apron", + "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", + "413": "assault rifle, assault gun", + "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", + "415": "bakery, bakeshop, bakehouse", + "416": "balance beam, beam", + "417": "balloon", + "418": "ballpoint, ballpoint pen, ballpen, Biro", + "419": "Band Aid", + "420": "banjo", + "421": "bannister, banister, balustrade, balusters, handrail", + "422": "barbell", + "423": "barber chair", + "424": "barbershop", + "425": "barn", + "426": "barometer", + "427": "barrel, cask", + "428": "barrow, garden cart, lawn cart, wheelbarrow", + "429": "baseball", + "430": "basketball", + "431": "bassinet", + "432": "bassoon", + "433": "bathing cap, swimming cap", + "434": "bath towel", + "435": "bathtub, bathing tub, bath, tub", + "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", + "437": "beacon, lighthouse, beacon light, pharos", + "438": "beaker", + "439": "bearskin, busby, shako", + "440": "beer bottle", + "441": "beer glass", + "442": "bell cote, bell cot", + "443": "bib", + "444": "bicycle-built-for-two, tandem bicycle, tandem", + "445": "bikini, two-piece", + "446": "binder, ring-binder", + "447": "binoculars, field glasses, opera glasses", + "448": "birdhouse", + "449": "boathouse", + "450": "bobsled, bobsleigh, bob", + "451": "bolo tie, bolo, bola tie, bola", + "452": "bonnet, poke bonnet", + "453": "bookcase", + "454": "bookshop, bookstore, bookstall", + "455": "bottlecap", + "456": "bow", + "457": "bow tie, bow-tie, bowtie", + "458": "brass, memorial tablet, plaque", + "459": "brassiere, bra, bandeau", + "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", + "461": "breastplate, aegis, egis", + "462": "broom", + "463": "bucket, pail", + "464": "buckle", + "465": "bulletproof vest", + "466": "bullet train, bullet", + "467": "butcher shop, meat market", + "468": "cab, hack, taxi, taxicab", + "469": "caldron, cauldron", + "470": "candle, taper, wax light", + "471": "cannon", + "472": "canoe", + "473": "can opener, tin opener", + "474": "cardigan", + "475": "car mirror", + "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", + "477": "carpenter's kit, tool kit", + "478": "carton", + "479": "car wheel", + "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", + "481": "cassette", + "482": "cassette player", + "483": "castle", + "484": "catamaran", + "485": "CD player", + "486": "cello, violoncello", + "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", + "488": "chain", + "489": "chainlink fence", + "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", + "491": "chain saw, chainsaw", + "492": "chest", + "493": "chiffonier, commode", + "494": "chime, bell, gong", + "495": "china cabinet, china closet", + "496": "Christmas stocking", + "497": "church, church building", + "498": "cinema, movie theater, movie theatre, movie house, picture palace", + "499": "cleaver, meat cleaver, chopper", + "500": "cliff dwelling", + "501": "cloak", + "502": "clog, geta, patten, sabot", + "503": "cocktail shaker", + "504": "coffee mug", + "505": "coffeepot", + "506": "coil, spiral, volute, whorl, helix", + "507": "combination lock", + "508": "computer keyboard, keypad", + "509": "confectionery, confectionary, candy store", + "510": "container ship, containership, container vessel", + "511": "convertible", + "512": "corkscrew, bottle screw", + "513": "cornet, horn, trumpet, trump", + "514": "cowboy boot", + "515": "cowboy hat, ten-gallon hat", + "516": "cradle", + "517": "crane", + "518": "crash helmet", + "519": "crate", + "520": "crib, cot", + "521": "Crock Pot", + "522": "croquet ball", + "523": "crutch", + "524": "cuirass", + "525": "dam, dike, dyke", + "526": "desk", + "527": "desktop computer", + "528": "dial telephone, dial phone", + "529": "diaper, nappy, napkin", + "530": "digital clock", + "531": "digital watch", + "532": "dining table, board", + "533": "dishrag, dishcloth", + "534": "dishwasher, dish washer, dishwashing machine", + "535": "disk brake, disc brake", + "536": "dock, dockage, docking facility", + "537": "dogsled, dog sled, dog sleigh", + "538": "dome", + "539": "doormat, welcome mat", + "540": "drilling platform, offshore rig", + "541": "drum, membranophone, tympan", + "542": "drumstick", + "543": "dumbbell", + "544": "Dutch oven", + "545": "electric fan, blower", + "546": "electric guitar", + "547": "electric locomotive", + "548": "entertainment center", + "549": "envelope", + "550": "espresso maker", + "551": "face powder", + "552": "feather boa, boa", + "553": "file, file cabinet, filing cabinet", + "554": "fireboat", + "555": "fire engine, fire truck", + "556": "fire screen, fireguard", + "557": "flagpole, flagstaff", + "558": "flute, transverse flute", + "559": "folding chair", + "560": "football helmet", + "561": "forklift", + "562": "fountain", + "563": "fountain pen", + "564": "four-poster", + "565": "freight car", + "566": "French horn, horn", + "567": "frying pan, frypan, skillet", + "568": "fur coat", + "569": "garbage truck, dustcart", + "570": "gasmask, respirator, gas helmet", + "571": "gas pump, gasoline pump, petrol pump, island dispenser", + "572": "goblet", + "573": "go-kart", + "574": "golf ball", + "575": "golfcart, golf cart", + "576": "gondola", + "577": "gong, tam-tam", + "578": "gown", + "579": "grand piano, grand", + "580": "greenhouse, nursery, glasshouse", + "581": "grille, radiator grille", + "582": "grocery store, grocery, food market, market", + "583": "guillotine", + "584": "hair slide", + "585": "hair spray", + "586": "half track", + "587": "hammer", + "588": "hamper", + "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", + "590": "hand-held computer, hand-held microcomputer", + "591": "handkerchief, hankie, hanky, hankey", + "592": "hard disc, hard disk, fixed disk", + "593": "harmonica, mouth organ, harp, mouth harp", + "594": "harp", + "595": "harvester, reaper", + "596": "hatchet", + "597": "holster", + "598": "home theater, home theatre", + "599": "honeycomb", + "600": "hook, claw", + "601": "hoopskirt, crinoline", + "602": "horizontal bar, high bar", + "603": "horse cart, horse-cart", + "604": "hourglass", + "605": "iPod", + "606": "iron, smoothing iron", + "607": "jack-o'-lantern", + "608": "jean, blue jean, denim", + "609": "jeep, landrover", + "610": "jersey, T-shirt, tee shirt", + "611": "jigsaw puzzle", + "612": "jinrikisha, ricksha, rickshaw", + "613": "joystick", + "614": "kimono", + "615": "knee pad", + "616": "knot", + "617": "lab coat, laboratory coat", + "618": "ladle", + "619": "lampshade, lamp shade", + "620": "laptop, laptop computer", + "621": "lawn mower, mower", + "622": "lens cap, lens cover", + "623": "letter opener, paper knife, paperknife", + "624": "library", + "625": "lifeboat", + "626": "lighter, light, igniter, ignitor", + "627": "limousine, limo", + "628": "liner, ocean liner", + "629": "lipstick, lip rouge", + "630": "Loafer", + "631": "lotion", + "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", + "633": "loupe, jeweler's loupe", + "634": "lumbermill, sawmill", + "635": "magnetic compass", + "636": "mailbag, postbag", + "637": "mailbox, letter box", + "638": "maillot", + "639": "maillot, tank suit", + "640": "manhole cover", + "641": "maraca", + "642": "marimba, xylophone", + "643": "mask", + "644": "matchstick", + "645": "maypole", + "646": "maze, labyrinth", + "647": "measuring cup", + "648": "medicine chest, medicine cabinet", + "649": "megalith, megalithic structure", + "650": "microphone, mike", + "651": "microwave, microwave oven", + "652": "military uniform", + "653": "milk can", + "654": "minibus", + "655": "miniskirt, mini", + "656": "minivan", + "657": "missile", + "658": "mitten", + "659": "mixing bowl", + "660": "mobile home, manufactured home", + "661": "Model T", + "662": "modem", + "663": "monastery", + "664": "monitor", + "665": "moped", + "666": "mortar", + "667": "mortarboard", + "668": "mosque", + "669": "mosquito net", + "670": "motor scooter, scooter", + "671": "mountain bike, all-terrain bike, off-roader", + "672": "mountain tent", + "673": "mouse, computer mouse", + "674": "mousetrap", + "675": "moving van", + "676": "muzzle", + "677": "nail", + "678": "neck brace", + "679": "necklace", + "680": "nipple", + "681": "notebook, notebook computer", + "682": "obelisk", + "683": "oboe, hautboy, hautbois", + "684": "ocarina, sweet potato", + "685": "odometer, hodometer, mileometer, milometer", + "686": "oil filter", + "687": "organ, pipe organ", + "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", + "689": "overskirt", + "690": "oxcart", + "691": "oxygen mask", + "692": "packet", + "693": "paddle, boat paddle", + "694": "paddlewheel, paddle wheel", + "695": "padlock", + "696": "paintbrush", + "697": "pajama, pyjama, pj's, jammies", + "698": "palace", + "699": "panpipe, pandean pipe, syrinx", + "700": "paper towel", + "701": "parachute, chute", + "702": "parallel bars, bars", + "703": "park bench", + "704": "parking meter", + "705": "passenger car, coach, carriage", + "706": "patio, terrace", + "707": "pay-phone, pay-station", + "708": "pedestal, plinth, footstall", + "709": "pencil box, pencil case", + "710": "pencil sharpener", + "711": "perfume, essence", + "712": "Petri dish", + "713": "photocopier", + "714": "pick, plectrum, plectron", + "715": "pickelhaube", + "716": "picket fence, paling", + "717": "pickup, pickup truck", + "718": "pier", + "719": "piggy bank, penny bank", + "720": "pill bottle", + "721": "pillow", + "722": "ping-pong ball", + "723": "pinwheel", + "724": "pirate, pirate ship", + "725": "pitcher, ewer", + "726": "plane, carpenter's plane, woodworking plane", + "727": "planetarium", + "728": "plastic bag", + "729": "plate rack", + "730": "plow, plough", + "731": "plunger, plumber's helper", + "732": "Polaroid camera, Polaroid Land camera", + "733": "pole", + "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", + "735": "poncho", + "736": "pool table, billiard table, snooker table", + "737": "pop bottle, soda bottle", + "738": "pot, flowerpot", + "739": "potter's wheel", + "740": "power drill", + "741": "prayer rug, prayer mat", + "742": "printer", + "743": "prison, prison house", + "744": "projectile, missile", + "745": "projector", + "746": "puck, hockey puck", + "747": "punching bag, punch bag, punching ball, punchball", + "748": "purse", + "749": "quill, quill pen", + "750": "quilt, comforter, comfort, puff", + "751": "racer, race car, racing car", + "752": "racket, racquet", + "753": "radiator", + "754": "radio, wireless", + "755": "radio telescope, radio reflector", + "756": "rain barrel", + "757": "recreational vehicle, RV, R.V.", + "758": "reel", + "759": "reflex camera", + "760": "refrigerator, icebox", + "761": "remote control, remote", + "762": "restaurant, eating house, eating place, eatery", + "763": "revolver, six-gun, six-shooter", + "764": "rifle", + "765": "rocking chair, rocker", + "766": "rotisserie", + "767": "rubber eraser, rubber, pencil eraser", + "768": "rugby ball", + "769": "rule, ruler", + "770": "running shoe", + "771": "safe", + "772": "safety pin", + "773": "saltshaker, salt shaker", + "774": "sandal", + "775": "sarong", + "776": "sax, saxophone", + "777": "scabbard", + "778": "scale, weighing machine", + "779": "school bus", + "780": "schooner", + "781": "scoreboard", + "782": "screen, CRT screen", + "783": "screw", + "784": "screwdriver", + "785": "seat belt, seatbelt", + "786": "sewing machine", + "787": "shield, buckler", + "788": "shoe shop, shoe-shop, shoe store", + "789": "shoji", + "790": "shopping basket", + "791": "shopping cart", + "792": "shovel", + "793": "shower cap", + "794": "shower curtain", + "795": "ski", + "796": "ski mask", + "797": "sleeping bag", + "798": "slide rule, slipstick", + "799": "sliding door", + "800": "slot, one-armed bandit", + "801": "snorkel", + "802": "snowmobile", + "803": "snowplow, snowplough", + "804": "soap dispenser", + "805": "soccer ball", + "806": "sock", + "807": "solar dish, solar collector, solar furnace", + "808": "sombrero", + "809": "soup bowl", + "810": "space bar", + "811": "space heater", + "812": "space shuttle", + "813": "spatula", + "814": "speedboat", + "815": "spider web, spider's web", + "816": "spindle", + "817": "sports car, sport car", + "818": "spotlight, spot", + "819": "stage", + "820": "steam locomotive", + "821": "steel arch bridge", + "822": "steel drum", + "823": "stethoscope", + "824": "stole", + "825": "stone wall", + "826": "stopwatch, stop watch", + "827": "stove", + "828": "strainer", + "829": "streetcar, tram, tramcar, trolley, trolley car", + "830": "stretcher", + "831": "studio couch, day bed", + "832": "stupa, tope", + "833": "submarine, pigboat, sub, U-boat", + "834": "suit, suit of clothes", + "835": "sundial", + "836": "sunglass", + "837": "sunglasses, dark glasses, shades", + "838": "sunscreen, sunblock, sun blocker", + "839": "suspension bridge", + "840": "swab, swob, mop", + "841": "sweatshirt", + "842": "swimming trunks, bathing trunks", + "843": "swing", + "844": "switch, electric switch, electrical switch", + "845": "syringe", + "846": "table lamp", + "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", + "848": "tape player", + "849": "teapot", + "850": "teddy, teddy bear", + "851": "television, television system", + "852": "tennis ball", + "853": "thatch, thatched roof", + "854": "theater curtain, theatre curtain", + "855": "thimble", + "856": "thresher, thrasher, threshing machine", + "857": "throne", + "858": "tile roof", + "859": "toaster", + "860": "tobacco shop, tobacconist shop, tobacconist", + "861": "toilet seat", + "862": "torch", + "863": "totem pole", + "864": "tow truck, tow car, wrecker", + "865": "toyshop", + "866": "tractor", + "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", + "868": "tray", + "869": "trench coat", + "870": "tricycle, trike, velocipede", + "871": "trimaran", + "872": "tripod", + "873": "triumphal arch", + "874": "trolleybus, trolley coach, trackless trolley", + "875": "trombone", + "876": "tub, vat", + "877": "turnstile", + "878": "typewriter keyboard", + "879": "umbrella", + "880": "unicycle, monocycle", + "881": "upright, upright piano", + "882": "vacuum, vacuum cleaner", + "883": "vase", + "884": "vault", + "885": "velvet", + "886": "vending machine", + "887": "vestment", + "888": "viaduct", + "889": "violin, fiddle", + "890": "volleyball", + "891": "waffle iron", + "892": "wall clock", + "893": "wallet, billfold, notecase, pocketbook", + "894": "wardrobe, closet, press", + "895": "warplane, military plane", + "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", + "897": "washer, automatic washer, washing machine", + "898": "water bottle", + "899": "water jug", + "900": "water tower", + "901": "whiskey jug", + "902": "whistle", + "903": "wig", + "904": "window screen", + "905": "window shade", + "906": "Windsor tie", + "907": "wine bottle", + "908": "wing", + "909": "wok", + "910": "wooden spoon", + "911": "wool, woolen, woollen", + "912": "worm fence, snake fence, snake-rail fence, Virginia fence", + "913": "wreck", + "914": "yawl", + "915": "yurt", + "916": "web site, website, internet site, site", + "917": "comic book", + "918": "crossword puzzle, crossword", + "919": "street sign", + "920": "traffic light, traffic signal, stoplight", + "921": "book jacket, dust cover, dust jacket, dust wrapper", + "922": "menu", + "923": "plate", + "924": "guacamole", + "925": "consomme", + "926": "hot pot, hotpot", + "927": "trifle", + "928": "ice cream, icecream", + "929": "ice lolly, lolly, lollipop, popsicle", + "930": "French loaf", + "931": "bagel, beigel", + "932": "pretzel", + "933": "cheeseburger", + "934": "hotdog, hot dog, red hot", + "935": "mashed potato", + "936": "head cabbage", + "937": "broccoli", + "938": "cauliflower", + "939": "zucchini, courgette", + "940": "spaghetti squash", + "941": "acorn squash", + "942": "butternut squash", + "943": "cucumber, cuke", + "944": "artichoke, globe artichoke", + "945": "bell pepper", + "946": "cardoon", + "947": "mushroom", + "948": "Granny Smith", + "949": "strawberry", + "950": "orange", + "951": "lemon", + "952": "fig", + "953": "pineapple, ananas", + "954": "banana", + "955": "jackfruit, jak, jack", + "956": "custard apple", + "957": "pomegranate", + "958": "hay", + "959": "carbonara", + "960": "chocolate sauce, chocolate syrup", + "961": "dough", + "962": "meat loaf, meatloaf", + "963": "pizza, pizza pie", + "964": "potpie", + "965": "burrito", + "966": "red wine", + "967": "espresso", + "968": "cup", + "969": "eggnog", + "970": "alp", + "971": "bubble", + "972": "cliff, drop, drop-off", + "973": "coral reef", + "974": "geyser", + "975": "lakeside, lakeshore", + "976": "promontory, headland, head, foreland", + "977": "sandbar, sand bar", + "978": "seashore, coast, seacoast, sea-coast", + "979": "valley, vale", + "980": "volcano", + "981": "ballplayer, baseball player", + "982": "groom, bridegroom", + "983": "scuba diver", + "984": "rapeseed", + "985": "daisy", + "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + "987": "corn", + "988": "acorn", + "989": "hip, rose hip, rosehip", + "990": "buckeye, horse chestnut, conker", + "991": "coral fungus", + "992": "agaric", + "993": "gyromitra", + "994": "stinkhorn, carrion fungus", + "995": "earthstar", + "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", + "997": "bolete", + "998": "ear, spike, capitulum", + "999": "toilet tissue, toilet paper, bathroom tissue" + }, + "image_size": 224, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "Afghan hound, Afghan": 160, + "African chameleon, Chamaeleo chamaeleon": 47, + "African crocodile, Nile crocodile, Crocodylus niloticus": 49, + "African elephant, Loxodonta africana": 386, + "African grey, African gray, Psittacus erithacus": 87, + "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus": 275, + "Airedale, Airedale terrier": 191, + "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier": 180, + "American alligator, Alligator mississipiensis": 50, + "American black bear, black bear, Ursus americanus, Euarctos americanus": 295, + "American chameleon, anole, Anolis carolinensis": 40, + "American coot, marsh hen, mud hen, water hen, Fulica americana": 137, + "American egret, great white heron, Egretta albus": 132, + "American lobster, Northern lobster, Maine lobster, Homarus americanus": 122, + "Angora, Angora rabbit": 332, + "Appenzeller": 240, + "Arabian camel, dromedary, Camelus dromedarius": 354, + "Arctic fox, white fox, Alopex lagopus": 279, + "Australian terrier": 193, + "Band Aid": 419, + "Bedlington terrier": 181, + "Bernese mountain dog": 239, + "Blenheim spaniel": 156, + "Border collie": 232, + "Border terrier": 182, + "Boston bull, Boston terrier": 195, + "Bouvier des Flandres, Bouviers des Flandres": 233, + "Brabancon griffon": 262, + "Brittany spaniel": 215, + "CD player": 485, + "Cardigan, Cardigan Welsh corgi": 264, + "Chesapeake Bay retriever": 209, + "Chihuahua": 151, + "Christmas stocking": 496, + "Crock Pot": 521, + "Dandie Dinmont, Dandie Dinmont terrier": 194, + "Doberman, Doberman pinscher": 236, + "Dungeness crab, Cancer magister": 118, + "Dutch oven": 544, + "Egyptian cat": 285, + "English foxhound": 167, + "English setter": 212, + "English springer, English springer spaniel": 217, + "EntleBucher": 241, + "Eskimo dog, husky": 248, + "European fire salamander, Salamandra salamandra": 25, + "European gallinule, Porphyrio porphyrio": 136, + "French bulldog": 245, + "French horn, horn": 566, + "French loaf": 930, + "German shepherd, German shepherd dog, German police dog, alsatian": 235, + "German short-haired pointer": 210, + "Gila monster, Heloderma suspectum": 45, + "Gordon setter": 214, + "Granny Smith": 948, + "Great Dane": 246, + "Great Pyrenees": 257, + "Greater Swiss Mountain dog": 238, + "Ibizan hound, Ibizan Podenco": 173, + "Indian cobra, Naja naja": 63, + "Indian elephant, Elephas maximus": 385, + "Irish setter, red setter": 213, + "Irish terrier": 184, + "Irish water spaniel": 221, + "Irish wolfhound": 170, + "Italian greyhound": 171, + "Japanese spaniel": 152, + "Kerry blue terrier": 183, + "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48, + "Labrador retriever": 208, + "Lakeland terrier": 189, + "Leonberg": 255, + "Lhasa, Lhasa apso": 204, + "Loafer": 630, + "Madagascar cat, ring-tailed lemur, Lemur catta": 383, + "Maltese dog, Maltese terrier, Maltese": 153, + "Mexican hairless": 268, + "Model T": 661, + "Newfoundland, Newfoundland dog": 256, + "Norfolk terrier": 185, + "Norwegian elkhound, elkhound": 174, + "Norwich terrier": 186, + "Old English sheepdog, bobtail": 229, + "Pekinese, Pekingese, Peke": 154, + "Pembroke, Pembroke Welsh corgi": 263, + "Persian cat": 283, + "Petri dish": 712, + "Polaroid camera, Polaroid Land camera": 732, + "Pomeranian": 259, + "Rhodesian ridgeback": 159, + "Rottweiler": 234, + "Saint Bernard, St Bernard": 247, + "Saluki, gazelle hound": 176, + "Samoyed, Samoyede": 258, + "Scotch terrier, Scottish terrier, Scottie": 199, + "Scottish deerhound, deerhound": 177, + "Sealyham terrier, Sealyham": 190, + "Shetland sheepdog, Shetland sheep dog, Shetland": 230, + "Shih-Tzu": 155, + "Siamese cat, Siamese": 284, + "Siberian husky": 250, + "Staffordshire bullterrier, Staffordshire bull terrier": 179, + "Sussex spaniel": 220, + "Tibetan mastiff": 244, + "Tibetan terrier, chrysanthemum dog": 200, + "Walker hound, Walker foxhound": 166, + "Weimaraner": 178, + "Welsh springer spaniel": 218, + "West Highland white terrier": 203, + "Windsor tie": 906, + "Yorkshire terrier": 187, + "abacus": 398, + "abaya": 399, + "academic gown, academic robe, judge's robe": 400, + "accordion, piano accordion, squeeze box": 401, + "acorn": 988, + "acorn squash": 941, + "acoustic guitar": 402, + "admiral": 321, + "affenpinscher, monkey pinscher, monkey dog": 252, + "agama": 42, + "agaric": 992, + "aircraft carrier, carrier, flattop, attack aircraft carrier": 403, + "airliner": 404, + "airship, dirigible": 405, + "albatross, mollymawk": 146, + "alligator lizard": 44, + "alp": 970, + "altar": 406, + "ambulance": 407, + "amphibian, amphibious vehicle": 408, + "analog clock": 409, + "anemone fish": 393, + "ant, emmet, pismire": 310, + "apiary, bee house": 410, + "apron": 411, + "armadillo": 363, + "artichoke, globe artichoke": 944, + "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412, + "assault rifle, assault gun": 413, + "axolotl, mud puppy, Ambystoma mexicanum": 29, + "baboon": 372, + "backpack, back pack, knapsack, packsack, rucksack, haversack": 414, + "badger": 362, + "bagel, beigel": 931, + "bakery, bakeshop, bakehouse": 415, + "balance beam, beam": 416, + "bald eagle, American eagle, Haliaeetus leucocephalus": 22, + "balloon": 417, + "ballplayer, baseball player": 981, + "ballpoint, ballpoint pen, ballpen, Biro": 418, + "banana": 954, + "banded gecko": 38, + "banjo": 420, + "bannister, banister, balustrade, balusters, handrail": 421, + "barbell": 422, + "barber chair": 423, + "barbershop": 424, + "barn": 425, + "barn spider, Araneus cavaticus": 73, + "barometer": 426, + "barracouta, snoek": 389, + "barrel, cask": 427, + "barrow, garden cart, lawn cart, wheelbarrow": 428, + "baseball": 429, + "basenji": 253, + "basketball": 430, + "basset, basset hound": 161, + "bassinet": 431, + "bassoon": 432, + "bath towel": 434, + "bathing cap, swimming cap": 433, + "bathtub, bathing tub, bath, tub": 435, + "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436, + "beacon, lighthouse, beacon light, pharos": 437, + "beagle": 162, + "beaker": 438, + "bearskin, busby, shako": 439, + "beaver": 337, + "bee": 309, + "bee eater": 92, + "beer bottle": 440, + "beer glass": 441, + "bell cote, bell cot": 442, + "bell pepper": 945, + "bib": 443, + "bicycle-built-for-two, tandem bicycle, tandem": 444, + "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349, + "bikini, two-piece": 445, + "binder, ring-binder": 446, + "binoculars, field glasses, opera glasses": 447, + "birdhouse": 448, + "bison": 347, + "bittern": 133, + "black and gold garden spider, Argiope aurantia": 72, + "black grouse": 80, + "black stork, Ciconia nigra": 128, + "black swan, Cygnus atratus": 100, + "black widow, Latrodectus mactans": 75, + "black-and-tan coonhound": 165, + "black-footed ferret, ferret, Mustela nigripes": 359, + "bloodhound, sleuthhound": 163, + "bluetick": 164, + "boa constrictor, Constrictor constrictor": 61, + "boathouse": 449, + "bobsled, bobsleigh, bob": 450, + "bolete": 997, + "bolo tie, bolo, bola tie, bola": 451, + "bonnet, poke bonnet": 452, + "book jacket, dust cover, dust jacket, dust wrapper": 921, + "bookcase": 453, + "bookshop, bookstore, bookstall": 454, + "borzoi, Russian wolfhound": 169, + "bottlecap": 455, + "bow": 456, + "bow tie, bow-tie, bowtie": 457, + "box turtle, box tortoise": 37, + "boxer": 242, + "brain coral": 109, + "brambling, Fringilla montifringilla": 10, + "brass, memorial tablet, plaque": 458, + "brassiere, bra, bandeau": 459, + "breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460, + "breastplate, aegis, egis": 461, + "briard": 226, + "broccoli": 937, + "broom": 462, + "brown bear, bruin, Ursus arctos": 294, + "bubble": 971, + "bucket, pail": 463, + "buckeye, horse chestnut, conker": 990, + "buckle": 464, + "bulbul": 16, + "bull mastiff": 243, + "bullet train, bullet": 466, + "bulletproof vest": 465, + "bullfrog, Rana catesbeiana": 30, + "burrito": 965, + "bustard": 138, + "butcher shop, meat market": 467, + "butternut squash": 942, + "cab, hack, taxi, taxicab": 468, + "cabbage butterfly": 324, + "cairn, cairn terrier": 192, + "caldron, cauldron": 469, + "can opener, tin opener": 473, + "candle, taper, wax light": 470, + "cannon": 471, + "canoe": 472, + "capuchin, ringtail, Cebus capucinus": 378, + "car mirror": 475, + "car wheel": 479, + "carbonara": 959, + "cardigan": 474, + "cardoon": 946, + "carousel, carrousel, merry-go-round, roundabout, whirligig": 476, + "carpenter's kit, tool kit": 477, + "carton": 478, + "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480, + "cassette": 481, + "cassette player": 482, + "castle": 483, + "catamaran": 484, + "cauliflower": 938, + "cello, violoncello": 486, + "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487, + "centipede": 79, + "chain": 488, + "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490, + "chain saw, chainsaw": 491, + "chainlink fence": 489, + "chambered nautilus, pearly nautilus, nautilus": 117, + "cheeseburger": 933, + "cheetah, chetah, Acinonyx jubatus": 293, + "chest": 492, + "chickadee": 19, + "chiffonier, commode": 493, + "chime, bell, gong": 494, + "chimpanzee, chimp, Pan troglodytes": 367, + "china cabinet, china closet": 495, + "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116, + "chocolate sauce, chocolate syrup": 960, + "chow, chow chow": 260, + "church, church building": 497, + "cicada, cicala": 316, + "cinema, movie theater, movie theatre, movie house, picture palace": 498, + "cleaver, meat cleaver, chopper": 499, + "cliff dwelling": 500, + "cliff, drop, drop-off": 972, + "cloak": 501, + "clog, geta, patten, sabot": 502, + "clumber, clumber spaniel": 216, + "cock": 7, + "cocker spaniel, English cocker spaniel, cocker": 219, + "cockroach, roach": 314, + "cocktail shaker": 503, + "coffee mug": 504, + "coffeepot": 505, + "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391, + "coil, spiral, volute, whorl, helix": 506, + "collie": 231, + "colobus, colobus monkey": 375, + "combination lock": 507, + "comic book": 917, + "common iguana, iguana, Iguana iguana": 39, + "common newt, Triturus vulgaris": 26, + "computer keyboard, keypad": 508, + "conch": 112, + "confectionery, confectionary, candy store": 509, + "consomme": 925, + "container ship, containership, container vessel": 510, + "convertible": 511, + "coral fungus": 991, + "coral reef": 973, + "corkscrew, bottle screw": 512, + "corn": 987, + "cornet, horn, trumpet, trump": 513, + "coucal": 91, + "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286, + "cowboy boot": 514, + "cowboy hat, ten-gallon hat": 515, + "coyote, prairie wolf, brush wolf, Canis latrans": 272, + "cradle": 516, + "crane": 517, + "crash helmet": 518, + "crate": 519, + "crayfish, crawfish, crawdad, crawdaddy": 124, + "crib, cot": 520, + "cricket": 312, + "croquet ball": 522, + "crossword puzzle, crossword": 918, + "crutch": 523, + "cucumber, cuke": 943, + "cuirass": 524, + "cup": 968, + "curly-coated retriever": 206, + "custard apple": 956, + "daisy": 985, + "dalmatian, coach dog, carriage dog": 251, + "dam, dike, dyke": 525, + "damselfly": 320, + "desk": 526, + "desktop computer": 527, + "dhole, Cuon alpinus": 274, + "dial telephone, dial phone": 528, + "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67, + "diaper, nappy, napkin": 529, + "digital clock": 530, + "digital watch": 531, + "dingo, warrigal, warragal, Canis dingo": 273, + "dining table, board": 532, + "dishrag, dishcloth": 533, + "dishwasher, dish washer, dishwashing machine": 534, + "disk brake, disc brake": 535, + "dock, dockage, docking facility": 536, + "dogsled, dog sled, dog sleigh": 537, + "dome": 538, + "doormat, welcome mat": 539, + "dough": 961, + "dowitcher": 142, + "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319, + "drake": 97, + "drilling platform, offshore rig": 540, + "drum, membranophone, tympan": 541, + "drumstick": 542, + "dugong, Dugong dugon": 149, + "dumbbell": 543, + "dung beetle": 305, + "ear, spike, capitulum": 998, + "earthstar": 995, + "echidna, spiny anteater, anteater": 102, + "eel": 390, + "eft": 27, + "eggnog": 969, + "electric fan, blower": 545, + "electric guitar": 546, + "electric locomotive": 547, + "electric ray, crampfish, numbfish, torpedo": 5, + "entertainment center": 548, + "envelope": 549, + "espresso": 967, + "espresso maker": 550, + "face powder": 551, + "feather boa, boa": 552, + "fiddler crab": 120, + "fig": 952, + "file, file cabinet, filing cabinet": 553, + "fire engine, fire truck": 555, + "fire screen, fireguard": 556, + "fireboat": 554, + "flagpole, flagstaff": 557, + "flamingo": 130, + "flat-coated retriever": 205, + "flatworm, platyhelminth": 110, + "flute, transverse flute": 558, + "fly": 308, + "folding chair": 559, + "football helmet": 560, + "forklift": 561, + "fountain": 562, + "fountain pen": 563, + "four-poster": 564, + "fox squirrel, eastern fox squirrel, Sciurus niger": 335, + "freight car": 565, + "frilled lizard, Chlamydosaurus kingi": 43, + "frying pan, frypan, skillet": 567, + "fur coat": 568, + "gar, garfish, garpike, billfish, Lepisosteus osseus": 395, + "garbage truck, dustcart": 569, + "garden spider, Aranea diademata": 74, + "garter snake, grass snake": 57, + "gas pump, gasoline pump, petrol pump, island dispenser": 571, + "gasmask, respirator, gas helmet": 570, + "gazelle": 353, + "geyser": 974, + "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388, + "giant schnauzer": 197, + "gibbon, Hylobates lar": 368, + "go-kart": 573, + "goblet": 572, + "golden retriever": 207, + "goldfinch, Carduelis carduelis": 11, + "goldfish, Carassius auratus": 1, + "golf ball": 574, + "golfcart, golf cart": 575, + "gondola": 576, + "gong, tam-tam": 577, + "goose": 99, + "gorilla, Gorilla gorilla": 366, + "gown": 578, + "grand piano, grand": 579, + "grasshopper, hopper": 311, + "great grey owl, great gray owl, Strix nebulosa": 24, + "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2, + "green lizard, Lacerta viridis": 46, + "green mamba": 64, + "green snake, grass snake": 55, + "greenhouse, nursery, glasshouse": 580, + "grey fox, gray fox, Urocyon cinereoargenteus": 280, + "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147, + "grille, radiator grille": 581, + "grocery store, grocery, food market, market": 582, + "groenendael": 224, + "groom, bridegroom": 982, + "ground beetle, carabid beetle": 302, + "guacamole": 924, + "guenon, guenon monkey": 370, + "guillotine": 583, + "guinea pig, Cavia cobaya": 338, + "gyromitra": 993, + "hair slide": 584, + "hair spray": 585, + "half track": 586, + "hammer": 587, + "hammerhead, hammerhead shark": 4, + "hamper": 588, + "hamster": 333, + "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589, + "hand-held computer, hand-held microcomputer": 590, + "handkerchief, hankie, hanky, hankey": 591, + "hard disc, hard disk, fixed disk": 592, + "hare": 331, + "harmonica, mouth organ, harp, mouth harp": 593, + "harp": 594, + "hartebeest": 351, + "harvester, reaper": 595, + "harvestman, daddy longlegs, Phalangium opilio": 70, + "hatchet": 596, + "hay": 958, + "head cabbage": 936, + "hen": 8, + "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996, + "hermit crab": 125, + "hip, rose hip, rosehip": 989, + "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344, + "hog, pig, grunter, squealer, Sus scrofa": 341, + "hognose snake, puff adder, sand viper": 54, + "holster": 597, + "home theater, home theatre": 598, + "honeycomb": 599, + "hook, claw": 600, + "hoopskirt, crinoline": 601, + "horizontal bar, high bar": 602, + "hornbill": 93, + "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66, + "horse cart, horse-cart": 603, + "hot pot, hotpot": 926, + "hotdog, hot dog, red hot": 934, + "hourglass": 604, + "house finch, linnet, Carpodacus mexicanus": 12, + "howler monkey, howler": 379, + "hummingbird": 94, + "hyena, hyaena": 276, + "iPod": 605, + "ibex, Capra ibex": 350, + "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296, + "ice cream, icecream": 928, + "ice lolly, lolly, lollipop, popsicle": 929, + "impala, Aepyceros melampus": 352, + "indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14, + "indri, indris, Indri indri, Indri brevicaudatus": 384, + "iron, smoothing iron": 606, + "isopod": 126, + "jacamar": 95, + "jack-o'-lantern": 607, + "jackfruit, jak, jack": 955, + "jaguar, panther, Panthera onca, Felis onca": 290, + "jay": 17, + "jean, blue jean, denim": 608, + "jeep, landrover": 609, + "jellyfish": 107, + "jersey, T-shirt, tee shirt": 610, + "jigsaw puzzle": 611, + "jinrikisha, ricksha, rickshaw": 612, + "joystick": 613, + "junco, snowbird": 13, + "keeshond": 261, + "kelpie": 227, + "killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148, + "kimono": 614, + "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121, + "king penguin, Aptenodytes patagonica": 145, + "king snake, kingsnake": 56, + "kit fox, Vulpes macrotis": 278, + "kite": 21, + "knee pad": 615, + "knot": 616, + "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105, + "komondor": 228, + "kuvasz": 222, + "lab coat, laboratory coat": 617, + "lacewing, lacewing fly": 318, + "ladle": 618, + "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301, + "lakeside, lakeshore": 975, + "lampshade, lamp shade": 619, + "langur": 374, + "laptop, laptop computer": 620, + "lawn mower, mower": 621, + "leaf beetle, chrysomelid": 304, + "leafhopper": 317, + "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34, + "lemon": 951, + "lens cap, lens cover": 622, + "leopard, Panthera pardus": 288, + "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387, + "letter opener, paper knife, paperknife": 623, + "library": 624, + "lifeboat": 625, + "lighter, light, igniter, ignitor": 626, + "limousine, limo": 627, + "limpkin, Aramus pictus": 135, + "liner, ocean liner": 628, + "lion, king of beasts, Panthera leo": 291, + "lionfish": 396, + "lipstick, lip rouge": 629, + "little blue heron, Egretta caerulea": 131, + "llama": 355, + "loggerhead, loggerhead turtle, Caretta caretta": 33, + "long-horned beetle, longicorn, longicorn beetle": 303, + "lorikeet": 90, + "lotion": 631, + "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632, + "loupe, jeweler's loupe": 633, + "lumbermill, sawmill": 634, + "lycaenid, lycaenid butterfly": 326, + "lynx, catamount": 287, + "macaque": 373, + "macaw": 88, + "magnetic compass": 635, + "magpie": 18, + "mailbag, postbag": 636, + "mailbox, letter box": 637, + "maillot": 638, + "maillot, tank suit": 639, + "malamute, malemute, Alaskan malamute": 249, + "malinois": 225, + "manhole cover": 640, + "mantis, mantid": 315, + "maraca": 641, + "marimba, xylophone": 642, + "marmoset": 377, + "marmot": 336, + "mashed potato": 935, + "mask": 643, + "matchstick": 644, + "maypole": 645, + "maze, labyrinth": 646, + "measuring cup": 647, + "meat loaf, meatloaf": 962, + "medicine chest, medicine cabinet": 648, + "meerkat, mierkat": 299, + "megalith, megalithic structure": 649, + "menu": 922, + "microphone, mike": 650, + "microwave, microwave oven": 651, + "military uniform": 652, + "milk can": 653, + "miniature pinscher": 237, + "miniature poodle": 266, + "miniature schnauzer": 196, + "minibus": 654, + "miniskirt, mini": 655, + "minivan": 656, + "mink": 357, + "missile": 657, + "mitten": 658, + "mixing bowl": 659, + "mobile home, manufactured home": 660, + "modem": 662, + "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus": 323, + "monastery": 663, + "mongoose": 298, + "monitor": 664, + "moped": 665, + "mortar": 666, + "mortarboard": 667, + "mosque": 668, + "mosquito net": 669, + "motor scooter, scooter": 670, + "mountain bike, 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"analog clock", + "count": 1, + "last_detected": "2024-11-07T12:17:47.055052", + "history": [ + { + "timestamp": "2024-11-07T12:17:47.055052", + "count": 0 + } + ] + }, + "dining table, board": { + "category": "dining table, board", + "count": 2, + "last_detected": "2024-11-07T12:17:47.188026", + "history": [ + { + "timestamp": "2024-11-07T12:17:47.120347", + "count": 0 + }, + { + "timestamp": "2024-11-07T12:17:47.188026", + "count": 1 + } + ] + } +} \ No newline at end of file diff --git a/chatbot/line_chart.png b/chatbot/line_chart.png new file mode 100644 index 0000000000000000000000000000000000000000..bf9f36b65edbbbe004bfd3f33aa7eb16c03c7f3e Binary files /dev/null and b/chatbot/line_chart.png differ diff --git a/chatbot/main.py.save b/chatbot/main.py.save new file mode 100644 index 0000000000000000000000000000000000000000..4235c15a1eaad0454f5170b5fb8be849873ef2ea --- /dev/null +++ b/chatbot/main.py.save @@ -0,0 +1,52 @@ +import gradio as grimport os +import keras +import keras_nlp + +import os + +os.environ["KERAS_BACKEND"] = "jax" +# Avoid memory fragmentation on JAX backend. +os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00" + +import os + +# Set Kaggle API credentials +os.environ["KAGGLE_USERNAME"] = "rogerkorantenng" +os.environ["KAGGLE_KEY"] = "9a33b6e88bcb6058b1281d777fa6808d" + +# Load environment variables +load_dotenv() + +# Replace this with the path or method to load your local model +gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en") + +def generate_response(message, history): + # Format the conversation history for the local model + formatted_history = [] + for user, assistant in history: + formatted_history.append(f"Instruction:\n{user}\n\nResponse:\n{assistant}") + + # Add the latest user message to the history + formatted_history.append(f"Instruction:\n{message}\n\nResponse:\n") + + # Join formatted history into a single string for input + input_text = "\n".join(formatted_history) + + # Generate response from the local model + # Make sure to adjust this part according to your model's API + response = gemma_lm.generate(input_text, max_length=256) + + # Extract the response text + # Adjust the response extraction based on the actual structure of your model's output + return response[0] # Change this line if necessary + +# Create the Gradio interface +gr.ChatInterface( + generate_response, + chatbot=gr.Chatbot(height=300), + textbox=gr.Textbox(placeholder="You can ask me anything", container=False, scale=7), + title="Local Model Chat Bot", + retry_btn=None, + undo_btn="Delete Previous", + clear_btn="Clear" +).launch(share=True) diff --git a/chatbot/main2.py b/chatbot/main2.py new file mode 100644 index 0000000000000000000000000000000000000000..fecf07a24bf4e10c9bf31f8c5379ef6940842205 --- /dev/null +++ b/chatbot/main2.py @@ -0,0 +1,48 @@ +import gradio as gr +import os +import keras_nlp +from transformers import AutoModelForCausalLM + +# Set Kaggle API credentials using values from environment variables +os.environ["KAGGLE_USERNAME"] = os.environ.get("KAGGLE_USERNAME") +os.environ["KAGGLE_KEY"] = os.environ.get("KAGGLE_KEY") + + +# Load LoRA weights if you have them +LoRA_weights_path = "fined-tuned-model.lora.h5" +gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en") +gemma_lm.backbone.enable_lora(rank=4) # Enable LoRA with rank 4 +gemma_lm.preprocessor.sequence_length = 512 # Limit sequence length +gemma_lm.backbone.load_lora_weights(LoRA_weights_path) # Load LoRA weights + +# Define the response generation function +def generate_response(message): + # Create a prompt template + template = "Instruction:\n{instruction}\n\nResponse:\n{response}" + + # Create the prompt with the current message + prompt = template.format(instruction=message, response="") + print("Prompt:\n", prompt) + + # Generate response from the model + response = gemma_lm.generate(prompt, max_length=256) + # Only keep the generated response + response = response.split("Response:")[-1].strip() + + print("Generated Response:\n", response) + + # Extract and return the generated response text + return response # Adjust this if your model's output structure differs + +# Create the Gradio chat interface +interface = gr.Interface( + fn=generate_response, # Function that generates responses + inputs=gr.Textbox(placeholder="Hello, I am Sage, your mental health advisor", lines=2, scale=7), + outputs=gr.Textbox(), + title="Sage, your Mental Health Advisor", +# description="Chat with Sage, your mental health advisor.", +# live=True +) +proxy_prefix = os.environ.get("PROXY_PREFIX") +# Launch the Gradio app +interface.launch(server_name="0.0.0.0", server_port=8080, root_path=proxy_prefix, share=True) diff --git a/chatbot/model_config.json b/chatbot/model_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0045ce3b942e9be61dc7c71a261d31cd5017bed9 --- /dev/null +++ b/chatbot/model_config.json @@ -0,0 +1,27 @@ +{ + "gpt-4": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview", + "api_key": "XTtd4wdC0mlEdbQqp0KWOe9aWrlg4z4HurxXlgBf5ASUvrB9iPkwJQQJ99AKACHYHv6XJ3w3AAABACOGaeSg", + "model_path": null + }, + "gpt-4o": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview", + "api_key": "XTtd4wdC0mlEdbQqp0KWOe9aWrlg4z4HurxXlgBf5ASUvrB9iPkwJQQJ99AKACHYHv6XJ3w3AAABACOGaeSg", + "model_path": null + }, + "gpt-35-turbo": { + "endpoint": "https://rogerkoranteng.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-08-01-preview", + "api_key": "cfe75d17d47c40318db6230d2677d84f", + "model_path": null + }, + "gpt-4-32k": { + "endpoint": "https://roger-m38orjxq-australiaeast.openai.azure.com/openai/deployments/gpt-4-32k/chat/completions?api-version=2024-08-01-preview", + "api_key": "AebO97c0w0lnrppFNpbuPVueKth7HfGOdMbKeaX5R2Uql6wvJA5qJQQJ99AKACL93NaXJ3w3AAABACOGh8MD", + "model_path": null + }, + "microsoft/Phi-3.5-mini-instruct": { + "endpoint": null, + "model_path": "./models/microsoft/Phi-3.5-mini-instruct", + "api_key": null + } +} \ No newline at end of file diff --git a/chatbot/models/500/config.json b/chatbot/models/500/config.json new file mode 100644 index 0000000000000000000000000000000000000000..bb4d6017a5101c73754210f69fa831ea678bd28e --- /dev/null +++ b/chatbot/models/500/config.json @@ -0,0 +1,2026 @@ +{ + "_name_or_path": "google/vit-base-patch16-224", + "architectures": [ + "ViTForImageClassification" + ], + "attention_probs_dropout_prob": 0.0, + "encoder_stride": 16, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.0, + "hidden_size": 768, + "id2label": { + "0": "tench, Tinca tinca", + "1": "goldfish, Carassius auratus", + "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", + "3": "tiger shark, Galeocerdo cuvieri", + "4": "hammerhead, hammerhead shark", + "5": "electric ray, crampfish, numbfish, torpedo", + "6": "stingray", + "7": "cock", + "8": "hen", + "9": "ostrich, Struthio camelus", + "10": "brambling, Fringilla montifringilla", + "11": "goldfinch, Carduelis carduelis", + "12": "house finch, linnet, Carpodacus mexicanus", + "13": "junco, snowbird", + "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", + "15": "robin, American robin, Turdus migratorius", + "16": "bulbul", + "17": "jay", + "18": "magpie", + "19": "chickadee", + "20": "water ouzel, dipper", + "21": "kite", + "22": "bald eagle, American eagle, Haliaeetus leucocephalus", + "23": "vulture", + "24": "great grey owl, great gray owl, Strix nebulosa", + "25": "European fire salamander, Salamandra salamandra", + "26": "common newt, Triturus vulgaris", + "27": "eft", + "28": "spotted salamander, Ambystoma maculatum", + "29": "axolotl, mud puppy, Ambystoma mexicanum", + "30": "bullfrog, Rana catesbeiana", + "31": "tree frog, tree-frog", + "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", + "33": "loggerhead, loggerhead turtle, Caretta caretta", + "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", + "35": "mud turtle", + "36": "terrapin", + "37": "box turtle, box tortoise", + "38": "banded gecko", + "39": "common iguana, iguana, Iguana iguana", + "40": "American chameleon, anole, Anolis carolinensis", + "41": "whiptail, whiptail lizard", + "42": "agama", + "43": "frilled lizard, Chlamydosaurus kingi", + "44": "alligator lizard", + "45": "Gila monster, Heloderma suspectum", + "46": "green lizard, Lacerta viridis", + "47": "African chameleon, Chamaeleo chamaeleon", + "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", + "49": "African crocodile, Nile crocodile, Crocodylus niloticus", + "50": "American alligator, Alligator mississipiensis", + "51": "triceratops", + "52": "thunder snake, worm snake, Carphophis amoenus", + "53": "ringneck snake, ring-necked snake, ring snake", + "54": "hognose snake, puff adder, sand viper", + "55": "green snake, grass snake", + "56": "king snake, kingsnake", + "57": "garter snake, grass snake", + "58": "water snake", + "59": "vine snake", + "60": "night snake, Hypsiglena torquata", + "61": "boa constrictor, Constrictor constrictor", + "62": "rock python, rock snake, Python sebae", + "63": "Indian cobra, Naja naja", + "64": "green mamba", + "65": "sea snake", + "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", + "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", + "68": "sidewinder, horned rattlesnake, Crotalus cerastes", + "69": "trilobite", + "70": "harvestman, daddy longlegs, Phalangium opilio", + "71": "scorpion", + "72": "black and gold garden spider, Argiope aurantia", + "73": "barn spider, Araneus cavaticus", + "74": "garden spider, Aranea diademata", + "75": "black widow, Latrodectus mactans", + "76": "tarantula", + "77": "wolf spider, hunting spider", + "78": "tick", + "79": "centipede", + "80": "black grouse", + "81": "ptarmigan", + "82": "ruffed grouse, partridge, Bonasa umbellus", + "83": "prairie chicken, prairie grouse, prairie fowl", + "84": "peacock", + "85": "quail", + "86": "partridge", + "87": "African grey, African gray, Psittacus erithacus", + "88": "macaw", + "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", + "90": "lorikeet", + "91": "coucal", + "92": "bee eater", + "93": "hornbill", + "94": "hummingbird", + "95": "jacamar", + "96": "toucan", + "97": "drake", + "98": "red-breasted merganser, Mergus serrator", + "99": "goose", + "100": "black swan, Cygnus atratus", + "101": "tusker", + "102": "echidna, spiny anteater, anteater", + "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", + "104": "wallaby, brush kangaroo", + "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", + "106": "wombat", + "107": "jellyfish", + "108": "sea anemone, anemone", + "109": "brain coral", + "110": "flatworm, platyhelminth", + "111": "nematode, nematode worm, roundworm", + "112": "conch", + "113": "snail", + "114": "slug", + "115": "sea slug, nudibranch", + "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", + "117": "chambered nautilus, pearly nautilus, nautilus", + "118": "Dungeness crab, Cancer magister", + "119": "rock crab, Cancer irroratus", + "120": "fiddler crab", + "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", + "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", + "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", + "124": "crayfish, crawfish, crawdad, crawdaddy", + "125": "hermit crab", + "126": "isopod", + "127": "white stork, Ciconia ciconia", + "128": "black stork, Ciconia nigra", + "129": "spoonbill", + "130": "flamingo", + "131": "little blue heron, Egretta caerulea", + "132": "American egret, great white heron, Egretta albus", + "133": "bittern", + "134": "crane", + "135": "limpkin, Aramus pictus", + "136": "European gallinule, Porphyrio porphyrio", + "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", + "138": "bustard", + "139": "ruddy turnstone, Arenaria interpres", + "140": "red-backed sandpiper, dunlin, Erolia alpina", + "141": "redshank, Tringa totanus", + "142": "dowitcher", + "143": "oystercatcher, oyster catcher", + "144": "pelican", + "145": "king penguin, Aptenodytes patagonica", + "146": "albatross, mollymawk", + "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", + "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", + "149": "dugong, Dugong dugon", + "150": "sea lion", + "151": "Chihuahua", + "152": "Japanese spaniel", + "153": "Maltese dog, Maltese terrier, Maltese", + "154": "Pekinese, Pekingese, Peke", + "155": "Shih-Tzu", + "156": "Blenheim spaniel", + "157": "papillon", + "158": "toy terrier", + "159": "Rhodesian ridgeback", + "160": "Afghan hound, Afghan", + "161": "basset, basset hound", + "162": "beagle", + "163": "bloodhound, sleuthhound", + "164": "bluetick", + "165": "black-and-tan coonhound", + "166": "Walker hound, Walker foxhound", + "167": "English foxhound", + "168": "redbone", + "169": "borzoi, Russian wolfhound", + "170": "Irish wolfhound", + "171": "Italian greyhound", + "172": "whippet", + "173": "Ibizan hound, Ibizan Podenco", + "174": "Norwegian elkhound, elkhound", + "175": "otterhound, otter hound", + "176": "Saluki, gazelle hound", + "177": "Scottish deerhound, deerhound", + "178": "Weimaraner", + "179": "Staffordshire bullterrier, Staffordshire bull terrier", + "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", + "181": "Bedlington terrier", + "182": "Border terrier", + "183": "Kerry blue terrier", + "184": "Irish terrier", + "185": "Norfolk terrier", + "186": "Norwich terrier", + "187": "Yorkshire terrier", + "188": "wire-haired fox terrier", + "189": "Lakeland terrier", + "190": "Sealyham terrier, Sealyham", + "191": "Airedale, Airedale terrier", + "192": "cairn, cairn terrier", + "193": "Australian terrier", + "194": "Dandie Dinmont, Dandie Dinmont terrier", + "195": "Boston bull, Boston terrier", + "196": "miniature schnauzer", + "197": "giant schnauzer", + "198": "standard schnauzer", + "199": "Scotch terrier, Scottish terrier, Scottie", + "200": "Tibetan terrier, chrysanthemum dog", + "201": "silky terrier, Sydney silky", + "202": "soft-coated wheaten terrier", + "203": "West Highland white terrier", + "204": "Lhasa, Lhasa apso", + "205": "flat-coated retriever", + "206": "curly-coated retriever", + "207": "golden retriever", + "208": "Labrador retriever", + "209": "Chesapeake Bay retriever", + "210": "German short-haired pointer", + "211": "vizsla, Hungarian pointer", + "212": "English setter", + "213": "Irish setter, red setter", + "214": "Gordon setter", + "215": "Brittany spaniel", + "216": "clumber, clumber spaniel", + "217": "English springer, English springer spaniel", + "218": "Welsh springer spaniel", + "219": "cocker spaniel, English cocker spaniel, cocker", + "220": "Sussex spaniel", + "221": "Irish water spaniel", + "222": "kuvasz", + "223": "schipperke", + "224": "groenendael", + "225": "malinois", + "226": "briard", + "227": "kelpie", + "228": "komondor", + "229": "Old English sheepdog, bobtail", + "230": "Shetland sheepdog, Shetland sheep dog, Shetland", + "231": "collie", + "232": "Border collie", + "233": "Bouvier des Flandres, Bouviers des Flandres", + "234": "Rottweiler", + "235": "German shepherd, German shepherd dog, German police dog, alsatian", + "236": "Doberman, Doberman pinscher", + "237": "miniature pinscher", + "238": "Greater Swiss Mountain dog", + "239": "Bernese mountain dog", + "240": "Appenzeller", + "241": "EntleBucher", + "242": "boxer", + "243": "bull mastiff", + "244": "Tibetan mastiff", + "245": "French bulldog", + "246": "Great Dane", + "247": "Saint Bernard, St Bernard", + "248": "Eskimo dog, husky", + "249": "malamute, malemute, Alaskan malamute", + "250": "Siberian husky", + "251": "dalmatian, coach dog, carriage dog", + "252": "affenpinscher, monkey pinscher, monkey dog", + "253": "basenji", + "254": "pug, pug-dog", + "255": "Leonberg", + "256": "Newfoundland, Newfoundland dog", + "257": "Great Pyrenees", + "258": "Samoyed, Samoyede", + "259": "Pomeranian", + "260": "chow, chow chow", + "261": "keeshond", + "262": "Brabancon griffon", + "263": "Pembroke, Pembroke Welsh corgi", + "264": "Cardigan, Cardigan Welsh corgi", + "265": "toy poodle", + "266": "miniature poodle", + "267": "standard poodle", + "268": "Mexican hairless", + "269": "timber wolf, grey wolf, gray wolf, Canis lupus", + "270": "white wolf, Arctic wolf, Canis lupus tundrarum", + "271": "red wolf, maned wolf, Canis rufus, Canis niger", + "272": "coyote, prairie wolf, brush wolf, Canis latrans", + "273": "dingo, warrigal, warragal, Canis dingo", + "274": "dhole, Cuon alpinus", + "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", + "276": "hyena, hyaena", + "277": "red fox, Vulpes vulpes", + "278": "kit fox, Vulpes macrotis", + "279": "Arctic fox, white fox, Alopex lagopus", + "280": "grey fox, gray fox, Urocyon cinereoargenteus", + "281": "tabby, tabby cat", + "282": "tiger cat", + "283": "Persian cat", + "284": "Siamese cat, Siamese", + "285": "Egyptian cat", + "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", + "287": "lynx, catamount", + "288": "leopard, Panthera pardus", + "289": "snow leopard, ounce, Panthera uncia", + "290": "jaguar, panther, Panthera onca, Felis onca", + "291": "lion, king of beasts, Panthera leo", + "292": "tiger, Panthera tigris", + "293": "cheetah, chetah, Acinonyx jubatus", + "294": "brown bear, bruin, Ursus arctos", + "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", + "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", + "297": "sloth bear, Melursus ursinus, Ursus ursinus", + "298": "mongoose", + "299": "meerkat, mierkat", + "300": "tiger beetle", + "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", + "302": "ground beetle, carabid beetle", + "303": "long-horned beetle, longicorn, longicorn beetle", + "304": "leaf beetle, chrysomelid", + "305": "dung beetle", + "306": "rhinoceros beetle", + "307": "weevil", + "308": "fly", + "309": "bee", + "310": "ant, emmet, pismire", + "311": "grasshopper, hopper", + "312": "cricket", + "313": "walking stick, walkingstick, stick insect", + "314": "cockroach, roach", + "315": "mantis, mantid", + "316": "cicada, cicala", + "317": "leafhopper", + "318": "lacewing, lacewing fly", + "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + "320": "damselfly", + "321": "admiral", + "322": "ringlet, ringlet butterfly", + "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", + "324": "cabbage butterfly", + "325": "sulphur butterfly, sulfur butterfly", + "326": "lycaenid, lycaenid butterfly", + "327": "starfish, sea star", + "328": "sea urchin", + "329": "sea cucumber, holothurian", + "330": "wood rabbit, cottontail, cottontail rabbit", + "331": "hare", + "332": "Angora, Angora rabbit", + "333": "hamster", + "334": "porcupine, hedgehog", + "335": "fox squirrel, eastern fox squirrel, Sciurus niger", + "336": "marmot", + "337": "beaver", + "338": "guinea pig, Cavia cobaya", + "339": "sorrel", + "340": "zebra", + "341": "hog, pig, grunter, squealer, Sus scrofa", + "342": "wild boar, boar, Sus scrofa", + "343": "warthog", + "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "345": "ox", + "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", + "347": "bison", + "348": "ram, tup", + "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", + "350": "ibex, Capra ibex", + "351": "hartebeest", + "352": "impala, Aepyceros melampus", + "353": "gazelle", + "354": "Arabian camel, dromedary, Camelus dromedarius", + "355": "llama", + "356": "weasel", + "357": "mink", + "358": "polecat, fitch, foulmart, foumart, Mustela putorius", + "359": "black-footed ferret, ferret, Mustela nigripes", + "360": "otter", + "361": "skunk, polecat, wood pussy", + "362": "badger", + "363": "armadillo", + "364": "three-toed sloth, ai, Bradypus tridactylus", + "365": "orangutan, orang, orangutang, Pongo pygmaeus", + "366": "gorilla, Gorilla gorilla", + "367": "chimpanzee, chimp, Pan troglodytes", + "368": "gibbon, Hylobates lar", + "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", + "370": "guenon, guenon monkey", + "371": "patas, hussar monkey, Erythrocebus patas", + "372": "baboon", + "373": "macaque", + "374": "langur", + "375": "colobus, colobus monkey", + "376": "proboscis monkey, Nasalis larvatus", + "377": "marmoset", + "378": "capuchin, ringtail, Cebus capucinus", + "379": "howler monkey, howler", + "380": "titi, titi monkey", + "381": "spider monkey, Ateles geoffroyi", + "382": "squirrel monkey, Saimiri sciureus", + "383": "Madagascar cat, ring-tailed lemur, Lemur catta", + "384": "indri, indris, Indri indri, Indri brevicaudatus", + "385": "Indian elephant, Elephas maximus", + "386": "African elephant, Loxodonta africana", + "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", + "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", + "389": "barracouta, snoek", + "390": "eel", + "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", + "392": "rock beauty, Holocanthus tricolor", + "393": "anemone fish", + "394": "sturgeon", + "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", + "396": "lionfish", + "397": "puffer, pufferfish, blowfish, globefish", + "398": "abacus", + "399": "abaya", + "400": "academic gown, academic robe, judge's robe", + "401": "accordion, piano accordion, squeeze box", + "402": "acoustic guitar", + "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", + "404": "airliner", + "405": "airship, dirigible", + "406": "altar", + "407": "ambulance", + "408": "amphibian, amphibious vehicle", + "409": "analog clock", + "410": "apiary, bee house", + "411": "apron", + "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", + "413": "assault rifle, assault gun", + "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", + "415": "bakery, bakeshop, bakehouse", + "416": "balance beam, beam", + "417": "balloon", + "418": "ballpoint, ballpoint pen, ballpen, Biro", + "419": "Band Aid", + "420": "banjo", + "421": "bannister, banister, balustrade, balusters, handrail", + "422": "barbell", + "423": "barber chair", + "424": "barbershop", + "425": "barn", + "426": "barometer", + "427": "barrel, cask", + "428": "barrow, garden cart, lawn cart, wheelbarrow", + "429": "baseball", + "430": "basketball", + "431": "bassinet", + "432": "bassoon", + "433": "bathing cap, swimming cap", + "434": "bath towel", + "435": "bathtub, bathing tub, bath, tub", + "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", + "437": "beacon, lighthouse, beacon light, pharos", + "438": "beaker", + "439": "bearskin, busby, shako", + "440": "beer bottle", + "441": "beer glass", + "442": "bell cote, bell cot", + "443": "bib", + "444": "bicycle-built-for-two, tandem bicycle, tandem", + "445": "bikini, two-piece", + "446": "binder, ring-binder", + "447": "binoculars, field glasses, opera glasses", + "448": "birdhouse", + "449": "boathouse", + "450": "bobsled, bobsleigh, bob", + "451": "bolo tie, bolo, bola tie, bola", + "452": "bonnet, poke bonnet", + "453": "bookcase", + "454": "bookshop, bookstore, bookstall", + "455": "bottlecap", + "456": "bow", + "457": "bow tie, bow-tie, bowtie", + "458": "brass, memorial tablet, plaque", + "459": "brassiere, bra, bandeau", + "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", + "461": "breastplate, aegis, egis", + "462": "broom", + "463": "bucket, pail", + "464": "buckle", + "465": "bulletproof vest", + "466": "bullet train, bullet", + "467": "butcher shop, meat market", + "468": "cab, hack, taxi, taxicab", + "469": "caldron, cauldron", + "470": "candle, taper, wax light", + "471": "cannon", + "472": "canoe", + "473": "can opener, tin opener", + "474": "cardigan", + "475": "car mirror", + "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", + "477": "carpenter's kit, tool kit", + "478": "carton", + "479": "car wheel", + "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", + "481": "cassette", + "482": "cassette player", + "483": "castle", + "484": "catamaran", + "485": "CD player", + "486": "cello, violoncello", + "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", + "488": "chain", + "489": "chainlink fence", + "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", + "491": "chain saw, chainsaw", + "492": "chest", + "493": "chiffonier, commode", + "494": "chime, bell, gong", + "495": "china cabinet, china closet", + "496": "Christmas stocking", + "497": "church, church building", + "498": "cinema, movie theater, movie theatre, movie house, picture palace", + "499": "cleaver, meat cleaver, chopper", + "500": "cliff dwelling", + "501": "cloak", + "502": "clog, geta, patten, sabot", + "503": "cocktail shaker", + "504": "coffee mug", + "505": "coffeepot", + "506": "coil, spiral, volute, whorl, helix", + "507": "combination lock", + "508": "computer keyboard, keypad", + "509": "confectionery, confectionary, candy store", + "510": "container ship, containership, container vessel", + "511": "convertible", + "512": "corkscrew, bottle screw", + "513": "cornet, horn, trumpet, trump", + "514": "cowboy boot", + "515": "cowboy hat, ten-gallon hat", + "516": "cradle", + "517": "crane", + "518": "crash helmet", + "519": "crate", + "520": "crib, cot", + "521": "Crock Pot", + "522": "croquet ball", + "523": "crutch", + "524": "cuirass", + "525": "dam, dike, dyke", + "526": "desk", + "527": "desktop computer", + "528": "dial telephone, dial phone", + "529": "diaper, nappy, napkin", + "530": "digital clock", + "531": "digital watch", + "532": "dining table, board", + "533": "dishrag, dishcloth", + "534": "dishwasher, dish washer, dishwashing machine", + "535": "disk brake, disc brake", + "536": "dock, dockage, docking facility", + "537": "dogsled, dog sled, dog sleigh", + "538": "dome", + "539": "doormat, welcome mat", + "540": "drilling platform, offshore rig", + "541": "drum, membranophone, tympan", + "542": "drumstick", + "543": "dumbbell", + "544": "Dutch oven", + "545": "electric fan, blower", + "546": "electric guitar", + "547": "electric locomotive", + "548": "entertainment center", + "549": "envelope", + "550": "espresso maker", + "551": "face powder", + "552": "feather boa, boa", + "553": "file, file cabinet, filing cabinet", + "554": "fireboat", + "555": "fire engine, fire truck", + "556": "fire screen, fireguard", + "557": "flagpole, flagstaff", + "558": "flute, transverse flute", + "559": "folding chair", + "560": "football helmet", + "561": "forklift", + "562": "fountain", + "563": "fountain pen", + "564": "four-poster", + "565": "freight car", + "566": "French horn, horn", + "567": "frying pan, frypan, skillet", + "568": "fur coat", + "569": "garbage truck, dustcart", + "570": "gasmask, respirator, gas helmet", + "571": "gas pump, gasoline pump, petrol pump, island dispenser", + "572": "goblet", + "573": "go-kart", + "574": "golf ball", + "575": "golfcart, golf cart", + "576": "gondola", + "577": "gong, tam-tam", + "578": "gown", + "579": "grand piano, grand", + "580": "greenhouse, nursery, glasshouse", + "581": "grille, radiator grille", + "582": "grocery store, grocery, food market, market", + "583": "guillotine", + "584": "hair slide", + "585": "hair spray", + "586": "half track", + "587": "hammer", + "588": "hamper", + "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", + "590": "hand-held computer, hand-held microcomputer", + "591": "handkerchief, hankie, hanky, hankey", + "592": "hard disc, hard disk, fixed disk", + "593": "harmonica, mouth organ, harp, mouth harp", + "594": "harp", + "595": "harvester, reaper", + "596": "hatchet", + "597": "holster", + "598": "home theater, home theatre", + "599": "honeycomb", + "600": "hook, claw", + "601": "hoopskirt, crinoline", + "602": "horizontal bar, high bar", + "603": "horse cart, horse-cart", + "604": "hourglass", + "605": "iPod", + "606": "iron, smoothing iron", + "607": "jack-o'-lantern", + "608": "jean, blue jean, denim", + "609": "jeep, landrover", + "610": "jersey, T-shirt, tee shirt", + "611": "jigsaw puzzle", + "612": "jinrikisha, ricksha, rickshaw", + "613": "joystick", + "614": "kimono", + "615": "knee pad", + "616": "knot", + "617": "lab coat, laboratory coat", + "618": "ladle", + "619": "lampshade, lamp shade", + "620": "laptop, laptop computer", + "621": "lawn mower, mower", + "622": "lens cap, lens cover", + "623": "letter opener, paper knife, paperknife", + "624": "library", + "625": "lifeboat", + "626": "lighter, light, igniter, ignitor", + "627": "limousine, limo", + "628": "liner, ocean liner", + "629": "lipstick, lip rouge", + "630": "Loafer", + "631": "lotion", + "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", + "633": "loupe, jeweler's loupe", + "634": "lumbermill, sawmill", + "635": "magnetic compass", + "636": "mailbag, postbag", + "637": "mailbox, letter box", + "638": "maillot", + "639": "maillot, tank suit", + "640": "manhole cover", + "641": "maraca", + "642": "marimba, xylophone", + "643": "mask", + "644": "matchstick", + "645": "maypole", + "646": "maze, labyrinth", + "647": "measuring cup", + "648": "medicine chest, medicine cabinet", + "649": "megalith, megalithic structure", + "650": "microphone, mike", + "651": "microwave, microwave oven", + "652": "military uniform", + "653": "milk can", + "654": "minibus", + "655": "miniskirt, mini", + "656": "minivan", + "657": "missile", + "658": "mitten", + "659": "mixing bowl", + "660": "mobile home, manufactured home", + "661": "Model T", + "662": "modem", + "663": "monastery", + "664": "monitor", + "665": "moped", + "666": "mortar", + "667": "mortarboard", + "668": "mosque", + "669": "mosquito net", + "670": "motor scooter, scooter", + "671": "mountain bike, all-terrain bike, off-roader", + "672": "mountain tent", + "673": "mouse, computer mouse", + "674": "mousetrap", + "675": "moving van", + "676": "muzzle", + "677": "nail", + "678": "neck brace", + "679": "necklace", + "680": "nipple", + "681": "notebook, notebook computer", + "682": "obelisk", + "683": "oboe, hautboy, hautbois", + "684": "ocarina, sweet potato", + "685": "odometer, hodometer, mileometer, milometer", + "686": "oil filter", + "687": "organ, pipe organ", + "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", + "689": "overskirt", + "690": "oxcart", + "691": "oxygen mask", + "692": "packet", + "693": "paddle, boat paddle", + "694": "paddlewheel, paddle wheel", + "695": "padlock", + "696": "paintbrush", + "697": "pajama, pyjama, pj's, jammies", + "698": "palace", + "699": "panpipe, pandean pipe, syrinx", + "700": "paper towel", + "701": "parachute, chute", + "702": "parallel bars, bars", + "703": "park bench", + "704": "parking meter", + "705": "passenger car, coach, carriage", + "706": "patio, terrace", + "707": "pay-phone, pay-station", + "708": "pedestal, plinth, footstall", + "709": "pencil box, pencil case", + "710": "pencil sharpener", + "711": "perfume, essence", + "712": "Petri dish", + "713": "photocopier", + "714": "pick, plectrum, plectron", + "715": "pickelhaube", + "716": "picket fence, paling", + "717": "pickup, pickup truck", + "718": "pier", + "719": "piggy bank, penny bank", + "720": "pill bottle", + "721": "pillow", + "722": "ping-pong ball", + "723": "pinwheel", + "724": "pirate, pirate ship", + "725": "pitcher, ewer", + "726": "plane, carpenter's plane, woodworking plane", + "727": "planetarium", + "728": "plastic bag", + "729": "plate rack", + "730": "plow, plough", + "731": "plunger, plumber's helper", + "732": "Polaroid camera, Polaroid Land camera", + "733": "pole", + "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", + "735": "poncho", + "736": "pool table, billiard table, snooker table", + "737": "pop bottle, soda bottle", + "738": "pot, flowerpot", + "739": "potter's wheel", + "740": "power drill", + "741": "prayer rug, prayer mat", + "742": "printer", + "743": "prison, prison house", + "744": "projectile, missile", + "745": "projector", + "746": "puck, hockey puck", + "747": "punching bag, punch bag, punching ball, punchball", + "748": "purse", + "749": "quill, quill pen", + "750": "quilt, comforter, comfort, puff", + "751": "racer, race car, racing car", + "752": "racket, racquet", + "753": "radiator", + "754": "radio, wireless", + "755": "radio telescope, radio reflector", + "756": "rain barrel", + "757": "recreational vehicle, RV, R.V.", + "758": "reel", + "759": "reflex camera", + "760": "refrigerator, icebox", + "761": "remote control, remote", + "762": "restaurant, eating house, eating place, eatery", + "763": "revolver, six-gun, six-shooter", + "764": "rifle", + "765": "rocking chair, rocker", + "766": "rotisserie", + "767": "rubber eraser, rubber, pencil eraser", + "768": "rugby ball", + "769": "rule, ruler", + "770": "running shoe", + "771": "safe", + "772": "safety pin", + "773": "saltshaker, salt shaker", + "774": "sandal", + "775": "sarong", + "776": "sax, saxophone", + "777": "scabbard", + "778": "scale, weighing machine", + "779": "school bus", + "780": "schooner", + "781": "scoreboard", + "782": "screen, CRT screen", + "783": "screw", + "784": "screwdriver", + "785": "seat belt, seatbelt", + "786": "sewing machine", + "787": "shield, buckler", + "788": "shoe shop, shoe-shop, shoe store", + "789": "shoji", + "790": "shopping basket", + "791": "shopping cart", + "792": "shovel", + "793": "shower cap", + "794": "shower curtain", + "795": "ski", + "796": "ski mask", + "797": "sleeping bag", + "798": "slide rule, slipstick", + "799": "sliding door", + "800": "slot, one-armed bandit", + "801": "snorkel", + "802": "snowmobile", + "803": "snowplow, snowplough", + "804": "soap dispenser", + "805": "soccer ball", + "806": "sock", + "807": "solar dish, solar collector, solar furnace", + "808": "sombrero", + "809": "soup bowl", + "810": "space bar", + "811": "space heater", + "812": "space shuttle", + "813": "spatula", + "814": "speedboat", + "815": "spider web, spider's web", + "816": "spindle", + "817": "sports car, sport car", + "818": "spotlight, spot", + "819": "stage", + "820": "steam locomotive", + "821": "steel arch bridge", + "822": "steel drum", + "823": "stethoscope", + "824": "stole", + "825": "stone wall", + "826": "stopwatch, stop watch", + "827": "stove", + "828": "strainer", + "829": "streetcar, tram, tramcar, trolley, trolley car", + "830": "stretcher", + "831": "studio couch, day bed", + "832": "stupa, tope", + "833": "submarine, pigboat, sub, U-boat", + "834": "suit, suit of clothes", + "835": "sundial", + "836": "sunglass", + "837": "sunglasses, dark glasses, shades", + "838": "sunscreen, sunblock, sun blocker", + "839": "suspension bridge", + "840": "swab, swob, mop", + "841": "sweatshirt", + "842": "swimming trunks, bathing trunks", + "843": "swing", + "844": "switch, electric switch, electrical switch", + "845": "syringe", + "846": "table lamp", + "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", + "848": "tape player", + "849": "teapot", + "850": "teddy, teddy bear", + "851": "television, television system", + "852": "tennis ball", + "853": "thatch, thatched roof", + "854": "theater curtain, theatre curtain", + "855": "thimble", + "856": "thresher, thrasher, threshing machine", + "857": "throne", + "858": "tile roof", + "859": "toaster", + "860": "tobacco shop, tobacconist shop, tobacconist", + "861": "toilet seat", + "862": "torch", + "863": "totem pole", + "864": "tow truck, tow car, wrecker", + "865": "toyshop", + "866": "tractor", + "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", + "868": "tray", + "869": "trench coat", + "870": "tricycle, trike, velocipede", + "871": "trimaran", + "872": "tripod", + "873": "triumphal arch", + "874": "trolleybus, trolley coach, trackless trolley", + "875": "trombone", + "876": "tub, vat", + "877": "turnstile", + "878": "typewriter keyboard", + "879": "umbrella", + "880": "unicycle, monocycle", + "881": "upright, upright piano", + "882": "vacuum, vacuum cleaner", + "883": "vase", + "884": "vault", + "885": "velvet", + "886": "vending machine", + "887": "vestment", + "888": "viaduct", + "889": "violin, fiddle", + "890": "volleyball", + "891": "waffle iron", + "892": "wall clock", + "893": "wallet, billfold, notecase, pocketbook", + "894": "wardrobe, closet, press", + "895": "warplane, military plane", + "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", + "897": "washer, automatic washer, washing machine", + "898": "water bottle", + "899": "water jug", + "900": "water tower", + "901": "whiskey jug", + "902": "whistle", + "903": "wig", + "904": "window screen", + "905": "window shade", + "906": "Windsor tie", + "907": "wine bottle", + "908": "wing", + "909": "wok", + "910": "wooden spoon", + "911": "wool, woolen, woollen", + "912": "worm fence, snake fence, snake-rail fence, Virginia fence", + "913": "wreck", + "914": "yawl", + "915": "yurt", + "916": "web site, website, internet site, site", + "917": "comic book", + "918": "crossword puzzle, crossword", + "919": "street sign", + "920": "traffic light, traffic signal, stoplight", + "921": "book jacket, dust cover, dust jacket, dust wrapper", + "922": "menu", + "923": "plate", + "924": "guacamole", + "925": "consomme", + "926": "hot pot, hotpot", + "927": "trifle", + "928": "ice cream, icecream", + "929": "ice lolly, lolly, lollipop, popsicle", + "930": "French loaf", + "931": "bagel, beigel", + "932": "pretzel", + "933": "cheeseburger", + "934": "hotdog, hot dog, red hot", + "935": "mashed potato", + "936": "head cabbage", + "937": "broccoli", + "938": "cauliflower", + "939": "zucchini, courgette", + "940": "spaghetti squash", + "941": "acorn squash", + "942": "butternut squash", + "943": "cucumber, cuke", + "944": "artichoke, globe artichoke", + "945": "bell pepper", + "946": "cardoon", + "947": "mushroom", + "948": "Granny Smith", + "949": "strawberry", + "950": "orange", + "951": "lemon", + "952": "fig", + "953": "pineapple, ananas", + "954": "banana", + "955": "jackfruit, jak, jack", + "956": "custard apple", + "957": "pomegranate", + "958": "hay", + "959": "carbonara", + "960": "chocolate sauce, chocolate syrup", + "961": "dough", + "962": "meat loaf, meatloaf", + "963": "pizza, pizza pie", + "964": "potpie", + "965": "burrito", + "966": "red wine", + "967": "espresso", + "968": "cup", + "969": "eggnog", + "970": "alp", + "971": "bubble", + "972": "cliff, drop, drop-off", + "973": "coral reef", + "974": "geyser", + "975": "lakeside, lakeshore", + "976": "promontory, headland, head, foreland", + "977": "sandbar, sand bar", + "978": "seashore, coast, seacoast, sea-coast", + "979": "valley, vale", + "980": "volcano", + "981": "ballplayer, baseball player", + "982": "groom, bridegroom", + "983": "scuba diver", + "984": "rapeseed", + "985": "daisy", + "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + "987": "corn", + "988": "acorn", + "989": "hip, rose hip, rosehip", + "990": "buckeye, horse chestnut, conker", + "991": "coral fungus", + "992": "agaric", + "993": "gyromitra", + "994": "stinkhorn, carrion fungus", + "995": "earthstar", + "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", + "997": "bolete", + "998": "ear, spike, capitulum", + "999": "toilet tissue, toilet paper, bathroom tissue" + }, + "image_size": 224, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "Afghan hound, Afghan": 160, + "African chameleon, Chamaeleo chamaeleon": 47, + "African crocodile, Nile crocodile, Crocodylus niloticus": 49, + "African elephant, Loxodonta africana": 386, + "African grey, African gray, Psittacus erithacus": 87, + "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus": 275, + "Airedale, Airedale terrier": 191, + "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier": 180, + "American alligator, Alligator mississipiensis": 50, + "American black bear, black bear, Ursus americanus, Euarctos americanus": 295, + "American chameleon, anole, Anolis carolinensis": 40, + "American coot, marsh hen, mud hen, water hen, Fulica americana": 137, + "American egret, great white heron, Egretta albus": 132, + "American lobster, Northern lobster, Maine lobster, Homarus americanus": 122, + "Angora, Angora rabbit": 332, + "Appenzeller": 240, + "Arabian camel, dromedary, Camelus dromedarius": 354, + "Arctic fox, white fox, Alopex lagopus": 279, + "Australian terrier": 193, + "Band Aid": 419, + "Bedlington terrier": 181, + "Bernese mountain dog": 239, + "Blenheim spaniel": 156, + "Border collie": 232, + "Border terrier": 182, + "Boston bull, Boston terrier": 195, + "Bouvier des Flandres, Bouviers des Flandres": 233, + "Brabancon griffon": 262, + "Brittany spaniel": 215, + "CD player": 485, + "Cardigan, Cardigan Welsh corgi": 264, + "Chesapeake Bay retriever": 209, + "Chihuahua": 151, + "Christmas stocking": 496, + "Crock Pot": 521, + "Dandie Dinmont, Dandie Dinmont terrier": 194, + "Doberman, Doberman pinscher": 236, + "Dungeness crab, Cancer magister": 118, + "Dutch oven": 544, + "Egyptian cat": 285, + "English foxhound": 167, + "English setter": 212, + "English springer, English springer spaniel": 217, + "EntleBucher": 241, + "Eskimo dog, husky": 248, + "European fire salamander, Salamandra salamandra": 25, + "European gallinule, Porphyrio porphyrio": 136, + "French bulldog": 245, + "French horn, horn": 566, + "French loaf": 930, + "German shepherd, German shepherd dog, German police dog, alsatian": 235, + "German short-haired pointer": 210, + "Gila monster, Heloderma suspectum": 45, + "Gordon setter": 214, + "Granny Smith": 948, + "Great Dane": 246, + "Great Pyrenees": 257, + "Greater Swiss Mountain dog": 238, + "Ibizan hound, Ibizan Podenco": 173, + "Indian cobra, Naja naja": 63, + "Indian elephant, Elephas maximus": 385, + "Irish setter, red setter": 213, + "Irish terrier": 184, + "Irish water spaniel": 221, + "Irish wolfhound": 170, + "Italian greyhound": 171, + "Japanese spaniel": 152, + "Kerry blue terrier": 183, + "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48, + "Labrador retriever": 208, + "Lakeland terrier": 189, + "Leonberg": 255, + "Lhasa, Lhasa apso": 204, + "Loafer": 630, + "Madagascar cat, ring-tailed lemur, Lemur catta": 383, + "Maltese dog, Maltese terrier, Maltese": 153, + "Mexican hairless": 268, + "Model T": 661, + "Newfoundland, Newfoundland dog": 256, + "Norfolk terrier": 185, + "Norwegian elkhound, elkhound": 174, + "Norwich terrier": 186, + "Old English sheepdog, bobtail": 229, + "Pekinese, Pekingese, Peke": 154, + "Pembroke, Pembroke Welsh corgi": 263, + "Persian cat": 283, + "Petri dish": 712, + "Polaroid camera, Polaroid Land camera": 732, + "Pomeranian": 259, + "Rhodesian ridgeback": 159, + "Rottweiler": 234, + "Saint Bernard, St Bernard": 247, + "Saluki, gazelle hound": 176, + "Samoyed, Samoyede": 258, + "Scotch terrier, Scottish terrier, Scottie": 199, + "Scottish deerhound, deerhound": 177, + "Sealyham terrier, Sealyham": 190, + "Shetland sheepdog, Shetland sheep dog, Shetland": 230, + "Shih-Tzu": 155, + "Siamese cat, Siamese": 284, + "Siberian husky": 250, + "Staffordshire bullterrier, Staffordshire bull terrier": 179, + "Sussex spaniel": 220, + "Tibetan mastiff": 244, + "Tibetan terrier, chrysanthemum dog": 200, + "Walker hound, Walker foxhound": 166, + "Weimaraner": 178, + "Welsh springer spaniel": 218, + "West Highland white terrier": 203, + "Windsor tie": 906, + "Yorkshire terrier": 187, + "abacus": 398, + "abaya": 399, + "academic gown, academic robe, judge's robe": 400, + "accordion, piano accordion, squeeze box": 401, + "acorn": 988, + "acorn squash": 941, + "acoustic guitar": 402, + "admiral": 321, + "affenpinscher, monkey pinscher, monkey dog": 252, + "agama": 42, + "agaric": 992, + "aircraft carrier, carrier, flattop, attack aircraft carrier": 403, + "airliner": 404, + "airship, dirigible": 405, + "albatross, mollymawk": 146, + "alligator lizard": 44, + "alp": 970, + "altar": 406, + "ambulance": 407, + "amphibian, amphibious vehicle": 408, + "analog clock": 409, + "anemone fish": 393, + "ant, emmet, pismire": 310, + "apiary, bee house": 410, + "apron": 411, + "armadillo": 363, + "artichoke, globe artichoke": 944, + "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412, + "assault rifle, assault gun": 413, + "axolotl, mud puppy, Ambystoma mexicanum": 29, + "baboon": 372, + "backpack, back pack, knapsack, packsack, rucksack, haversack": 414, + "badger": 362, + "bagel, beigel": 931, + "bakery, bakeshop, bakehouse": 415, + "balance beam, beam": 416, + "bald eagle, American eagle, Haliaeetus leucocephalus": 22, + "balloon": 417, + "ballplayer, baseball player": 981, + "ballpoint, ballpoint pen, ballpen, Biro": 418, + "banana": 954, + "banded gecko": 38, + "banjo": 420, + "bannister, banister, balustrade, balusters, handrail": 421, + "barbell": 422, + "barber chair": 423, + "barbershop": 424, + "barn": 425, + "barn spider, Araneus cavaticus": 73, + "barometer": 426, + "barracouta, snoek": 389, + "barrel, cask": 427, + "barrow, garden cart, lawn cart, wheelbarrow": 428, + "baseball": 429, + "basenji": 253, + "basketball": 430, + "basset, basset hound": 161, + "bassinet": 431, + "bassoon": 432, + "bath towel": 434, + "bathing cap, swimming cap": 433, + "bathtub, bathing tub, bath, tub": 435, + "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436, + "beacon, lighthouse, beacon light, pharos": 437, + "beagle": 162, + "beaker": 438, + "bearskin, busby, shako": 439, + "beaver": 337, + "bee": 309, + "bee eater": 92, + "beer bottle": 440, + "beer glass": 441, + "bell cote, bell cot": 442, + "bell pepper": 945, + "bib": 443, + "bicycle-built-for-two, tandem bicycle, tandem": 444, + "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349, + "bikini, two-piece": 445, + "binder, ring-binder": 446, + "binoculars, field glasses, opera glasses": 447, + "birdhouse": 448, + "bison": 347, + "bittern": 133, + "black and gold garden spider, Argiope aurantia": 72, + "black grouse": 80, + "black stork, Ciconia nigra": 128, + "black swan, Cygnus atratus": 100, + "black widow, Latrodectus mactans": 75, + "black-and-tan coonhound": 165, + "black-footed ferret, ferret, Mustela nigripes": 359, + "bloodhound, sleuthhound": 163, + "bluetick": 164, + "boa constrictor, Constrictor constrictor": 61, + "boathouse": 449, + "bobsled, bobsleigh, bob": 450, + "bolete": 997, + "bolo tie, bolo, bola tie, bola": 451, + "bonnet, poke bonnet": 452, + "book jacket, dust cover, dust jacket, dust wrapper": 921, + "bookcase": 453, + "bookshop, bookstore, bookstall": 454, + "borzoi, Russian wolfhound": 169, + "bottlecap": 455, + "bow": 456, + "bow tie, bow-tie, bowtie": 457, + "box turtle, box tortoise": 37, + "boxer": 242, + "brain coral": 109, + "brambling, Fringilla montifringilla": 10, + "brass, memorial tablet, plaque": 458, + "brassiere, bra, bandeau": 459, + "breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460, + "breastplate, aegis, egis": 461, + "briard": 226, + "broccoli": 937, + "broom": 462, + "brown bear, bruin, Ursus arctos": 294, + "bubble": 971, + "bucket, pail": 463, + "buckeye, horse chestnut, conker": 990, + "buckle": 464, + "bulbul": 16, + "bull mastiff": 243, + "bullet train, bullet": 466, + "bulletproof vest": 465, + "bullfrog, Rana catesbeiana": 30, + "burrito": 965, + "bustard": 138, + "butcher shop, meat market": 467, + "butternut squash": 942, + "cab, hack, taxi, taxicab": 468, + "cabbage butterfly": 324, + "cairn, cairn terrier": 192, + "caldron, cauldron": 469, + "can opener, tin opener": 473, + "candle, taper, wax light": 470, + "cannon": 471, + "canoe": 472, + "capuchin, ringtail, Cebus capucinus": 378, + "car mirror": 475, + "car wheel": 479, + "carbonara": 959, + "cardigan": 474, + "cardoon": 946, + "carousel, carrousel, merry-go-round, roundabout, whirligig": 476, + "carpenter's kit, tool kit": 477, + "carton": 478, + "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480, + "cassette": 481, + "cassette player": 482, + "castle": 483, + "catamaran": 484, + "cauliflower": 938, + "cello, violoncello": 486, + "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487, + "centipede": 79, + "chain": 488, + "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490, + "chain saw, chainsaw": 491, + "chainlink fence": 489, + "chambered nautilus, pearly nautilus, nautilus": 117, + "cheeseburger": 933, + "cheetah, chetah, Acinonyx jubatus": 293, + "chest": 492, + "chickadee": 19, + "chiffonier, commode": 493, + "chime, bell, gong": 494, + "chimpanzee, chimp, Pan troglodytes": 367, + "china cabinet, china closet": 495, + "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116, + "chocolate sauce, chocolate syrup": 960, + "chow, chow chow": 260, + "church, church building": 497, + "cicada, cicala": 316, + "cinema, movie theater, movie theatre, movie house, picture palace": 498, + "cleaver, meat cleaver, chopper": 499, + "cliff dwelling": 500, + "cliff, drop, drop-off": 972, + "cloak": 501, + "clog, geta, patten, sabot": 502, + "clumber, clumber spaniel": 216, + "cock": 7, + "cocker spaniel, English cocker spaniel, cocker": 219, + "cockroach, roach": 314, + "cocktail shaker": 503, + "coffee mug": 504, + "coffeepot": 505, + "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391, + "coil, spiral, volute, whorl, helix": 506, + "collie": 231, + "colobus, colobus monkey": 375, + "combination lock": 507, + "comic book": 917, + "common iguana, iguana, Iguana iguana": 39, + "common newt, Triturus vulgaris": 26, + "computer keyboard, keypad": 508, + "conch": 112, + "confectionery, confectionary, candy store": 509, + "consomme": 925, + "container ship, containership, container vessel": 510, + "convertible": 511, + "coral fungus": 991, + "coral reef": 973, + "corkscrew, bottle screw": 512, + "corn": 987, + "cornet, horn, trumpet, trump": 513, + "coucal": 91, + "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286, + "cowboy boot": 514, + "cowboy hat, ten-gallon hat": 515, + "coyote, prairie wolf, brush wolf, Canis latrans": 272, + "cradle": 516, + "crane": 517, + "crash helmet": 518, + "crate": 519, + "crayfish, crawfish, crawdad, crawdaddy": 124, + "crib, cot": 520, + "cricket": 312, + "croquet ball": 522, + "crossword puzzle, crossword": 918, + "crutch": 523, + "cucumber, cuke": 943, + "cuirass": 524, + "cup": 968, + "curly-coated retriever": 206, + "custard apple": 956, + "daisy": 985, + "dalmatian, coach dog, carriage dog": 251, + "dam, dike, dyke": 525, + "damselfly": 320, + "desk": 526, + "desktop computer": 527, + "dhole, Cuon alpinus": 274, + "dial telephone, dial phone": 528, + "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67, + "diaper, nappy, napkin": 529, + "digital clock": 530, + "digital watch": 531, + "dingo, warrigal, warragal, Canis dingo": 273, + "dining table, board": 532, + "dishrag, dishcloth": 533, + "dishwasher, dish washer, dishwashing machine": 534, + "disk brake, disc brake": 535, + "dock, dockage, docking facility": 536, + "dogsled, dog sled, dog sleigh": 537, + "dome": 538, + "doormat, welcome mat": 539, + "dough": 961, + "dowitcher": 142, + "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319, + "drake": 97, + "drilling platform, offshore rig": 540, + "drum, membranophone, tympan": 541, + "drumstick": 542, + "dugong, Dugong dugon": 149, + "dumbbell": 543, + "dung beetle": 305, + "ear, spike, capitulum": 998, + "earthstar": 995, + "echidna, spiny anteater, anteater": 102, + "eel": 390, + "eft": 27, + "eggnog": 969, + "electric fan, blower": 545, + "electric guitar": 546, + "electric locomotive": 547, + "electric ray, crampfish, numbfish, torpedo": 5, + "entertainment center": 548, + "envelope": 549, + "espresso": 967, + "espresso maker": 550, + "face powder": 551, + "feather boa, boa": 552, + "fiddler crab": 120, + "fig": 952, + "file, file cabinet, filing cabinet": 553, + "fire engine, fire truck": 555, + "fire screen, fireguard": 556, + "fireboat": 554, + "flagpole, flagstaff": 557, + "flamingo": 130, + "flat-coated retriever": 205, + "flatworm, platyhelminth": 110, + "flute, transverse flute": 558, + "fly": 308, + "folding chair": 559, + "football helmet": 560, + "forklift": 561, + "fountain": 562, + "fountain pen": 563, + "four-poster": 564, + "fox squirrel, eastern fox squirrel, Sciurus niger": 335, + "freight car": 565, + "frilled lizard, Chlamydosaurus kingi": 43, + "frying pan, frypan, skillet": 567, + "fur coat": 568, + "gar, garfish, garpike, billfish, Lepisosteus osseus": 395, + "garbage truck, dustcart": 569, + "garden spider, Aranea diademata": 74, + "garter snake, grass snake": 57, + "gas pump, gasoline pump, petrol pump, island dispenser": 571, + "gasmask, respirator, gas helmet": 570, + "gazelle": 353, + "geyser": 974, + "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388, + "giant schnauzer": 197, + "gibbon, Hylobates lar": 368, + "go-kart": 573, + "goblet": 572, + "golden retriever": 207, + "goldfinch, Carduelis carduelis": 11, + "goldfish, Carassius auratus": 1, + "golf ball": 574, + "golfcart, golf cart": 575, + "gondola": 576, + "gong, tam-tam": 577, + "goose": 99, + "gorilla, Gorilla gorilla": 366, + "gown": 578, + "grand piano, grand": 579, + "grasshopper, hopper": 311, + "great grey owl, great gray owl, Strix nebulosa": 24, + "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2, + "green lizard, Lacerta viridis": 46, + "green mamba": 64, + "green snake, grass snake": 55, + "greenhouse, nursery, glasshouse": 580, + "grey fox, gray fox, Urocyon cinereoargenteus": 280, + "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147, + "grille, radiator grille": 581, + "grocery store, grocery, food market, market": 582, + "groenendael": 224, + "groom, bridegroom": 982, + "ground beetle, carabid beetle": 302, + "guacamole": 924, + "guenon, guenon monkey": 370, + "guillotine": 583, + "guinea pig, Cavia cobaya": 338, + "gyromitra": 993, + "hair slide": 584, + "hair spray": 585, + "half track": 586, + "hammer": 587, + "hammerhead, hammerhead shark": 4, + "hamper": 588, + "hamster": 333, + "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589, + "hand-held computer, hand-held microcomputer": 590, + "handkerchief, hankie, hanky, hankey": 591, + "hard disc, hard disk, fixed disk": 592, + "hare": 331, + "harmonica, mouth organ, harp, mouth harp": 593, + "harp": 594, + "hartebeest": 351, + "harvester, reaper": 595, + "harvestman, daddy longlegs, Phalangium opilio": 70, + "hatchet": 596, + "hay": 958, + "head cabbage": 936, + "hen": 8, + "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996, + "hermit crab": 125, + "hip, rose hip, rosehip": 989, + "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344, + "hog, pig, grunter, squealer, Sus scrofa": 341, + "hognose snake, puff adder, sand viper": 54, + "holster": 597, + "home theater, home theatre": 598, + "honeycomb": 599, + "hook, claw": 600, + "hoopskirt, crinoline": 601, + "horizontal bar, high bar": 602, + "hornbill": 93, + "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66, + "horse cart, horse-cart": 603, + "hot pot, hotpot": 926, + "hotdog, hot dog, red hot": 934, + "hourglass": 604, + "house finch, linnet, Carpodacus mexicanus": 12, + "howler monkey, howler": 379, + "hummingbird": 94, + "hyena, hyaena": 276, + "iPod": 605, + "ibex, Capra ibex": 350, + "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296, + "ice cream, icecream": 928, + "ice lolly, lolly, lollipop, popsicle": 929, + "impala, Aepyceros melampus": 352, + "indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14, + "indri, indris, Indri indri, Indri brevicaudatus": 384, + "iron, smoothing iron": 606, + "isopod": 126, + "jacamar": 95, + "jack-o'-lantern": 607, + "jackfruit, jak, jack": 955, + "jaguar, panther, Panthera onca, Felis onca": 290, + "jay": 17, + "jean, blue jean, denim": 608, + "jeep, landrover": 609, + "jellyfish": 107, + "jersey, T-shirt, tee shirt": 610, + "jigsaw puzzle": 611, + "jinrikisha, ricksha, rickshaw": 612, + "joystick": 613, + "junco, snowbird": 13, + "keeshond": 261, + "kelpie": 227, + "killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148, + "kimono": 614, + "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121, + "king penguin, Aptenodytes patagonica": 145, + "king snake, kingsnake": 56, + "kit fox, Vulpes macrotis": 278, + "kite": 21, + "knee pad": 615, + "knot": 616, + "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105, + "komondor": 228, + "kuvasz": 222, + "lab coat, laboratory coat": 617, + "lacewing, lacewing fly": 318, + "ladle": 618, + "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301, + "lakeside, lakeshore": 975, + "lampshade, lamp shade": 619, + "langur": 374, + "laptop, laptop computer": 620, + "lawn mower, mower": 621, + "leaf beetle, chrysomelid": 304, + "leafhopper": 317, + "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34, + "lemon": 951, + "lens cap, lens cover": 622, + "leopard, Panthera pardus": 288, + "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387, + "letter opener, paper knife, paperknife": 623, + "library": 624, + "lifeboat": 625, + "lighter, light, igniter, ignitor": 626, + "limousine, limo": 627, + "limpkin, Aramus pictus": 135, + "liner, ocean liner": 628, + "lion, king of beasts, Panthera leo": 291, + "lionfish": 396, + "lipstick, lip rouge": 629, + "little blue heron, Egretta caerulea": 131, + "llama": 355, + "loggerhead, loggerhead turtle, Caretta caretta": 33, + "long-horned beetle, longicorn, longicorn beetle": 303, + "lorikeet": 90, + "lotion": 631, + "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632, + "loupe, jeweler's loupe": 633, + "lumbermill, sawmill": 634, + "lycaenid, lycaenid butterfly": 326, + "lynx, catamount": 287, + "macaque": 373, + "macaw": 88, + "magnetic compass": 635, + "magpie": 18, + "mailbag, postbag": 636, + "mailbox, letter box": 637, + "maillot": 638, + "maillot, tank suit": 639, + "malamute, malemute, Alaskan malamute": 249, + "malinois": 225, + "manhole cover": 640, + "mantis, mantid": 315, + "maraca": 641, + "marimba, xylophone": 642, + "marmoset": 377, + "marmot": 336, + "mashed potato": 935, + "mask": 643, + "matchstick": 644, + "maypole": 645, + "maze, labyrinth": 646, + "measuring cup": 647, + "meat loaf, meatloaf": 962, + "medicine chest, medicine cabinet": 648, + "meerkat, mierkat": 299, + "megalith, megalithic structure": 649, + "menu": 922, + "microphone, mike": 650, + "microwave, microwave oven": 651, + "military uniform": 652, + "milk can": 653, + "miniature pinscher": 237, + "miniature poodle": 266, + "miniature schnauzer": 196, + "minibus": 654, + "miniskirt, mini": 655, + "minivan": 656, + "mink": 357, + "missile": 657, + "mitten": 658, + "mixing bowl": 659, + "mobile home, manufactured home": 660, + "modem": 662, + "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus": 323, + "monastery": 663, + "mongoose": 298, + "monitor": 664, + "moped": 665, + "mortar": 666, + "mortarboard": 667, + "mosque": 668, + "mosquito net": 669, + "motor scooter, scooter": 670, + "mountain bike, all-terrain bike, off-roader": 671, + "mountain tent": 672, + "mouse, computer mouse": 673, + "mousetrap": 674, + "moving van": 675, + "mud turtle": 35, + "mushroom": 947, + "muzzle": 676, + "nail": 677, + "neck brace": 678, + "necklace": 679, + "nematode, nematode worm, roundworm": 111, + "night snake, Hypsiglena torquata": 60, + "nipple": 680, + "notebook, notebook computer": 681, + "obelisk": 682, + "oboe, hautboy, hautbois": 683, + "ocarina, sweet potato": 684, + "odometer, hodometer, mileometer, milometer": 685, + "oil filter": 686, + "orange": 950, + "orangutan, orang, orangutang, Pongo pygmaeus": 365, + "organ, pipe organ": 687, + "oscilloscope, scope, cathode-ray oscilloscope, CRO": 688, + "ostrich, Struthio camelus": 9, + "otter": 360, + "otterhound, otter hound": 175, + "overskirt": 689, + "ox": 345, + "oxcart": 690, + "oxygen mask": 691, + "oystercatcher, oyster catcher": 143, + "packet": 692, + "paddle, boat paddle": 693, + "paddlewheel, paddle wheel": 694, + "padlock": 695, + "paintbrush": 696, + "pajama, pyjama, pj's, jammies": 697, + "palace": 698, + "panpipe, pandean pipe, syrinx": 699, + "paper towel": 700, + "papillon": 157, + "parachute, chute": 701, + "parallel bars, bars": 702, + "park bench": 703, + "parking meter": 704, + "partridge": 86, + "passenger car, coach, carriage": 705, + "patas, hussar monkey, Erythrocebus patas": 371, + "patio, terrace": 706, + "pay-phone, pay-station": 707, + "peacock": 84, + "pedestal, plinth, footstall": 708, + "pelican": 144, + "pencil box, pencil case": 709, + "pencil sharpener": 710, + "perfume, essence": 711, + "photocopier": 713, + "pick, plectrum, plectron": 714, + "pickelhaube": 715, + "picket fence, paling": 716, + "pickup, pickup truck": 717, + "pier": 718, + "piggy bank, penny bank": 719, + "pill bottle": 720, + "pillow": 721, + "pineapple, ananas": 953, + "ping-pong ball": 722, + "pinwheel": 723, + "pirate, pirate ship": 724, + "pitcher, ewer": 725, + "pizza, pizza pie": 963, + "plane, carpenter's plane, woodworking plane": 726, + "planetarium": 727, + "plastic bag": 728, + "plate": 923, + "plate rack": 729, + "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103, + "plow, plough": 730, + "plunger, plumber's helper": 731, + "pole": 733, + "polecat, fitch, foulmart, foumart, Mustela putorius": 358, + "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734, + "pomegranate": 957, + "poncho": 735, + "pool table, billiard table, snooker table": 736, + "pop bottle, soda bottle": 737, + "porcupine, hedgehog": 334, + "pot, flowerpot": 738, + "potpie": 964, + "potter's wheel": 739, + "power drill": 740, + "prairie chicken, prairie grouse, prairie fowl": 83, + "prayer rug, prayer mat": 741, + "pretzel": 932, + "printer": 742, + "prison, prison house": 743, + "proboscis monkey, Nasalis larvatus": 376, + "projectile, missile": 744, + "projector": 745, + "promontory, headland, head, foreland": 976, + "ptarmigan": 81, + "puck, hockey puck": 746, + "puffer, pufferfish, blowfish, globefish": 397, + "pug, pug-dog": 254, + "punching bag, punch bag, punching ball, punchball": 747, + "purse": 748, + "quail": 85, + "quill, quill pen": 749, + "quilt, comforter, comfort, puff": 750, + "racer, race car, racing car": 751, + "racket, racquet": 752, + "radiator": 753, + "radio telescope, radio reflector": 755, + "radio, wireless": 754, + "rain barrel": 756, + "ram, tup": 348, + "rapeseed": 984, + "recreational vehicle, RV, R.V.": 757, + "red fox, Vulpes vulpes": 277, + "red wine": 966, + "red wolf, maned wolf, Canis rufus, Canis niger": 271, + "red-backed sandpiper, dunlin, Erolia alpina": 140, + "red-breasted merganser, Mergus serrator": 98, + "redbone": 168, + "redshank, Tringa totanus": 141, + "reel": 758, + "reflex camera": 759, + "refrigerator, icebox": 760, + "remote control, remote": 761, + "restaurant, eating house, eating place, eatery": 762, + "revolver, six-gun, six-shooter": 763, + "rhinoceros beetle": 306, + "rifle": 764, + "ringlet, ringlet butterfly": 322, + "ringneck snake, ring-necked snake, ring snake": 53, + 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"use_cache": true, + "vocab_size": 49152 +} diff --git a/chatbot/models/HuggingFaceTB/SmolLM2-1.7B-Instruct/generation_config.json b/chatbot/models/HuggingFaceTB/SmolLM2-1.7B-Instruct/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..a1f56f6cefa8ac63cd73706c996d29c4381801e9 --- /dev/null +++ b/chatbot/models/HuggingFaceTB/SmolLM2-1.7B-Instruct/generation_config.json @@ -0,0 +1,7 @@ +{ + "_from_model_config": true, + "bos_token_id": 1, + "eos_token_id": 2, + "pad_token_id": 2, + "transformers_version": "4.46.2" +} diff --git a/chatbot/models/HuggingFaceTB/SmolLM2-1.7B-Instruct/merges.txt b/chatbot/models/HuggingFaceTB/SmolLM2-1.7B-Instruct/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..69503b13f727ba3812b6803e97442a6de05ef5eb --- /dev/null +++ b/chatbot/models/HuggingFaceTB/SmolLM2-1.7B-Instruct/merges.txt @@ -0,0 +1,48901 @@ +#version: 0.2 +Ġ t +Ġ a +i n +h e +Ġ Ġ +r e +o n +e r +Ġt he +a t +Ġ s +Ġ o +e n +Ġ c +e s +Ġ w +n d +i t +o r +i s +a l +Ġ p +in g +Ġ f +a n +e d +Ġ b +o u +a r +Ġ in +Ġo f +Ġ m +Ġa nd +i on +i c +Ġ d +Ġt o +ĠĠ ĠĠ +l e +r o +a s +en t +Ġ h +Ġt h +c t +Ġ e +Ġ re +e l +o m +i l +s t +Ġ l +Ġ n +e t +i m +v e +o l +at ion +Ġ g +i d +Ġ T +s e +Ġ is +u r +u t +r a +l y +c e +o t +â Ģ +c h +o w +i g +Ġb e +Ġ u +Ġf or +Ġs t +Ġ y +Ġ A +v er +a m +ĠĠ Ġ +Ġ S +Ġ on +u l +i r +Ġth at +Ġ I +Ġ C +a y +i f +it h +a d +Ġc on +Ġy ou +Ġa s +Ġp ro +he r +o d +Ġw ith +t er +ĠĠĠĠ ĠĠĠĠ +Ġa n +Ġo r +Ġw h +Ġ it +m ent +Ġa re +Ġa l +g e +es s +is t +Ġe x +Ġ ( +er s +Ġd e +at e +a b +i es +o p +Ġ M +t h +e ct +re s +u s +Ġ P +ĠT he +Ġc om +i v +es t +u m +it y +Ġ he +q u +Ġ v +a c +il l +Ġ B +o re +e m +Ġw e +Ġs u +p p +o s +k e +a nd +ro m +n t +l d +or t +a in +an t +i ve +ig h +o c +Ġ H +Ġ W +i al +Ġc h +Ġb y +Ġ r +u d +Ġ E +ĠĠĠĠ ĠĠĠ +Ġc an +âĢ Ļ +Ġa t +u n +Ġn e +Ġh a +Ġ D +- - +u re +Ġ le +Ġ F +Ġs e +Ġ R +Ġf rom +Ġ en +r i +p e +ic al +ar t +o g +Ġw as +p t +ion s +p l +r ou +Ġn ot +Ġ N +Ġs h +Ġ im +igh t +Ġ = +ou t +Ċ ĠĠĠĠĠĠĠ +al l +Ġ L +Ġth is +Ġ G +Ġa b +a g +re d +p er +Ġha ve +Ġw or +Ġ âĢ +g h +ou r +in e +i z +Ġin t +om e +Ċ ĠĠĠĠĠĠĠĠ +u st +Ġu s +Ġyou r +ou ld +a ct +Ċ ĠĠĠ +a ge +o st +Ġp l +Ġ " +ar d +el l +t her +Ġthe ir +ul t +el f +at ed +f f +ic h +en d +an s +ou s +id e +r u +ation s +Ġ O +Ġa d +a k +Ġw he +d u +c l +Ġcon t +Ġre s +as t +Ġ k +Ġthe y +Ġcom p +T he +i b +' s +or m +i p +c c +Ġd is +Ġal l +a p +am e +Ġ U +ab le +on g +e ar +e re +i e +as s +Ġim p +a re +Ġw ill +an ce +ĠT h +in d +Ġwh ich +o od +ar y +Ġ J +u al +v el +ĠI n +e p +en ce +Ġd o +on e +k s +Ġc l +Ġm ore +r y +i a +Ġt e +Ġ j +ig n +u e +ent s +re at +Ġm e +Ġo ther +Ġu n +i le +Ġh as +a ch +Ġm an +im e +ct ion +il d +s o +res s +c es +Ġa r +Ġab out +Ġb ut +a v +Ġa pp +Ġp er +o o +ic e +it ion +at er +el y +âĢ Ŀ +Ġs p +a ke +as e +Ġe v +Ġo ut +or s +ac k +en s +Ġon e +ver y +f orm +Ġ if +-- -- +or y +Ġs o +or d +a ce +in t +Ġwe re +b er +nd er +) . +Ġl i +Ġal so +ou nt +Ġp art +Ġcom m +Ġthe m +o se +a u +an g +c i +Ġst ud +c on +r it +i re +Ġp e +u b +n ow +Ġ qu +Ġu p +Ġs y +ing s +Ġwh o +Ġint o +ĠâĢ ľ +ou nd +is h +Ġp re +Ġthe se +Ġit s +ĠS t +ol og +if f +pe c +ĊĠĠĠĠĠĠĠĠ ĠĠĠ +rou gh +r ic +Ġf e +Ġbe c +Ġs ome +Ġt ra +a il +Ġ ' +Ġh ow +Ġs elf +re e +Ġin d +it ies +) , +k ing +Ġwhe n +ay s +p h +ver s +Ġe m +Ġh is +Ġ ro +if ic +Ġo ur +Ġm ay +Ġt ime +Ġu nder +ĠI t +ment s +Ġ K +at es +op le +n e +it e +Ġc ol +Ġthe re +Ġb et +ur n +c re +ĠTh is +Ġth an +ou gh +y s +Ġd iff +at ing +o b +t e +w e +ĠC h +at h +j ect +Ġt r +al ly +l ow +er v +Ġg o +m s +Ġcon s +Ġa g +Ġ ra +Ġo ver +le ct +Ġre c +x t +Ġp r +p le +ter n +i an +Ġa cc +Ġk now +c ess +Ġpe ople +Ġli ke +o ve +Ġhe l +Ġa ct +at a +on s +Ġd es +l ic +cl ud +i ous +# # +Ġy ear +ar n +Ġsu ch +Ġre l +Ġ V +Ġ Y +Ġbe en +ro w +e f +Ġu se +re n +Ġhel p +Ġne w +g et +) : +al th +ir st +er t +Ġ - +Ġwh at +au se +et h +l es +Ġw ould +Ġne ed +Ġth rough +f ul +st em +ur ing +rou nd +Ġs a +Ġa m +Ġe ff +Ġwor k +ic s +vel op +o v 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"Ġdiffering": 17547, + "Context": 17548, + "odont": 17549, + "Ġcryptocur": 17550, + "Ġheavier": 17551, + "ĠTro": 17552, + "ĠPubl": 17553, + "Ġtouched": 17554, + "ĠConstruction": 17555, + "Modern": 17556, + "Ġsubtract": 17557, + "erred": 17558, + "Ġlamp": 17559, + "Ġbiography": 17560, + "Ġseventh": 17561, + "workers": 17562, + "Ġconstell": 17563, + "Result": 17564, + "beta": 17565, + "ĠTu": 17566, + "ĠHispanic": 17567, + "ĠLang": 17568, + "ĠInitial": 17569, + "POST": 17570, + "Ġknees": 17571, + "Ġsooner": 17572, + "Ġoccupy": 17573, + "Ġsuccesses": 17574, + "ĠStew": 17575, + "Ġvegg": 17576, + "Ġturbines": 17577, + "resol": 17578, + "ĠApplying": 17579, + "ĠPortugal": 17580, + "phy": 17581, + "Ġdams": 17582, + "Ġware": 17583, + "Ġvacation": 17584, + "Ġ'%": 17585, + "Ġfeeds": 17586, + "because": 17587, + "Ġpolitically": 17588, + "modern": 17589, + "ĠDoctor": 17590, + "Ġpulp": 17591, + "Ġfisheries": 17592, + "?!": 17593, + "Ġexpon": 17594, + "Rad": 17595, + "Ġpools": 17596, + "Output": 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+ "Ġexcel": 17646, + "Total": 17647, + "Ġproactive": 17648, + "ĠHyper": 17649, + "Ġcompassionate": 17650, + "ogly": 17651, + "ĠFestival": 17652, + "break": 17653, + "Ġpave": 17654, + "utenant": 17655, + "Enter": 17656, + "mitt": 17657, + "ĠScripture": 17658, + "Ġsealed": 17659, + "Ġenrolled": 17660, + "-%": 17661, + "Ġtide": 17662, + "Ġboil": 17663, + "ĠGuinea": 17664, + "Ġcommercially": 17665, + "ĠTechnologies": 17666, + "uddenly": 17667, + "ĠRon": 17668, + "sheet": 17669, + "Ġanchor": 17670, + "ĠEC": 17671, + "ĠDur": 17672, + "IH": 17673, + "Ġcourtesy": 17674, + "Ġmistaken": 17675, + "Ġsurrender": 17676, + "ĠPent": 17677, + "Ġairport": 17678, + "DT": 17679, + "timeout": 17680, + "ĠShel": 17681, + "Ġacquiring": 17682, + "ĠAB": 17683, + "allel": 17684, + "Ġfractures": 17685, + "Ġerected": 17686, + "ĠPoor": 17687, + "ĠCrime": 17688, + "ĠNear": 17689, + "Ġmarry": 17690, + "Ġdepicting": 17691, + "orations": 17692, + "ĠMcK": 17693, + "oof": 17694, + "construction": 17695, + "ĠEric": 17696, 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"Ġfilaments": 30556, + "Ġsummarizes": 30557, + "Ġphonics": 30558, + "ĠElectricity": 30559, + "juven": 30560, + "aphyloc": 30561, + "Sche": 30562, + "Ġinadvert": 30563, + "abric": 30564, + "ĠArms": 30565, + "ĠValidation": 30566, + "å½": 30567, + "ĠLoren": 30568, + "ggy": 30569, + "Allow": 30570, + "Ġthrives": 30571, + "Ġlibrarians": 30572, + "Ġreplica": 30573, + "Tex": 30574, + "solution": 30575, + "('_": 30576, + "ĠResilience": 30577, + "ĠPhone": 30578, + "Ġfurnished": 30579, + "predictions": 30580, + "à¥ĩ": 30581, + "Ġbullied": 30582, + "ĠBeauty": 30583, + "Ġpragmatic": 30584, + "ĠKarn": 30585, + "ermal": 30586, + "Ġtrek": 30587, + "Ġwheelchair": 30588, + "ĠLiberation": 30589, + "ĠPhotoshop": 30590, + "Ġflattened": 30591, + "ĠPyramid": 30592, + "Ġpledged": 30593, + "Ġpredation": 30594, + "Ġfloats": 30595, + "Ġtorped": 30596, + "Ġqueens": 30597, + "Ġorchestr": 30598, + "Ġpatriarchal": 30599, + "Boolean": 30600, + "trial": 30601, + "atoms": 30602, + "ĠOst": 30603, + "ensure": 30604, + 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"ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 30799, + "Tele": 30800, + "oining": 30801, + "Ġtertiary": 30802, + "ĠMood": 30803, + "REQU": 30804, + "Params": 30805, + "Ġnuisance": 30806, + "Ġconfinement": 30807, + "Ġspleen": 30808, + "ĠDoct": 30809, + "Ġlatitudes": 30810, + "ĠWheat": 30811, + "Ġintrusion": 30812, + "Ġdivergent": 30813, + "Ġentrepreneurial": 30814, + "Ġdemolished": 30815, + "Incorpor": 30816, + "lys": 30817, + "ĠHelping": 30818, + "Healthy": 30819, + "Ġpirate": 30820, + "inism": 30821, + "fft": 30822, + "Ġintegrates": 30823, + "Ġlymphoma": 30824, + "ר": 30825, + "Ġlas": 30826, + "Ġconfisc": 30827, + "Ġordained": 30828, + "Ġrepercussions": 30829, + "ĠTort": 30830, + "ĠWinn": 30831, + "Ġurges": 30832, + "Ġconceal": 30833, + "establish": 30834, + "Ġpairing": 30835, + "Ġinterfering": 30836, + "ĠSoul": 30837, + "ĠFlying": 30838, + "Ġlifecycle": 30839, + "Ġfirearms": 30840, + "ĠTownship": 30841, + "Ġdenominator": 30842, + "iqued": 30843, + "otechn": 30844, + "sell": 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"apons": 31146, + ".\".": 31147, + "ĠEncyclop": 31148, + "Ġmenstruation": 31149, + "ĠPlymouth": 31150, + "gat": 31151, + "umann": 31152, + "Ġ\"'": 31153, + "Ġparity": 31154, + "Ġbiographical": 31155, + "============": 31156, + "ĠSurveillance": 31157, + "Ġsurvives": 31158, + "Ġhearings": 31159, + "ĠRespiratory": 31160, + "Ġchimney": 31161, + "RR": 31162, + "finder": 31163, + "Ġaunt": 31164, + "racks": 31165, + "ftp": 31166, + "Ġhumane": 31167, + "ushi": 31168, + "devices": 31169, + "Ġtablespoon": 31170, + "ĠChicken": 31171, + "Mont": 31172, + "Ãĥ": 31173, + "ĠINT": 31174, + "ĠAPIs": 31175, + "Positive": 31176, + "ĠBav": 31177, + "approximately": 31178, + "Ġcrypto": 31179, + "Mer": 31180, + "ĠOT": 31181, + "Ġbehold": 31182, + "Ġheadings": 31183, + "rice": 31184, + "ĠBerm": 31185, + "Ġexploits": 31186, + "Ġshading": 31187, + "Software": 31188, + "ilion": 31189, + "Ġantic": 31190, + "ĠPracticing": 31191, + "ĠCastro": 31192, + "Ġmesmer": 31193, + "/<": 31194, + "Ġ(*": 31195, + "ĠMeyer": 31196, + "ĠHers": 31197, + "ĠLoop": 31198, + "ĠChurches": 31199, + "Ġrecommending": 31200, + "iatric": 31201, + "PubMedGoogle": 31202, + "Drop": 31203, + "NESS": 31204, + "ĠStroke": 31205, + "ĠRevere": 31206, + "pathic": 31207, + "Ġverdict": 31208, + "Ġvertebrates": 31209, + "Ġworshipped": 31210, + "PLA": 31211, + "atism": 31212, + "Ġwarts": 31213, + "ĠHook": 31214, + "ĠGly": 31215, + "Ġweakening": 31216, + "uvian": 31217, + "Ġhymns": 31218, + "ĠInflamm": 31219, + "Ġspectators": 31220, + "Ġfooth": 31221, + "Ġtwisting": 31222, + "ĠGastro": 31223, + "atchewan": 31224, + "Ġabreast": 31225, + "ĠDJ": 31226, + "Ġsurrog": 31227, + "afer": 31228, + "Ġhottest": 31229, + "Ġtumult": 31230, + "Ġallevi": 31231, + "Library": 31232, + "Ġthirds": 31233, + "ĠSara": 31234, + "Ġbullets": 31235, + "canvas": 31236, + "ĠPMC": 31237, + "Ġbattling": 31238, + "Ġcategorize": 31239, + "Ġsulphur": 31240, + "vir": 31241, + "Ġcosting": 31242, + "Ġforthcoming": 31243, + "Ġpharmacy": 31244, + "omorphic": 31245, + "tun": 31246, + "emics": 31247, + "ĠNaturally": 31248, + "Ġsilently": 31249, + "giene": 31250, + "Mental": 31251, + "Ġmyocard": 31252, + "Ġfamed": 31253, + "Ġcheek": 31254, + "Ġerase": 31255, + "topics": 31256, + "Ġneurode": 31257, + "locked": 31258, + "ĠImmune": 31259, + "ĠLudwig": 31260, + "AWS": 31261, + "Ġsid": 31262, + "Ġischem": 31263, + "Ġblisters": 31264, + "ĠConsortium": 31265, + "Shape": 31266, + "Ġknight": 31267, + "Ġharb": 31268, + "Keeping": 31269, + "Ġgranular": 31270, + "Ġcoercion": 31271, + "bp": 31272, + "opold": 31273, + "ĠFerg": 31274, + "Ġmetre": 31275, + "ĠSalem": 31276, + "Ġaltru": 31277, + "Ġarbitration": 31278, + "Ġinaccessible": 31279, + "Ġorg": 31280, + "Ġexoplan": 31281, + "rious": 31282, + "ĠLt": 31283, + "Ġmodernization": 31284, + "checks": 31285, + "ĠAsthma": 31286, + "Signs": 31287, + "Ġconsolidated": 31288, + "Ġcascade": 31289, + "hoea": 31290, + "ĠCorinthians": 31291, + "nine": 31292, + "ĠMaz": 31293, + "ĠBin": 31294, + "unknown": 31295, + "ĠRoth": 31296, + "asser": 31297, + "ĠTrace": 31298, + "dirs": 31299, + "professional": 31300, + "Ġtaxonomy": 31301, + "Ir": 31302, + "ĠMist": 31303, + "ortment": 31304, + "Ġratt": 31305, + "cessions": 31306, + "everse": 31307, + "Ġhistogram": 31308, + "ĠThermal": 31309, + "Side": 31310, + "Ġdeletion": 31311, + "Ġunconst": 31312, + "ĠChocolate": 31313, + "ucose": 31314, + "Ġresearches": 31315, + "compare": 31316, + "ĠHumanities": 31317, + "ĠADD": 31318, + "Ġbotan": 31319, + "evaluation": 31320, + "echo": 31321, + "Execution": 31322, + "fan": 31323, + "toe": 31324, + "Ġspotlight": 31325, + "Ġpedal": 31326, + "ĠNGO": 31327, + "ĠAxis": 31328, + "avier": 31329, + "ĠTraditions": 31330, + "ĠTerry": 31331, + "Electric": 31332, + "Cancer": 31333, + "hey": 31334, + "ĠFashion": 31335, + "ognition": 31336, + "ĠAmish": 31337, + "Ġк": 31338, + "Ġabandonment": 31339, + "Experts": 31340, + "Offic": 31341, + "Ġstadium": 31342, + "ĠThousands": 31343, + "Ġodors": 31344, + "Ġconveys": 31345, + "ummies": 31346, + "Kit": 31347, + "Ġpolitely": 31348, + "ĠVenet": 31349, + "ĠChronicle": 31350, + "loo": 31351, + "Ġfus": 31352, + "Ġmedial": 31353, + "Ġstranded": 31354, + "ĠExcited": 31355, + "CADE": 31356, + "ĠYahweh": 31357, + "ĠVladimir": 31358, + "icum": 31359, + "Ġhid": 31360, + "ĠUz": 31361, + "Ġlayouts": 31362, + "Ġrainforests": 31363, + "Ġsophist": 31364, + "Ġterrorists": 31365, + "ĠArguments": 31366, + "tysburg": 31367, + "dar": 31368, + "Ġinterim": 31369, + "Ġlocality": 31370, + "ĠNeolithic": 31371, + "Ġultrason": 31372, + "matched": 31373, + "voltage": 31374, + "Ġpinch": 31375, + "Ġtattoo": 31376, + "opedic": 31377, + "ĠBUT": 31378, + "Ġtraverse": 31379, + "Ġemits": 31380, + "ĠSharp": 31381, + "Resources": 31382, + "Ġinvariably": 31383, + "PCR": 31384, + "kil": 31385, + "omials": 31386, + "Ġproclamation": 31387, + "tainment": 31388, + "avering": 31389, + ",,,,,,,,": 31390, + "Ġneonatal": 31391, + "fx": 31392, + "rack": 31393, + "llo": 31394, + "goal": 31395, + "ĠManip": 31396, + "ĠGuides": 31397, + "Ġseekers": 31398, + "ĠDataset": 31399, + "ĠOrient": 31400, + "radle": 31401, + "ĠAnalytics": 31402, + "ĠEnhanced": 31403, + "Ġridiculous": 31404, + "|','": 31405, + "Ġmasonry": 31406, + "agi": 31407, + "Ġrails": 31408, + "Ġpowdered": 31409, + "аÑĤ": 31410, + "wrapper": 31411, + "scalar": 31412, + "Pick": 31413, + "asarray": 31414, + "Ġjer": 31415, + "Ġfirewall": 31416, + "ĠJerry": 31417, + "]=": 31418, + "catch": 31419, + "verting": 31420, + "ĠMatch": 31421, + "Ġsept": 31422, + "Ġactivates": 31423, + "Ġpotentials": 31424, + "Ġradios": 31425, + "ĠFraser": 31426, + "Ġundist": 31427, + "ĠHousehold": 31428, + "Specific": 31429, + "browser": 31430, + "lm": 31431, + "inished": 31432, + "Ġgoose": 31433, + "essim": 31434, + "Ġflashes": 31435, + "ĠScar": 31436, + "ĠGeo": 31437, + "Lord": 31438, + "Ġhij": 31439, + "Ġproactively": 31440, + "iev": 31441, + "Ġguerr": 31442, + "Ġbattalion": 31443, + "initializer": 31444, + "Ġrecombination": 31445, + "Ġunreasonable": 31446, + "Mic": 31447, + "Tools": 31448, + "meg": 31449, + "ĠTalking": 31450, + "ĠAry": 31451, + "ectin": 31452, + "Ġresumed": 31453, + "ĠProtestants": 31454, + "Ġblossoms": 31455, + "Ġamusement": 31456, + "reeks": 31457, + "Ġsymmetric": 31458, + "burse": 31459, + "Ġcyberbullying": 31460, + "fighting": 31461, + "ب": 31462, + "ĠTus": 31463, + "čĊĠĠĠĠĠĠĠĠĠĠĠĠ": 31464, + "itone": 31465, + "ĠSpar": 31466, + "ĠSEC": 31467, + "ipolar": 31468, + "Ġtallest": 31469, + "Ġsucceeding": 31470, + "Ġdreaming": 31471, + "Ġsparkling": 31472, + "ĠStockholm": 31473, + "Ġplankton": 31474, + "ĠServe": 31475, + "spoken": 31476, + "Ġsynonyms": 31477, + "Ġpuppies": 31478, + "Lee": 31479, + "Site": 31480, + "Ġbacon": 31481, + "Ġconced": 31482, + "Ġans": 31483, + "Ġranch": 31484, + "Ġeroded": 31485, + "Ġgraphite": 31486, + "Ġpreached": 31487, + "destroy": 31488, + "Ġinclination": 31489, + "Ġshovel": 31490, + "Ġreshape": 31491, + "ĠCiv": 31492, + "ĠUTC": 31493, + "Ġdrier": 31494, + "Ġinduces": 31495, + "localhost": 31496, + "Ġadvertisement": 31497, + "Ġinex": 31498, + "urai": 31499, + "Ġteamm": 31500, + "ĠFormer": 31501, + "Ġaquifer": 31502, + "AGES": 31503, + "Ġsadly": 31504, + "thereum": 31505, + "Ġteas": 31506, + "Ġafflicted": 31507, + "Ġhandheld": 31508, + "marked": 31509, + "Ġfraudulent": 31510, + "Ġtropics": 31511, + "Ġfrig": 31512, + "odon": 31513, + "ĠWalt": 31514, + "epid": 31515, + "Ġrecol": 31516, + "Ġdetectable": 31517, + "rers": 31518, + "Ġadherent": 31519, + "Ġposing": 31520, + "ĠGeoff": 31521, + "Ġtrusting": 31522, + "Ġepidemiological": 31523, + "Migration": 31524, + "atz": 31525, + "Ġjargon": 31526, + "ported": 31527, + "idson": 31528, + "loom": 31529, + "Tell": 31530, + "ĠMight": 31531, + "ĠPie": 31532, + "ĠKatherine": 31533, + "Ġresultant": 31534, + "Guide": 31535, + "Secondly": 31536, + "Ġrepositories": 31537, + "orating": 31538, + "teness": 31539, + "ERC": 31540, + "termedi": 31541, + "Ġunearthed": 31542, + "Ġdred": 31543, + "ĠBend": 31544, + "ĠHier": 31545, + "amming": 31546, + "Ġfavourable": 31547, + "ĠRodrig": 31548, + "Ġlawsuits": 31549, + "Clear": 31550, + "Ġbureaucracy": 31551, + "Ġgust": 31552, + "Ġrushing": 31553, + "ĠFerr": 31554, + "Ġcommissions": 31555, + "Ġlongstanding": 31556, + "ABA": 31557, + "Ġimpartial": 31558, + "enzie": 31559, + "absolute": 31560, + "ĠAuschwitz": 31561, + "Ġquer": 31562, + "Ġtownship": 31563, + "Ġresponders": 31564, + "Ġfavors": 31565, + "Ġnegligible": 31566, + "ĠPrague": 31567, + "rar": 31568, + "informatics": 31569, + "alias": 31570, + "Ġmedically": 31571, + "ĠCampus": 31572, + "Ġinhalation": 31573, + "Ġbiomarkers": 31574, + "Safety": 31575, + "ĠPall": 31576, + "addWidget": 31577, + "Ġpharmacist": 31578, + "Ġprincipally": 31579, + "otypic": 31580, + "HV": 31581, + "tion": 31582, + "ĠChern": 31583, + "ĠRebecca": 31584, + "ĠWeber": 31585, + "Ġecclesiastical": 31586, + "Ġautomate": 31587, + "Ġsurveying": 31588, + "ĠRobotics": 31589, + "Ġmisconception": 31590, + "Ġdiscrepancy": 31591, + "Ġcried": 31592, + "opin": 31593, + "ĠDesigning": 31594, + "Ġtactic": 31595, + "GRO": 31596, + "lip": 31597, + "ĠSSD": 31598, + "Ġprinces": 31599, + "Ġgrandchildren": 31600, + "Ġreciprocal": 31601, + "Dar": 31602, + "hang": 31603, + "áº": 31604, + "prod": 31605, + "ĠSeb": 31606, + "ĠAhmed": 31607, + "Ġinverted": 31608, + "male": 31609, + "pv": 31610, + "Ġtherein": 31611, + "ITES": 31612, + "ĠTransmission": 31613, + "Ġdelegate": 31614, + ">=": 31615, + "yield": 31616, + "iminary": 31617, + "ĠJak": 31618, + "ĠKoh": 31619, + "Ġaccents": 31620, + "ĠEarlier": 31621, + "Fac": 31622, + "Ġthrilled": 31623, + "Ġcervix": 31624, + "delivery": 31625, + "Ġstren": 31626, + "Ġdirective": 31627, + "ĠAttack": 31628, + "Ġtasting": 31629, + "oya": 31630, + "Ġintellectually": 31631, + "ĠCSV": 31632, + "Ġslept": 31633, + "anse": 31634, + "odend": 31635, + "Ġsolic": 31636, + "ĠInstitutions": 31637, + "Ġcirculated": 31638, + "IK": 31639, + "ĠHelps": 31640, + "Ġtedious": 31641, + "Ġepigenetic": 31642, + "BF": 31643, + "ovis": 31644, + "Ġhandmade": 31645, + "dummy": 31646, + "elian": 31647, + "ĠLac": 31648, + "Ġpatiently": 31649, + "Ġhospitalized": 31650, + "Ġnarrower": 31651, + "Ġpioneered": 31652, + "ĠCassini": 31653, + "IU": 31654, + "Rout": 31655, + "Ġshook": 31656, + "aspx": 31657, + "nering": 31658, + "Ġti": 31659, + "ĠInteractions": 31660, + "Canadian": 31661, + "Ġbombard": 31662, + "rush": 31663, + "lli": 31664, + "ĠEducators": 31665, + "ĠAnything": 31666, + "iago": 31667, + "meth": 31668, + "inol": 31669, + "ĠEz": 31670, + "Ġflowed": 31671, + "Ġsalient": 31672, + "ĠCec": 31673, + "akra": 31674, + "=='": 31675, + "Ġcritiques": 31676, + "Ġeyesight": 31677, + "customer": 31678, + "Ġterrifying": 31679, + "Ġhref": 31680, + "Ġgenotype": 31681, + "Ġdedicate": 31682, + "ĠOpera": 31683, + "ĠBuildings": 31684, + "Ġreconnaissance": 31685, + "Ġvernacular": 31686, + "Ser": 31687, + "ratch": 31688, + "Ġdummy": 31689, + "Ġhass": 31690, + "ptr": 31691, + "ĠInequ": 31692, + "Ġmeadows": 31693, + "Ġequipping": 31694, + "ĠPapua": 31695, + "Ġcontraception": 31696, + "Ġskiing": 31697, + "Ġaureus": 31698, + "ĠLords": 31699, + "Ġclerk": 31700, + "Ġensuing": 31701, + "Ġimpactful": 31702, + "Ġtutors": 31703, + "Ġhydroph": 31704, + "Ġcardinal": 31705, + "TeX": 31706, + "HF": 31707, + "bps": 31708, + "Ġeq": 31709, + "measures": 31710, + "mostly": 31711, + "Ġdenoted": 31712, + "academic": 31713, + "Impact": 31714, + "Ġunrealistic": 31715, + "ĠPresbyterian": 31716, + "Paper": 31717, + "çĽ": 31718, + "imon": 31719, + "odiac": 31720, + "Ġunic": 31721, + "ĠScandinavian": 31722, + "ĠBehaviour": 31723, + "ĠLCD": 31724, + "ĠJin": 31725, + "Ġconsortium": 31726, + "Ġdiaries": 31727, + "ĠTelegraph": 31728, + "Ġrhymes": 31729, + "ол": 31730, + "ĠPompe": 31731, + "ĠSwe": 31732, + "ĠRacial": 31733, + "ribly": 31734, + "Ġbitcoin": 31735, + "Ġbanning": 31736, + "Ġmasked": 31737, + "ĠHellen": 31738, + "ĠExercises": 31739, + "mable": 31740, + "money": 31741, + "kef": 31742, + "Ġnotified": 31743, + "deletion": 31744, + "ĠBeethoven": 31745, + "Ġacademy": 31746, + "riday": 31747, + "inetics": 31748, + "Ġsymptomatic": 31749, + "lawful": 31750, + "Ġamyloid": 31751, + "ï¸ı": 31752, + "bered": 31753, + "Ġurination": 31754, + "Ġpolluting": 31755, + "Ġfootsteps": 31756, + "ĠLearners": 31757, + "Ġdetectives": 31758, + "Ġtroubling": 31759, + "ĠOutcomes": 31760, + "furt": 31761, + "inox": 31762, + "Ġalters": 31763, + "ĠAsper": 31764, + "landers": 31765, + "Ġtoolkit": 31766, + "Ġtumours": 31767, + "ĠChau": 31768, + "Ġovercrow": 31769, + "Ġrelocated": 31770, + "Ġmeaningless": 31771, + "ĠPhysicians": 31772, + "rystall": 31773, + "little": 31774, + "Ġdislike": 31775, + "Ġspins": 31776, + "ĠVisitors": 31777, + "ĠOxygen": 31778, + "Ġskeletons": 31779, + "Ġflavon": 31780, + "Ġcirculatory": 31781, + "oggles": 31782, + "cus": 31783, + "tier": 31784, + "Ġaust": 31785, + "Ġsprayed": 31786, + "profits": 31787, + "ĠCraft": 31788, + "artes": 31789, + "ĠMalay": 31790, + "********************************": 31791, + "Ġfaculties": 31792, + "Happy": 31793, + "Ġbeak": 31794, + "ĠMell": 31795, + "ĠDop": 31796, + "ĠGur": 31797, + "ás": 31798, + "-)": 31799, + "timer": 31800, + "Ġfeline": 31801, + "ematic": 31802, + "Ġsparks": 31803, + "quez": 31804, + "ĠImpacts": 31805, + "Transform": 31806, + "ĠParticipation": 31807, + "ĠLiverpool": 31808, + "Programming": 31809, + "Germany": 31810, + "Ġexcurs": 31811, + "irement": 31812, + "aji": 31813, + "Ġpictured": 31814, + "ILE": 31815, + "Ġsimplistic": 31816, + "Ġindefinitely": 31817, + "Ġtyranny": 31818, + ":\")": 31819, + "Rap": 31820, + "Ġwatt": 31821, + "ĠSever": 31822, + "ĠJazz": 31823, + "('<": 31824, + "Ġastrology": 31825, + "Ġheterosexual": 31826, + "Ġappendix": 31827, + "Ġmusculoskeletal": 31828, + "ĠPaint": 31829, + "quarter": 31830, + "ĠDas": 31831, + "ĠRank": 31832, + "Ġclash": 31833, + "ĠNewfoundland": 31834, + "Ġdolls": 31835, + "Ġaffirmative": 31836, + "Ġnotebooks": 31837, + "׾": 31838, + "Ġaqueous": 31839, + "Ġscrolling": 31840, + "Ġattic": 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35267, + "Ġpastry": 35268, + "ĠPersians": 35269, + "Ġdissimilar": 35270, + "ĠDoesn": 35271, + "Interfaces": 35272, + "Ġsubsidiary": 35273, + "Ġpaleont": 35274, + "Ġprostitution": 35275, + "ĠHunger": 35276, + "roves": 35277, + "Ġenvy": 35278, + "')]": 35279, + "Ġpriced": 35280, + "ĠOrganize": 35281, + "ĠMetro": 35282, + "understand": 35283, + "Ġdiscounts": 35284, + "ĠGlacier": 35285, + "ĠWarming": 35286, + "ĠYose": 35287, + "ĠManila": 35288, + "ĠPrecision": 35289, + "Ġrotates": 35290, + "Ġnarrowly": 35291, + "ĠInvol": 35292, + "Ġdystop": 35293, + "ĠWouldn": 35294, + "Ġcancelled": 35295, + "Ġchiropractic": 35296, + "NULL": 35297, + "ĠMilwaukee": 35298, + "ĠInteger": 35299, + "ĠObservation": 35300, + "Ġcadmium": 35301, + "ĠMysteries": 35302, + "Tuesday": 35303, + "elo": 35304, + "Ġcoma": 35305, + "ĠGHz": 35306, + "Ġsyst": 35307, + "ISO": 35308, + "Ġsnoring": 35309, + "Ġclustered": 35310, + "Ġsynchronization": 35311, + "Ġcrusher": 35312, + "ĠAztec": 35313, + "Ġincompet": 35314, + "Ġlumps": 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"Ġfairer": 40859, + "Fer": 40860, + "fection": 40861, + "thello": 40862, + "Identity": 40863, + "ĠColeman": 40864, + "ĠRodriguez": 40865, + "Ġinnumerable": 40866, + "seat": 40867, + "ĠESP": 40868, + "Ġleaked": 40869, + "Ġdisillusion": 40870, + "ĠStamp": 40871, + "compress": 40872, + "Appro": 40873, + "Ġfertilize": 40874, + "Ġanthropological": 40875, + "ĠMarshal": 40876, + "ĠMoshe": 40877, + "ĠThreatened": 40878, + "ĠPlatforms": 40879, + "Easy": 40880, + "Ġdurations": 40881, + "thorne": 40882, + "ĠWade": 40883, + "plog": 40884, + "Ġunconsciously": 40885, + "thews": 40886, + "ĠKeynes": 40887, + "divisions": 40888, + "Handle": 40889, + "Util": 40890, + "ĠBLM": 40891, + "ĠTucson": 40892, + "moves": 40893, + "arative": 40894, + "Ġnave": 40895, + "ĠRV": 40896, + "ĠKod": 40897, + "Ġdefender": 40898, + "manage": 40899, + "Ġbarracks": 40900, + "Ġvillains": 40901, + "Ġplainly": 40902, + "ĠEVs": 40903, + "Ġsurfaced": 40904, + "Ġinductive": 40905, + "ĠPURPOSE": 40906, + "vah": 40907, + "Ġsoot": 40908, + "Arr": 40909, + "ĠInterstate": 40910, + "Ġclimbers": 40911, + "Ġnonex": 40912, + "Ġmolded": 40913, + "bourg": 40914, + "Ġoversees": 40915, + "responsive": 40916, + "ĠVedas": 40917, + "Ġsurrogate": 40918, + "covering": 40919, + "Ġbordered": 40920, + "ĠSEL": 40921, + "ĠPablo": 40922, + "ĠArabidopsis": 40923, + "ĠCircular": 40924, + "rotsky": 40925, + "ĠHabit": 40926, + "ĠEurasia": 40927, + "Dictionary": 40928, + "ĠTomb": 40929, + "quiring": 40930, + "Ġnecks": 40931, + "Ġdisordered": 40932, + "Ġjohn": 40933, + "ĠSto": 40934, + "othermia": 40935, + "genome": 40936, + "Ġfourteenth": 40937, + "ĠSheep": 40938, + "SSL": 40939, + "ä¸Ĭ": 40940, + "Ġamplifiers": 40941, + "нÑĭ": 40942, + "predicted": 40943, + "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 40944, + "Ġabolish": 40945, + "Ġanthrax": 40946, + "confirmed": 40947, + "Ġmortgages": 40948, + "Din": 40949, + "liquid": 40950, + "Ġwreat": 40951, + "ibou": 40952, + "Ġsubcontinent": 40953, + "ĠArsen": 40954, + "ĠEmpty": 40955, + "Ġcombatting": 40956, + "Ġplugins": 40957, + "Ġcannib": 40958, + "Ġpsychiatrists": 40959, + "ytocin": 40960, + "ĠRaising": 40961, + "ĠBruno": 40962, + "ĠThreats": 40963, + "Ġcarcasses": 40964, + "Ġbots": 40965, + "sta": 40966, + "igible": 40967, + "ĠHog": 40968, + "ĠJE": 40969, + "ĠYom": 40970, + "Ġmoderated": 40971, + "Ġwoodpec": 40972, + "Ġsuspend": 40973, + "ĠParliamentary": 40974, + "anasia": 40975, + "Ġgrapefruit": 40976, + "avas": 40977, + "scipy": 40978, + "idelberg": 40979, + "warnings": 40980, + "Ġstaircase": 40981, + "ĠMaharashtra": 40982, + "Sand": 40983, + "walking": 40984, + "Ġvase": 40985, + "ĠBrom": 40986, + "ĠUAE": 40987, + "ĠAbnormal": 40988, + "aturation": 40989, + "ĠDiary": 40990, + "URI": 40991, + "FTA": 40992, + "æľ¬": 40993, + "ä½ľ": 40994, + "ĠMutual": 40995, + "ĠAuthentication": 40996, + "ĠKEY": 40997, + "ĠBIM": 40998, + "apur": 40999, + "unding": 41000, + "ĠAdri": 41001, + "ĠColour": 41002, + "ICH": 41003, + "ĠAntony": 41004, + "Ġsonic": 41005, + "abilistic": 41006, + "ĠBoyd": 41007, + "Ġosmosis": 41008, + "ĠPharise": 41009, + "cnn": 41010, + "urgeon": 41011, + "kerel": 41012, + "Ġspindle": 41013, + "Ġcommute": 41014, + "Ġindiscrim": 41015, + "ovsk": 41016, + "Ġnumerals": 41017, + "Ġuri": 41018, + "films": 41019, + "Potential": 41020, + "ĠSurrounding": 41021, + "Tax": 41022, + "Ġtonal": 41023, + "âĢļ": 41024, + "ĠWatching": 41025, + "ĠLICENSE": 41026, + "ĠGan": 41027, + "ĠGenet": 41028, + "Ġhazel": 41029, + "Ġtributary": 41030, + "nod": 41031, + "Ġadverb": 41032, + "ologne": 41033, + "Ġmaladaptive": 41034, + "ĠAssessments": 41035, + "Ġdeleting": 41036, + "Ġbruising": 41037, + "Ġhawk": 41038, + "dB": 41039, + "mene": 41040, + "yrus": 41041, + "ĠSpy": 41042, + "advent": 41043, + "ĠDV": 41044, + "reddit": 41045, + "ecological": 41046, + "Stone": 41047, + "(\".": 41048, + "Ġforearm": 41049, + "Ġlifetimes": 41050, + "ĠHerbal": 41051, + "slope": 41052, + "AMPLE": 41053, + "ĠLeicester": 41054, + "Ġordinances": 41055, + "HCR": 41056, + "hai": 41057, + "tv": 41058, + "enact": 41059, + "otrans": 41060, + "ĠBau": 41061, + "ĠThousand": 41062, + "Ġunclean": 41063, + "Ġunidentified": 41064, + "conversion": 41065, + "Ġpreprocessing": 41066, + "Ġunderlie": 41067, + "covers": 41068, + "sufficiency": 41069, + "Ġcontractual": 41070, + "ĠCorpus": 41071, + "ĠMacro": 41072, + "Ġiconography": 41073, + "QUE": 41074, + "Ġlagoon": 41075, + "Customer": 41076, + "ĠAyurvedic": 41077, + "+',": 41078, + "Cour": 41079, + "Prin": 41080, + "SERV": 41081, + "Ġplywood": 41082, + "ĠCasp": 41083, + "ĠRitual": 41084, + "Ġqubits": 41085, + "ASP": 41086, + "Ġvegetarians": 41087, + "Ġreproducible": 41088, + "Ġmanipulations": 41089, + "Ġrepayment": 41090, + "/')": 41091, + "Near": 41092, + "mf": 41093, + "Ġextermin": 41094, + "reduced": 41095, + "cessive": 41096, + "Ġentrances": 41097, + "ĠWebsites": 41098, + "paragraph": 41099, + "ĠShim": 41100, + "Ġpainkillers": 41101, + "ĠPerse": 41102, + "Ġspeedy": 41103, + "Ġdishwas": 41104, + "Ġgrabbing": 41105, + "ĠFleming": 41106, + "Ġirresist": 41107, + "nda": 41108, + "Ġreiter": 41109, + "ĠCain": 41110, + "ĠGad": 41111, + "Generic": 41112, + "ĠBrigham": 41113, + "Ġretailer": 41114, + "Ġplutonium": 41115, + "thorn": 41116, + "ĠNutrient": 41117, + "ĠLig": 41118, + "ĠKlan": 41119, + "Ġrefurb": 41120, + "vester": 41121, + "posp": 41122, + "spaces": 41123, + "Ġconcentric": 41124, + "brev": 41125, + "Ġstimulants": 41126, + "oderma": 41127, + "è¦ģ": 41128, + "iou": 41129, + "ĠBella": 41130, + "Ġscribes": 41131, + "atteries": 41132, + "ĠCyrus": 41133, + "ĠBurton": 41134, + "Ġparasit": 41135, + "Ġphosphory": 41136, + "Ġmimicking": 41137, + "Ġfutile": 41138, + "literals": 41139, + "ĠBringing": 41140, + "Ġacquaintance": 41141, + "Slow": 41142, + "Upload": 41143, + "jang": 41144, + "slavery": 41145, + "ÅĦ": 41146, + "aru": 41147, + "Ġanne": 41148, + "ĠAddition": 41149, + "Ġmischie": 41150, + "Ġtimest": 41151, + "ãģ«": 41152, + "connections": 41153, + "ĠATM": 41154, + "Monitoring": 41155, + "Ġpluralism": 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"Ġvariously": 41354, + "Ġuntapped": 41355, + "Ġdeduction": 41356, + "-----------------------------------": 41357, + "osphorus": 41358, + "ĠPathology": 41359, + "Ġgranules": 41360, + "Ġotters": 41361, + "ĠCeres": 41362, + "JO": 41363, + "Rod": 41364, + "ulmonary": 41365, + "ĠBess": 41366, + "aunder": 41367, + "ĠVideos": 41368, + "ĠClaire": 41369, + "Ġmotility": 41370, + "timezone": 41371, + "summer": 41372, + "Ġcarnivorous": 41373, + "ĠUber": 41374, + "ĠJill": 41375, + "ĠKeller": 41376, + "Ġregurg": 41377, + "completed": 41378, + "arches": 41379, + "âĢľ.": 41380, + "rada": 41381, + "Ġsequel": 41382, + "Ġsqrt": 41383, + "Ġanteced": 41384, + "Ġmisfortune": 41385, + "Pin": 41386, + "Ġtungsten": 41387, + "entities": 41388, + "Ġeerie": 41389, + "ĠWille": 41390, + "Ġunanswered": 41391, + "expert": 41392, + "Ġilliterate": 41393, + "Ġscreaming": 41394, + "Ġuniverses": 41395, + "ĠHistorians": 41396, + "ĠKoreans": 41397, + "ĠBrotherhood": 41398, + "ĠFeelings": 41399, + "Ġphylogeny": 41400, + "Ġgiraffe": 41401, + "tear": 41402, + "ĠTiny": 41403, + "ĠBard": 41404, + "Ġoxal": 41405, + "Ġµm": 41406, + "@@": 41407, + "Ġou": 41408, + "ĠCoy": 41409, + "Ġsyringe": 41410, + "ĠCompos": 41411, + "ĠActing": 41412, + "Ġutilised": 41413, + "ãģĹ": 41414, + "clicked": 41415, + "Ġsprang": 41416, + "bohydrate": 41417, + "kinesis": 41418, + "Ġrename": 41419, + "Ġure": 41420, + "ĠDoll": 41421, + "ĠRheumat": 41422, + "Ġrogue": 41423, + "ertations": 41424, + "armament": 41425, + "')(": 41426, + "ĠColored": 41427, + "Ġstressing": 41428, + "Ġarcheological": 41429, + "ĠParadox": 41430, + "Ġsolubility": 41431, + "Mom": 41432, + "ĠTart": 41433, + "icky": 41434, + "Ġincrements": 41435, + "notify": 41436, + "Ġwasteful": 41437, + "ĠElectoral": 41438, + "Scope": 41439, + "Ġtightening": 41440, + "Attr": 41441, + "PON": 41442, + "Ġcpu": 41443, + "Ġstocking": 41444, + "Ġdeceive": 41445, + "ĠDere": 41446, + "Ġequate": 41447, + "manufact": 41448, + "Ġharden": 41449, + "Ġsensibilities": 41450, + "Ġfurthermore": 41451, + "CSI": 41452, + "[:,:,": 41453, + "latent": 41454, + "ог": 41455, + "Pattern": 41456, + "Reducing": 41457, + "forestry": 41458, + "responses": 41459, + "ĠGlossary": 41460, + "Crypt": 41461, + "Done": 41462, + "Fixed": 41463, + "Ice": 41464, + "MARY": 41465, + "}(": 41466, + "å¿": 41467, + "Ġhoo": 41468, + "ĠMesh": 41469, + "ĠEure": 41470, + "ĠFlem": 41471, + "ĠRash": 41472, + "ĠOW": 41473, + "Ġeffluent": 41474, + "escape": 41475, + "Ġtotalitarian": 41476, + "zzi": 41477, + "pubmed": 41478, + "大": 41479, + "ĠMirror": 41480, + "egg": 41481, + "stere": 41482, + "Ġgills": 41483, + "egy": 41484, + "Chart": 41485, + "Andrew": 41486, + "ĠLockheed": 41487, + "Ġprerequisites": 41488, + "Bottom": 41489, + "Ġaversion": 41490, + "Ġbouncing": 41491, + "acer": 41492, + "ĠHare": 41493, + "ĠErik": 41494, + "Ġunquestion": 41495, + "theory": 41496, + "ophones": 41497, + "ĠFloyd": 41498, + "Ġinformally": 41499, + "Ġcharger": 41500, + "Preventing": 41501, + "Ġeradicated": 41502, + 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grass snake", + "58": "water snake", + "59": "vine snake", + "60": "night snake, Hypsiglena torquata", + "61": "boa constrictor, Constrictor constrictor", + "62": "rock python, rock snake, Python sebae", + "63": "Indian cobra, Naja naja", + "64": "green mamba", + "65": "sea snake", + "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", + "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", + "68": "sidewinder, horned rattlesnake, Crotalus cerastes", + "69": "trilobite", + "70": "harvestman, daddy longlegs, Phalangium opilio", + "71": "scorpion", + "72": "black and gold garden spider, Argiope aurantia", + "73": "barn spider, Araneus cavaticus", + "74": "garden spider, Aranea diademata", + "75": "black widow, Latrodectus mactans", + "76": "tarantula", + "77": "wolf spider, hunting spider", + "78": "tick", + "79": "centipede", + "80": "black grouse", + "81": "ptarmigan", + "82": "ruffed grouse, partridge, Bonasa umbellus", + "83": "prairie chicken, prairie grouse, prairie fowl", + "84": "peacock", + "85": "quail", + "86": "partridge", + "87": "African grey, African gray, Psittacus erithacus", + "88": "macaw", + "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", + "90": "lorikeet", + "91": "coucal", + "92": "bee eater", + "93": "hornbill", + "94": "hummingbird", + "95": "jacamar", + "96": "toucan", + "97": "drake", + "98": "red-breasted merganser, Mergus serrator", + "99": "goose", + "100": "black swan, Cygnus atratus", + "101": "tusker", + "102": "echidna, spiny anteater, anteater", + "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", + "104": "wallaby, brush kangaroo", + "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", + "106": "wombat", + "107": "jellyfish", + "108": "sea anemone, anemone", + "109": "brain coral", + "110": "flatworm, platyhelminth", + "111": "nematode, nematode worm, roundworm", + "112": "conch", + "113": "snail", + "114": "slug", + "115": "sea slug, nudibranch", + "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", + "117": "chambered nautilus, pearly nautilus, nautilus", + "118": "Dungeness crab, Cancer magister", + "119": "rock crab, Cancer irroratus", + "120": "fiddler crab", + "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", + "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", + "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", + "124": "crayfish, crawfish, crawdad, crawdaddy", + "125": "hermit crab", + "126": "isopod", + "127": "white stork, Ciconia ciconia", + "128": "black stork, Ciconia nigra", + "129": "spoonbill", + "130": "flamingo", + "131": "little blue heron, Egretta caerulea", + "132": "American egret, great white heron, Egretta albus", + "133": "bittern", + "134": "crane", + "135": "limpkin, Aramus pictus", + "136": "European gallinule, Porphyrio porphyrio", + "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", + "138": "bustard", + "139": "ruddy turnstone, Arenaria interpres", + "140": "red-backed sandpiper, dunlin, Erolia alpina", + "141": "redshank, Tringa totanus", + "142": "dowitcher", + "143": "oystercatcher, oyster catcher", + "144": "pelican", + "145": "king penguin, Aptenodytes patagonica", + "146": "albatross, mollymawk", + "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", + "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", + "149": "dugong, Dugong dugon", + "150": "sea lion", + "151": "Chihuahua", + "152": "Japanese spaniel", + "153": "Maltese dog, Maltese terrier, Maltese", + "154": "Pekinese, Pekingese, Peke", + "155": "Shih-Tzu", + "156": "Blenheim spaniel", + "157": "papillon", + "158": "toy terrier", + "159": "Rhodesian ridgeback", + "160": "Afghan hound, Afghan", + "161": "basset, basset hound", + "162": "beagle", + "163": "bloodhound, sleuthhound", + "164": "bluetick", + "165": "black-and-tan coonhound", + "166": "Walker hound, Walker foxhound", + "167": "English foxhound", + "168": "redbone", + "169": "borzoi, Russian wolfhound", + "170": "Irish wolfhound", + "171": "Italian greyhound", + "172": "whippet", + "173": "Ibizan hound, Ibizan Podenco", + "174": "Norwegian elkhound, elkhound", + "175": "otterhound, otter hound", + "176": "Saluki, gazelle hound", + "177": "Scottish deerhound, deerhound", + "178": "Weimaraner", + "179": "Staffordshire bullterrier, Staffordshire bull terrier", + "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", + "181": "Bedlington terrier", + "182": "Border terrier", + "183": "Kerry blue terrier", + "184": "Irish terrier", + "185": "Norfolk terrier", + "186": "Norwich terrier", + "187": "Yorkshire terrier", + "188": "wire-haired fox terrier", + "189": "Lakeland terrier", + "190": "Sealyham terrier, Sealyham", + "191": "Airedale, Airedale terrier", + "192": "cairn, cairn terrier", + "193": "Australian terrier", + "194": "Dandie Dinmont, Dandie Dinmont terrier", + "195": "Boston bull, Boston terrier", + "196": "miniature schnauzer", + "197": "giant schnauzer", + "198": "standard schnauzer", + "199": "Scotch terrier, Scottish terrier, Scottie", + "200": "Tibetan terrier, chrysanthemum dog", + "201": "silky terrier, Sydney silky", + "202": "soft-coated wheaten terrier", + "203": "West Highland white terrier", + "204": "Lhasa, Lhasa apso", + "205": "flat-coated retriever", + "206": "curly-coated retriever", + "207": "golden retriever", + "208": "Labrador retriever", + "209": "Chesapeake Bay retriever", + "210": "German short-haired pointer", + "211": "vizsla, Hungarian pointer", + "212": "English setter", + "213": "Irish setter, red setter", + "214": "Gordon setter", + "215": "Brittany spaniel", + "216": "clumber, clumber spaniel", + "217": "English springer, English springer spaniel", + "218": "Welsh springer spaniel", + "219": "cocker spaniel, English cocker spaniel, cocker", + "220": "Sussex spaniel", + "221": "Irish water spaniel", + "222": "kuvasz", + "223": "schipperke", + "224": "groenendael", + "225": "malinois", + "226": "briard", + "227": "kelpie", + "228": "komondor", + "229": "Old English sheepdog, bobtail", + "230": "Shetland sheepdog, Shetland sheep dog, Shetland", + "231": "collie", + "232": "Border collie", + "233": "Bouvier des Flandres, Bouviers des Flandres", + "234": "Rottweiler", + "235": "German shepherd, German shepherd dog, German police dog, alsatian", + "236": "Doberman, Doberman pinscher", + "237": "miniature pinscher", + "238": "Greater Swiss Mountain dog", + "239": "Bernese mountain dog", + "240": "Appenzeller", + "241": "EntleBucher", + "242": "boxer", + "243": "bull mastiff", + "244": "Tibetan mastiff", + "245": "French bulldog", + "246": "Great Dane", + "247": "Saint Bernard, St Bernard", + "248": "Eskimo dog, husky", + "249": "malamute, malemute, Alaskan malamute", + "250": "Siberian husky", + "251": "dalmatian, coach dog, carriage dog", + "252": "affenpinscher, monkey pinscher, monkey dog", + "253": "basenji", + "254": "pug, pug-dog", + "255": "Leonberg", + "256": "Newfoundland, Newfoundland dog", + "257": "Great Pyrenees", + "258": "Samoyed, Samoyede", + "259": "Pomeranian", + "260": "chow, chow chow", + "261": "keeshond", + "262": "Brabancon griffon", + "263": "Pembroke, Pembroke Welsh corgi", + "264": "Cardigan, Cardigan Welsh corgi", + "265": "toy poodle", + "266": "miniature poodle", + "267": "standard poodle", + "268": "Mexican hairless", + "269": "timber wolf, grey wolf, gray wolf, Canis lupus", + "270": "white wolf, Arctic wolf, Canis lupus tundrarum", + "271": "red wolf, maned wolf, Canis rufus, Canis niger", + "272": "coyote, prairie wolf, brush wolf, Canis latrans", + "273": "dingo, warrigal, warragal, Canis dingo", + "274": "dhole, Cuon alpinus", + "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", + "276": "hyena, hyaena", + "277": "red fox, Vulpes vulpes", + "278": "kit fox, Vulpes macrotis", + "279": "Arctic fox, white fox, Alopex lagopus", + "280": "grey fox, gray fox, Urocyon cinereoargenteus", + "281": "tabby, tabby cat", + "282": "tiger cat", + "283": "Persian cat", + "284": "Siamese cat, Siamese", + "285": "Egyptian cat", + "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", + "287": "lynx, catamount", + "288": "leopard, Panthera pardus", + "289": "snow leopard, ounce, Panthera uncia", + "290": "jaguar, panther, Panthera onca, Felis onca", + "291": "lion, king of beasts, Panthera leo", + "292": "tiger, Panthera tigris", + "293": "cheetah, chetah, Acinonyx jubatus", + "294": "brown bear, bruin, Ursus arctos", + "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", + "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", + "297": "sloth bear, Melursus ursinus, Ursus ursinus", + "298": "mongoose", + "299": "meerkat, mierkat", + "300": "tiger beetle", + "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", + "302": "ground beetle, carabid beetle", + "303": "long-horned beetle, longicorn, longicorn beetle", + "304": "leaf beetle, chrysomelid", + "305": "dung beetle", + "306": "rhinoceros beetle", + "307": "weevil", + "308": "fly", + "309": "bee", + "310": "ant, emmet, pismire", + "311": "grasshopper, hopper", + "312": "cricket", + "313": "walking stick, walkingstick, stick insect", + "314": "cockroach, roach", + "315": "mantis, mantid", + "316": "cicada, cicala", + "317": "leafhopper", + "318": "lacewing, lacewing fly", + "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", + "320": "damselfly", + "321": "admiral", + "322": "ringlet, ringlet butterfly", + "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", + "324": "cabbage butterfly", + "325": "sulphur butterfly, sulfur butterfly", + "326": "lycaenid, lycaenid butterfly", + "327": "starfish, sea star", + "328": "sea urchin", + "329": "sea cucumber, holothurian", + "330": "wood rabbit, cottontail, cottontail rabbit", + "331": "hare", + "332": "Angora, Angora rabbit", + "333": "hamster", + "334": "porcupine, hedgehog", + "335": "fox squirrel, eastern fox squirrel, Sciurus niger", + "336": "marmot", + "337": "beaver", + "338": "guinea pig, Cavia cobaya", + "339": "sorrel", + "340": "zebra", + "341": "hog, pig, grunter, squealer, Sus scrofa", + "342": "wild boar, boar, Sus scrofa", + "343": "warthog", + "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", + "345": "ox", + "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", + "347": "bison", + "348": "ram, tup", + "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", + "350": "ibex, Capra ibex", + "351": "hartebeest", + "352": "impala, Aepyceros melampus", + "353": "gazelle", + "354": "Arabian camel, dromedary, Camelus dromedarius", + "355": "llama", + "356": "weasel", + "357": "mink", + "358": "polecat, fitch, foulmart, foumart, Mustela putorius", + "359": "black-footed ferret, ferret, Mustela nigripes", + "360": "otter", + "361": "skunk, polecat, wood pussy", + "362": "badger", + "363": "armadillo", + "364": "three-toed sloth, ai, Bradypus tridactylus", + "365": "orangutan, orang, orangutang, Pongo pygmaeus", + "366": "gorilla, Gorilla gorilla", + "367": "chimpanzee, chimp, Pan troglodytes", + "368": "gibbon, Hylobates lar", + "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", + "370": "guenon, guenon monkey", + "371": "patas, hussar monkey, Erythrocebus patas", + "372": "baboon", + "373": "macaque", + "374": "langur", + "375": "colobus, colobus monkey", + "376": "proboscis monkey, Nasalis larvatus", + "377": "marmoset", + "378": "capuchin, ringtail, Cebus capucinus", + "379": "howler monkey, howler", + "380": "titi, titi monkey", + "381": "spider monkey, Ateles geoffroyi", + "382": "squirrel monkey, Saimiri sciureus", + "383": "Madagascar cat, ring-tailed lemur, Lemur catta", + "384": "indri, indris, Indri indri, Indri brevicaudatus", + "385": "Indian elephant, Elephas maximus", + "386": "African elephant, Loxodonta africana", + "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", + "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", + "389": "barracouta, snoek", + "390": "eel", + "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", + "392": "rock beauty, Holocanthus tricolor", + "393": "anemone fish", + "394": "sturgeon", + "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", + "396": "lionfish", + "397": "puffer, pufferfish, blowfish, globefish", + "398": "abacus", + "399": "abaya", + "400": "academic gown, academic robe, judge's robe", + "401": "accordion, piano accordion, squeeze box", + "402": "acoustic guitar", + "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", + "404": "airliner", + "405": "airship, dirigible", + "406": "altar", + "407": "ambulance", + "408": "amphibian, amphibious vehicle", + "409": "analog clock", + "410": "apiary, bee house", + "411": "apron", + "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", + "413": "assault rifle, assault gun", + "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", + "415": "bakery, bakeshop, bakehouse", + "416": "balance beam, beam", + "417": "balloon", + "418": "ballpoint, ballpoint pen, ballpen, Biro", + "419": "Band Aid", + "420": "banjo", + "421": "bannister, banister, balustrade, balusters, handrail", + "422": "barbell", + "423": "barber chair", + "424": "barbershop", + "425": "barn", + "426": "barometer", + "427": "barrel, cask", + "428": "barrow, garden cart, lawn cart, wheelbarrow", + "429": "baseball", + "430": "basketball", + "431": "bassinet", + "432": "bassoon", + "433": "bathing cap, swimming cap", + "434": "bath towel", + "435": "bathtub, bathing tub, bath, tub", + "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", + "437": "beacon, lighthouse, beacon light, pharos", + "438": "beaker", + "439": "bearskin, busby, shako", + "440": "beer bottle", + "441": "beer glass", + "442": "bell cote, bell cot", + "443": "bib", + "444": "bicycle-built-for-two, tandem bicycle, tandem", + "445": "bikini, two-piece", + "446": "binder, ring-binder", + "447": "binoculars, field glasses, opera glasses", + "448": "birdhouse", + "449": "boathouse", + "450": "bobsled, bobsleigh, bob", + "451": "bolo tie, bolo, bola tie, bola", + "452": "bonnet, poke bonnet", + "453": "bookcase", + "454": "bookshop, bookstore, bookstall", + "455": "bottlecap", + "456": "bow", + "457": "bow tie, bow-tie, bowtie", + "458": "brass, memorial tablet, plaque", + "459": "brassiere, bra, bandeau", + "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", + "461": "breastplate, aegis, egis", + "462": "broom", + "463": "bucket, pail", + "464": "buckle", + "465": "bulletproof vest", + "466": "bullet train, bullet", + "467": "butcher shop, meat market", + "468": "cab, hack, taxi, taxicab", + "469": "caldron, cauldron", + "470": "candle, taper, wax light", + "471": "cannon", + "472": "canoe", + "473": "can opener, tin opener", + "474": "cardigan", + "475": "car mirror", + "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", + "477": "carpenter's kit, tool kit", + "478": "carton", + "479": "car wheel", + "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", + "481": "cassette", + "482": "cassette player", + "483": "castle", + "484": "catamaran", + "485": "CD player", + "486": "cello, violoncello", + "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", + "488": "chain", + "489": "chainlink fence", + "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", + "491": "chain saw, chainsaw", + "492": "chest", + "493": "chiffonier, commode", + "494": "chime, bell, gong", + "495": "china cabinet, china closet", + "496": "Christmas stocking", + "497": "church, church building", + "498": "cinema, movie theater, movie theatre, movie house, picture palace", + "499": "cleaver, meat cleaver, chopper", + "500": "cliff dwelling", + "501": "cloak", + "502": "clog, geta, patten, sabot", + "503": "cocktail shaker", + "504": "coffee mug", + "505": "coffeepot", + "506": "coil, spiral, volute, whorl, helix", + "507": "combination lock", + "508": "computer keyboard, keypad", + "509": "confectionery, confectionary, candy store", + "510": "container ship, containership, container vessel", + "511": "convertible", + "512": "corkscrew, bottle screw", + "513": "cornet, horn, trumpet, trump", + "514": "cowboy boot", + "515": "cowboy hat, ten-gallon hat", + "516": "cradle", + "517": "crane", + "518": "crash helmet", + "519": "crate", + "520": "crib, cot", + "521": "Crock Pot", + "522": "croquet ball", + "523": "crutch", + "524": "cuirass", + "525": "dam, dike, dyke", + "526": "desk", + "527": "desktop computer", + "528": "dial telephone, dial phone", + "529": "diaper, nappy, napkin", + "530": "digital clock", + "531": "digital watch", + "532": "dining table, board", + "533": "dishrag, dishcloth", + "534": "dishwasher, dish washer, dishwashing machine", + "535": "disk brake, disc brake", + "536": "dock, dockage, docking facility", + "537": "dogsled, dog sled, dog sleigh", + "538": "dome", + "539": "doormat, welcome mat", + "540": "drilling platform, offshore rig", + "541": "drum, membranophone, tympan", + "542": "drumstick", + "543": "dumbbell", + "544": "Dutch oven", + "545": "electric fan, blower", + "546": "electric guitar", + "547": "electric locomotive", + "548": "entertainment center", + "549": "envelope", + "550": "espresso maker", + "551": "face powder", + "552": "feather boa, boa", + "553": "file, file cabinet, filing cabinet", + "554": "fireboat", + "555": "fire engine, fire truck", + "556": "fire screen, fireguard", + "557": "flagpole, flagstaff", + "558": "flute, transverse flute", + "559": "folding chair", + "560": "football helmet", + "561": "forklift", + "562": "fountain", + "563": "fountain pen", + "564": "four-poster", + "565": "freight car", + "566": "French horn, horn", + "567": "frying pan, frypan, skillet", + "568": "fur coat", + "569": "garbage truck, dustcart", + "570": "gasmask, respirator, gas helmet", + "571": "gas pump, gasoline pump, petrol pump, island dispenser", + "572": "goblet", + "573": "go-kart", + "574": "golf ball", + "575": "golfcart, golf cart", + "576": "gondola", + "577": "gong, tam-tam", + "578": "gown", + "579": "grand piano, grand", + "580": "greenhouse, nursery, glasshouse", + "581": "grille, radiator grille", + "582": "grocery store, grocery, food market, market", + "583": "guillotine", + "584": "hair slide", + "585": "hair spray", + "586": "half track", + "587": "hammer", + "588": "hamper", + "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", + "590": "hand-held computer, hand-held microcomputer", + "591": "handkerchief, hankie, hanky, hankey", + "592": "hard disc, hard disk, fixed disk", + "593": "harmonica, mouth organ, harp, mouth harp", + "594": "harp", + "595": "harvester, reaper", + "596": "hatchet", + "597": "holster", + "598": "home theater, home theatre", + "599": "honeycomb", + "600": "hook, claw", + "601": "hoopskirt, crinoline", + "602": "horizontal bar, high bar", + "603": "horse cart, horse-cart", + "604": "hourglass", + "605": "iPod", + "606": "iron, smoothing iron", + "607": "jack-o'-lantern", + "608": "jean, blue jean, denim", + "609": "jeep, landrover", + "610": "jersey, T-shirt, tee shirt", + "611": "jigsaw puzzle", + "612": "jinrikisha, ricksha, rickshaw", + "613": "joystick", + "614": "kimono", + "615": "knee pad", + "616": "knot", + "617": "lab coat, laboratory coat", + "618": "ladle", + "619": "lampshade, lamp shade", + "620": "laptop, laptop computer", + "621": "lawn mower, mower", + "622": "lens cap, lens cover", + "623": "letter opener, paper knife, paperknife", + "624": "library", + "625": "lifeboat", + "626": "lighter, light, igniter, ignitor", + "627": "limousine, limo", + "628": "liner, ocean liner", + "629": "lipstick, lip rouge", + "630": "Loafer", + "631": "lotion", + "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", + "633": "loupe, jeweler's loupe", + "634": "lumbermill, sawmill", + "635": "magnetic compass", + "636": "mailbag, postbag", + "637": "mailbox, letter box", + "638": "maillot", + "639": "maillot, tank suit", + "640": "manhole cover", + "641": "maraca", + "642": "marimba, xylophone", + "643": "mask", + "644": "matchstick", + "645": "maypole", + "646": "maze, labyrinth", + "647": "measuring cup", + "648": "medicine chest, medicine cabinet", + "649": "megalith, megalithic structure", + "650": "microphone, mike", + "651": "microwave, microwave oven", + "652": "military uniform", + "653": "milk can", + "654": "minibus", + "655": "miniskirt, mini", + "656": "minivan", + "657": "missile", + "658": "mitten", + "659": "mixing bowl", + "660": "mobile home, manufactured home", + "661": "Model T", + "662": "modem", + "663": "monastery", + "664": "monitor", + "665": "moped", + "666": "mortar", + "667": "mortarboard", + "668": "mosque", + "669": "mosquito net", + "670": "motor scooter, scooter", + "671": "mountain bike, all-terrain bike, off-roader", + "672": "mountain tent", + "673": "mouse, computer mouse", + "674": "mousetrap", + "675": "moving van", + "676": "muzzle", + "677": "nail", + "678": "neck brace", + "679": "necklace", + "680": "nipple", + "681": "notebook, notebook computer", + "682": "obelisk", + "683": "oboe, hautboy, hautbois", + "684": "ocarina, sweet potato", + "685": "odometer, hodometer, mileometer, milometer", + "686": "oil filter", + "687": "organ, pipe organ", + "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", + "689": "overskirt", + "690": "oxcart", + "691": "oxygen mask", + "692": "packet", + "693": "paddle, boat paddle", + "694": "paddlewheel, paddle wheel", + "695": "padlock", + "696": "paintbrush", + "697": "pajama, pyjama, pj's, jammies", + "698": "palace", + "699": "panpipe, pandean pipe, syrinx", + "700": "paper towel", + "701": "parachute, chute", + "702": "parallel bars, bars", + "703": "park bench", + "704": "parking meter", + "705": "passenger car, coach, carriage", + "706": "patio, terrace", + "707": "pay-phone, pay-station", + "708": "pedestal, plinth, footstall", + "709": "pencil box, pencil case", + "710": "pencil sharpener", + "711": "perfume, essence", + "712": "Petri dish", + "713": "photocopier", + "714": "pick, plectrum, plectron", + "715": "pickelhaube", + "716": "picket fence, paling", + "717": "pickup, pickup truck", + "718": "pier", + "719": "piggy bank, penny bank", + "720": "pill bottle", + "721": "pillow", + "722": "ping-pong ball", + "723": "pinwheel", + "724": "pirate, pirate ship", + "725": "pitcher, ewer", + "726": "plane, carpenter's plane, woodworking plane", + "727": "planetarium", + "728": "plastic bag", + "729": "plate rack", + "730": "plow, plough", + "731": "plunger, plumber's helper", + "732": "Polaroid camera, Polaroid Land camera", + "733": "pole", + "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", + "735": "poncho", + "736": "pool table, billiard table, snooker table", + "737": "pop bottle, soda bottle", + "738": "pot, flowerpot", + "739": "potter's wheel", + "740": "power drill", + "741": "prayer rug, prayer mat", + "742": "printer", + "743": "prison, prison house", + "744": "projectile, missile", + "745": "projector", + "746": "puck, hockey puck", + "747": "punching bag, punch bag, punching ball, punchball", + "748": "purse", + "749": "quill, quill pen", + "750": "quilt, comforter, comfort, puff", + "751": "racer, race car, racing car", + "752": "racket, racquet", + "753": "radiator", + "754": "radio, wireless", + "755": "radio telescope, radio reflector", + "756": "rain barrel", + "757": "recreational vehicle, RV, R.V.", + "758": "reel", + "759": "reflex camera", + "760": "refrigerator, icebox", + "761": "remote control, remote", + "762": "restaurant, eating house, eating place, eatery", + "763": "revolver, six-gun, six-shooter", + "764": "rifle", + "765": "rocking chair, rocker", + "766": "rotisserie", + "767": "rubber eraser, rubber, pencil eraser", + "768": "rugby ball", + "769": "rule, ruler", + "770": "running shoe", + "771": "safe", + "772": "safety pin", + "773": "saltshaker, salt shaker", + "774": "sandal", + "775": "sarong", + "776": "sax, saxophone", + "777": "scabbard", + "778": "scale, weighing machine", + "779": "school bus", + "780": "schooner", + "781": "scoreboard", + "782": "screen, CRT screen", + "783": "screw", + "784": "screwdriver", + "785": "seat belt, seatbelt", + "786": "sewing machine", + "787": "shield, buckler", + "788": "shoe shop, shoe-shop, shoe store", + "789": "shoji", + "790": "shopping basket", + "791": "shopping cart", + "792": "shovel", + "793": "shower cap", + "794": "shower curtain", + "795": "ski", + "796": "ski mask", + "797": "sleeping bag", + "798": "slide rule, slipstick", + "799": "sliding door", + "800": "slot, one-armed bandit", + "801": "snorkel", + "802": "snowmobile", + "803": "snowplow, snowplough", + "804": "soap dispenser", + "805": "soccer ball", + "806": "sock", + "807": "solar dish, solar collector, solar furnace", + "808": "sombrero", + "809": "soup bowl", + "810": "space bar", + "811": "space heater", + "812": "space shuttle", + "813": "spatula", + "814": "speedboat", + "815": "spider web, spider's web", + "816": "spindle", + "817": "sports car, sport car", + "818": "spotlight, spot", + "819": "stage", + "820": "steam locomotive", + "821": "steel arch bridge", + "822": "steel drum", + "823": "stethoscope", + "824": "stole", + "825": "stone wall", + "826": "stopwatch, stop watch", + "827": "stove", + "828": "strainer", + "829": "streetcar, tram, tramcar, trolley, trolley car", + "830": "stretcher", + "831": "studio couch, day bed", + "832": "stupa, tope", + "833": "submarine, pigboat, sub, U-boat", + "834": "suit, suit of clothes", + "835": "sundial", + "836": "sunglass", + "837": "sunglasses, dark glasses, shades", + "838": "sunscreen, sunblock, sun blocker", + "839": "suspension bridge", + "840": "swab, swob, mop", + "841": "sweatshirt", + "842": "swimming trunks, bathing trunks", + "843": "swing", + "844": "switch, electric switch, electrical switch", + "845": "syringe", + "846": "table lamp", + "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", + "848": "tape player", + "849": "teapot", + "850": "teddy, teddy bear", + "851": "television, television system", + "852": "tennis ball", + "853": "thatch, thatched roof", + "854": "theater curtain, theatre curtain", + "855": "thimble", + "856": "thresher, thrasher, threshing machine", + "857": "throne", + "858": "tile roof", + "859": "toaster", + "860": "tobacco shop, tobacconist shop, tobacconist", + "861": "toilet seat", + "862": "torch", + "863": "totem pole", + "864": "tow truck, tow car, wrecker", + "865": "toyshop", + "866": "tractor", + "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", + "868": "tray", + "869": "trench coat", + "870": "tricycle, trike, velocipede", + "871": "trimaran", + "872": "tripod", + "873": "triumphal arch", + "874": "trolleybus, trolley coach, trackless trolley", + "875": "trombone", + "876": "tub, vat", + "877": "turnstile", + "878": "typewriter keyboard", + "879": "umbrella", + "880": "unicycle, monocycle", + "881": "upright, upright piano", + "882": "vacuum, vacuum cleaner", + "883": "vase", + "884": "vault", + "885": "velvet", + "886": "vending machine", + "887": "vestment", + "888": "viaduct", + "889": "violin, fiddle", + "890": "volleyball", + "891": "waffle iron", + "892": "wall clock", + "893": "wallet, billfold, notecase, pocketbook", + "894": "wardrobe, closet, press", + "895": "warplane, military plane", + "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", + "897": "washer, automatic washer, washing machine", + "898": "water bottle", + "899": "water jug", + "900": "water tower", + "901": "whiskey jug", + "902": "whistle", + "903": "wig", + "904": "window screen", + "905": "window shade", + "906": "Windsor tie", + "907": "wine bottle", + "908": "wing", + "909": "wok", + "910": "wooden spoon", + "911": "wool, woolen, woollen", + "912": "worm fence, snake fence, snake-rail fence, Virginia fence", + "913": "wreck", + "914": "yawl", + "915": "yurt", + "916": "web site, website, internet site, site", + "917": "comic book", + "918": "crossword puzzle, crossword", + "919": "street sign", + "920": "traffic light, traffic signal, stoplight", + "921": "book jacket, dust cover, dust jacket, dust wrapper", + "922": "menu", + "923": "plate", + "924": "guacamole", + "925": "consomme", + "926": "hot pot, hotpot", + "927": "trifle", + "928": "ice cream, icecream", + "929": "ice lolly, lolly, lollipop, popsicle", + "930": "French loaf", + "931": "bagel, beigel", + "932": "pretzel", + "933": "cheeseburger", + "934": "hotdog, hot dog, red hot", + "935": "mashed potato", + "936": "head cabbage", + "937": "broccoli", + "938": "cauliflower", + "939": "zucchini, courgette", + "940": "spaghetti squash", + "941": "acorn squash", + "942": "butternut squash", + "943": "cucumber, cuke", + "944": "artichoke, globe artichoke", + "945": "bell pepper", + "946": "cardoon", + "947": "mushroom", + "948": "Granny Smith", + "949": "strawberry", + "950": "orange", + "951": "lemon", + "952": "fig", + "953": "pineapple, ananas", + "954": "banana", + "955": "jackfruit, jak, jack", + "956": "custard apple", + "957": "pomegranate", + "958": "hay", + "959": "carbonara", + "960": "chocolate sauce, chocolate syrup", + "961": "dough", + "962": "meat loaf, meatloaf", + "963": "pizza, pizza pie", + "964": "potpie", + "965": "burrito", + "966": "red wine", + "967": "espresso", + "968": "cup", + "969": "eggnog", + "970": "alp", + "971": "bubble", + "972": "cliff, drop, drop-off", + "973": "coral reef", + "974": "geyser", + "975": "lakeside, lakeshore", + "976": "promontory, headland, head, foreland", + "977": "sandbar, sand bar", + "978": "seashore, coast, seacoast, sea-coast", + "979": "valley, vale", + "980": "volcano", + "981": "ballplayer, baseball player", + "982": "groom, bridegroom", + "983": "scuba diver", + "984": "rapeseed", + "985": "daisy", + "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", + "987": "corn", + "988": "acorn", + "989": "hip, rose hip, rosehip", + "990": "buckeye, horse chestnut, conker", + "991": "coral fungus", + "992": "agaric", + "993": "gyromitra", + "994": "stinkhorn, carrion fungus", + "995": "earthstar", + "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", + "997": "bolete", + "998": "ear, spike, capitulum", + "999": "toilet tissue, toilet paper, bathroom tissue" + }, + "image_size": 224, + "initializer_range": 0.02, + "intermediate_size": 3072, + "label2id": { + "Afghan hound, Afghan": 160, + "African chameleon, Chamaeleo chamaeleon": 47, + "African crocodile, Nile crocodile, Crocodylus niloticus": 49, + "African elephant, Loxodonta africana": 386, + "African grey, African gray, Psittacus erithacus": 87, + "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus": 275, + "Airedale, Airedale terrier": 191, + "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier": 180, + "American alligator, Alligator mississipiensis": 50, + "American black bear, black bear, Ursus americanus, Euarctos americanus": 295, + "American chameleon, anole, Anolis carolinensis": 40, + "American coot, marsh hen, mud hen, water hen, Fulica americana": 137, + "American egret, great white heron, Egretta albus": 132, + "American lobster, Northern lobster, Maine lobster, Homarus americanus": 122, + "Angora, Angora rabbit": 332, + "Appenzeller": 240, + "Arabian camel, dromedary, Camelus dromedarius": 354, + "Arctic fox, white fox, Alopex lagopus": 279, + "Australian terrier": 193, + "Band Aid": 419, + "Bedlington terrier": 181, + "Bernese mountain dog": 239, + "Blenheim spaniel": 156, + "Border collie": 232, + "Border terrier": 182, + "Boston bull, Boston terrier": 195, + "Bouvier des Flandres, Bouviers des Flandres": 233, + "Brabancon griffon": 262, + "Brittany spaniel": 215, + "CD player": 485, + "Cardigan, Cardigan Welsh corgi": 264, + "Chesapeake Bay retriever": 209, + "Chihuahua": 151, + "Christmas stocking": 496, + "Crock Pot": 521, + "Dandie Dinmont, Dandie Dinmont terrier": 194, + "Doberman, Doberman pinscher": 236, + "Dungeness crab, Cancer magister": 118, + "Dutch oven": 544, + "Egyptian cat": 285, + "English foxhound": 167, + "English setter": 212, + "English springer, English springer spaniel": 217, + "EntleBucher": 241, + "Eskimo dog, husky": 248, + "European fire salamander, Salamandra salamandra": 25, + "European gallinule, Porphyrio porphyrio": 136, + "French bulldog": 245, + "French horn, horn": 566, + "French loaf": 930, + "German shepherd, German shepherd dog, German police dog, alsatian": 235, + "German short-haired pointer": 210, + "Gila monster, Heloderma suspectum": 45, + "Gordon setter": 214, + "Granny Smith": 948, + "Great Dane": 246, + "Great Pyrenees": 257, + "Greater Swiss Mountain dog": 238, + "Ibizan hound, Ibizan Podenco": 173, + "Indian cobra, Naja naja": 63, + "Indian elephant, Elephas maximus": 385, + "Irish setter, red setter": 213, + "Irish terrier": 184, + "Irish water spaniel": 221, + "Irish wolfhound": 170, + "Italian greyhound": 171, + "Japanese spaniel": 152, + "Kerry blue terrier": 183, + "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis": 48, + "Labrador retriever": 208, + "Lakeland terrier": 189, + "Leonberg": 255, + "Lhasa, Lhasa apso": 204, + "Loafer": 630, + "Madagascar cat, ring-tailed lemur, Lemur catta": 383, + "Maltese dog, Maltese terrier, Maltese": 153, + "Mexican hairless": 268, + "Model T": 661, + "Newfoundland, Newfoundland dog": 256, + "Norfolk terrier": 185, + "Norwegian elkhound, elkhound": 174, + "Norwich terrier": 186, + "Old English sheepdog, bobtail": 229, + "Pekinese, Pekingese, Peke": 154, + "Pembroke, Pembroke Welsh corgi": 263, + "Persian cat": 283, + "Petri dish": 712, + "Polaroid camera, Polaroid Land camera": 732, + "Pomeranian": 259, + "Rhodesian ridgeback": 159, + "Rottweiler": 234, + "Saint Bernard, St Bernard": 247, + "Saluki, gazelle hound": 176, + "Samoyed, Samoyede": 258, + "Scotch terrier, Scottish terrier, Scottie": 199, + "Scottish deerhound, deerhound": 177, + "Sealyham terrier, Sealyham": 190, + "Shetland sheepdog, Shetland sheep dog, Shetland": 230, + "Shih-Tzu": 155, + "Siamese cat, Siamese": 284, + "Siberian husky": 250, + "Staffordshire bullterrier, Staffordshire bull terrier": 179, + "Sussex spaniel": 220, + "Tibetan mastiff": 244, + "Tibetan terrier, chrysanthemum dog": 200, + "Walker hound, Walker foxhound": 166, + "Weimaraner": 178, + "Welsh springer spaniel": 218, + "West Highland white terrier": 203, + "Windsor tie": 906, + "Yorkshire terrier": 187, + "abacus": 398, + "abaya": 399, + "academic gown, academic robe, judge's robe": 400, + "accordion, piano accordion, squeeze box": 401, + "acorn": 988, + "acorn squash": 941, + "acoustic guitar": 402, + "admiral": 321, + "affenpinscher, monkey pinscher, monkey dog": 252, + "agama": 42, + "agaric": 992, + "aircraft carrier, carrier, flattop, attack aircraft carrier": 403, + "airliner": 404, + "airship, dirigible": 405, + "albatross, mollymawk": 146, + "alligator lizard": 44, + "alp": 970, + "altar": 406, + "ambulance": 407, + "amphibian, amphibious vehicle": 408, + "analog clock": 409, + "anemone fish": 393, + "ant, emmet, pismire": 310, + "apiary, bee house": 410, + "apron": 411, + "armadillo": 363, + "artichoke, globe artichoke": 944, + "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin": 412, + "assault rifle, assault gun": 413, + "axolotl, mud puppy, Ambystoma mexicanum": 29, + "baboon": 372, + "backpack, back pack, knapsack, packsack, rucksack, haversack": 414, + "badger": 362, + "bagel, beigel": 931, + "bakery, bakeshop, bakehouse": 415, + "balance beam, beam": 416, + "bald eagle, American eagle, Haliaeetus leucocephalus": 22, + "balloon": 417, + "ballplayer, baseball player": 981, + "ballpoint, ballpoint pen, ballpen, Biro": 418, + "banana": 954, + "banded gecko": 38, + "banjo": 420, + "bannister, banister, balustrade, balusters, handrail": 421, + "barbell": 422, + "barber chair": 423, + "barbershop": 424, + "barn": 425, + "barn spider, Araneus cavaticus": 73, + "barometer": 426, + "barracouta, snoek": 389, + "barrel, cask": 427, + "barrow, garden cart, lawn cart, wheelbarrow": 428, + "baseball": 429, + "basenji": 253, + "basketball": 430, + "basset, basset hound": 161, + "bassinet": 431, + "bassoon": 432, + "bath towel": 434, + "bathing cap, swimming cap": 433, + "bathtub, bathing tub, bath, tub": 435, + "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon": 436, + "beacon, lighthouse, beacon light, pharos": 437, + "beagle": 162, + "beaker": 438, + "bearskin, busby, shako": 439, + "beaver": 337, + "bee": 309, + "bee eater": 92, + "beer bottle": 440, + "beer glass": 441, + "bell cote, bell cot": 442, + "bell pepper": 945, + "bib": 443, + "bicycle-built-for-two, tandem bicycle, tandem": 444, + "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis": 349, + "bikini, two-piece": 445, + "binder, ring-binder": 446, + "binoculars, field glasses, opera glasses": 447, + "birdhouse": 448, + "bison": 347, + "bittern": 133, + "black and gold garden spider, Argiope aurantia": 72, + "black grouse": 80, + "black stork, Ciconia nigra": 128, + "black swan, Cygnus atratus": 100, + "black widow, Latrodectus mactans": 75, + "black-and-tan coonhound": 165, + "black-footed ferret, ferret, Mustela nigripes": 359, + "bloodhound, sleuthhound": 163, + "bluetick": 164, + "boa constrictor, Constrictor constrictor": 61, + "boathouse": 449, + "bobsled, bobsleigh, bob": 450, + "bolete": 997, + "bolo tie, bolo, bola tie, bola": 451, + "bonnet, poke bonnet": 452, + "book jacket, dust cover, dust jacket, dust wrapper": 921, + "bookcase": 453, + "bookshop, bookstore, bookstall": 454, + "borzoi, Russian wolfhound": 169, + "bottlecap": 455, + "bow": 456, + "bow tie, bow-tie, bowtie": 457, + "box turtle, box tortoise": 37, + "boxer": 242, + "brain coral": 109, + "brambling, Fringilla montifringilla": 10, + "brass, memorial tablet, plaque": 458, + "brassiere, bra, bandeau": 459, + "breakwater, groin, groyne, mole, bulwark, seawall, jetty": 460, + "breastplate, aegis, egis": 461, + "briard": 226, + "broccoli": 937, + "broom": 462, + "brown bear, bruin, Ursus arctos": 294, + "bubble": 971, + "bucket, pail": 463, + "buckeye, horse chestnut, conker": 990, + "buckle": 464, + "bulbul": 16, + "bull mastiff": 243, + "bullet train, bullet": 466, + "bulletproof vest": 465, + "bullfrog, Rana catesbeiana": 30, + "burrito": 965, + "bustard": 138, + "butcher shop, meat market": 467, + "butternut squash": 942, + "cab, hack, taxi, taxicab": 468, + "cabbage butterfly": 324, + "cairn, cairn terrier": 192, + "caldron, cauldron": 469, + "can opener, tin opener": 473, + "candle, taper, wax light": 470, + "cannon": 471, + "canoe": 472, + "capuchin, ringtail, Cebus capucinus": 378, + "car mirror": 475, + "car wheel": 479, + "carbonara": 959, + "cardigan": 474, + "cardoon": 946, + "carousel, carrousel, merry-go-round, roundabout, whirligig": 476, + "carpenter's kit, tool kit": 477, + "carton": 478, + "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM": 480, + "cassette": 481, + "cassette player": 482, + "castle": 483, + "catamaran": 484, + "cauliflower": 938, + "cello, violoncello": 486, + "cellular telephone, cellular phone, cellphone, cell, mobile phone": 487, + "centipede": 79, + "chain": 488, + "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour": 490, + "chain saw, chainsaw": 491, + "chainlink fence": 489, + "chambered nautilus, pearly nautilus, nautilus": 117, + "cheeseburger": 933, + "cheetah, chetah, Acinonyx jubatus": 293, + "chest": 492, + "chickadee": 19, + "chiffonier, commode": 493, + "chime, bell, gong": 494, + "chimpanzee, chimp, Pan troglodytes": 367, + "china cabinet, china closet": 495, + "chiton, coat-of-mail shell, sea cradle, polyplacophore": 116, + "chocolate sauce, chocolate syrup": 960, + "chow, chow chow": 260, + "church, church building": 497, + "cicada, cicala": 316, + "cinema, movie theater, movie theatre, movie house, picture palace": 498, + "cleaver, meat cleaver, chopper": 499, + "cliff dwelling": 500, + "cliff, drop, drop-off": 972, + "cloak": 501, + "clog, geta, patten, sabot": 502, + "clumber, clumber spaniel": 216, + "cock": 7, + "cocker spaniel, English cocker spaniel, cocker": 219, + "cockroach, roach": 314, + "cocktail shaker": 503, + "coffee mug": 504, + "coffeepot": 505, + "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch": 391, + "coil, spiral, volute, whorl, helix": 506, + "collie": 231, + "colobus, colobus monkey": 375, + "combination lock": 507, + "comic book": 917, + "common iguana, iguana, Iguana iguana": 39, + "common newt, Triturus vulgaris": 26, + "computer keyboard, keypad": 508, + "conch": 112, + "confectionery, confectionary, candy store": 509, + "consomme": 925, + "container ship, containership, container vessel": 510, + "convertible": 511, + "coral fungus": 991, + "coral reef": 973, + "corkscrew, bottle screw": 512, + "corn": 987, + "cornet, horn, trumpet, trump": 513, + "coucal": 91, + "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor": 286, + "cowboy boot": 514, + "cowboy hat, ten-gallon hat": 515, + "coyote, prairie wolf, brush wolf, Canis latrans": 272, + "cradle": 516, + "crane": 517, + "crash helmet": 518, + "crate": 519, + "crayfish, crawfish, crawdad, crawdaddy": 124, + "crib, cot": 520, + "cricket": 312, + "croquet ball": 522, + "crossword puzzle, crossword": 918, + "crutch": 523, + "cucumber, cuke": 943, + "cuirass": 524, + "cup": 968, + "curly-coated retriever": 206, + "custard apple": 956, + "daisy": 985, + "dalmatian, coach dog, carriage dog": 251, + "dam, dike, dyke": 525, + "damselfly": 320, + "desk": 526, + "desktop computer": 527, + "dhole, Cuon alpinus": 274, + "dial telephone, dial phone": 528, + "diamondback, diamondback rattlesnake, Crotalus adamanteus": 67, + "diaper, nappy, napkin": 529, + "digital clock": 530, + "digital watch": 531, + "dingo, warrigal, warragal, Canis dingo": 273, + "dining table, board": 532, + "dishrag, dishcloth": 533, + "dishwasher, dish washer, dishwashing machine": 534, + "disk brake, disc brake": 535, + "dock, dockage, docking facility": 536, + "dogsled, dog sled, dog sleigh": 537, + "dome": 538, + "doormat, welcome mat": 539, + "dough": 961, + "dowitcher": 142, + "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk": 319, + "drake": 97, + "drilling platform, offshore rig": 540, + "drum, membranophone, tympan": 541, + "drumstick": 542, + "dugong, Dugong dugon": 149, + "dumbbell": 543, + "dung beetle": 305, + "ear, spike, capitulum": 998, + "earthstar": 995, + "echidna, spiny anteater, anteater": 102, + "eel": 390, + "eft": 27, + "eggnog": 969, + "electric fan, blower": 545, + "electric guitar": 546, + "electric locomotive": 547, + "electric ray, crampfish, numbfish, torpedo": 5, + "entertainment center": 548, + "envelope": 549, + "espresso": 967, + "espresso maker": 550, + "face powder": 551, + "feather boa, boa": 552, + "fiddler crab": 120, + "fig": 952, + "file, file cabinet, filing cabinet": 553, + "fire engine, fire truck": 555, + "fire screen, fireguard": 556, + "fireboat": 554, + "flagpole, flagstaff": 557, + "flamingo": 130, + "flat-coated retriever": 205, + "flatworm, platyhelminth": 110, + "flute, transverse flute": 558, + "fly": 308, + "folding chair": 559, + "football helmet": 560, + "forklift": 561, + "fountain": 562, + "fountain pen": 563, + "four-poster": 564, + "fox squirrel, eastern fox squirrel, Sciurus niger": 335, + "freight car": 565, + "frilled lizard, Chlamydosaurus kingi": 43, + "frying pan, frypan, skillet": 567, + "fur coat": 568, + "gar, garfish, garpike, billfish, Lepisosteus osseus": 395, + "garbage truck, dustcart": 569, + "garden spider, Aranea diademata": 74, + "garter snake, grass snake": 57, + "gas pump, gasoline pump, petrol pump, island dispenser": 571, + "gasmask, respirator, gas helmet": 570, + "gazelle": 353, + "geyser": 974, + "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca": 388, + "giant schnauzer": 197, + "gibbon, Hylobates lar": 368, + "go-kart": 573, + "goblet": 572, + "golden retriever": 207, + "goldfinch, Carduelis carduelis": 11, + "goldfish, Carassius auratus": 1, + "golf ball": 574, + "golfcart, golf cart": 575, + "gondola": 576, + "gong, tam-tam": 577, + "goose": 99, + "gorilla, Gorilla gorilla": 366, + "gown": 578, + "grand piano, grand": 579, + "grasshopper, hopper": 311, + "great grey owl, great gray owl, Strix nebulosa": 24, + "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias": 2, + "green lizard, Lacerta viridis": 46, + "green mamba": 64, + "green snake, grass snake": 55, + "greenhouse, nursery, glasshouse": 580, + "grey fox, gray fox, Urocyon cinereoargenteus": 280, + "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus": 147, + "grille, radiator grille": 581, + "grocery store, grocery, food market, market": 582, + "groenendael": 224, + "groom, bridegroom": 982, + "ground beetle, carabid beetle": 302, + "guacamole": 924, + "guenon, guenon monkey": 370, + "guillotine": 583, + "guinea pig, Cavia cobaya": 338, + "gyromitra": 993, + "hair slide": 584, + "hair spray": 585, + "half track": 586, + "hammer": 587, + "hammerhead, hammerhead shark": 4, + "hamper": 588, + "hamster": 333, + "hand blower, blow dryer, blow drier, hair dryer, hair drier": 589, + "hand-held computer, hand-held microcomputer": 590, + "handkerchief, hankie, hanky, hankey": 591, + "hard disc, hard disk, fixed disk": 592, + "hare": 331, + "harmonica, mouth organ, harp, mouth harp": 593, + "harp": 594, + "hartebeest": 351, + "harvester, reaper": 595, + "harvestman, daddy longlegs, Phalangium opilio": 70, + "hatchet": 596, + "hay": 958, + "head cabbage": 936, + "hen": 8, + "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa": 996, + "hermit crab": 125, + "hip, rose hip, rosehip": 989, + "hippopotamus, hippo, river horse, Hippopotamus amphibius": 344, + "hog, pig, grunter, squealer, Sus scrofa": 341, + "hognose snake, puff adder, sand viper": 54, + "holster": 597, + "home theater, home theatre": 598, + "honeycomb": 599, + "hook, claw": 600, + "hoopskirt, crinoline": 601, + "horizontal bar, high bar": 602, + "hornbill": 93, + "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus": 66, + "horse cart, horse-cart": 603, + "hot pot, hotpot": 926, + "hotdog, hot dog, red hot": 934, + "hourglass": 604, + "house finch, linnet, Carpodacus mexicanus": 12, + "howler monkey, howler": 379, + "hummingbird": 94, + "hyena, hyaena": 276, + "iPod": 605, + "ibex, Capra ibex": 350, + "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus": 296, + "ice cream, icecream": 928, + "ice lolly, lolly, lollipop, popsicle": 929, + "impala, Aepyceros melampus": 352, + "indigo bunting, indigo finch, indigo bird, Passerina cyanea": 14, + "indri, indris, Indri indri, Indri brevicaudatus": 384, + "iron, smoothing iron": 606, + "isopod": 126, + "jacamar": 95, + "jack-o'-lantern": 607, + "jackfruit, jak, jack": 955, + "jaguar, panther, Panthera onca, Felis onca": 290, + "jay": 17, + "jean, blue jean, denim": 608, + "jeep, landrover": 609, + "jellyfish": 107, + "jersey, T-shirt, tee shirt": 610, + "jigsaw puzzle": 611, + "jinrikisha, ricksha, rickshaw": 612, + "joystick": 613, + "junco, snowbird": 13, + "keeshond": 261, + "kelpie": 227, + "killer whale, killer, orca, grampus, sea wolf, Orcinus orca": 148, + "kimono": 614, + "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica": 121, + "king penguin, Aptenodytes patagonica": 145, + "king snake, kingsnake": 56, + "kit fox, Vulpes macrotis": 278, + "kite": 21, + "knee pad": 615, + "knot": 616, + "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus": 105, + "komondor": 228, + "kuvasz": 222, + "lab coat, laboratory coat": 617, + "lacewing, lacewing fly": 318, + "ladle": 618, + "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle": 301, + "lakeside, lakeshore": 975, + "lampshade, lamp shade": 619, + "langur": 374, + "laptop, laptop computer": 620, + "lawn mower, mower": 621, + "leaf beetle, chrysomelid": 304, + "leafhopper": 317, + "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea": 34, + "lemon": 951, + "lens cap, lens cover": 622, + "leopard, Panthera pardus": 288, + "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens": 387, + "letter opener, paper knife, paperknife": 623, + "library": 624, + "lifeboat": 625, + "lighter, light, igniter, ignitor": 626, + "limousine, limo": 627, + "limpkin, Aramus pictus": 135, + "liner, ocean liner": 628, + "lion, king of beasts, Panthera leo": 291, + "lionfish": 396, + "lipstick, lip rouge": 629, + "little blue heron, Egretta caerulea": 131, + "llama": 355, + "loggerhead, loggerhead turtle, Caretta caretta": 33, + "long-horned beetle, longicorn, longicorn beetle": 303, + "lorikeet": 90, + "lotion": 631, + "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system": 632, + "loupe, jeweler's loupe": 633, + "lumbermill, sawmill": 634, + "lycaenid, lycaenid butterfly": 326, + "lynx, catamount": 287, + "macaque": 373, + "macaw": 88, + "magnetic compass": 635, + "magpie": 18, + "mailbag, postbag": 636, + "mailbox, letter box": 637, + "maillot": 638, + "maillot, tank suit": 639, + "malamute, malemute, Alaskan malamute": 249, + "malinois": 225, + "manhole cover": 640, + "mantis, mantid": 315, + "maraca": 641, + "marimba, xylophone": 642, + "marmoset": 377, + "marmot": 336, + "mashed potato": 935, + "mask": 643, + "matchstick": 644, + "maypole": 645, + "maze, labyrinth": 646, + "measuring cup": 647, + "meat loaf, meatloaf": 962, + "medicine chest, medicine cabinet": 648, + "meerkat, mierkat": 299, + "megalith, megalithic structure": 649, + "menu": 922, + "microphone, mike": 650, + "microwave, microwave oven": 651, + "military uniform": 652, + "milk can": 653, + "miniature pinscher": 237, + "miniature poodle": 266, + "miniature schnauzer": 196, + "minibus": 654, + "miniskirt, mini": 655, + "minivan": 656, + "mink": 357, + "missile": 657, + "mitten": 658, + "mixing bowl": 659, + "mobile home, manufactured home": 660, + "modem": 662, + "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus": 323, + "monastery": 663, + "mongoose": 298, + "monitor": 664, + "moped": 665, + "mortar": 666, + "mortarboard": 667, + "mosque": 668, + "mosquito net": 669, + "motor scooter, scooter": 670, + "mountain bike, all-terrain bike, off-roader": 671, + "mountain tent": 672, + "mouse, computer mouse": 673, + "mousetrap": 674, + "moving van": 675, + "mud turtle": 35, + "mushroom": 947, + "muzzle": 676, + "nail": 677, + "neck brace": 678, + "necklace": 679, + "nematode, nematode worm, roundworm": 111, + "night snake, Hypsiglena torquata": 60, + "nipple": 680, + "notebook, notebook computer": 681, + "obelisk": 682, + "oboe, hautboy, hautbois": 683, + "ocarina, sweet potato": 684, + "odometer, hodometer, mileometer, milometer": 685, + "oil filter": 686, + "orange": 950, + "orangutan, orang, orangutang, Pongo pygmaeus": 365, + "organ, pipe organ": 687, + "oscilloscope, scope, cathode-ray oscilloscope, CRO": 688, + "ostrich, Struthio camelus": 9, + "otter": 360, + "otterhound, otter hound": 175, + "overskirt": 689, + "ox": 345, + "oxcart": 690, + "oxygen mask": 691, + "oystercatcher, oyster catcher": 143, + "packet": 692, + "paddle, boat paddle": 693, + "paddlewheel, paddle wheel": 694, + "padlock": 695, + "paintbrush": 696, + "pajama, pyjama, pj's, jammies": 697, + "palace": 698, + "panpipe, pandean pipe, syrinx": 699, + "paper towel": 700, + "papillon": 157, + "parachute, chute": 701, + "parallel bars, bars": 702, + "park bench": 703, + "parking meter": 704, + "partridge": 86, + "passenger car, coach, carriage": 705, + "patas, hussar monkey, Erythrocebus patas": 371, + "patio, terrace": 706, + "pay-phone, pay-station": 707, + "peacock": 84, + "pedestal, plinth, footstall": 708, + "pelican": 144, + "pencil box, pencil case": 709, + "pencil sharpener": 710, + "perfume, essence": 711, + "photocopier": 713, + "pick, plectrum, plectron": 714, + "pickelhaube": 715, + "picket fence, paling": 716, + "pickup, pickup truck": 717, + "pier": 718, + "piggy bank, penny bank": 719, + "pill bottle": 720, + "pillow": 721, + "pineapple, ananas": 953, + "ping-pong ball": 722, + "pinwheel": 723, + "pirate, pirate ship": 724, + "pitcher, ewer": 725, + "pizza, pizza pie": 963, + "plane, carpenter's plane, woodworking plane": 726, + "planetarium": 727, + "plastic bag": 728, + "plate": 923, + "plate rack": 729, + "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus": 103, + "plow, plough": 730, + "plunger, plumber's helper": 731, + "pole": 733, + "polecat, fitch, foulmart, foumart, Mustela putorius": 358, + "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria": 734, + "pomegranate": 957, + "poncho": 735, + "pool table, billiard table, snooker table": 736, + "pop bottle, soda bottle": 737, + "porcupine, hedgehog": 334, + "pot, flowerpot": 738, + "potpie": 964, + "potter's wheel": 739, + "power drill": 740, + "prairie chicken, prairie grouse, prairie fowl": 83, + "prayer rug, prayer mat": 741, + "pretzel": 932, + "printer": 742, + "prison, prison house": 743, + "proboscis monkey, Nasalis larvatus": 376, + "projectile, missile": 744, + "projector": 745, + "promontory, headland, head, foreland": 976, + "ptarmigan": 81, + "puck, hockey puck": 746, + "puffer, pufferfish, blowfish, globefish": 397, + "pug, pug-dog": 254, + "punching bag, punch bag, punching ball, punchball": 747, + "purse": 748, + "quail": 85, + "quill, quill pen": 749, + "quilt, comforter, comfort, puff": 750, + "racer, race car, racing car": 751, + "racket, racquet": 752, + "radiator": 753, + "radio telescope, radio reflector": 755, + "radio, wireless": 754, + "rain barrel": 756, + "ram, tup": 348, + "rapeseed": 984, + "recreational vehicle, RV, R.V.": 757, + "red fox, Vulpes vulpes": 277, + "red wine": 966, + "red wolf, maned wolf, Canis rufus, Canis niger": 271, + "red-backed sandpiper, dunlin, Erolia alpina": 140, + "red-breasted merganser, Mergus serrator": 98, + "redbone": 168, + "redshank, Tringa totanus": 141, + "reel": 758, + "reflex camera": 759, + "refrigerator, icebox": 760, + "remote control, remote": 761, + "restaurant, eating house, eating place, eatery": 762, + "revolver, six-gun, six-shooter": 763, + "rhinoceros beetle": 306, + "rifle": 764, + "ringlet, ringlet butterfly": 322, + "ringneck snake, ring-necked snake, ring snake": 53, + "robin, American robin, Turdus migratorius": 15, + "rock beauty, Holocanthus tricolor": 392, + "rock crab, Cancer irroratus": 119, + "rock python, rock snake, Python sebae": 62, + "rocking chair, rocker": 765, + "rotisserie": 766, + "rubber eraser, rubber, pencil eraser": 767, + "ruddy turnstone, Arenaria interpres": 139, + "ruffed grouse, partridge, Bonasa umbellus": 82, + "rugby ball": 768, + "rule, ruler": 769, + "running shoe": 770, + "safe": 771, + "safety pin": 772, + "saltshaker, salt shaker": 773, + "sandal": 774, + "sandbar, sand bar": 977, + "sarong": 775, + "sax, saxophone": 776, + "scabbard": 777, + "scale, weighing machine": 778, + "schipperke": 223, + "school bus": 779, + "schooner": 780, + "scoreboard": 781, + "scorpion": 71, + "screen, CRT screen": 782, + "screw": 783, + "screwdriver": 784, + "scuba diver": 983, + "sea anemone, anemone": 108, + "sea cucumber, holothurian": 329, + "sea lion": 150, + "sea slug, nudibranch": 115, + "sea snake": 65, + "sea urchin": 328, + "seashore, coast, seacoast, sea-coast": 978, + "seat belt, seatbelt": 785, + "sewing machine": 786, + "shield, buckler": 787, + "shoe shop, shoe-shop, shoe store": 788, + "shoji": 789, + "shopping basket": 790, + "shopping cart": 791, + "shovel": 792, + "shower cap": 793, + "shower curtain": 794, + "siamang, Hylobates syndactylus, Symphalangus syndactylus": 369, + "sidewinder, horned rattlesnake, Crotalus cerastes": 68, + "silky terrier, Sydney silky": 201, + "ski": 795, + "ski mask": 796, + "skunk, polecat, wood pussy": 361, + "sleeping bag": 797, + "slide rule, slipstick": 798, + "sliding door": 799, + "slot, one-armed bandit": 800, + "sloth bear, Melursus ursinus, Ursus ursinus": 297, + "slug": 114, + "snail": 113, + "snorkel": 801, + "snow leopard, ounce, Panthera uncia": 289, + "snowmobile": 802, + "snowplow, snowplough": 803, + "soap dispenser": 804, + "soccer ball": 805, + "sock": 806, + "soft-coated wheaten terrier": 202, + "solar dish, solar collector, solar furnace": 807, + "sombrero": 808, + "sorrel": 339, + "soup bowl": 809, + "space bar": 810, + "space heater": 811, + "space shuttle": 812, + "spaghetti squash": 940, + "spatula": 813, + "speedboat": 814, + "spider monkey, Ateles geoffroyi": 381, + "spider web, spider's web": 815, + "spindle": 816, + "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish": 123, + "spoonbill": 129, + "sports car, sport car": 817, + "spotlight, spot": 818, + "spotted salamander, Ambystoma maculatum": 28, + "squirrel monkey, Saimiri sciureus": 382, + "stage": 819, + "standard poodle": 267, + "standard schnauzer": 198, + "starfish, sea star": 327, + "steam locomotive": 820, + "steel arch bridge": 821, + "steel drum": 822, + "stethoscope": 823, + "stingray": 6, + "stinkhorn, carrion fungus": 994, + "stole": 824, + "stone wall": 825, + "stopwatch, stop watch": 826, + "stove": 827, + "strainer": 828, + "strawberry": 949, + "street sign": 919, + "streetcar, tram, tramcar, trolley, trolley car": 829, + "stretcher": 830, + "studio couch, day bed": 831, + "stupa, tope": 832, + "sturgeon": 394, + "submarine, pigboat, sub, U-boat": 833, + "suit, suit of clothes": 834, + "sulphur butterfly, sulfur 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"▁approximation": 16845, + "dam": 16846, + "▁fog": 16847, + "кор": 16848, + "▁Writ": 16849, + "▁ling": 16850, + "▁писа": 16851, + "▁Mars": 16852, + "otti": 16853, + "Enum": 16854, + "▁Trib": 16855, + "▁merc": 16856, + "zung": 16857, + "vanced": 16858, + "cfg": 16859, + "нах": 16860, + "schen": 16861, + "\"].": 16862, + "bek": 16863, + "▁ster": 16864, + "jp": 16865, + "▁Rap": 16866, + "▁recording": 16867, + "▁peint": 16868, + "▁lets": 16869, + "änge": 16870, + ">\";": 16871, + "▁місце": 16872, + "▁caval": 16873, + "▁CSV": 16874, + "▁entstand": 16875, + "▁helper": 16876, + "endet": 16877, + "▁Gram": 16878, + "▁Diego": 16879, + "▁Bishop": 16880, + "TAG": 16881, + "▁ecc": 16882, + "▁Een": 16883, + "▁AV": 16884, + "City": 16885, + "▁Guide": 16886, + "hind": 16887, + "rical": 16888, + "▁Основ": 16889, + "Bus": 16890, + "▁zunächst": 16891, + "▁tick": 16892, + "▁Colonel": 16893, + "Thanks": 16894, + "▁ferm": 16895, + "▁granted": 16896, + "▁threshold": 16897, + "omorphic": 16898, + "▁Hun": 16899, + "enis": 16900, + "▁прав": 16901, + "▁які": 16902, + "PG": 16903, + "▁ws": 16904, + "▁technical": 16905, + "estro": 16906, + "klär": 16907, + "vars": 16908, + "ocrat": 16909, + "▁општи": 16910, + "onso": 16911, + "iba": 16912, + "▁Save": 16913, + "▁programa": 16914, + "▁въ": 16915, + "▁invån": 16916, + ">()": 16917, + "▁mejor": 16918, + "▁слова": 16919, + "▁replacement": 16920, + "▁impr": 16921, + "▁Francesco": 16922, + "▁Hotel": 16923, + "▁UPDATE": 16924, + "▁музы": 16925, + "ugs": 16926, + "vard": 16927, + "▁faz": 16928, + "inton": 16929, + "▁arts": 16930, + "▁Ky": 16931, + "▁Ils": 16932, + "▁sera": 16933, + "▁Volume": 16934, + "▁giugno": 16935, + "▁asym": 16936, + "▁Pir": 16937, + "▁NAS": 16938, + "▁Tam": 16939, + "ěl": 16940, + "Sequ": 16941, + "kmal": 16942, + "▁Eins": 16943, + "▁компа": 16944, + "obe": 16945, + "oor": 16946, + "▁heap": 16947, + "ctl": 16948, + "▁separately": 16949, + "reader": 16950, + "▁significantly": 16951, + "▁Lag": 16952, + "notes": 16953, + "▁sele": 16954, + "▁dedicated": 16955, + "▁Host": 16956, + "choice": 16957, + "wing": 16958, + "▁Titel": 16959, + "▁befindet": 16960, + "large": 16961, + "▁conten": 16962, + "JavaScript": 16963, + "▁deser": 16964, + "▁Gordon": 16965, + "спе": 16966, + "▁patri": 16967, + "▁Random": 16968, + "▁Returns": 16969, + "ым": 16970, + "рома": 16971, + "▁Studies": 16972, + "Sl": 16973, + "▁frü": 16974, + "TEXT": 16975, + "inate": 16976, + "▁Tol": 16977, + "▁everywhere": 16978, + "arta": 16979, + "▁orbit": 16980, + "▁Aires": 16981, + "▁Iss": 16982, + "▁też": 16983, + "▁diverse": 16984, + "▁numeric": 16985, + "maz": 16986, + "▁mise": 16987, + "▁battery": 16988, + "▁Akadem": 16989, + "нение": 16990, + "▁simultane": 16991, + "▁Dead": 16992, + "▁clust": 16993, + "▁otro": 16994, + "▁cerca": 16995, + "()`,": 16996, + "roz": 16997, + "ăt": 16998, + "▁MO": 16999, + "riften": 17000, + "important": 17001, + "▁jeho": 17002, + "▁findViewById": 17003, + "▁consequence": 17004, + "▁measured": 17005, + "ishes": 17006, + "▁sze": 17007, + "iendo": 17008, + "▁Wahl": 17009, + "strip": 17010, + "ARD": 17011, + "▁opacity": 17012, + "WORD": 17013, + "▁Ві": 17014, + "▁Location": 17015, + "rai": 17016, + "пен": 17017, + "▁rif": 17018, + "aussian": 17019, + "FileName": 17020, + "▁disco": 17021, + "ilen": 17022, + "▁vagy": 17023, + "licity": 17024, + "Border": 17025, + "▁Track": 17026, + "бом": 17027, + "fact": 17028, + "oka": 17029, + "▁gior": 17030, + "▁XVII": 17031, + "▁där": 17032, + "Site": 17033, + "ało": 17034, + "ská": 17035, + "▁pixels": 17036, + "vity": 17037, + "jQuery": 17038, + "▁sculpt": 17039, + "▁cargo": 17040, + "▁directive": 17041, + "▁wal": 17042, + "▁conna": 17043, + "▁Through": 17044, + "▁этом": 17045, + "Static": 17046, + "omsnitt": 17047, + "▁rund": 17048, + "▁claimed": 17049, + "зня": 17050, + "sha": 17051, + "▁rag": 17052, + "crement": 17053, + "▁fünf": 17054, + "▁rival": 17055, + "rin": 17056, + "slash": 17057, + "▁thirty": 17058, + "sleep": 17059, + "ологи": 17060, + "SM": 17061, + "gate": 17062, + "izations": 17063, + "vik": 17064, + "▁bless": 17065, + "▁Illinois": 17066, + "▁TE": 17067, + "uting": 17068, + "▁solving": 17069, + "GER": 17070, + "▁XIV": 17071, + "▁Indians": 17072, + "express": 17073, + "▁Heil": 17074, + "▁mujer": 17075, + "▁invånare": 17076, + "']);": 17077, + "▁aur": 17078, + "boost": 17079, + "GO": 17080, + "▁nin": 17081, + "tok": 17082, + "god": 17083, + "oter": 17084, + ")$$": 17085, + "▁descend": 17086, + "рю": 17087, + "▁Language": 17088, + "▁diver": 17089, + "▁Assuming": 17090, + "▁frequent": 17091, + "чні": 17092, + "▁Biography": 17093, + ",[": 17094, + "urm": 17095, + "▁walked": 17096, + "▁federal": 17097, + "▁Michigan": 17098, + "▁facts": 17099, + "▁Integr": 17100, + "LES": 17101, + "▁Alan": 17102, + "▁coup": 17103, + "Ber": 17104, + "▁particles": 17105, + "ће": 17106, + "Inflater": 17107, + "+(": 17108, + "Bound": 17109, + "▁Sü": 17110, + "Audio": 17111, + "citet": 17112, + "yect": 17113, + "▁nr": 17114, + "xe": 17115, + "▁Brun": 17116, + "▁_,": 17117, + "avor": 17118, + "▁discipl": 17119, + "alm": 17120, + "▁ноября": 17121, + "▁SSL": 17122, + "▁Kaiser": 17123, + "▁recher": 17124, + "ygon": 17125, + "▁regardless": 17126, + "▁configur": 17127, + "▁unnecess": 17128, + "▁Clark": 17129, + "PHP": 17130, + "▁FALSE": 17131, + "▁pad": 17132, + "$}": 17133, + "▁valu": 17134, + "▁disease": 17135, + "▁maior": 17136, + "▁hommes": 17137, + "▁Edition": 17138, + "slant": 17139, + "▁ending": 17140, + "▁settled": 17141, + "urus": 17142, + "hed": 17143, + "Pattern": 17144, + "▁година": 17145, + "▁Philadel": 17146, + "tikzpicture": 17147, + "▁coal": 17148, + "▁sede": 17149, + "▁satisfies": 17150, + "▁trim": 17151, + "▁bat": 17152, + "▁américain": 17153, + "▁luglio": 17154, + "▁поча": 17155, + "ffff": 17156, + "▁Target": 17157, + "generate": 17158, + "▁Zie": 17159, + "ția": 17160, + "▁gard": 17161, + "▁workers": 17162, + "▁Job": 17163, + "▁urban": 17164, + "ahlen": 17165, + "▁Building": 17166, + "▁neu": 17167, + "▁chron": 17168, + "▁Earl": 17169, + "gro": 17170, + "USE": 17171, + "▁XII": 17172, + "▁wealth": 17173, + "inae": 17174, + "▁Бра": 17175, + "▁libert": 17176, + "iros": 17177, + ":$": 17178, + "lee": 17179, + "ieves": 17180, + "▁Justice": 17181, + "▁oil": 17182, + "▁Athlet": 17183, + "▁clo": 17184, + "Scale": 17185, + "▁lips": 17186, + "▁april": 17187, + "▁impression": 17188, + "▁perce": 17189, + "▁участи": 17190, + "vil": 17191, + "éch": 17192, + "▁equality": 17193, + "▁мет": 17194, + "▁annotation": 17195, + "ernal": 17196, + "▁Mach": 17197, + "▁intitul": 17198, + "problem": 17199, + "ющих": 17200, + "oplus": 17201, + "▁thousands": 17202, + "▁calculations": 17203, + "umps": 17204, + "▁triangle": 17205, + "phal": 17206, + "▁Dorf": 17207, + "▁dollars": 17208, + "▁denen": 17209, + "lès": 17210, + "olid": 17211, + "▁Results": 17212, + "▁Stadium": 17213, + "▁Desp": 17214, + "▁Eisen": 17215, + "imir": 17216, + "▁sotto": 17217, + "▁či": 17218, + "atable": 17219, + "orum": 17220, + "▁convergence": 17221, + "▁jeune": 17222, + "oking": 17223, + "▁живо": 17224, + "aining": 17225, + "pointer": 17226, + "culo": 17227, + "▁jsou": 17228, + "▁grab": 17229, + "akte": 17230, + "▁hoping": 17231, + "▁Mak": 17232, + "▁sag": 17233, + "origine": 17234, + "▁послед": 17235, + "▁Veg": 17236, + "▁theoret": 17237, + "▁Tru": 17238, + "nement": 17239, + "▁faces": 17240, + "Hor": 17241, + "Join": 17242, + "arel": 17243, + "▁около": 17244, + "However": 17245, + "▁catal": 17246, + "bourg": 17247, + "▁mysqli": 17248, + "acions": 17249, + "▁Initial": 17250, + "▁rain": 17251, + "iture": 17252, + "▁Sciences": 17253, + "▁Kreis": 17254, + ".__": 17255, + "▁cinq": 17256, + "▁Auß": 17257, + "ithmet": 17258, + "itors": 17259, + "amazon": 17260, + "▁gap": 17261, + "▁ignored": 17262, + "adv": 17263, + "кої": 17264, + "▁часть": 17265, + "▁corpor": 17266, + "цер": 17267, + "▁crime": 17268, + "uous": 17269, + "▁налази": 17270, + "DataFrame": 17271, + "води": 17272, + "Ign": 17273, + "▁Lincoln": 17274, + "▁menos": 17275, + "▁Luft": 17276, + "▁Lind": 17277, + "▁Cook": 17278, + "▁materials": 17279, + "apped": 17280, + "ignore": 17281, + "▁откры": 17282, + "fried": 17283, + "▁gouvernement": 17284, + "▁fired": 17285, + "▁screenshot": 17286, + "сен": 17287, + "▁[(": 17288, + "▁организа": 17289, + "Graphics": 17290, + "▁проти": 17291, + "▁phen": 17292, + "craft": 17293, + "▁brain": 17294, + "▁Como": 17295, + "▁Everything": 17296, + "anes": 17297, + "IGN": 17298, + "▁nederbörd": 17299, + "▁Forest": 17300, + "zahl": 17301, + "▁Among": 17302, + "Qt": 17303, + "▁togg": 17304, + "▁variant": 17305, + "▁hill": 17306, + "писи": 17307, + "colon": 17308, + "▁dicembre": 17309, + "гор": 17310, + "▁Wind": 17311, + "ünstler": 17312, + "▁=\\": 17313, + "saved": 17314, + "▁nej": 17315, + "unte": 17316, + "utto": 17317, + "▁recens": 17318, + "▁sick": 17319, + "▁desen": 17320, + "UST": 17321, + "▁worst": 17322, + "▁Angel": 17323, + "odox": 17324, + "▁Province": 17325, + "▁Maz": 17326, + "▁agreement": 17327, + "▁Bass": 17328, + "▁segunda": 17329, + "onces": 17330, + "▁Linki": 17331, + "▁CL": 17332, + "▁já": 17333, + "itement": 17334, + "▁área": 17335, + "▁scalar": 17336, + "▁Рес": 17337, + "awt": 17338, + "sieme": 17339, + "▁juni": 17340, + "▁худож": 17341, + "ikus": 17342, + "▁lid": 17343, + "ppel": 17344, + "avi": 17345, + "▁balance": 17346, + "ipping": 17347, + "cussion": 17348, + "ческих": 17349, + "(\".": 17350, + "Also": 17351, + "▁whis": 17352, + "HOME": 17353, + "▁brown": 17354, + "▁día": 17355, + "▁può": 17356, + "plotlib": 17357, + "▁Jahrhunderts": 17358, + "DK": 17359, + "▁anchor": 17360, + "...]": 17361, + "▁Austria": 17362, + "▁marca": 17363, + "▁gez": 17364, + "iously": 17365, + "▁lazy": 17366, + "xa": 17367, + "▁Channel": 17368, + "▁neuen": 17369, + "das": 17370, + "▁searched": 17371, + "▁staat": 17372, + "▁Так": 17373, + "▁Josef": 17374, + "▁Sher": 17375, + "pois": 17376, + "▁enem": 17377, + "▁accessing": 17378, + "▁неко": 17379, + "▁furono": 17380, + "▁pseudo": 17381, + "?>": 17382, + "▁estadoun": 17383, + "▁Види": 17384, + "▁motiv": 17385, + "▁recall": 17386, + "isson": 17387, + "ób": 17388, + ")--": 17389, + "▁Erz": 17390, + "▁савез": 17391, + "Direct": 17392, + "соб": 17393, + "▁sho": 17394, + "völker": 17395, + "Ap": 17396, + "gens": 17397, + "ништво": 17398, + "▁Amsterdam": 17399, + "usk": 17400, + "пло": 17401, + "▁simulation": 17402, + "▁BC": 17403, + "▁Woj": 17404, + "autom": 17405, + "Alex": 17406, + "▁economic": 17407, + "гом": 17408, + "ikai": 17409, + "▁altre": 17410, + "▁'-": 17411, + "▁Weg": 17412, + "NotFound": 17413, + "йской": 17414, + "▁converting": 17415, + "phabet": 17416, + "atrice": 17417, + "bourne": 17418, + "alom": 17419, + "▁comparing": 17420, + "▁Zo": 17421, + "▁fla": 17422, + "вая": 17423, + "▁entra": 17424, + "▁charset": 17425, + "developers": 17426, + "ística": 17427, + "}>": 17428, + "▁Jazz": 17429, + "▁Howard": 17430, + "шта": 17431, + "▁clone": 17432, + "door": 17433, + "▁Pin": 17434, + "***": 17435, + "▁silent": 17436, + "ecycle": 17437, + "isce": 17438, + "▁mud": 17439, + "▁Display": 17440, + "▁lip": 17441, + "▁использова": 17442, + "▁characteristic": 17443, + "▁sb": 17444, + "firebase": 17445, + "▁Bew": 17446, + "Calendar": 17447, + "▁uso": 17448, + "èse": 17449, + "▁Rat": 17450, + "▁esper": 17451, + "▁throwing": 17452, + "▁rodz": 17453, + "▁yards": 17454, + "▁grass": 17455, + "▁marker": 17456, + "▁Kos": 17457, + "Theta": 17458, + "▁organis": 17459, + "kernel": 17460, + "▁personas": 17461, + "keep": 17462, + "▁exclaimed": 17463, + "oslav": 17464, + "▁Entertain": 17465, + "нер": 17466, + "▁inwon": 17467, + "▁Rand": 17468, + "reduce": 17469, + "fac": 17470, + "expression": 17471, + "yj": 17472, + "▁differenti": 17473, + "aglia": 17474, + "▁templates": 17475, + "▁mű": 17476, + "▁prv": 17477, + "▁mois": 17478, + "▁gewann": 17479, + "▁була": 17480, + "bibli": 17481, + "demo": 17482, + "▁Anderson": 17483, + "▁ред": 17484, + "▁porque": 17485, + "▁Pologne": 17486, + "▁trip": 17487, + "▁exemple": 17488, + "▁Internacional": 17489, + "▁као": 17490, + "Insert": 17491, + "general": 17492, + "SESSION": 17493, + "berga": 17494, + "hält": 17495, + "unas": 17496, + "мира": 17497, + "▁yields": 17498, + "mapsto": 17499, + "spot": 17500, + "▁+\\": 17501, + "лла": 17502, + "▁precisely": 17503, + "▁член": 17504, + "shadow": 17505, + "Are": 17506, + "unal": 17507, + "▁dispar": 17508, + "▁título": 17509, + "nest": 17510, + "▁Low": 17511, + "▁prot": 17512, + "▁Costa": 17513, + "named": 17514, + "▁gained": 17515, + "lesia": 17516, + "▁administration": 17517, + "Import": 17518, + "branch": 17519, + "▁sympath": 17520, + "voj": 17521, + "▁EC": 17522, + "▁municipio": 17523, + "▁animated": 17524, + "▁directories": 17525, + "▁roof": 17526, + "ząd": 17527, + "imet": 17528, + "proto": 17529, + "bla": 17530, + ":]": 17531, + "have": 17532, + "atem": 17533, + "▁ns": 17534, + "▁sector": 17535, + "three": 17536, + "owane": 17537, + "wers": 17538, + "ових": 17539, + "rence": 17540, + "▁extr": 17541, + "igten": 17542, + "▁occident": 17543, + "ță": 17544, + "▁eat": 17545, + "▁hydro": 17546, + "ubernetes": 17547, + "[@": 17548, + "▁Moon": 17549, + "▁Sho": 17550, + "▁elsewhere": 17551, + "üller": 17552, + "Upload": 17553, + "ланд": 17554, + "▁För": 17555, + "wissenschaft": 17556, + "KS": 17557, + "▁physics": 17558, + "tz": 17559, + "▁серед": 17560, + "▁Arbeit": 17561, + "▁мест": 17562, + "▁Gebiet": 17563, + "▁insect": 17564, + "Ah": 17565, + "izado": 17566, + "▁temple": 17567, + "▁annual": 17568, + "stad": 17569, + "▁habitat": 17570, + "▁AB": 17571, + "wort": 17572, + "▁repos": 17573, + "▁Neu": 17574, + "▁$(\".": 17575, + "Vorlage": 17576, + "▁reprezent": 17577, + "estanden": 17578, + "Intern": 17579, + ".`": 17580, + "▁failing": 17581, + "▁Material": 17582, + "▁effectively": 17583, + "телем": 17584, + "▁гла": 17585, + "▁nahm": 17586, + "▁differently": 17587, + "extension": 17588, + "▁Verm": 17589, + "enabled": 17590, + "configure": 17591, + "nio": 17592, + "ciones": 17593, + "▁Beach": 17594, + "сона": 17595, + "▁copying": 17596, + "▁україн": 17597, + "▁призна": 17598, + "zh": 17599, + "Desktop": 17600, + "▁sost": 17601, + "▁subsequently": 17602, + "▁Lehr": 17603, + "▁ó": 17604, + "lär": 17605, + "odor": 17606, + "phon": 17607, + "nc": 17608, + "iterator": 17609, + "▁эти": 17610, + "▁europé": 17611, + "▁Toronto": 17612, + "ódigo": 17613, + "▁posto": 17614, + "ffe": 17615, + "▁crew": 17616, + "▁Schwar": 17617, + "Sa": 17618, + "square": 17619, + "▁beside": 17620, + "▁Мі": 17621, + "▁ath": 17622, + "▁advent": 17623, + "cji": 17624, + "written": 17625, + "▁russ": 17626, + "rost": 17627, + "HI": 17628, + "▁dice": 17629, + "cca": 17630, + "▁dép": 17631, + "ply": 17632, + "bigg": 17633, + "ział": 17634, + "ütt": 17635, + "▁одно": 17636, + "JECT": 17637, + "ському": 17638, + "nos": 17639, + "mock": 17640, + "Launch": 17641, + "same": 17642, + "▁jobs": 17643, + "▁widely": 17644, + "▁defines": 17645, + "▁Pse": 17646, + "▁neighbour": 17647, + "ющие": 17648, + "▁closer": 17649, + "▁располо": 17650, + "▁clubs": 17651, + "fly": 17652, + "шим": 17653, + "▁suffered": 17654, + "▁nar": 17655, + "▁lavor": 17656, + "Extension": 17657, + "itionally": 17658, + "▁grace": 17659, + "▁Campeonato": 17660, + "▁Christmas": 17661, + "middle": 17662, + "othek": 17663, + "elements": 17664, + "▁sondern": 17665, + "▁tarde": 17666, + "▁permanent": 17667, + "▁conclude": 17668, + "Seg": 17669, + "▁акаде": 17670, + "}\",": 17671, + "▁февраля": 17672, + "řed": 17673, + "▁IL": 17674, + "jud": 17675, + "▁USS": 17676, + "▁Nature": 17677, + "ifference": 17678, + "Serializer": 17679, + "▁twelve": 17680, + "tid": 17681, + "мия": 17682, + "ческого": 17683, + "▁calendar": 17684, + "concat": 17685, + "▁intersection": 17686, + "▁PA": 17687, + "azure": 17688, + "▁située": 17689, + "▁kinds": 17690, + "▁ausge": 17691, + "▁rural": 17692, + "Theme": 17693, + "▁tale": 17694, + "noindent": 17695, + "going": 17696, + "rx": 17697, + "agi": 17698, + "wrapper": 17699, + "▁Coast": 17700, + "mbH": 17701, + "▁перед": 17702, + "spre": 17703, + "▁}\\": 17704, + "▁LI": 17705, + "znam": 17706, + "itled": 17707, + "Sample": 17708, + "uliar": 17709, + "*\\": 17710, + "▁resistance": 17711, + "stock": 17712, + "ked": 17713, + "▁HE": 17714, + "▁possession": 17715, + "▁Ring": 17716, + "▁magyar": 17717, + "outs": 17718, + "▁Secretary": 17719, + "nde": 17720, + "▁Wald": 17721, + "-(": 17722, + "▁ISO": 17723, + "▁afternoon": 17724, + "ionen": 17725, + "▁stops": 17726, + "▁constants": 17727, + "guard": 17728, + "bow": 17729, + "▁ers": 17730, + "▁Firebase": 17731, + "▁Clear": 17732, + "▁Holy": 17733, + "Win": 17734, + "▁titles": 17735, + "▁трав": 17736, + "▁contrib": 17737, + "häng": 17738, + "▁photograph": 17739, + "▁Distribution": 17740, + "ifts": 17741, + "▁aunque": 17742, + "comb": 17743, + "ADD": 17744, + "▁publication": 17745, + "▁служ": 17746, + "▁кня": 17747, + "▁ayant": 17748, + "▁restore": 17749, + "▁belief": 17750, + "▁vég": 17751, + "▁extensions": 17752, + "▁decom": 17753, + "вший": 17754, + "WT": 17755, + "▁parti": 17756, + "▁gioc": 17757, + "▁мира": 17758, + "▁issu": 17759, + "pipe": 17760, + "▁props": 17761, + "▁willing": 17762, + "▁nest": 17763, + "aso": 17764, + "pot": 17765, + "▁handles": 17766, + "▁фо": 17767, + "▁moder": 17768, + "▁ebenfalls": 17769, + "▁fighting": 17770, + "umbn": 17771, + "▁transparent": 17772, + "▁Krist": 17773, + "▁homes": 17774, + "▁voyage": 17775, + "Failed": 17776, + "▁Bird": 17777, + "▁Heart": 17778, + "Counter": 17779, + "▁Scottish": 17780, + "ática": 17781, + "▁arbeit": 17782, + "^{-\\": 17783, + "▁Sor": 17784, + "▁engaged": 17785, + "▁aside": 17786, + "▁Fou": 17787, + "▁wiel": 17788, + "▁reconst": 17789, + "ousin": 17790, + "▁hosted": 17791, + "▁classe": 17792, + "▁contest": 17793, + "...\"": 17794, + "мом": 17795, + "▁bean": 17796, + "gem": 17797, + "▁consultato": 17798, + "▁bio": 17799, + "▁subjects": 17800, + "boBox": 17801, + "▁Schrift": 17802, + "▁dinner": 17803, + "ăr": 17804, + "▁równ": 17805, + "▁%%": 17806, + "bage": 17807, + "▁veröff": 17808, + "▁detected": 17809, + "ienn": 17810, + "rose": 17811, + "▁Ton": 17812, + "Complete": 17813, + "▁proto": 17814, + "ichts": 17815, + "STAT": 17816, + "Checked": 17817, + "▁inten": 17818, + "▁smile": 17819, + "▁strip": 17820, + "neut": 17821, + "');\r": 17822, + "four": 17823, + "▁todas": 17824, + "Controls": 17825, + "▁thorough": 17826, + "rup": 17827, + "▁држави": 17828, + "ită": 17829, + "Protocol": 17830, + "Ка": 17831, + "▁expanded": 17832, + "extra": 17833, + "oport": 17834, + "▁Станов": 17835, + "leases": 17836, + "▁notion": 17837, + "▁guest": 17838, + "▁Islands": 17839, + "icked": 17840, + "▁Dave": 17841, + "▁reflection": 17842, + "liv": 17843, + "ální": 17844, + "▁revealed": 17845, + "▁sog": 17846, + "▁Tax": 17847, + "▁periodo": 17848, + "▁Weltkrie": 17849, + "catalina": 17850, + "qué": 17851, + "▁Father": 17852, + "▁Bir": 17853, + "expect": 17854, + "▁regression": 17855, + "iné": 17856, + "▁dabei": 17857, + "perm": 17858, + "мене": 17859, + "▁Abd": 17860, + "▁CF": 17861, + "arks": 17862, + "resolve": 17863, + "wedge": 17864, + "▁initialization": 17865, + "▁Véase": 17866, + "▁приня": 17867, + "stmt": 17868, + "▁income": 17869, + "MY": 17870, + "▁odkazy": 17871, + "▁Siehe": 17872, + "▁bodies": 17873, + "▁soc": 17874, + "Random": 17875, + "▁senza": 17876, + "ablo": 17877, + "▁regarded": 17878, + "onCreate": 17879, + "▁Magazine": 17880, + "▁Raf": 17881, + "▁Buenos": 17882, + "ил": 17883, + ")));": 17884, + "capt": 17885, + "redirect": 17886, + "▁petit": 17887, + "▁farm": 17888, + "▁rôle": 17889, + "▁статьи": 17890, + "    ": 17891, + "subfigure": 17892, + "èces": 17893, + "ziel": 17894, + "▁окон": 17895, + "EE": 17896, + "mee": 17897, + "▁perten": 17898, + "▁représent": 17899, + "▁LA": 17900, + "?'": 17901, + "▁тру": 17902, + "▁rational": 17903, + "osof": 17904, + "▁kne": 17905, + "▁artists": 17906, + "Flow": 17907, + "▁Аль": 17908, + "izard": 17909, + "▁numero": 17910, + "actic": 17911, + "▁destruct": 17912, + "▁Пра": 17913, + "onsieur": 17914, + "qt": 17915, + "abestanden": 17916, + "ność": 17917, + "Connect": 17918, + "▁oracle": 17919, + "▁Stockholm": 17920, + "sizeof": 17921, + "▁gemäß": 17922, + "ACT": 17923, + "▁expert": 17924, + "utions": 17925, + "▁hacia": 17926, + "▁logger": 17927, + "▁fool": 17928, + "rypto": 17929, + "ær": 17930, + "▁cidade": 17931, + "▁составе": 17932, + "oker": 17933, + "▁Transfer": 17934, + "▁denied": 17935, + "Track": 17936, + "▁radi": 17937, + "zec": 17938, + "▁Historic": 17939, + "▁Einwohner": 17940, + "кою": 17941, + "▁хра": 17942, + "▁Category": 17943, + "▁Disney": 17944, + "▁swap": 17945, + "Begin": 17946, + "▁mientras": 17947, + "▁dance": 17948, + "▁tête": 17949, + "▁droit": 17950, + "erta": 17951, + "▁birds": 17952, + "▁convin": 17953, + "parator": 17954, + "дра": 17955, + "▁ES": 17956, + "▁Ressources": 17957, + "EGIN": 17958, + "ücke": 17959, + "▁Cruz": 17960, + "abling": 17961, + "▁\"@": 17962, + "▁metres": 17963, + "▁Beg": 17964, + "▁Gründ": 17965, + "▁Boh": 17966, + "▁mile": 17967, + "▁Technology": 17968, + "\"+": 17969, + "acco": 17970, + "▁ss": 17971, + "▁Fed": 17972, + "▁Hend": 17973, + "usch": 17974, + "itä": 17975, + "folk": 17976, + "▁absor": 17977, + "antal": 17978, + "odge": 17979, + "▁WHEN": 17980, + "▁Externí": 17981, + "▁Regiment": 17982, + "▁evaluation": 17983, + "▁Tai": 17984, + "▁vocals": 17985, + "▁experimental": 17986, + "embed": 17987, + "▁Minn": 17988, + "▁вме": 17989, + "prec": 17990, + "every": 17991, + "▁hoof": 17992, + "▁Fernando": 17993, + "▁Bibliographie": 17994, + "▁nag": 17995, + "amerikanischer": 17996, + "▁marks": 17997, + "▁UTC": 17998, + "▁uncertain": 17999, + "дия": 18000, + "olia": 18001, + "▁cup": 18002, + "▁fille": 18003, + "▁dok": 18004, + "useppe": 18005, + "esterd": 18006, + "▁Brand": 18007, + "▁Third": 18008, + "PP": 18009, + "nodes": 18010, + "▁Pad": 18011, + "▁loved": 18012, + "swing": 18013, + "▁surprised": 18014, + "ardi": 18015, + "▁GR": 18016, + "]\"": 18017, + "▁equally": 18018, + "ihe": 18019, + "care": 18020, + "писок": 18021, + "lijk": 18022, + "rinn": 18023, + "▁\\[\\": 18024, + "▁sons": 18025, + "▁tät": 18026, + "icamente": 18027, + "▁listing": 18028, + "iellement": 18029, + "▁nyelven": 18030, + "▁ds": 18031, + "▁agricult": 18032, + "▁Hermann": 18033, + "▁besides": 18034, + "progress": 18035, + "▁peculiar": 18036, + "focus": 18037, + "cn": 18038, + "-$": 18039, + "ственный": 18040, + "ourg": 18041, + "▁wyn": 18042, + "▁conducted": 18043, + "▁Становништво": 18044, + "connected": 18045, + "▁bott": 18046, + "▁смер": 18047, + "▁Poz": 18048, + "unct": 18049, + "conda": 18050, + "▁савезној": 18051, + "▁havet": 18052, + "ligt": 18053, + "orted": 18054, + "▁entering": 18055, + "multip": 18056, + "▁Temple": 18057, + "▁Plant": 18058, + "typeof": 18059, + "▁Vlad": 18060, + "▁qued": 18061, + "▁reste": 18062, + "▁май": 18063, + "▁Very": 18064, + "ambiguation": 18065, + "▁challeng": 18066, + "▁respective": 18067, + "▁тор": 18068, + "Ctrl": 18069, + "▁absence": 18070, + "aru": 18071, + "вое": 18072, + "▁först": 18073, + "▁sq": 18074, + "▁Emperor": 18075, + "▁Ign": 18076, + "▁това": 18077, + ":`": 18078, + "adoop": 18079, + "▁Madame": 18080, + "▁gruppo": 18081, + "stud": 18082, + "▁externas": 18083, + "▁Александр": 18084, + "▁dign": 18085, + "▁живе": 18086, + "Amount": 18087, + "▁correlate": 18088, + "▁Fant": 18089, + "▁rails": 18090, + "fp": 18091, + "министратив": 18092, + "▁bought": 18093, + "▁filters": 18094, + "▁ancora": 18095, + "▁partner": 18096, + "▁quand": 18097, + "symbol": 18098, + "ulating": 18099, + "▁zd": 18100, + "awn": 18101, + "▁Grant": 18102, + "because": 18103, + "rable": 18104, + "\\}": 18105, + "ísticas": 18106, + "▁уче": 18107, + "▁période": 18108, + "▁ske": 18109, + "▁Anyway": 18110, + "▁indexes": 18111, + "▁directions": 18112, + "▁RAM": 18113, + "chrome": 18114, + "▁apost": 18115, + "▁warnings": 18116, + "▁Airport": 18117, + "VI": 18118, + "abile": 18119, + "▁lord": 18120, + "provider": 18121, + "▁Ji": 18122, + "ostream": 18123, + "▁gemeente": 18124, + "tableView": 18125, + "Extra": 18126, + "cursor": 18127, + "eground": 18128, + "▁Moz": 18129, + "▁rib": 18130, + "▁morph": 18131, + "loads": 18132, + "elsk": 18133, + "▁MAX": 18134, + "▁Santiago": 18135, + "▁Him": 18136, + "codes": 18137, + "▁lanz": 18138, + "▁counts": 18139, + "rinningsområ": 18140, + "щё": 18141, + "▁spé": 18142, + "▁pierws": 18143, + "▁Sver": 18144, + "▁acknow": 18145, + "Boolean": 18146, + "▁фамили": 18147, + "▁Senate": 18148, + "шов": 18149, + "agers": 18150, + "▁Nueva": 18151, + "bil": 18152, + "kiem": 18153, + "▁Mey": 18154, + "wij": 18155, + "▁GmbH": 18156, + "validation": 18157, + "▁ensuite": 18158, + "inking": 18159, + "▁campion": 18160, + "▁financial": 18161, + "izon": 18162, + "Headers": 18163, + "▁deprecated": 18164, + "▁fonction": 18165, + "REG": 18166, + "▁volumes": 18167, + "▁Chi": 18168, + "▁encountered": 18169, + "lak": 18170, + "рая": 18171, + "▁continues": 18172, + "▁~[": 18173, + "uerte": 18174, + "▁\\;": 18175, + "▁Dok": 18176, + "▁weights": 18177, + "▁rh": 18178, + "▁Napole": 18179, + "▁naturally": 18180, + "sku": 18181, + "pas": 18182, + "▁gegründ": 18183, + "etr": 18184, + "▁Ku": 18185, + "icted": 18186, + "▁fabric": 18187, + "▁ASC": 18188, + "▁Entertainment": 18189, + "▁energ": 18190, + "клад": 18191, + "omon": 18192, + "theme": 18193, + "▁харак": 18194, + "▁draft": 18195, + "▁channels": 18196, + "▁desert": 18197, + "▁través": 18198, + "▁Lock": 18199, + "▁siendo": 18200, + "фек": 18201, + "même": 18202, + "▁packet": 18203, + "▁Mountain": 18204, + "▁Fahr": 18205, + "braio": 18206, + "пере": 18207, + "▁genannt": 18208, + "▁deployment": 18209, + "Pal": 18210, + "ног": 18211, + "стру": 18212, + "Prim": 18213, + "für": 18214, + "▁dangerous": 18215, + "▁szám": 18216, + "reck": 18217, + "▁popup": 18218, + "icky": 18219, + "inar": 18220, + "cowo": 18221, + "нцикло": 18222, + "ítás": 18223, + "▁plugins": 18224, + "▁driven": 18225, + "лев": 18226, + "▁\"(": 18227, + "tta": 18228, + "▁Ú": 18229, + "▁eb": 18230, + "▁'';": 18231, + "▁knock": 18232, + "▁основа": 18233, + "▁maison": 18234, + "гля": 18235, + "▁Honor": 18236, + "tail": 18237, + "ritz": 18238, + "▁guys": 18239, + "▁combinations": 18240, + "ondere": 18241, + "▁Ald": 18242, + "▁fiddle": 18243, + "дав": 18244, + "urd": 18245, + "▁projection": 18246, + "▁También": 18247, + "verb": 18248, + "▁terre": 18249, + "rugu": 18250, + "▁september": 18251, + "▁=": 18572, + "▁Beat": 18573, + "▁Sax": 18574, + "vertical": 18575, + "кто": 18576, + "▁plants": 18577, + "▁Références": 18578, + "▁ogni": 18579, + "▁curs": 18580, + "▁SK": 18581, + "они": 18582, + "▁destac": 18583, + "\");\r": 18584, + "▁Sure": 18585, + "▁partido": 18586, + "▁Folge": 18587, + "▁Moore": 18588, + "▁wz": 18589, + "скус": 18590, + "ltre": 18591, + "ondo": 18592, + "▁pose": 18593, + "imos": 18594, + "бой": 18595, + "ципа": 18596, + "jus": 18597, + ".....": 18598, + "▁época": 18599, + "▁quanto": 18600, + "▁Support": 18601, + "geschichte": 18602, + "SERVER": 18603, + "▁Georges": 18604, + "enum": 18605, + "▁herm": 18606, + "▁nebo": 18607, + "▁Chr": 18608, + "character": 18609, + "▁***": 18610, + "▁Forsch": 18611, + "iami": 18612, + "▁¿": 18613, + "cych": 18614, + "▁fifth": 18615, + "sent": 18616, + "▁anderem": 18617, + "▁proportion": 18618, + "▁prest": 18619, + "▁Girl": 18620, + "▁drama": 18621, + "wand": 18622, + "▁Mail": 18623, + "▁Lux": 18624, + "▁který": 18625, + "▁Gesellschaft": 18626, + "▁Hinweis": 18627, + "nisse": 18628, + "▁mondo": 18629, + "Eq": 18630, + "▁perí": 18631, + "▁eastern": 18632, + "▁UEFA": 18633, + "uale": 18634, + "▁convex": 18635, + "▁поль": 18636, + "▁Hey": 18637, + "zenie": 18638, + "initely": 18639, + "▁Zusammen": 18640, + "SSL": 18641, + "ocal": 18642, + "▁canal": 18643, + "voy": 18644, + "▁Кри": 18645, + "▁között": 18646, + "▁cars": 18647, + "▁versión": 18648, + "Environment": 18649, + "Her": 18650, + "▁señ": 18651, + "▁spatial": 18652, + "ymi": 18653, + "Fire": 18654, + "▁veget": 18655, + "▁Wie": 18656, + "▁znaj": 18657, + "▁damage": 18658, + 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"▁Audio": 21764, + "▁(\\<": 21765, + "riority": 21766, + "▁nit": 21767, + "▁представи": 21768, + "▁Vie": 21769, + "▁wür": 21770, + "▁Hold": 21771, + "▁Sad": 21772, + "▁Tochter": 21773, + "▁oltre": 21774, + "▁Activ": 21775, + "▁Jason": 21776, + "▁wieku": 21777, + "▁regards": 21778, + "▁taste": 21779, + "agnostic": 21780, + "лася": 21781, + "▁Self": 21782, + "▁apr": 21783, + "▁Deep": 21784, + "scop": 21785, + "Activ": 21786, + "▁typedef": 21787, + "ContentView": 21788, + "compiler": 21789, + "▁Roth": 21790, + "xc": 21791, + "зик": 21792, + "▁largo": 21793, + "▁Rena": 21794, + "heiten": 21795, + "▁platforms": 21796, + "ulla": 21797, + "▁glance": 21798, + "▁mascul": 21799, + "▁mex": 21800, + "▁Jorge": 21801, + "▁funcion": 21802, + "choose": 21803, + "▁reviews": 21804, + "▁Alban": 21805, + "▁Glo": 21806, + "▁Species": 21807, + "▁Fame": 21808, + "▁Roll": 21809, + "▁Puerto": 21810, + "▁\\)": 21811, + "ymnas": 21812, + "environ": 21813, + "▁iphone": 21814, + "▁Wrestling": 21815, + "ały": 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"inject": 21920, + "▁';": 21921, + "änn": 21922, + "▁zelf": 21923, + "▁Entwicklung": 21924, + "ária": 21925, + "onomy": 21926, + "▁svil": 21927, + "iese": 21928, + "▁conser": 21929, + "▁nim": 21930, + "▁rész": 21931, + "▁Итали": 21932, + "▁partici": 21933, + "▁Lion": 21934, + "sr": 21935, + "always": 21936, + "▁Владимир": 21937, + "ческие": 21938, + "[,": 21939, + "▁Definition": 21940, + "nant": 21941, + "oem": 21942, + "Ids": 21943, + "▁вне": 21944, + "▁[...]": 21945, + "▁направ": 21946, + "▁GO": 21947, + "▁års": 21948, + "▁után": 21949, + "▁outros": 21950, + "▁región": 21951, + "▁Mong": 21952, + "▁filme": 21953, + "▁triple": 21954, + "▁spons": 21955, + "Develop": 21956, + "▁outcome": 21957, + "▁Bible": 21958, + "▁имени": 21959, + "Canvas": 21960, + "пута": 21961, + "curr": 21962, + "ások": 21963, + "){\\": 21964, + "ningar": 21965, + "`;": 21966, + "▁Flash": 21967, + ":#": 21968, + "must": 21969, + "cpu": 21970, + "▁formats": 21971, + "Har": 21972, + "▁episodio": 21973, + "▁Rosa": 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"species": 24091, + "▁injury": 24092, + "▁самы": 24093, + "▁lattice": 24094, + "Material": 24095, + "andenburg": 24096, + "▁huvudstaden": 24097, + "story": 24098, + "▁varying": 24099, + "▁követ": 24100, + "▁Российской": 24101, + "irse": 24102, + "▁drum": 24103, + "Pressed": 24104, + "Lar": 24105, + "▁Agu": 24106, + "▁weil": 24107, + "▁commence": 24108, + "▁Según": 24109, + "Gesture": 24110, + "Shape": 24111, + "▁Vors": 24112, + "▁succès": 24113, + "▁corrected": 24114, + "Kar": 24115, + "▁cruel": 24116, + "▁politico": 24117, + "▁Schriftsteller": 24118, + "▁risult": 24119, + "etu": 24120, + "archiv": 24121, + "▁género": 24122, + "▁Lü": 24123, + "▁triumph": 24124, + "ORS": 24125, + "Lu": 24126, + "▁personnel": 24127, + "▁Hills": 24128, + "asset": 24129, + "domin": 24130, + "Receive": 24131, + "▁Oak": 24132, + "▁Kno": 24133, + "▁Theory": 24134, + "irie": 24135, + "owan": 24136, + "▁estava": 24137, + "▁executes": 24138, + "йт": 24139, + "ópez": 24140, + "поло": 24141, + "ética": 24142, + "▁название": 24143, + "▁converges": 24144, + "▁notre": 24145, + "▁populated": 24146, + "▁movements": 24147, + "▁statistical": 24148, + "▁Zweiten": 24149, + "quin": 24150, + "▁importantes": 24151, + "▁klein": 24152, + "▁Segunda": 24153, + "schließend": 24154, + "Failure": 24155, + "nar": 24156, + "dag": 24157, + "▁ruolo": 24158, + "▁fiction": 24159, + "▁использу": 24160, + "▁crisis": 24161, + "▁Getting": 24162, + ",%": 24163, + "▁армии": 24164, + "▁campus": 24165, + "▁footer": 24166, + "▁días": 24167, + "бан": 24168, + "▁liberty": 24169, + "▁gh": 24170, + "▁chamber": 24171, + "▁districts": 24172, + "▁excited": 24173, + "▁canción": 24174, + "tero": 24175, + "▁Working": 24176, + "▁części": 24177, + "льный": 24178, + "▁forum": 24179, + "▁Ehe": 24180, + "▁ката": 24181, + "itations": 24182, + "Tools": 24183, + "achiv": 24184, + "▁cres": 24185, + "asto": 24186, + "▁rever": 24187, + "▁nazionale": 24188, + "▁doors": 24189, + "▁Nancy": 24190, + "▁islands": 24191, + "Imp": 24192, + "▁Chair": 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"▁modific": 24670, + "notification": 24671, + "▁denn": 24672, + "▁algo": 24673, + "▁Spo": 24674, + "▁mistrzost": 24675, + "/:": 24676, + "▁apresent": 24677, + "▁прод": 24678, + "Volume": 24679, + "ską": 24680, + "protected": 24681, + "▁Turkish": 24682, + "azy": 24683, + "▁pouv": 24684, + "▁período": 24685, + "skog": 24686, + "▁entropy": 24687, + "zed": 24688, + "тори": 24689, + "▁lij": 24690, + "boards": 24691, + "▁стату": 24692, + "Bool": 24693, + "▁polity": 24694, + "@\",": 24695, + "▁рік": 24696, + "née": 24697, + "▁Zug": 24698, + "▁Uniti": 24699, + "émet": 24700, + "atience": 24701, + "dimen": 24702, + "▁Steven": 24703, + "Ha": 24704, + "ACTION": 24705, + "▁wand": 24706, + "▁Navar": 24707, + "▁січня": 24708, + "Watch": 24709, + "▁Stuart": 24710, + "▁zde": 24711, + "▁контро": 24712, + "dataset": 24713, + "yó": 24714, + "▁Bush": 24715, + "▁себя": 24716, + "▁worthy": 24717, + "▁Ble": 24718, + "▁propor": 24719, + "▁Village": 24720, + "▁ry": 24721, + "▁voit": 24722, + "▁копия": 24723, + 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"Typ": 24933, + "▁político": 24934, + "▁RewriteCond": 24935, + "▁hoped": 24936, + "▁anxious": 24937, + "Liter": 24938, + "jahr": 24939, + "▁assemble": 24940, + "▁crypt": 24941, + "lahoma": 24942, + "ASH": 24943, + "▁Бри": 24944, + "▁Cic": 24945, + "twitter": 24946, + "hyper": 24947, + "▁Tell": 24948, + "ільки": 24949, + "вобо": 24950, + "▁bazie": 24951, + "▁contemporary": 24952, + "▁Parameter": 24953, + "stwa": 24954, + "▁bekend": 24955, + "cock": 24956, + "previous": 24957, + "enska": 24958, + "▁caller": 24959, + "]])": 24960, + "▁Raz": 24961, + "▁Selon": 24962, + "▁proposal": 24963, + "▁bý": 24964, + "▁Sied": 24965, + "▁Arbeits": 24966, + "▁pride": 24967, + "▁slope": 24968, + "idé": 24969, + "gradient": 24970, + "▁Джерела": 24971, + "▁SH": 24972, + "▁разрабо": 24973, + "iversity": 24974, + "сподар": 24975, + "\\{\\": 24976, + "▁стали": 24977, + "▁Einzel": 24978, + "▁rgba": 24979, + "▁Anim": 24980, + "▁alles": 24981, + "бар": 24982, + "erte": 24983, + "▁réalisé": 24984, + "Institut": 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25924, + "IOS": 25925, + "▁британ": 25926, + "▁bout": 25927, + "▁автор": 25928, + "ництво": 25929, + "ето": 25930, + "lera": 25931, + "cls": 25932, + "▁Ley": 25933, + "amy": 25934, + "agens": 25935, + "ashed": 25936, + "▁okrę": 25937, + "гро": 25938, + "ellett": 25939, + "▁Fellow": 25940, + "▁manifold": 25941, + "$),": 25942, + "lder": 25943, + "▁voz": 25944, + "▁begg": 25945, + "▁baron": 25946, + "▁fid": 25947, + "▁firing": 25948, + "ilda": 25949, + "dek": 25950, + "AU": 25951, + "itare": 25952, + "▁Ara": 25953, + "▁Exit": 25954, + "▁cinemat": 25955, + "▁intros": 25956, + "▁contacts": 25957, + "пени": 25958, + "▁möglich": 25959, + "▁Singapore": 25960, + "ström": 25961, + "▁Hern": 25962, + "▁sixth": 25963, + "▁publications": 25964, + "vie": 25965, + "▁Hat": 25966, + "▁accepting": 25967, + "ác": 25968, + "stwo": 25969, + "▁quietly": 25970, + "Photo": 25971, + "▁basket": 25972, + "▁eigenvalues": 25973, + "▁médec": 25974, + "▁Olimp": 25975, + "▁церков": 25976, + "alin": 25977, + "consum": 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26347, + "▁свои": 26348, + "MIT": 26349, + "made": 26350, + "▁locomot": 26351, + "хода": 26352, + "FP": 26353, + "▁pm": 26354, + ".*;": 26355, + "▁Hamm": 26356, + "`}": 26357, + "LayoutInflater": 26358, + "==\"": 26359, + "▁Eur": 26360, + "▁dogs": 26361, + "жении": 26362, + "▁azon": 26363, + "▁emulator": 26364, + "▁ricon": 26365, + "beeld": 26366, + "▁ну": 26367, + "▁approximate": 26368, + "LM": 26369, + "▁Bond": 26370, + "▁enh": 26371, + "ędz": 26372, + "▁solit": 26373, + "RelativeLayout": 26374, + "eteor": 26375, + "amentos": 26376, + "▁indirect": 26377, + "iből": 26378, + "▁gros": 26379, + "▁Originals": 26380, + "commands": 26381, + "Export": 26382, + "▁Avec": 26383, + "▁solemn": 26384, + "▁correction": 26385, + "▁проводи": 26386, + "▁Mosk": 26387, + "▁подо": 26388, + "▁gebied": 26389, + "▁następ": 26390, + "▁Driver": 26391, + "▁Ook": 26392, + "▁Vec": 26393, + "▁lungo": 26394, + "ficos": 26395, + "▁svol": 26396, + "▁kid": 26397, + "nja": 26398, + "▁Hr": 26399, + "▁поддер": 26400, + 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28500, + "нім": 28501, + "near": 28502, + "▁shame": 28503, + "▁moyenne": 28504, + "▁CP": 28505, + "preis": 28506, + "▁passenger": 28507, + "lek": 28508, + "ionales": 28509, + "kafka": 28510, + "▁participe": 28511, + "▁membership": 28512, + "[_": 28513, + "lando": 28514, + "stelling": 28515, + "Sem": 28516, + "gon": 28517, + "▁Correct": 28518, + "▁valle": 28519, + "▁readily": 28520, + "▁Dokument": 28521, + "honneur": 28522, + "▁testim": 28523, + "ulative": 28524, + "doFilter": 28525, + "▁dominant": 28526, + "ammer": 28527, + "▁која": 28528, + "▁Monsieur": 28529, + "zeg": 28530, + "▁війни": 28531, + "▁Fo": 28532, + "▁Amy": 28533, + "▁¡": 28534, + "▁február": 28535, + "▁downloading": 28536, + "▁leng": 28537, + "\\}$,": 28538, + "▁neat": 28539, + "▁Cache": 28540, + "ICATION": 28541, + "▁deve": 28542, + "▁sorrow": 28543, + "slow": 28544, + "▁hinaus": 28545, + "▁reconoc": 28546, + "▁Linked": 28547, + "▁Shaw": 28548, + "market": 28549, + "▁Dic": 28550, + "▁Ski": 28551, + "▁delimiter": 28552, + "▁MainActivity": 28553, + "▁Musical": 28554, + "▁Reyn": 28555, + "ScrollView": 28556, + "▁conventional": 28557, + "ença": 28558, + "▁refactor": 28559, + "'-": 28560, + "▁Hed": 28561, + "sprech": 28562, + "▁athlet": 28563, + "▁especies": 28564, + "▁Schön": 28565, + "▁kleinen": 28566, + "шко": 28567, + "▁Йо": 28568, + "▁Happy": 28569, + "multirow": 28570, + "▁augusti": 28571, + "▁Gand": 28572, + "▁appointment": 28573, + "▁Mediabestanden": 28574, + "Three": 28575, + "▁Kenneth": 28576, + "NEW": 28577, + "▁Notification": 28578, + "▁Marx": 28579, + "▁insc": 28580, + "Mor": 28581, + "вый": 28582, + "väst": 28583, + "vidia": 28584, + "▁demonstrated": 28585, + "fonts": 28586, + "▁kamen": 28587, + "▁Ster": 28588, + "▁mieszkańców": 28589, + "▁Koh": 28590, + "~$\\": 28591, + "»).": 28592, + "rene": 28593, + "insic": 28594, + "ická": 28595, + "xygen": 28596, + "▁mn": 28597, + "▁sched": 28598, + "ASC": 28599, + "Ig": 28600, + "▁Constant": 28601, + "▁opportun": 28602, + "▁MyClass": 28603, + "sef": 28604, + "oped": 28605, + "▁injured": 28606, + "VIS": 28607, + "▁Pero": 28608, + "▁Until": 28609, + "▁flesh": 28610, + "orphism": 28611, + "▁Portal": 28612, + "▁gminy": 28613, + "▁власти": 28614, + "▁Nä": 28615, + "ктиче": 28616, + "▁hrab": 28617, + "▁Cub": 28618, + "avoir": 28619, + "▁Lars": 28620, + "▁Бело": 28621, + "▁seizoen": 28622, + "▁Genomsnitt": 28623, + "▁Lil": 28624, + "▁Pool": 28625, + "▁Dios": 28626, + "TX": 28627, + "aes": 28628, + "autore": 28629, + "Alpha": 28630, + "states": 28631, + "Lab": 28632, + "nederbörd": 28633, + "erton": 28634, + "▁brid": 28635, + "▁richt": 28636, + "▁Ela": 28637, + "▁сла": 28638, + "▁weapon": 28639, + "▁combatt": 28640, + "agar": 28641, + "▁regnig": 28642, + "▁utilisé": 28643, + "▁servir": 28644, + "▁brick": 28645, + "▁gateway": 28646, + "▁torraste": 28647, + "▁procedures": 28648, + "▁årsnederbörd": 28649, + "▁Genomsnittlig": 28650, + "чёт": 28651, + "▁områ": 28652, + "▁regnigaste": 28653, + "▁честь": 28654, + "▁amid": 28655, + "▁grateful": 28656, + "▁DIS": 28657, + "DAY": 28658, + "▁ору": 28659, + "▁rivière": 28660, + "heure": 28661, + "▁Richmond": 28662, + "▁Compar": 28663, + "▁Нор": 28664, + "DOC": 28665, + "esia": 28666, + "calc": 28667, + "▁IU": 28668, + "▁vorg": 28669, + "▁habían": 28670, + "çoit": 28671, + "▁arist": 28672, + "▁кли": 28673, + "▁Sue": 28674, + "▁Touch": 28675, + "▁Writing": 28676, + "ifiable": 28677, + "▁wc": 28678, + "▁withdraw": 28679, + "зар": 28680, + "▁presently": 28681, + "▁FK": 28682, + "▁prakt": 28683, + "▁colored": 28684, + "usb": 28685, + "▁Perú": 28686, + "▁plata": 28687, + "▁wishes": 28688, + "▁кам": 28689, + "azar": 28690, + "ável": 28691, + "▁lamp": 28692, + "bishop": 28693, + "▁inclusion": 28694, + "jq": 28695, + "arth": 28696, + "▁Flag": 28697, + "▁нор": 28698, + "ædia": 28699, + "UNCTION": 28700, + "▁Bahnhof": 28701, + "▁approaching": 28702, + "▁Gött": 28703, + "▁cube": 28704, + "▁argued": 28705, + "▁Things": 28706, + "Gui": 28707, + "дови": 28708, + "▁recre": 28709, + "▁réseau": 28710, + "▁significa": 28711, + "Git": 28712, + "gebracht": 28713, + "▁liga": 28714, + "▁assured": 28715, + "alus": 28716, + "рит": 28717, + "▁энциклопеди": 28718, + "▁%).": 28719, + "▁Première": 28720, + "▁declarations": 28721, + "▁tricky": 28722, + "▁profiles": 28723, + "▁Fon": 28724, + "▁Jas": 28725, + "âr": 28726, + "babel": 28727, + "▁Friday": 28728, + "▁június": 28729, + "▁cols": 28730, + "▁EXISTS": 28731, + "▁Italiana": 28732, + "▁authorization": 28733, + "▁sulle": 28734, + "▁Emb": 28735, + "▁Variable": 28736, + "trees": 28737, + "▁Fly": 28738, + "riors": 28739, + "▁damals": 28740, + "▁findet": 28741, + "▁Sept": 28742, + "▁mundial": 28743, + "▁removal": 28744, + "▁longitude": 28745, + "clic": 28746, + "▁fade": 28747, + "▁gradle": 28748, + "▁zák": 28749, + "▁timing": 28750, + "trightarrow": 28751, + "atia": 28752, + "-.": 28753, + "uche": 28754, + "▁serialize": 28755, + "▁Hmm": 28756, + "▁Representatives": 28757, + "bah": 28758, + "rend": 28759, + "assador": 28760, + "▁shield": 28761, + "ucion": 28762, + "▁américaine": 28763, + "zę": 28764, + "villa": 28765, + "▁hombre": 28766, + "áss": 28767, + "▁SF": 28768, + "▁repeating": 28769, + "▁criter": 28770, + "▁Struct": 28771, + "???": 28772, + "▁cheap": 28773, + "▁rings": 28774, + "abhäng": 28775, + "▁corte": 28776, + "▁administ": 28777, + "ixon": 28778, + "gypt": 28779, + "▁puntos": 28780, + "▁mezi": 28781, + "▁pochod": 28782, + "isko": 28783, + "nię": 28784, + "▁осу": 28785, + "▁ár": 28786, + "тельной": 28787, + "▁Metropolitan": 28788, + "jin": 28789, + "zess": 28790, + "▁віці": 28791, + "▁conflicts": 28792, + "ijst": 28793, + "▁Market": 28794, + "стров": 28795, + "▁\",\"": 28796, + "▁Scroll": 28797, + "gun": 28798, + "тара": 28799, + "▁amateur": 28800, + "▁róż": 28801, + "poss": 28802, + "▁generalized": 28803, + "▁Harm": 28804, + "cita": 28805, + "▁Switzerland": 28806, + "icola": 28807, + "▁muit": 28808, + "located": 28809, + "▁có": 28810, + "▁arose": 28811, + "▁communauté": 28812, + "})^": 28813, + "visibility": 28814, + "ída": 28815, + "▁FB": 28816, + "▁Freund": 28817, + "gat": 28818, + "\":{\"": 28819, + "intellij": 28820, + "ifie": 28821, + "hmen": 28822, + "▁édition": 28823, + "▁које": 28824, + "▁інших": 28825, + "oming": 28826, + "▁arquitect": 28827, + "▁Presidente": 28828, + "▁Під": 28829, + "▁cabin": 28830, + "Theorem": 28831, + "▁Gay": 28832, + "ifice": 28833, + "▁hect": 28834, + "lą": 28835, + "irmingham": 28836, + "▁semantic": 28837, + "▁Louisiana": 28838, + "▁sacrifice": 28839, + "▁Christoph": 28840, + "▁Executive": 28841, + "_+": 28842, + "ják": 28843, + "▁seria": 28844, + "▁Overflow": 28845, + "▁Lucy": 28846, + "▁melhor": 28847, + "▁voices": 28848, + "cza": 28849, + "▁капи": 28850, + "▁университета": 28851, + "INCT": 28852, + "▁coloc": 28853, + "▁prue": 28854, + "▁geomet": 28855, + "▁diretto": 28856, + "reso": 28857, + "▁Akt": 28858, + "▁unh": 28859, + "▁сери": 28860, + "▁Alert": 28861, + "Wel": 28862, + "audi": 28863, + "äler": 28864, + "▁guests": 28865, + "▁иде": 28866, + "Studio": 28867, + "▁кате": 28868, + "▁exponent": 28869, + "rze": 28870, + "pmod": 28871, + "rolle": 28872, + "▁Limited": 28873, + "Allemagne": 28874, + "▁pity": 28875, + "▁lä": 28876, + "▁runner": 28877, + "kende": 28878, + "EQ": 28879, + "▁MM": 28880, + "szág": 28881, + "поді": 28882, + "▁regret": 28883, + "▁publié": 28884, + "▁departamento": 28885, + "▁accused": 28886, + "hp": 28887, + "▁Pfl": 28888, + "▁Sint": 28889, + "▁ekonom": 28890, + "ractor": 28891, + "▁Пів": 28892, + "▁awful": 28893, + "ować": 28894, + "]->": 28895, + "▁Fine": 28896, + "Са": 28897, + "tis": 28898, + "éta": 28899, + "▁Роди": 28900, + "▁Düsseldorf": 28901, + "LOB": 28902, + "osas": 28903, + "werke": 28904, + "▁lance": 28905, + "▁листопада": 28906, + "▁incomplete": 28907, + "▁Picture": 28908, + "('\\": 28909, + "esters": 28910, + "▁belonged": 28911, + "▁Sank": 28912, + "ammed": 28913, + "▁repositories": 28914, + "▁addr": 28915, + "Collect": 28916, + "Hot": 28917, + "▁tyl": 28918, + "▁instanceof": 28919, + "▁bonus": 28920, + "ový": 28921, + "▁моря": 28922, + "▁interactive": 28923, + "▁Mys": 28924, + "▁Edmund": 28925, + "fileName": 28926, + "emor": 28927, + "▁Три": 28928, + "▁Rosen": 28929, + "▁Prima": 28930, + "▁voting": 28931, + "▁XP": 28932, + "▁Zero": 28933, + "▁Led": 28934, + "amsung": 28935, + "▁enables": 28936, + "▁redirects": 28937, + "AST": 28938, + "Paint": 28939, + "acker": 28940, + "lecht": 28941, + "▁chairman": 28942, + "▁Aven": 28943, + "▁Sach": 28944, + "(\"<": 28945, + "кер": 28946, + "▁mistakes": 28947, + "▁Weit": 28948, + "▁prowad": 28949, + "▁didnt": 28950, + "énario": 28951, + "unless": 28952, + "▁backwards": 28953, + "boa": 28954, + "duino": 28955, + "```": 28956, + "stor": 28957, + "Completion": 28958, + "puesta": 28959, + "▁dinast": 28960, + "últ": 28961, + "▁SY": 28962, + "ifolia": 28963, + "œuvres": 28964, + "▁racing": 28965, + "▁cabinet": 28966, + "▁cutting": 28967, + "▁thumb": 28968, + "▁Кара": 28969, + "highlight": 28970, + "куп": 28971, + "▁sd": 28972, + "▁національ": 28973, + "▁campagne": 28974, + "▁registers": 28975, + "▁educational": 28976, + "▁pesar": 28977, + "üge": 28978, + "▁oro": 28979, + "burgo": 28980, + "▁Athletics": 28981, + "▁MTV": 28982, + "getMessage": 28983, + "▁Hyp": 28984, + "▁victim": 28985, + "))\\": 28986, + "▁drums": 28987, + "hostname": 28988, + "tał": 28989, + "making": 28990, + "▁powiat": 28991, + "őd": 28992, + "threads": 28993, + "▁absolv": 28994, + "▁люди": 28995, + "▁stepped": 28996, + "exist": 28997, + "▁NK": 28998, + "▁ves": 28999, + "istiche": 29000, + "%'": 29001, + "ativos": 29002, + "▁такой": 29003, + "▁MongoDB": 29004, + "▁Ung": 29005, + "▁Рус": 29006, + "▁elim": 29007, + "▁Fif": 29008, + "icación": 29009, + "▁Tennis": 29010, + "▁Jefferson": 29011, + "ján": 29012, + "fog": 29013, + "anha": 29014, + "zor": 29015, + "▁університе": 29016, + "ahu": 29017, + "iada": 29018, + "Sdk": 29019, + "Setting": 29020, + "▁Kill": 29021, + "▁Wend": 29022, + "▁bald": 29023, + "▁Kub": 29024, + "▁visto": 29025, + "▁jeunes": 29026, + "collections": 29027, + "ací": 29028, + "вропей": 29029, + "▁arise": 29030, + "оні": 29031, + "MAIN": 29032, + "доступ": 29033, + "▁berg": 29034, + "▁criticism": 29035, + "▁Torre": 29036, + "▁descript": 29037, + "ières": 29038, + "▁estudio": 29039, + "▁ili": 29040, + "▁militare": 29041, + "▁Clara": 29042, + "▁Ellen": 29043, + "limited": 29044, + "лм": 29045, + "▁Españ": 29046, + "▁infinitely": 29047, + "America": 29048, + "ouc": 29049, + "glass": 29050, + "▁rud": 29051, + "▁zat": 29052, + "▁rin": 29053, + "▁Bibliografía": 29054, + "▁merchant": 29055, + "tensorflow": 29056, + "▁dér": 29057, + "▁ActiveRecord": 29058, + "IES": 29059, + "▁linker": 29060, + "▁estudios": 29061, + "cdnjs": 29062, + "▁Государ": 29063, + "ánchez": 29064, + "appe": 29065, + "club": 29066, + "▁další": 29067, + "▁Algorithm": 29068, + "dfs": 29069, + "▁Bac": 29070, + "▁кафе": 29071, + "▁&=\\": 29072, + "▁ат": 29073, + "▁Глав": 29074, + "▁Mou": 29075, + "Machine": 29076, + "(...)": 29077, + 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30866, + "光": 30867, + "白": 30868, + "心": 30869, + "த": 30870, + "ज": 30871, + "设": 30872, + "ί": 30873, + "路": 30874, + "ग": 30875, + "∥": 30876, + "한": 30877, + "最": 30878, + "Ћ": 30879, + "手": 30880, + "ս": 30881, + "？": 30882, + "型": 30883, + "ầ": 30884, + "セ": 30885, + "建": 30886, + "ェ": 30887, + "主": 30888, + "시": 30889, + "대": 30890, + "ῆ": 30891, + "‡": 30892, + "集": 30893, + "დ": 30894, + "目": 30895, + "Ρ": 30896, + "ァ": 30897, + "度": 30898, + "長": 30899, + "星": 30900, + "ノ": 30901, + "ộ": 30902, + "가": 30903, + "五": 30904, + "چ": 30905, + "로": 30906, + "ョ": 30907, + "重": 30908, + "于": 30909, + "发": 30910, + "史": 30911, + "ظ": 30912, + "ช": 30913, + "え": 30914, + "國": 30915, + "ĭ": 30916, + "ப": 30917, + "인": 30918, + "你": 30919, + "駅": 30920, + "‒": 30921, + "♥": 30922, + "多": 30923, + "ħ": 30924, + "Қ": 30925, + "ồ": 30926, + "士": 30927, + "四": 30928, + "┴": 30929, + "ம": 30930, + "司": 30931, + "ে": 30932, + "ὰ": 30933, + "∂": 30934, + "╬": 30935, + "次": 30936, + "Ľ": 30937, + "⟶": 30938, + "立": 30939, + "点": 30940, + "音": 30941, + "⠀": 30942, + "器": 30943, + "하": 30944, + "井": 30945, + "存": 30946, + "ֹ": 30947, + "当": 30948, + "Ë": 30949, + "★": 30950, + "寺": 30951, + "性": 30952, + "也": 30953, + "め": 30954, + "だ": 30955, + "位": 30956, + "ങ": 30957, + "ہ": 30958, + "值": 30959, + "古": 30960, + "გ": 30961, + "ব": 30962, + "院": 30963, + "േ": 30964, + "▶": 30965, + "ர": 30966, + "界": 30967, + "語": 30968, + "സ": 30969, + "수": 30970, + "ǒ": 30971, + "愛": 30972, + "✔": 30973, + "時": 30974, + "ọ": 30975, + "റ": 30976, + "մ": 30977, + "ケ": 30978, + "东": 30979, + "同": 30980, + "주": 30981, + "保": 30982, + "Õ": 30983, + "ố": 30984, + "ἰ": 30985, + "青": 30986, + "ゴ": 30987, + "体": 30988, + "清": 30989, + "相": 30990, + "จ": 30991, + "ء": 30992, + "情": 30993, + "𝕜": 30994, + "ক": 30995, + "ḫ": 30996, + "ờ": 30997, + "将": 30998, + "族": 30999, + "동": 31000, + "Υ": 31001, + "┌": 31002, + "ボ": 31003, + "宮": 31004, + "』": 31005, + "ম": 31006, + "『": 31007, + "ļ": 31008, + "श": 31009, + "ป": 31010, + "Ա": 31011, + "ब": 31012, + "자": 31013, + "政": 31014, + "ா": 31015, + "间": 31016, + "fi": 31017, + "松": 31018, + "ṃ": 31019, + "始": 31020, + "息": 31021, + "少": 31022, + "教": 31023, + "获": 31024, + "列": 31025, + "开": 31026, + "ტ": 31027, + "ワ": 31028, + "კ": 31029, + "科": 31030, + "春": 31031, + "治": 31032, + "吉": 31033, + "ས": 31034, + "ศ": 31035, + "ɒ": 31036, + "台": 31037, + "ネ": 31038, + "း": 31039, + "ĩ": 31040, + "工": 31041, + "ά": 31042, + "知": 31043, + "八": 31044, + "場": 31045, + "画": 31046, + "百": 31047, + "☆": 31048, + "記": 31049, + "得": 31050, + "ソ": 31051, + "氏": 31052, + "ာ": 31053, + "에": 31054, + "ল": 31055, + "ṛ": 31056, + "关": 31057, + "ġ": 31058, + "έ": 31059, + "∑": 31060, + "ベ": 31061, + "标": 31062, + "니": 31063, + "ὴ": 31064, + "ֵ": 31065, + "外": 31066, + "♠": 31067, + "わ": 31068, + "間": 31069, + "ภ": 31070, + "校": 31071, + "制": 31072, + "แ": 31073, + "力": 31074, + "門": 31075, + "好": 31076, + "ғ": 31077, + "Ù": 31078, + "ℓ": 31079, + "ֶ": 31080, + "는": 31081, + "┐": 31082, + "∗": 31083, + "指": 31084, + "色": 31085, + "返": 31086, + "馬": 31087, + "请": 31088, + "≫": 31089, + "風": 31090, + "ό": 31091, + "接": 31092, + "서": 31093, + "↳": 31094, + "せ": 31095, + "志": 31096, + "̲": 31097, + "魔": 31098, + "ң": 31099, + "更": 31100, + "程": 31101, + "김": 31102, + "郡": 31103, + "ོ": 31104, + "ũ": 31105, + "ച": 31106, + "利": 31107, + "県": 31108, + "周": 31109, + "そ": 31110, + "や": 31111, + "谷": 31112, + "香": 31113, + "♯": 31114, + "じ": 31115, + "،": 31116, + "期": 31117, + "∅": 31118, + "┘": 31119, + "初": 31120, + "福": 31121, + "片": 31122, + "ザ": 31123, + "動": 31124, + "参": 31125, + "성": 31126, + "Ə": 31127, + "╦": 31128, + "어": 31129, + "ხ": 31130, + "義": 31131, + "च": 31132, + "象": 31133, + "功": 31134, + "♂": 31135, + "도": 31136, + "고": 31137, + "过": 31138, + "վ": 31139, + "皇": 31140, + "特": 31141, + "ậ": 31142, + "长": 31143, + "英": 31144, + "ấ": 31145, + "ണ": 31146, + "Ъ": 31147, + "স": 31148, + "其": 31149, + "ত": 31150, + "流": 31151, + "除": 31152, + "일": 31153, + "ু": 31154, + "្": 31155, + "永": 31156, + "直": 31157, + "상": 31158, + "千": 31159, + "ắ": 31160, + "館": 31161, + "Ť": 31162, + "朝": 31163, + "ட": 31164, + "ɣ": 31165, + "单": 31166, + "ʀ": 31167, + "格": 31168, + "德": 31169, + "전": 31170, + "☺": 31171, + "ピ": 31172, + "歌": 31173, + "进": 31174, + "限": 31175, + "夫": 31176, + "트": 31177, + "⊢": 31178, + "園": 31179, + "量": 31180, + "土": 31181, + "放": 31182, + "码": 31183, + "等": 31184, + "系": 31185, + "∼": 31186, + "華": 31187, + "↵": 31188, + "소": 31189, + "常": 31190, + "否": 31191, + "見": 31192, + "源": 31193, + "ׁ": 31194, + "实": 31195, + "博": 31196, + "라": 31197, + "원": 31198, + "보": 31199, + "⊕": 31200, + "解": 31201, + "〜": 31202, + "男": 31203, + "দ": 31204, + "ポ": 31205, + "ろ": 31206, + "나": 31207, + "ག": 31208, + "無": 31209, + "Û": 31210, + "̥": 31211, + "ұ": 31212, + "查": 31213, + "̣": 31214, + "╗": 31215, + "╩": 31216, + "条": 31217, + "য": 31218, + "ὁ": 31219, + "後": 31220, + "他": 31221, + "网": 31222, + "ல": 31223, + "≃": 31224, + "화": 31225, + "ە": 31226, + "阿": 31227, + "ေ": 31228, + "户": 31229, + "∫": 31230, + "구": 31231, + "ར": 31232, + "မ": 31233, + "▸": 31234, + "լ": 31235, + "○": 31236, + "命": 31237, + "就": 31238, + "龍": 31239, + "君": 31240, + "夏": 31241, + "": 31242, + "言": 31243, + "先": 31244, + "➜": 31245, + "შ": 31246, + "ძ": 31247, + "ਾ": 31248, + "வ": 31249, + "ど": 31250, + "ヒ": 31251, + "ไ": 31252, + "ன": 31253, + "ば": 31254, + "ギ": 31255, + "գ": 31256, + "ἄ": 31257, + "ヤ": 31258, + "典": 31259, + "府": 31260, + "̄": 31261, + "신": 31262, + "组": 31263, + "改": 31264, + "ὲ": 31265, + "华": 31266, + "与": 31267, + "调": 31268, + "╝": 31269, + "ヴ": 31270, + "ქ": 31271, + "由": 31272, + "修": 31273, + "學": 31274, + "♣": 31275, + "消": 31276, + "符": 31277, + "ʌ": 31278, + "부": 31279, + "ớ": 31280, + "‾": 31281, + "▲": 31282, + "录": 31283, + "ള": 31284, + "연": 31285, + "을": 31286, + "ひ": 31287, + "영": 31288, + "┤": 31289, + "已": 31290, + "陽": 31291, + "င": 31292, + "국": 31293, + "容": 31294, + "未": 31295, + "宗": 31296, + "ᴇ": 31297, + "び": 31298, + "장": 31299, + "龙": 31300, + "්": 31301, + "提": 31302, + "ĝ": 31303, + "六": 31304, + "形": 31305, + "제": 31306, + "Հ": 31307, + "伊": 31308, + "ϵ": 31309, + "ข": 31310, + "Ű": 31311, + "ゃ": 31312, + "火": 31313, + "Ṣ": 31314, + "佐": 31315, + "⊥": 31316, + "̪": 31317, + "ứ": 31318, + "□": 31319, + "结": 31320, + "九": 31321, + "雄": 31322, + "թ": 31323, + "ា": 31324, + "而": 31325, + "བ": 31326, + "우": 31327, + "张": 31328, + "ट": 31329, + "ष": 31330, + "向": 31331, + "ῥ": 31332, + "选": 31333, + "공": 31334, + "ゲ": 31335, + "ʐ": 31336, + "仁": 31337, + "堂": 31338, + "ך": 31339, + "ု": 31340, + "ἔ": 31341, + "അ": 31342, + "ề": 31343, + "ད": 31344, + "선": 31345, + "오": 31346, + "久": 31347, + "œ": 31348, + "义": 31349, + "अ": 31350, + "╔": 31351, + "无": 31352, + "
": 31353, + "은": 31354, + "ʷ": 31355, + "那": 31356, + "線": 31357, + "务": 31358, + "基": 31359, + "属": 31360, + "配": 31361, + "미": 31362, + "軍": 31363, + "โ": 31364, + "津": 31365, + "完": 31366, + "研": 31367, + "注": 31368, + "失": 31369, + "应": 31370, + "က": 31371, + "╚": 31372, + "友": 31373, + "章": 31374, + "Ψ": 31375, + "求": 31376, + "ण": 31377, + "경": 31378, + "‬": 31379, + "भ": 31380, + "们": 31381, + "模": 31382, + "需": 31383, + "ச": 31384, + "電": 31385, + "প": 31386, + "դ": 31387, + "へ": 31388, + "此": 31389, + "夜": 31390, + "或": 31391, + "橋": 31392, + "根": 31393, + "Ī": 31394, + "玉": 31395, + "ู": 31396, + "ṅ": 31397, + "交": 31398, + "品": 31399, + "良": 31400, + "ང": 31401, + "ォ": 31402, + "则": 31403, + "開": 31404, + "Ζ": 31405, + "문": 31406, + "被": 31407, + "조": 31408, + "株": 31409, + "记": 31410, + "會": 31411, + "经": 31412, + "ू": 31413, + "ょ": 31414, + "转": 31415, + "崎": 31416, + "마": 31417, + "⌘": 31418, + "比": 31419, + "造": 31420, + "ܐ": 31421, + "ื": 31422, + "没": 31423, + "现": 31424, + "七": 31425, + "Ά": 31426, + "商": 31427, + "ை": 31428, + "机": 31429, + "阳": 31430, + "ĉ": 31431, + "角": 31432, + "站": 31433, + "բ": 31434, + "해": 31435, + "及": 31436, + "ध": 31437, + "術": 31438, + "认": 31439, + "‘": 31440, + "创": 31441, + "編": 31442, + "ղ": 31443, + "ḩ": 31444, + "伝": 31445, + "岡": 31446, + "ड": 31447, + "ホ": 31448, + "港": 31449, + "任": 31450, + "登": 31451, + "ི": 31452, + "็": 31453, + "布": 31454, + "究": 31455, + "帝": 31456, + "여": 31457, + "산": 31458, + "န": 31459, + "◦": 31460, + "密": 31461, + "变": 31462, + "序": 31463, + "♀": 31464, + "∣": 31465, + "计": 31466, + "曲": 31467, + "Ă": 31468, + "ύ": 31469, + "ʋ": 31470, + "传": 31471, + "】": 31472, + "包": 31473, + "意": 31474, + "去": 31475, + "沙": 31476, + "⸮": 31477, + "【": 31478, + "写": 31479, + "超": 31480, + "ய": 31481, + "今": 31482, + "┈": 31483, + "森": 31484, + "ි": 31485, + "⊗": 31486, + "비": 31487, + "հ": 31488, + "Ḩ": 31489, + "ǫ": 31490, + "黄": 31491, + "∙": 31492, + "드": 31493, + "🌍": 31494, + "景": 31495, 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"pad_token": "<|endoftext|>", + "padding_side": "left", + "sp_model_kwargs": {}, + "tokenizer_class": "LlamaTokenizer", + "unk_token": "", + "use_default_system_prompt": false +} diff --git a/chatbot/models/openai-community/gpt2/config.json b/chatbot/models/openai-community/gpt2/config.json new file mode 100644 index 0000000000000000000000000000000000000000..e96c9d535360db34158598589de006a82030af71 --- /dev/null +++ b/chatbot/models/openai-community/gpt2/config.json @@ -0,0 +1,39 @@ +{ + "_name_or_path": "openai-community/gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 50256, + "embd_pdrop": 0.1, + "eos_token_id": 50256, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 768, + "n_head": 12, + "n_inner": null, + "n_layer": 12, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "torch_dtype": "float32", + "transformers_version": "4.46.2", + "use_cache": true, + "vocab_size": 50257 +} diff --git a/chatbot/models/openai-community/gpt2/generation_config.json b/chatbot/models/openai-community/gpt2/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..42ea62038a77f74f5238e905881cf9a7efecf046 --- /dev/null +++ b/chatbot/models/openai-community/gpt2/generation_config.json @@ -0,0 +1,6 @@ +{ + "_from_model_config": true, + "bos_token_id": 50256, + "eos_token_id": 50256, + "transformers_version": "4.46.2" +} diff --git a/chatbot/models/openai-community/gpt2/merges.txt b/chatbot/models/openai-community/gpt2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..226b0752cac7789c48f0cb3ec53eda48b7be36cc --- /dev/null +++ b/chatbot/models/openai-community/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - +al l +Ġ O +ac k +Ġc h +Ġ le +i es +re d +ar d +â Ģ +ou t +Ġ J +Ġa b +e ar +i v +al ly +ou r +o st +g h +p t +Ġp l +as t +Ġc an +a k +om e +u d +T he +Ġh is +Ġd o +Ġg o +Ġh as +g e +' t +Ġ U +r ou +Ġs a +Ġ j +Ġb ut +Ġw or +Ġa ll +e ct +Ġ k +am e +Ġw ill +o k +Ġw he +Ġthe y +id e +0 1 +f f +ic h +p l +t her +Ġt r +. . +Ġin t +i e +u re +ag e +Ġn e +i al +a p +in e +ic e +Ġm e +Ġo ut +an s +on e +on g +ion s +Ġwh o +Ġ K +Ġu p +Ġthe ir +Ġa d +Ġ 3 +Ġu s +at ed +ou s +Ġm ore +u e +o g +ĠS t +in d +i ke +Ġs o +im e +p er +. " +b er +i z +a ct +Ġon e +Ġsa id +Ġ - +a re +Ġyou r +c c +ĠT h +Ġc l +e p +a ke +ab le +i p +Ġcon t +Ġwh ich +i a +Ġ im +Ġab out +Ġwe re +ver y +u b +Ġh ad +Ġ en +Ġcom p +, " +ĠI n +Ġu n +Ġa g +i re +ac e +a u +ar y +Ġw ould +as s +r y +Ġ âĢ +c l +o ok +e re +s o +Ġ V +ig n +i b +Ġof f +Ġt e +v en +Ġ Y +i le +o se +it e +or m +Ġ2 01 +Ġre s +Ġm an +Ġp er +Ġo ther +or d +ul t +Ġbe en +Ġl ike +as e +an ce +k s +ay s +ow n +en ce +Ġd is +ct ion +Ġan y +Ġa pp +Ġs p +in t +res s +ation s +a il +Ġ 4 +ic al +Ġthe m +Ġhe r +ou nt +ĠC h +Ġa r +Ġ if +Ġthe re +Ġp e +Ġy ear +a v +Ġm y +Ġs ome +Ġwhe n +ou gh +ac h +Ġth an +r u +on d +ic k +Ġo ver +ve l +Ġ qu +Ċ Ċ +Ġs c +re at +re e +ĠI t +ou nd +p ort +Ġal so +Ġp art +f ter +Ġk n +Ġbe c +Ġt ime +en s +Ġ 5 +op le +Ġwh at +Ġn o +d u +m er +an g +Ġn ew +-- -- +Ġg et +or y +it ion +ing s +Ġj ust +Ġint o +Ġ 0 +ent s +o ve +t e +Ġpe ople +Ġp re +Ġit s +Ġre c +Ġt w +i an +ir st +ar k +or s +Ġwor k +ad e +o b +Ġs he +Ġo ur +w n +in k +l ic +Ġ1 9 +ĠH e +is 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"Ġtablets": 17255, + "include": 17256, + "Range": 17257, + "Ġcaut": 17258, + "Ġlogs": 17259, + "Ġmounting": 17260, + "Ġunaware": 17261, + "Ġdynamics": 17262, + "ĠPalestine": 17263, + "ĠQuarter": 17264, + "ĠPurple": 17265, + "Ġma": 17266, + "ĠImport": 17267, + "Ġcollections": 17268, + "ciation": 17269, + "Ġsuccessor": 17270, + "Ġclone": 17271, + "Ġaiming": 17272, + "Ġpossessed": 17273, + "Ġsticking": 17274, + "Ġshaking": 17275, + "Ġlocate": 17276, + "ĠHockey": 17277, + "Turn": 17278, + "170": 17279, + "Ġfifteen": 17280, + "ĠHarrison": 17281, + "Ġcontinuously": 17282, + "ĠTC": 17283, + "ĠValent": 17284, + "ĠRescue": 17285, + "Ġbypass": 17286, + "amount": 17287, + "Ġmast": 17288, + "Ġprotects": 17289, + "Ġartistic": 17290, + "Ġsometime": 17291, + "Ġshoe": 17292, + "Ġshouted": 17293, + "ificant": 17294, + "etitive": 17295, + "ĠRegister": 17296, + "ĠJin": 17297, + "Ġconcentrated": 17298, + "lington": 17299, + "onies": 17300, + "Ġgenerator": 17301, + "yrim": 17302, + "ĠArmen": 17303, + "Ġclearing": 17304, + "ido": 17305, + "ĠTW": 17306, + "alph": 17307, + "Ġladies": 17308, + "Hard": 17309, + "Ġdialog": 17310, + "Ġinputs": 17311, + "æľ": 17312, + "Ġposes": 17313, + "Ġslots": 17314, + "ĠPremium": 17315, + "Ġleaks": 17316, + "Ġbosses": 17317, + "Ġ113": 17318, + "course": 17319, + "Acc": 17320, + "ĠNewton": 17321, + "ĠAustria": 17322, + "ĠMage": 17323, + "Ġteaches": 17324, + "abad": 17325, + "Ġwears": 17326, + "Ġcyl": 17327, + "Ġcurse": 17328, + "ĠSales": 17329, + "ĠWings": 17330, + "Ġpsy": 17331, + "Ġgaps": 17332, + "ĠIceland": 17333, + "ĠPinterest": 17334, + "Ġlandlord": 17335, + "Ġdefinitions": 17336, + "ĠKer": 17337, + "Ġsufficiently": 17338, + "ĠPence": 17339, + "ĠArchitect": 17340, + "Ġsurpass": 17341, + "Ġ114": 17342, + "Ġsuperhero": 17343, + "ĠDisease": 17344, + "Ġpriests": 17345, + "ĠCulture": 17346, + "Ġdefinitive": 17347, + "Ġsecretly": 17348, + "ĠDance": 17349, + "install": 17350, + "chief": 17351, + "ĠJessica": 17352, + "Would": 17353, + "Updated": 17354, + "Ġlocker": 17355, + "ĠKay": 17356, + "Ġmemorial": 17357, + "è¦": 17358, + "fat": 17359, + "Ġdisgu": 17360, + "Ġflavors": 17361, + "ĠBaseball": 17362, + "ĠResistance": 17363, + "Ġkicks": 17364, + "Ġenv": 17365, + "Ġteenagers": 17366, + "Dark": 17367, + "ĠCAR": 17368, + "Ġhalt": 17369, + "ĠLG": 17370, + "ĠGabriel": 17371, + "Ġfever": 17372, + "Ġsatur": 17373, + "Ġmall": 17374, + "Ġaffiliate": 17375, + "ĠSleep": 17376, + "ĠSpecific": 17377, + "ĠVel": 17378, + "Ġjar": 17379, + "ĠSacred": 17380, + "ĠEdwards": 17381, + "ĠACL": 17382, + "Ġretained": 17383, + "ĠGiant": 17384, + "Ġlimitation": 17385, + "inces": 17386, + "Ġrefusal": 17387, + "ĠTale": 17388, + "ĠButler": 17389, + "Ġaccidents": 17390, + "ĠCSS": 17391, + "Ġimported": 17392, + "ĠCopy": 17393, + "α": 17394, + "ERT": 17395, + "zel": 17396, + "Ġdivisions": 17397, + "hots": 17398, + "ĠAlb": 17399, + "ĠDS": 17400, + "Loader": 17401, + "Washington": 17402, + "atisf": 17403, + "ĠCreative": 17404, + "\\.": 17405, + "ĠAutom": 17406, + "redict": 17407, + "Ġreceptor": 17408, + "ĠCarlos": 17409, + "Method": 17410, + "oka": 17411, + "Ġmalicious": 17412, + "Ġstepping": 17413, + ",[": 17414, + "ĠDad": 17415, + "Ġattraction": 17416, + "ĠEffects": 17417, + "ĠPirate": 17418, + "ĠCer": 17419, + "ĠIndustry": 17420, + "ĠRud": 17421, + "Ġcharter": 17422, + "Ġdining": 17423, + "Ġinsists": 17424, + "Ġconfigure": 17425, + "Ġ(#": 17426, + "ĠSimple": 17427, + "ĠScroll": 17428, + "UTC": 17429, + "175": 17430, + "ĠKon": 17431, + "Ġmarketplace": 17432, + "ĠãĤ": 17433, + "Ġrefres": 17434, + "Ġgates": 17435, + "erred": 17436, + "ĠPod": 17437, + "Ġbehave": 17438, + "Frank": 17439, + "node": 17440, + "Ġendorsed": 17441, + "hett": 17442, + "asive": 17443, + "ĠHomeland": 17444, + "Ġrides": 17445, + "ĠLeave": 17446, + "erness": 17447, + "Ġflooding": 17448, + "AFP": 17449, + "Ġrisen": 17450, + "Ġcontinually": 17451, + "Ġunanim": 17452, + "ĠContract": 17453, + "ĠPas": 17454, + "Ġguided": 17455, + "ĠChile": 17456, + "bd": 17457, + "Ġsucc": 17458, 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17510, + "GH": 17511, + "Ġmanipulation": 17512, + "BY": 17513, + "Ġspear": 17514, + "Ġbreasts": 17515, + "Ġvolcan": 17516, + "ilia": 17517, + "Material": 17518, + "Ġformats": 17519, + "ĠBast": 17520, + "Ġparliamentary": 17521, + "Ġsnake": 17522, + "Ġservants": 17523, + "ĠTrudeau": 17524, + "ĠGrim": 17525, + "ĠArabic": 17526, + "ĠSCP": 17527, + "ĠBoys": 17528, + "station": 17529, + "Ġprospective": 17530, + "orde": 17531, + "initialized": 17532, + "Ġbored": 17533, + "ABLE": 17534, + "Ġaccessed": 17535, + "Ġtaxi": 17536, + "ĠShell": 17537, + "aiden": 17538, + "ursed": 17539, + "inates": 17540, + "ĠInsurance": 17541, + "ĠPete": 17542, + "September": 17543, + "650": 17544, + "Ġadventures": 17545, + "ĠCover": 17546, + "Ġtribute": 17547, + "Ġsketch": 17548, + "Ġempower": 17549, + "ĠØ": 17550, + "ĠGlenn": 17551, + "ĠDaw": 17552, + "=\\\"": 17553, + "ĠPolitics": 17554, + "Ġguides": 17555, + "Ġdioxide": 17556, + "ĠGore": 17557, + "ĠBright": 17558, + "ĠSierra": 17559, + "Ġvalued": 17560, + "cond": 17561, + "Ġpointer": 17562, + "Select": 17563, + "Ġrisky": 17564, + "Ġabsorb": 17565, + "images": 17566, + "Ġrefuses": 17567, + "Ġbonuses": 17568, + "___": 17569, + "Ġhilar": 17570, + "ĠFeatures": 17571, + "220": 17572, + "ĠCollector": 17573, + "Foot": 17574, + "Ġ1964": 17575, + "culus": 17576, + "Ġdawn": 17577, + "Ġworkout": 17578, + "ĠLO": 17579, + "Ġphilosophical": 17580, + "ĠSandy": 17581, + "ĠYouth": 17582, + "Ġliable": 17583, + "Af": 17584, + "blue": 17585, + "Ġoverturn": 17586, + "lessness": 17587, + "ĠTribune": 17588, + "ĠIng": 17589, + "Ġfactories": 17590, + "Ġcatches": 17591, + "Ġprone": 17592, + "Ġmatrix": 17593, + "Ġlogin": 17594, + "Ġinacc": 17595, + "Ġexert": 17596, + "sys": 17597, + "Ġneedle": 17598, + "ĠQur": 17599, + "Ġnotified": 17600, + "oulder": 17601, + "tx": 17602, + "Ġreminds": 17603, + "Ġpublishers": 17604, + "Ġnort": 17605, + "Ġgit": 17606, + "Ġflies": 17607, + "ĠEmily": 17608, + "Ġflowing": 17609, + "ĠAlien": 17610, + "ĠStrateg": 17611, + "Ġhardest": 17612, + "Ġmodification": 17613, + "API": 17614, + "ĠMY": 17615, + "Ġcrashes": 17616, + "stairs": 17617, + "number": 17618, + "Ġurging": 17619, + "channel": 17620, + "ĠFalcon": 17621, + "Ġinhabitants": 17622, + "Ġterrifying": 17623, + "Ġutilize": 17624, + "Ġbanner": 17625, + "Ġcigarettes": 17626, + "Ġsenses": 17627, + "ĠHolmes": 17628, + "Ġpractition": 17629, + "ĠPhillips": 17630, + "otto": 17631, + "Ġcompile": 17632, + "Model": 17633, + "ĠKo": 17634, + "Ġ[]": 17635, + "Americans": 17636, + "ĠTerms": 17637, + "Ġmedications": 17638, + "ĠAna": 17639, + "Ġfundamentally": 17640, + "ĠNotice": 17641, + "Ġweaker": 17642, + "Ġ0000": 17643, + "Ġgarlic": 17644, + "Ġoutbreak": 17645, + "Ġeconomist": 17646, + "ĠBirth": 17647, + "Ġobstacles": 17648, + "arcer": 17649, + "ĠOrthodox": 17650, + "Ġplacebo": 17651, + "ĠCrew": 17652, + "aspberry": 17653, + "ĠAngels": 17654, + "Ġdischarge": 17655, + "Ġdestructive": 17656, + "117": 17657, + "ĠRising": 17658, + "Ġdairy": 17659, + "late": 17660, + "Ġcollision": 17661, + "ĠTigers": 17662, + "eanor": 17663, + "ocumented": 17664, + "ĠInvalid": 17665, + "Ġdont": 17666, + "ĠLiter": 17667, + "ĠVa": 17668, + "Ġhydrogen": 17669, + "Ġvariants": 17670, + "ĠBrowns": 17671, + "Ġ1965": 17672, + "Ġindigenous": 17673, + "Ġtrades": 17674, + "Ġremainder": 17675, + "Ġswept": 17676, + "ĠImpact": 17677, + "Ġredist": 17678, + "Ġunint": 17679, + "graduate": 17680, + "ãĥķ": 17681, + "ĠWILL": 17682, + "ãģ®ç": 17683, + "ĠCritical": 17684, + "Ġfisher": 17685, + "Ġvicious": 17686, + "Ġreversed": 17687, + "Year": 17688, + "ĠSox": 17689, + "Ġshootings": 17690, + "Ġfilming": 17691, + "Ġtouchdowns": 17692, + "aires": 17693, + "mel": 17694, + "Ġgrandfather": 17695, + "Ġaffection": 17696, + "ingle": 17697, + "Ġoverly": 17698, + "Additional": 17699, + "Ġsupreme": 17700, + "ĠGrad": 17701, + "Ġsporting": 17702, + "Ġmercy": 17703, + "ĠBrooks": 17704, + "ounty": 17705, + "Ġperforms": 17706, + "Ġtightly": 17707, + "Ġdemons": 17708, + "Ġkillings": 17709, + "Ġfaction": 17710, + "ĠNova": 17711, + "auts": 17712, + "Ġundoubtedly": 17713, + "arin": 17714, + "Ġunderway": 17715, + "rak": 17716, + "Ġliv": 17717, + "ĠRegion": 17718, + "Ġbriefing": 17719, + "sers": 17720, + "cloud": 17721, + "ĠMik": 17722, + "usp": 17723, + "Ġprediction": 17724, + "azor": 17725, + "Ġportable": 17726, + "ĠGand": 17727, + "Ġpresenting": 17728, + "Ġ1080": 17729, + "»": 17730, + "ushi": 17731, + "ĠSpark": 17732, + "thereum": 17733, + "Ġjustification": 17734, + "ĠNy": 17735, + "Ġcontractors": 17736, + "mingham": 17737, + "ĠStyle": 17738, + "åħ": 17739, + "ĠChronicles": 17740, + "ĠPicture": 17741, + "Ġproving": 17742, + "Ġwives": 17743, + "sett": 17744, + "Ġmolecules": 17745, + "ĠFairy": 17746, + "Ġconsisting": 17747, + "Ġpier": 17748, + "alone": 17749, + "inition": 17750, + "Ġnucle": 17751, + "json": 17752, + "Ġgotta": 17753, + "Ġmobil": 17754, + "Ġverbal": 17755, + "arium": 17756, + "Ġmonument": 17757, + "ucked": 17758, + "Ġ256": 17759, + "Tech": 17760, + "minecraft": 17761, + "ĠTrack": 17762, + "Ġtile": 17763, + "Ġcompatibility": 17764, + "asis": 17765, + "Ġsadd": 17766, + "Ġinstructed": 17767, + "ĠMueller": 17768, + "Ġlethal": 17769, + "Ġhormone": 17770, + "Ġorche": 17771, + "else": 17772, + "Ġskelet": 17773, + "Ġentertaining": 17774, + "Ġminimize": 17775, + "again": 17776, + "Ġundergo": 17777, + "Ġconstraints": 17778, + "Ġcigarette": 17779, + "ĠIslamist": 17780, + "Ġtravels": 17781, + "ĠPanthers": 17782, + "lings": 17783, + "Care": 17784, + "Ġlawsuits": 17785, + "uras": 17786, + "Ġcryst": 17787, + "Ġlowered": 17788, + "Ġaerial": 17789, + "Ġcombinations": 17790, + "Ġhaun": 17791, + "Ġcha": 17792, + "Ġvine": 17793, + "Ġquantities": 17794, + "Ġlinking": 17795, + "bank": 17796, + "Ġsoy": 17797, + "Bill": 17798, + "ĠAngela": 17799, + "Ġrecipient": 17800, + "ĠProtest": 17801, + "Ġsocket": 17802, + "Ġsolidarity": 17803, + "ĠâĨ": 17804, + "mill": 17805, + "Ġvaries": 17806, + "ĠPakistani": 17807, + "Dragon": 17808, + "Ġune": 17809, + "Ġhorizon": 17810, + "³³³³³³³³": 17811, + "Ġprovinces": 17812, + "Ġfrankly": 17813, + "Ġenacted": 17814, + "notes": 17815, + "['": 17816, + "Ġ192": 17817, + "ocracy": 17818, + "Ġendorsement": 17819, + "Ġovertime": 17820, + "True": 17821, + "Lab": 17822, + "licted": 17823, + "ĠDNC": 17824, + "Ġbeats": 17825, + "ĠJamie": 17826, + "152": 17827, + "ĠINT": 17828, + "Contact": 17829, + "Ġaccounted": 17830, + "hash": 17831, + "ĠPackers": 17832, + "pires": 17833, + "Ġlesbian": 17834, + "Ġamendments": 17835, + "Ġhopeful": 17836, + "ĠFinland": 17837, + "Ġspotlight": 17838, + "Ġconfigured": 17839, + "Ġtroubled": 17840, + "Ġgaze": 17841, + "ĠCalgary": 17842, + "Ġreliability": 17843, + "Ġinsurg": 17844, + "swer": 17845, + "buy": 17846, + "ĠSkin": 17847, + "Ġpixels": 17848, + "Ġhandgun": 17849, + "Ġparas": 17850, + "Ġcategor": 17851, + "ĠEL": 17852, + "ĠRex": 17853, + "Indeed": 17854, + "Ġkinda": 17855, + "Ġconjunction": 17856, + "ĠBryan": 17857, + "ĠManufact": 17858, + "yang": 17859, + "Plus": 17860, + "SQL": 17861, + "ishment": 17862, + "Ġdominate": 17863, + "Ġnail": 17864, + "Ġoath": 17865, + "Ġerupt": 17866, + "ĠFine": 17867, + "itbart": 17868, + "ĠChip": 17869, + "ĠAbd": 17870, + "ĠNam": 17871, + "Ġbuyer": 17872, + "Ġdissent": 17873, + "Leaks": 17874, + "Contin": 17875, + "Ġrider": 17876, + "ĠSomeone": 17877, + "Ġillusion": 17878, + "cin": 17879, + "ĠBoeing": 17880, + "Ġinadequ": 17881, + "ovation": 17882, + "iants": 17883, + "Ġrebuild": 17884, + "450": 17885, + "ĠDestiny": 17886, + "SW": 17887, + "ĠTill": 17888, + "Hit": 17889, + "iaz": 17890, + "ĠBangl": 17891, + "achers": 17892, + "ĠReform": 17893, + "Ġsegments": 17894, + "Ġsystematic": 17895, + "dc": 17896, + "ĠConservatives": 17897, + "Ġportal": 17898, + "hor": 17899, + "ĠDragonbound": 17900, + "Ġdragged": 17901, + "omo": 17902, + "Ġthee": 17903, + "advert": 17904, + "ĠReports": 17905, + "ĠEt": 17906, + "Ġbarrels": 17907, + "August": 17908, + "Ġcomparisons": 17909, + "Ġhex": 17910, + "Ġanthrop": 17911, + "\"[": 17912, + "borough": 17913, + "abi": 17914, + "Ġpictured": 17915, + "playing": 17916, + "ĠAddress": 17917, + "ĠMirror": 17918, + "Smith": 17919, + "Ġtires": 17920, + "ĠNPR": 17921, + "AAAA": 17922, + "Ġclassification": 17923, + "ĠThan": 17924, + "ĠHarm": 17925, + "ĠRA": 17926, + "Ġrejection": 17927, + "mination": 17928, + "Ġranged": 17929, + "ĠFalls": 17930, + "DI": 17931, + "Host": 17932, + "ãĤ´": 17933, + "ĠExample": 17934, + "listed": 17935, + "thirds": 17936, + "Ġsafegu": 17937, + "brand": 17938, + "Ġprobable": 17939, + "Canada": 17940, + "ITION": 17941, + "ĠQaeda": 17942, + "Ġchick": 17943, + "Ġimports": 17944, + "hit": 17945, + "loc": 17946, + "WW": 17947, + "Ġblew": 17948, + "Ġanytime": 17949, + "Ġwholes": 17950, + "iked": 17951, + "Ġcalculation": 17952, + "create": 17953, + "ĠOri": 17954, + "Ġupgraded": 17955, + "Ġappar": 17956, + "utory": 17957, + "ĠMol": 17958, + "Brit": 17959, + "ĠJong": 17960, + "INAL": 17961, + "ĠStarting": 17962, + "Ġdice": 17963, + "urtle": 17964, + "Ġrelying": 17965, + "closure": 17966, + "Ġprofitable": 17967, + "Ġslaughter": 17968, + "ĠManual": 17969, + "caster": 17970, + "Ġ\"$": 17971, + "Ġfeather": 17972, + "ĠSimply": 17973, + "ieves": 17974, + "Ġdeterior": 17975, + "ĠPCI": 17976, + "Ġstamp": 17977, + "Ġflaws": 17978, + "Ġshade": 17979, + "hammer": 17980, + "Ġpassport": 17981, + "Ġconting": 17982, + "amel": 17983, + "Ġobservers": 17984, + "Ġneglect": 17985, + "ĠRB": 17986, + "ĠBrotherhood": 17987, + "Ġskeptical": 17988, + "family": 17989, + "usk": 17990, + "Ġemotionally": 17991, + "âĻ": 17992, + "ĠBeta": 17993, + "asonable": 17994, + "idity": 17995, + "ĠMul": 17996, + "Ġkicking": 17997, + "ĠCarm": 17998, + "ollah": 17999, + "VERTIS": 18000, + "ĠAthen": 18001, + "Ġladder": 18002, + "ĠBullet": 18003, + "å£": 18004, + "0001": 18005, + "ĠWildlife": 18006, + "ĠMask": 18007, + "ĠNan": 18008, + "Rev": 18009, + "Ġunacceptable": 18010, + "legal": 18011, + "Ġcrowded": 18012, + "agi": 18013, + "ĠCox": 18014, + "je": 18015, + "Ġmorality": 18016, + "Ġfuels": 18017, + "Ġcables": 18018, + "Ġmankind": 18019, + "ĠCaribbean": 18020, + "Ġanchor": 18021, + "Ġbyte": 18022, + "ĠOften": 18023, + "ĠOz": 18024, + "Ġcrafted": 18025, + "Ġhistorian": 18026, + "ĠWu": 18027, + "Ġtowers": 18028, + "ĠCitizens": 18029, + "Ġhelm": 18030, + "Ġcredentials": 18031, + "Ġsingular": 18032, + "ĠJesse": 18033, + "Ġtackles": 18034, + "Ġcontempt": 18035, + "Ġafore": 18036, + "ĠShadows": 18037, + "Ġnil": 18038, + "Ġurgent": 18039, + "apple": 18040, + "blood": 18041, + "Ġvon": 18042, + "Ġoffline": 18043, + "Ġbreathe": 18044, + "Ġjumps": 18045, + "Ġirrelevant": 18046, + "oxic": 18047, + "omal": 18048, + "important": 18049, + "Jim": 18050, + "Ġgloves": 18051, + "arming": 18052, + "depth": 18053, + "Ġtalents": 18054, + "ookie": 18055, + "ĠSB": 18056, + "Ġpalm": 18057, + "uffs": 18058, + "esta": 18059, + "IGH": 18060, + "Ġcanon": 18061, + "ĠVerizon": 18062, + "ĠPle": 18063, + "Ġcoupled": 18064, + "velt": 18065, + "Ġfundraising": 18066, + "ĠGetting": 18067, + "ĠDLC": 18068, + "Ġmathematical": 18069, + "ĠHS": 18070, + "ĠCardinals": 18071, + "telling": 18072, + "Ġsponsors": 18073, + "ĠÏ": 18074, + "ĠBulls": 18075, + "option": 18076, + "Ġpropose": 18077, + "Ġmemorable": 18078, + "Ġembraced": 18079, + "Ġdeclining": 18080, + "Health": 18081, + "eda": 18082, + "Ġ};": 18083, + "Ġspam": 18084, + "mile": 18085, + "Ġpitcher": 18086, + "ĠEight": 18087, + "Ġcaring": 18088, + "utic": 18089, + "role": 18090, + "Ġairline": 18091, + "ernandez": 18092, + "ĠAthlet": 18093, + "Ġcertification": 18094, + "uxe": 18095, + "riger": 18096, + "Ġempir": 18097, + "Ġsensation": 18098, + "Ġdism": 18099, + "Ġbolt": 18100, + "Ġevolve": 18101, + "House": 18102, + "Ġconsultation": 18103, + "ĠDuty": 18104, + "Ġtouches": 18105, + "ĠNathan": 18106, + "Ġfaint": 18107, + "had": 18108, + "\"(": 18109, + "ĠConsumer": 18110, + "ĠExtreme": 18111, + "Ġ127": 18112, + "ĠHerm": 18113, + "ĠSacrament": 18114, + "izoph": 18115, + "Ġanxious": 18116, + "ulously": 18117, + "Ġsocially": 18118, + "ĠUTC": 18119, + "Ġsolving": 18120, + "ĠLetter": 18121, + "History": 18122, + "educ": 18123, + "Price": 18124, + "));": 18125, + "Ġreload": 18126, + "amic": 18127, + "Ġpork": 18128, + "Ġdiscourse": 18129, + "Ġtournaments": 18130, + "airo": 18131, + "ĠKur": 18132, + "ĠCosta": 18133, + "Ġviolating": 18134, + "Ġinterfere": 18135, + "Ġrecreational": 18136, + "uffle": 18137, + "Ġspeeches": 18138, + "Ġneeding": 18139, + "Ġremembers": 18140, + "Ġcredited": 18141, + "nia": 18142, + "focused": 18143, + "amera": 18144, + "Ġbru": 18145, + "umbs": 18146, + "ĠCuban": 18147, + "Ġpreceding": 18148, + "Ġnonsense": 18149, + "acial": 18150, + "Ġsmartphones": 18151, + "ĠStories": 18152, + "Sports": 18153, + "ĠEmergency": 18154, + "ouncing": 18155, + "efined": 18156, + "Ġber": 18157, + "Ġconsulting": 18158, + "Ġmasters": 18159, + "heastern": 18160, + ".\"[": 18161, + "ĠRunning": 18162, + "Ġsuscept": 18163, + "ĠFeng": 18164, + "America": 18165, + "prises": 18166, + "stitial": 18167, + "ĠWeekly": 18168, + "ĠGreater": 18169, + "modules": 18170, + "ifter": 18171, + "Graphics": 18172, + "uler": 18173, + "Ġwholly": 18174, + "Ġsuppress": 18175, + "Ġconcealed": 18176, + "Ġhappily": 18177, + "Ġaccepts": 18178, + "ĠEnjoy": 18179, + "Ġrivers": 18180, + "ĠExcept": 18181, + "225": 18182, + "ĠNHS": 18183, + "ĠMcConnell": 18184, + "Ġpussy": 18185, + "ferred": 18186, + "utable": 18187, + "Ġattain": 18188, + "Ġ>=": 18189, + "Ġdeposits": 18190, + "rophic": 18191, + "Ġnotorious": 18192, + "ĠShaw": 18193, + "ilitation": 18194, + "Ġepidemic": 18195, + "allic": 18196, + "Ġsmallest": 18197, + "ovich": 18198, + "Ġaccessories": 18199, + "perties": 18200, + "Ġsurplus": 18201, + "ĠMech": 18202, + "Ġambig": 18203, + "ĠImmigration": 18204, + "Ġchim": 18205, + "eval": 18206, + "Ġpracticing": 18207, + "ĠMystery": 18208, + "Ġdomains": 18209, + "ĠSilicon": 18210, + "apps": 18211, + "Ġkilometers": 18212, + "ea": 18213, + "ĠSmash": 18214, + "Ġwarranty": 18215, + "Ġnost": 18216, + "sil": 18217, + "rev": 18218, + "Jon": 18219, + "ĠDublin": 18220, + "Ġtastes": 18221, + "Ġbout": 18222, + "great": 18223, + "error": 18224, + "Ġswitches": 18225, + "ĠBapt": 18226, + "DO": 18227, + "oki": 18228, + "Ġsourced": 18229, + "produ": 18230, + "Ġattachment": 18231, + "ĠIssue": 18232, + "ĠQuestion": 18233, + "Join": 18234, + "Ġfitted": 18235, + "Ġunlawful": 18236, + "^^": 18237, + "erek": 18238, + "Ġauthentication": 18239, + "Ġstole": 18240, + "Ġaccountability": 18241, + "label": 18242, + "Search": 18243, + "Ġalbeit": 18244, + "atican": 18245, + "funded": 18246, + "ĠAdding": 18247, + "ĠIQ": 18248, + "Ġsubmar": 18249, + "lit": 18250, + "aque": 18251, + "ĠLearning": 18252, + "Ġinteger": 18253, + "Master": 18254, + "ĠChrom": 18255, + "Ġpremier": 18256, + "Op": 18257, + "ĠLiu": 18258, + "Ġblessed": 18259, + "ĠGlobe": 18260, + "ĠResponse": 18261, + "Ġlegitim": 18262, + "ĠMerkel": 18263, + "Ġdisposal": 18264, + "´": 18265, + "Ġgauge": 18266, + "peat": 18267, + "Ġinduced": 18268, + "Ġquestionable": 18269, + "arthy": 18270, + "ĠVit": 18271, + "ĠFeed": 18272, + "Until": 18273, + "Ut": 18274, + "worthy": 18275, + "RY": 18276, + "ĠHerald": 18277, + "ĠHammer": 18278, + "Ġmedal": 18279, + "ĠRivers": 18280, + "ĠHack": 18281, + "Ġclarify": 18282, + "Ġtracked": 18283, + "Ġautonomous": 18284, + "Ġtenant": 18285, + "ĠQatar": 18286, + "erie": 18287, + "Ġgrim": 18288, + "ĠMonitor": 18289, + "Ġresistant": 18290, + "ĠSpec": 18291, + "ĠWells": 18292, + "NAS": 18293, + "148": 18294, + "Ġminers": 18295, + "iotics": 18296, + "Ġmisses": 18297, + "116": 18298, + "gian": 18299, + "git": 18300, + "ĠEyes": 18301, + "pres": 18302, + "Ġgraduated": 18303, + "Ġangel": 18304, + "Ġsynchron": 18305, + "Ġefficiently": 18306, + "Ġtransmitted": 18307, + "Harry": 18308, + "Ġglobally": 18309, + "ENCE": 18310, + "ĠMontana": 18311, + "raged": 18312, + "ĠPrevention": 18313, + "Ġpiss": 18314, + "ĠLl": 18315, + "Ġshelf": 18316, + "ĠBJP": 18317, + "ĠTestament": 18318, + "ĠLate": 18319, + "iker": 18320, + "ĠHapp": 18321, + "ĠJulian": 18322, + "hall": 18323, + "Ġspont": 18324, + "Ġshutdown": 18325, + "Ġinconsistent": 18326, + "Ġsubscribers": 18327, + "Ġskeleton": 18328, + "ĠNebraska": 18329, + "Ġinspire": 18330, + "ĠVoid": 18331, + "Feed": 18332, + "Ġangles": 18333, + "ĠSprings": 18334, + "Ġbenchmark": 18335, + "Ġvaccines": 18336, + "izophren": 18337, + "sexual": 18338, + "uffed": 18339, + "Ġshine": 18340, + "ĠKath": 18341, + "Ġgesture": 18342, + "inea": 18343, + "Ġrip": 18344, + "Ġoppression": 18345, + "Ġconscience": 18346, + "bt": 18347, + "ĠLum": 18348, + "Ġincidence": 18349, + "ĠFa": 18350, + "wr": 18351, + "Ġmineral": 18352, + "ĠSpurs": 18353, + "alky": 18354, + "Ġthunder": 18355, + "Ġopio": 18356, + "Being": 18357, + "ĠPalm": 18358, + "Ġwasted": 18359, + "Ġlb": 18360, + "iaries": 18361, + "ĠInitiative": 18362, + "Ġcurric": 18363, + "Ġmarker": 18364, + "ĠMcL": 18365, + "Ġextensions": 18366, + "ĠPv": 18367, + "ĠArms": 18368, + "Ġofferings": 18369, + "Ġdefenses": 18370, + "Ġvendor": 18371, + "Ġcontradict": 18372, + "ĠColin": 18373, + "Ġreddit": 18374, + "Ġperipher": 18375, + "122": 18376, + "Ġsins": 18377, + "Edit": 18378, + "ICT": 18379, + "Soft": 18380, + "ĠShah": 18381, + "Ġadministrator": 18382, + "ĠTrip": 18383, + "Ġpornography": 18384, + "Ġtuition": 18385, + "inence": 18386, + "ĠProgress": 18387, + "Ġcatalog": 18388, + "Ġsuite": 18389, + "Ġhike": 18390, + "Ġreproductive": 18391, + "engine": 18392, + "Ġdrought": 18393, + "ĠNoah": 18394, + "Ġ230": 18395, + "Ġdude": 18396, + "Ġrelaxed": 18397, + "Ġpartition": 18398, + "Ġparticipant": 18399, + "Ġtelesc": 18400, + "Ġfeas": 18401, + "ĠFF": 18402, + "owner": 18403, + "Ġsweeping": 18404, + "Ġlenses": 18405, + "Ġmatchup": 18406, + "ĠRepl": 18407, + "ournals": 18408, + "Ġcredible": 18409, + "Ġgrandmother": 18410, + "Ġthermal": 18411, + "Ġsubscribing": 18412, + "Ġidentities": 18413, + "colm": 18414, + "UCT": 18415, + "Ġreluctant": 18416, + "users": 18417, + "ĠCort": 18418, + "Ġassisted": 18419, + "OSS": 18420, + "ATIONS": 18421, + "ISH": 18422, + "Ġpharmaceutical": 18423, + "icable": 18424, + "adian": 18425, + "ĠSonic": 18426, + "ĠFury": 18427, + "ĠMong": 18428, + "AH": 18429, + "ĠPsychology": 18430, + "Ġphosph": 18431, + "Ġtreats": 18432, + "ŃĶ": 18433, + "Ġsteadily": 18434, + "ĠHello": 18435, + "Ġrelates": 18436, + "Ġclue": 18437, + "Expl": 18438, + "auth": 18439, + "Ġrevision": 18440, + "Ġeld": 18441, + "osion": 18442, + "Ġbron": 18443, + "144": 18444, + "rikes": 18445, + "Ġmines": 18446, + "Ġblanket": 18447, + "ĠFail": 18448, + "eled": 18449, + "ĠImagine": 18450, + "ĠPlanned": 18451, + "aic": 18452, + "Request": 18453, + "Mad": 18454, + "ĠHorse": 18455, + "ĠEagle": 18456, + "Ġcapac": 18457, + "157": 18458, + "Ġling": 18459, + "ĠNice": 18460, + "ĠParenthood": 18461, + "minster": 18462, + "ogs": 18463, + "ensitive": 18464, + "Nothing": 18465, + "Ġcarn": 18466, + "Fin": 18467, + "ĠPE": 18468, + "Ġrifles": 18469, + "ĠLP": 18470, + "Sand": 18471, + "ĠguiActive": 18472, + "Ġtourist": 18473, + "CNN": 18474, + "Ġunveiled": 18475, + "Ġpredecessor": 18476, + "}{": 18477, + "uber": 18478, + "Ġoffshore": 18479, + "Ġoptical": 18480, + "ĠRot": 18481, + "ĠPearl": 18482, + "eton": 18483, + "Ġstared": 18484, + "Ġfarther": 18485, + "atility": 18486, + "contin": 18487, + "ĠGy": 18488, + "ĠFoster": 18489, + "ĠCoc": 18490, + "rients": 18491, + "Ġdesigning": 18492, + "ĠEconomy": 18493, + "ONG": 18494, + "Women": 18495, + "ĠNancy": 18496, + "erver": 18497, + "Ġmascul": 18498, + "Ġcasualties": 18499, + "Ġ225": 18500, + "ĠSullivan": 18501, + "ĠChoice": 18502, + "Ġaster": 18503, + "ws": 18504, + "Ġhotels": 18505, + "Ġconsiderations": 18506, + "Ġcouch": 18507, + "ĠStrip": 18508, + "ĠGn": 18509, + "Ġmanipulate": 18510, + "lied": 18511, + "Ġsynthetic": 18512, + "Ġassaulted": 18513, + "Ġoffenses": 18514, + "ĠDrake": 18515, + "Ġimpe": 18516, + "October": 18517, + "ĠHeritage": 18518, + "hl": 18519, + "ĠBlair": 18520, + "Unlike": 18521, + "Ġgrief": 18522, + "Ġ450": 18523, + "Ġopted": 18524, + "Ġresignation": 18525, + "ilo": 18526, + "Ġverse": 18527, + "ĠTomb": 18528, + "Ġupt": 18529, + "Ġaired": 18530, + "ĠHook": 18531, + "ĠMLB": 18532, + "Ġassumes": 18533, + "outed": 18534, + "ĠVers": 18535, + "Ġinferior": 18536, + "Ġbundle": 18537, + "ĠDNS": 18538, + "ographer": 18539, + "Ġmultip": 18540, + "ĠSouls": 18541, + "Ġillustrated": 18542, + "Ġtactic": 18543, + "Ġdressing": 18544, + "Ġduo": 18545, + "Conf": 18546, + "Ġrelent": 18547, + "Ġcant": 18548, + "Ġscarce": 18549, + "Ġcandy": 18550, + "ĠCF": 18551, + "Ġaffiliated": 18552, + "Ġsprint": 18553, + "ylan": 18554, + "ĠGarcia": 18555, + "Ġjunk": 18556, + "Print": 18557, + "exec": 18558, + "Crit": 18559, + "Ġportrait": 18560, + "iries": 18561, + "ĠOFF": 18562, + "Ġdisputes": 18563, + "WR": 18564, + "Love": 18565, + "ãģĦ": 18566, + "ĠReyn": 18567, + "Ġhipp": 18568, + "opath": 18569, + "Ġfloors": 18570, + "ĠFeel": 18571, + "Ġworries": 18572, + "Ġsettlements": 18573, + "ĠPos": 18574, + "Ġmosque": 18575, + "Ġfinals": 18576, + "Ġcrushed": 18577, + "ĠProbably": 18578, + "ĠBot": 18579, + "ĠMans": 18580, + "ĠPeriod": 18581, + "Ġsovereignty": 18582, + "Ġseller": 18583, + "Ġapost": 18584, + "Ġamateur": 18585, + "Ġdorm": 18586, + "Ġconsuming": 18587, + "Ġarmour": 18588, + "ĠRoose": 18589, + "Ġintensive": 18590, + "Ġeliminating": 18591, + "ĠSunni": 18592, + "ĠAleppo": 18593, + "jin": 18594, + "Ġadvise": 18595, + "pal": 18596, + "ĠHalo": 18597, + "Ġdescent": 18598, + "Ġsimpler": 18599, + "Ġbooth": 18600, + "STR": 18601, + "Later": 18602, + "ĠCave": 18603, + "===": 18604, + "Ġmol": 18605, + "Ġfist": 18606, + "Ġshotgun": 18607, + "supp": 18608, + "Ġrobbery": 18609, + "Effect": 18610, + "Ġobscure": 18611, + "ĠProfessional": 18612, + "Ġembassy": 18613, + "Ġmilitant": 18614, + "Ġincarcer": 18615, + "Ġgenerates": 18616, + "Ġlaunches": 18617, + "Ġadministrators": 18618, + "Ġshaft": 18619, + "Ġcircular": 18620, + "Ġfreshman": 18621, + "ĠWes": 18622, + "ĠJoel": 18623, + "ĠDrew": 18624, + "ĠDuncan": 18625, + "ĠApparently": 18626, + "sight": 18627, + "ĠInternal": 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"ĠImportant": 28511, + "Shot": 28512, + "ĠRas": 28513, + "Ġtrainers": 28514, + "ĠBun": 28515, + "Working": 28516, + "Ġharmed": 28517, + "ĠPandora": 28518, + "ĠLTE": 28519, + "Ġmushroom": 28520, + "ĠCHAR": 28521, + "ĠFee": 28522, + "ĠMoy": 28523, + "Born": 28524, + "oliberal": 28525, + "ĠMartial": 28526, + "Ġgentlemen": 28527, + "Ġlingering": 28528, + "Official": 28529, + "Ġgraffiti": 28530, + "ĠNames": 28531, + "Der": 28532, + "Ġquint": 28533, + "istrate": 28534, + "azeera": 28535, + "ĠNOTICE": 28536, + "ĠFlorence": 28537, + "Ġpayable": 28538, + "Ġdepicts": 28539, + "ĠSpecies": 28540, + "Heart": 28541, + "âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ": 28542, + "Ġenclosed": 28543, + "Increases": 28544, + "Daily": 28545, + "ĠLis": 28546, + "Ġenactment": 28547, + "ĠBacon": 28548, + "ĠSteele": 28549, + "demand": 28550, + "Ġ183": 28551, + "Ġmouths": 28552, + "Ġstranded": 28553, + "Ġenhancement": 28554, + "011": 28555, + "ĠWhats": 28556, + "Ġhealed": 28557, + "eny": 28558, + "ĠRab": 28559, + "Ġ340": 28560, + "ĠLabyrinth": 28561, + "roach": 28562, + "ĠYosh": 28563, + "ĠClippers": 28564, + "Ġconcerts": 28565, + "Internet": 28566, + "355": 28567, + "Ġstickers": 28568, + "Ġtermed": 28569, + "ĠAxe": 28570, + "Ġgrandparents": 28571, + "France": 28572, + "ĠClim": 28573, + "ĠUh": 28574, + "ulic": 28575, + "Ġthrill": 28576, + "centric": 28577, + "ĠOverview": 28578, + "ĠConduct": 28579, + "Ġsubstantive": 28580, + "Ġ182": 28581, + "mur": 28582, + "Ġstray": 28583, + "ĠCoff": 28584, + "Ġrepetitive": 28585, + "ĠForgotten": 28586, + "Ġqualification": 28587, + "ewitness": 28588, + "ĠZimbabwe": 28589, + "Ġsimulated": 28590, + "ĠJD": 28591, + "253": 28592, + "ĠWare": 28593, + "Ġunsc": 28594, + "Times": 28595, + "Ġsummons": 28596, + "Ġdisconnected": 28597, + "Ġ184": 28598, + "cius": 28599, + "ĠGujar": 28600, + "odka": 28601, + "Ġerase": 28602, + "ĠTobacco": 28603, + "elected": 28604, + "Ġuncont": 28605, + "ĠShepard": 28606, + "ĠLamp": 28607, + "Ġalerted": 28608, + "Ġoperative": 28609, + "arna": 28610, + "uint": 28611, + "Ġnegligence": 28612, + "acements": 28613, + "Ġsupra": 28614, + "Ġprevail": 28615, + "ĠShark": 28616, + "Ġbelts": 28617, + "ãģ«": 28618, + "Ġtighter": 28619, + "Engineers": 28620, + "Ġinactive": 28621, + "Ġexponent": 28622, + "ĠWillie": 28623, + "aples": 28624, + "Ġheir": 28625, + "ĠHits": 28626, + "iann": 28627, + "ĠSays": 28628, + "Ġcurrents": 28629, + "ĠBengal": 28630, + "Ġarist": 28631, + "Buffer": 28632, + "Ġbreeze": 28633, + "ĠWesley": 28634, + "Cola": 28635, + "Ġpronoun": 28636, + "Ġdeed": 28637, + "ĠKling": 28638, + "Ġoft": 28639, + "Ġinflict": 28640, + "Ġpunishing": 28641, + "Ġnm": 28642, + "iku": 28643, + "ODUCT": 28644, + "014": 28645, + "Ġsubsidy": 28646, + "ĠDEA": 28647, + "ĠHerbert": 28648, + "ĠJal": 28649, + "Bank": 28650, + "Ġdeferred": 28651, + "Ġshipment": 28652, + "Bott": 28653, + "Ġalle": 28654, + "bearing": 28655, + "HTML": 28656, + "Offline": 28657, + "Ġ213": 28658, + "Ġscrolling": 28659, + "Ġscanned": 28660, + "ĠLibyan": 28661, + "ĠTOP": 28662, + "chrom": 28663, + "dt": 28664, + "column": 28665, + "PsyNetMessage": 28666, + "Zero": 28667, + "Ġtorso": 28668, + "050": 28669, + "âķIJ": 28670, + "Ġimperson": 28671, + "ĠSchwartz": 28672, + "udic": 28673, + "Ġpissed": 28674, + "ĠSapp": 28675, + "257": 28676, + "ĠISPs": 28677, + "ogl": 28678, + "Ġsupervised": 28679, + "Ġadolescent": 28680, + "Ġattained": 28681, + "ĠDelivery": 28682, + "ĠBunny": 28683, + "Ġ1937": 28684, + "Ġminiature": 28685, + "Ġos": 28686, + "Ġ370": 28687, + "608": 28688, + "ĠMourinho": 28689, + "Ġinnate": 28690, + "Ġtempo": 28691, + "ĠNM": 28692, + "ĠFallen": 28693, + "009": 28694, + "Ġprovocative": 28695, + "Streamer": 28696, + "ĠBenedict": 28697, + "ĠBolshe": 28698, + "Ġturtle": 28699, + "ĠPCB": 28700, + "ĠEqual": 28701, + "Director": 28702, + "ĠRend": 28703, + "Ġfluids": 28704, + "Authorities": 28705, + "Ġcousins": 28706, + "requency": 28707, + "ĠNeighbor": 28708, + "sets": 28709, + "shared": 28710, + "Charles": 28711, + "password": 28712, + "Ġgears": 28713, + "Ġ211": 28714, + "ĠHardware": 28715, + "rika": 28716, + "Ġupstream": 28717, + "Hom": 28718, + "Ġdisproportionately": 28719, + "ivities": 28720, + "Ġundefined": 28721, + "Ġelectrons": 28722, + "Ġcommemor": 28723, + "Eventually": 28724, + "Ġ><": 28725, + "Ġirresponsible": 28726, + "218": 28727, + "ĠReleased": 28728, + "ĠOVER": 28729, + "ĠIGN": 28730, + "ĠBread": 28731, + "stellar": 28732, + "ĠSage": 28733, + "tted": 28734, + "damage": 28735, + "edition": 28736, + "ĠPrec": 28737, + "Ġlime": 28738, + "Ġconfinement": 28739, + "Ġcalorie": 28740, + "weapon": 28741, + "Ġdiffering": 28742, + "ĠSina": 28743, + "mys": 28744, + "amd": 28745, + "Ġintricate": 28746, + "kk": 28747, + "ĠPAT": 28748, + "ão": 28749, + "stones": 28750, + "links": 28751, + "Ġranch": 28752, + "Semitic": 28753, + "Ġdifferentiate": 28754, + "ĠSinger": 28755, + "occupied": 28756, + "Ġfortress": 28757, + "cmd": 28758, + "Ġinterception": 28759, + "ĠAnkara": 28760, + "Ġrept": 28761, + "ĠSolitaire": 28762, + "Ġremake": 28763, + "pred": 28764, + "Ġdared": 28765, + "autions": 28766, + "ĠBACK": 28767, + "Running": 28768, + "Ġdebugging": 28769, + "Ġgraphs": 28770, + "399": 28771, + "ĠNigel": 28772, + "Ġbun": 28773, + "Ġpillow": 28774, + "Ġprogressed": 28775, + "fashioned": 28776, + "Ġobedience": 28777, + "ERN": 28778, + "Ġrehears": 28779, + "Cell": 28780, + "tl": 28781, + "Sher": 28782, + "Ġherald": 28783, + "ĠPayment": 28784, + "ĠCory": 28785, + "ĠDept": 28786, + "Ġrepent": 28787, + "ĠWeak": 28788, + "uckland": 28789, + "Ġpleasing": 28790, + "Ġshortages": 28791, + "Ġjurors": 28792, + "ĠKab": 28793, + "qqa": 28794, + "Anti": 28795, + "Ġwow": 28796, + "ĠRCMP": 28797, + "Ġtsun": 28798, + "ĠSic": 28799, + "Ġcomprises": 28800, + "Ġspies": 28801, + "Ġprecinct": 28802, + "nu": 28803, + "Ġurges": 28804, + "Ġtimed": 28805, + "Ġstripes": 28806, + "ĠBoots": 28807, + "Ġyen": 28808, + "Advanced": 28809, + "Ġdiscrete": 28810, + "ĠArchangel": 28811, + "employment": 28812, + "Diff": 28813, + "Ġmonuments": 28814, + "Ġ209": 28815, + "worker": 28816, + "Ġ196": 28817, + "ĠIg": 28818, + "utterstock": 28819, + "TPS": 28820, + "Jac": 28821, + "Ġhomelessness": 28822, + "Ġcommentator": 28823, + "Ġracially": 28824, + "fing": 28825, + "seed": 28826, + "Ele": 28827, + "ellation": 28828, + "Ġethanol": 28829, + "Ġparish": 28830, + "ĠDong": 28831, + "ĠAwakening": 28832, + "Ġdeviation": 28833, + "ĠBearing": 28834, + "ĠTsuk": 28835, + "Ġrecess": 28836, + "Ġlymph": 28837, + "ĠCannabis": 28838, + "åľ": 28839, + "ĠNEWS": 28840, + "Ġdra": 28841, + "ĠStefan": 28842, + "ĠWrong": 28843, + "ĠSAM": 28844, + "Ġloosely": 28845, + "Ġinterpreter": 28846, + "ĠPlain": 28847, + "Government": 28848, + "Ġbigotry": 28849, + "Ġgrenades": 28850, + "avez": 28851, + "pictured": 28852, + "Ġmandated": 28853, + "ĠMonk": 28854, + "ĠPedro": 28855, + "Ġlava": 28856, + "274": 28857, + "Ġcynical": 28858, + "ĠScrolls": 28859, + "locks": 28860, + "Mp": 28861, + "Ġcongregation": 28862, + "ornings": 28863, + "phil": 28864, + "ĠIbid": 28865, + "Ġferv": 28866, + "Ġdisappearing": 28867, + "Ġarrogant": 28868, + "syn": 28869, + "ĠMaver": 28870, + "ĠSuit": 28871, + "241": 28872, + "Ġabbre": 28873, + "ackers": 28874, + "Pa": 28875, + "ĠYel": 28876, + "Whenever": 28877, + "Ġ235": 28878, + "ĠVine": 28879, + "ĠAnat": 28880, + "Ġextinct": 28881, + "LET": 28882, + "Ġexecutable": 28883, + "VERS": 28884, + "oxide": 28885, + "DNA": 28886, + "ĠPrel": 28887, + "Ġresentment": 28888, + "Ġcomprise": 28889, + "ĠAviv": 28890, + "Ġinterceptions": 28891, + "Ġprolific": 28892, + "INA": 28893, + "ĠErin": 28894, + "thought": 28895, + "219": 28896, + "ĠPsychiatry": 28897, + "unky": 28898, + "chemist": 28899, + "Ho": 28900, + "ĠMcCoy": 28901, + "Ġbricks": 28902, + "Los": 28903, + "rily": 28904, + "ĠUSSR": 28905, + "Ġrud": 28906, + "Ġlaud": 28907, + "ĠWise": 28908, + "ĠEmerald": 28909, + "Ġrevived": 28910, + "Ġdamned": 28911, + "ĠRepair": 28912, + "idem": 28913, + "ctica": 28914, + "Ġpatriarch": 28915, + "ĠNurs": 28916, + "meg": 28917, + "Ġcheapest": 28918, + "reements": 28919, + "empty": 28920, + "ĠCelebr": 28921, + "Ġdeprivation": 28922, + "chanted": 28923, + "ĠThumbnails": 28924, + "Energy": 28925, + "ĠEthan": 28926, + "ĠQing": 28927, + "Ġopposes": 28928, + "WIND": 28929, + "vik": 28930, + "ĠMau": 28931, + "ĠSUB": 28932, + "667": 28933, + "GRE": 28934, + "ĠVolunte": 28935, + "nton": 28936, + "Cook": 28937, + "åIJ": 28938, + "esque": 28939, + "Ġplummet": 28940, + "Ġsuing": 28941, + "Ġpronounce": 28942, + "Ġresisting": 28943, + "ĠFishing": 28944, + "ĠTrials": 28945, + "Ġyell": 28946, + "Ġ310": 28947, + "Ġinduct": 28948, + "Ġpersonalized": 28949, + "often": 28950, + "Reb": 28951, + "EMBER": 28952, + "Ġviewpoint": 28953, + "Ġexistential": 28954, + "())": 28955, + "remove": 28956, + "MENTS": 28957, + "lasses": 28958, + "Ġevapor": 28959, + "Ġaisle": 28960, + "meta": 28961, + "Ġreflective": 28962, + "Ġentitlement": 28963, + "Ġdevised": 28964, + "music": 28965, + "ascade": 28966, + "Ġwinding": 28967, + "offset": 28968, + "Ġaccessibility": 28969, + "kered": 28970, + "Better": 28971, + "ĠJohnston": 28972, + "thinking": 28973, + "Snow": 28974, + "ĠCroatia": 28975, + "ĠAtomic": 28976, + "271": 28977, + "348": 28978, + "Ġtextbook": 28979, + "ĠSixth": 28980, + "ĠاÙĦ": 28981, + "Ġslider": 28982, + "ĠBurger": 28983, + "bol": 28984, + "Sync": 28985, + "Ġgrandchildren": 28986, + "Ġcerv": 28987, + "+)": 28988, + "Ġeternity": 28989, + "Ġtweeting": 28990, + "Ġspeculative": 28991, + "Ġpivotal": 28992, + "ĠWP": 28993, + "ĠTER": 28994, + "ynamic": 28995, + "Ġupl": 28996, + "ĠCats": 28997, + "perhaps": 28998, + "Ġclassmates": 28999, + "Ġblatant": 29000, + "'-": 29001, + "Ġlakh": 29002, + "antine": 29003, + "ĠBorg": 29004, + "iom": 29005, + "/(": 29006, + "ĠAthletic": 29007, + "Ġsar": 29008, + "OTA": 29009, + "ĠHoffman": 29010, + "Nevertheless": 29011, + "Ġadorable": 29012, + "Ġspawned": 29013, + "Associated": 29014, + "ĠDomestic": 29015, + "Ġimplant": 29016, + "ĠLuxem": 29017, + "ĠKens": 29018, + "Ġpumps": 29019, + "ĠSAT": 29020, + "Attributes": 29021, + "509": 29022, + "avour": 29023, + "Ġcentralized": 29024, + "ĠTN": 29025, + "Ġfreshly": 29026, + "ĠAchieve": 29027, + "Ġoutsiders": 29028, + "herty": 29029, + "ĠRee": 29030, + "ĠTowers": 29031, + "ĠDart": 29032, + "akable": 29033, + "Ġmp": 29034, + "ĠHeavenly": 29035, + "Ġripe": 29036, + "ĠCaroline": 29037, + "ryan": 29038, + "Ġclassics": 29039, + "Ġretiring": 29040, + "Ġ228": 29041, + "Ġah": 29042, + "Ġdealings": 29043, + "Ġpunching": 29044, + "ĠChapman": 29045, + "Options": 29046, + "maxwell": 29047, + "volume": 29048, + "Ġstal": 29049, + "Ġexported": 29050, + "ĠQuite": 29051, + "Ġnumerical": 29052, + "Burn": 29053, + "Fact": 29054, + "ĠKeystone": 29055, + "Ġtrending": 29056, + "Ġaltering": 29057, + "ĠAfricans": 29058, + "478": 29059, + "ĠMN": 29060, + "ĠKnock": 29061, + "Ġtemptation": 29062, + "Ġprestige": 29063, + "Overview": 29064, + "ĠTraditional": 29065, + "ĠBahrain": 29066, + "Private": 29067, + "ĠHOU": 29068, + "Ġbarr": 29069, + "ĠTat": 29070, + "Cube": 29071, + "USD": 29072, + "ĠGrande": 29073, + "ĠGat": 29074, + "ĠFlo": 29075, + "Ġresides": 29076, + "Ġindec": 29077, + "volent": 29078, + "Ġperpetual": 29079, + "ubes": 29080, + "Ġworldview": 29081, + "ĠQuantum": 29082, + "Ġfiltered": 29083, + "Ġensu": 29084, + "orgetown": 29085, + "ERSON": 29086, + "ĠMild": 29087, + "379": 29088, + "OTT": 29089, + "Ã¥": 29090, + "Ġvitamins": 29091, + "Ġribbon": 29092, + "Ġsincerely": 29093, + "ĠHin": 29094, + "Ġeighteen": 29095, + "Ġcontradictory": 29096, + "Ġglaring": 29097, + "Ġexpectancy": 29098, + "Ġconspir": 29099, + "Ġmonstrous": 29100, + "Ġ380": 29101, + "reci": 29102, + "Ġhandic": 29103, + "Ġpumped": 29104, + "Ġindicative": 29105, + "Ġrapp": 29106, + "Ġavail": 29107, + "ĠLEGO": 29108, + "ĠMarijuana": 29109, + "1985": 29110, + "erton": 29111, + "Ġtwentieth": 29112, + "################################": 29113, + "ĠSwamp": 29114, + "Ġvaluation": 29115, + "Ġaffiliates": 29116, + "adjusted": 29117, + "ĠFacility": 29118, + "262": 29119, + "Ġenzymes": 29120, + "itudinal": 29121, + "Ġimprint": 29122, + "Site": 29123, + "Ġinstaller": 29124, + "ĠTRA": 29125, + "mology": 29126, + "linear": 29127, + "ĠCollective": 29128, + "igating": 29129, + "ĠToken": 29130, + "Ġspeculated": 29131, + "KN": 29132, + "ĠCly": 29133, + "ority": 29134, + "Ġdefer": 29135, + "Ġinspectors": 29136, + "approved": 29137, + "RM": 29138, + "ĠSuns": 29139, + "Ġinforming": 29140, + "ĠSyracuse": 29141, + "ibli": 29142, + "765": 29143, + "Ġglove": 29144, + "Ġauthorize": 29145, + "âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦âĢ¦": 29146, + "ĠCruise": 29147, + "Ġcontracting": 29148, + "shell": 29149, + "IFE": 29150, + "ĠJewel": 29151, + "pract": 29152, + "ĠPhotoshop": 29153, + "ĠKnowing": 29154, + "harm": 29155, + "Ġattractions": 29156, + "adan": 29157, + "etus": 29158, + "018": 29159, + "wagen": 29160, + "Alt": 29161, + "Ġmultiply": 29162, + "Ġequilibrium": 29163, + ":{": 29164, + "ĠFighters": 29165, + "ĠEdgar": 29166, + "Ġfourteen": 29167, + "Govern": 29168, + "Ġmisuse": 29169, + 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"Ġmoons": 31093, + "Ġelectromagnetic": 31094, + "Ġamidst": 31095, + "ĠViktor": 31096, + "NESS": 31097, + "BACK": 31098, + "Ġcommute": 31099, + "ĠAnaheim": 31100, + "Ġfluctuations": 31101, + "640": 31102, + "Ġnoodles": 31103, + "ĠCopenhagen": 31104, + "ĠTide": 31105, + "ĠGrizz": 31106, + "ĠSEE": 31107, + "Ġpipelines": 31108, + "Ġscars": 31109, + "endo": 31110, + "agus": 31111, + "ĠETF": 31112, + "/#": 31113, + "ĠBecome": 31114, + "448": 31115, + "Ġvisc": 31116, + "ĠRecommended": 31117, + "Ġjumper": 31118, + "Ġcognition": 31119, + "Ġassassin": 31120, + "Ġwitnessing": 31121, + "ĠSetup": 31122, + "Ġlac": 31123, + "vim": 31124, + "ISM": 31125, + "pages": 31126, + "SSL": 31127, + "358": 31128, + "Ġadject": 31129, + "industrial": 31130, + "lore": 31131, + "chery": 31132, + "Ġglitter": 31133, + "Ġcalf": 31134, + "Florida": 31135, + "Ġspoilers": 31136, + "Ġsucceeds": 31137, + "Ġchanting": 31138, + "Ġslogans": 31139, + "ĠTracy": 31140, + "Visit": 31141, + "rology": 31142, + "Ġmornings": 31143, + 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31195, + "ĠHunters": 31196, + "gp": 31197, + "Ġprominence": 31198, + "Liber": 31199, + "ĠAncest": 31200, + "otechnology": 31201, + "Ġmocking": 31202, + "ĠTraff": 31203, + "ĸļ": 31204, + "Medium": 31205, + "Iraq": 31206, + "Ġpsychiatrist": 31207, + "Quantity": 31208, + "ĠLect": 31209, + "Ġnoisy": 31210, + "520": 31211, + "GY": 31212, + "Ġslapped": 31213, + "ĠMTV": 31214, + "Ġpara": 31215, + "pull": 31216, + "Multiple": 31217, + "asher": 31218, + "Ġnour": 31219, + "ĠSeg": 31220, + "Spell": 31221, + "vous": 31222, + "ordial": 31223, + "Senior": 31224, + "ĠGoldberg": 31225, + "ĠPlasma": 31226, + "need": 31227, + "Ġmessenger": 31228, + "eret": 31229, + "Ġteamed": 31230, + "Ġliteracy": 31231, + "ĠLeah": 31232, + "ĠDoyle": 31233, + "Ġemitted": 31234, + "UX": 31235, + "Ġevade": 31236, + "Ġmaze": 31237, + "Ġwrongly": 31238, + "ĠLars": 31239, + "Ġstereotype": 31240, + "Ġpledges": 31241, + "Ġaroma": 31242, + "ĠMET": 31243, + "Ġacre": 31244, + "ĠOD": 31245, + "Ġff": 31246, + "Ġbreweries": 31247, + 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31757, + "ãĤĴ": 31758, + "Ġreproduced": 31759, + "Ġformulation": 31760, + "jug": 31761, + "irez": 31762, + "gear": 31763, + "Ġcoached": 31764, + "MpServer": 31765, + "ĠSJ": 31766, + "ĠKw": 31767, + "Init": 31768, + "deal": 31769, + "ĠOro": 31770, + "ĠLoki": 31771, + "ĠSongs": 31772, + "Ġ232": 31773, + "ĠLouise": 31774, + "asionally": 31775, + "Ġuncond": 31776, + "ollywood": 31777, + "Ġprogressives": 31778, + "ĠEnough": 31779, + "ĠDoe": 31780, + "Ġwreckage": 31781, + "Ġbrushed": 31782, + "ĠBaseType": 31783, + "Ġzoning": 31784, + "ishable": 31785, + "hetically": 31786, + "ĠCaucus": 31787, + "ĠHue": 31788, + "Ġkarma": 31789, + "ĠSporting": 31790, + "Ġtrader": 31791, + "Ġseeming": 31792, + "ĠCapture": 31793, + "430": 31794, + "bish": 31795, + "Ġtunes": 31796, + "Ġindoors": 31797, + "ĠSphere": 31798, + "ĠDancing": 31799, + "TERN": 31800, + "Ġnob": 31801, + "ĠGST": 31802, + "maps": 31803, + "Ġpeppers": 31804, + "Fit": 31805, + "Ġoversees": 31806, + "ĠRabbi": 31807, + "ĠRuler": 31808, + 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"ĠCrossing": 31910, + "449": 31911, + "Ġholog": 31912, + "ĠPuzzles": 31913, + "ĠGoes": 31914, + "erenn": 31915, + "604": 31916, + "ãģı": 31917, + "ĠRafael": 31918, + "Ġatten": 31919, + "ĠEmanuel": 31920, + "Ġupro": 31921, + "ĠSusp": 31922, + "Psych": 31923, + "ĠTrainer": 31924, + "ĠNES": 31925, + "ĠHunts": 31926, + "becue": 31927, + "Ġcounselor": 31928, + "Rule": 31929, + "Ġtoxins": 31930, + "Ġbanners": 31931, + "rifice": 31932, + "Ġgreeting": 31933, + "Ġfrenzy": 31934, + "Ġallocate": 31935, + "Ġ*)": 31936, + "expr": 31937, + "503": 31938, + "ĠChick": 31939, + "ĠTorn": 31940, + "Ġconsolidation": 31941, + "ĠFletcher": 31942, + "switch": 31943, + "frac": 31944, + "clips": 31945, + "ĠMcKin": 31946, + "ĠLunar": 31947, + "Month": 31948, + "ITCH": 31949, + "Ġscholarly": 31950, + "raped": 31951, + "398": 31952, + "Ġ1910": 31953, + "Ġegreg": 31954, + "Ġinsecure": 31955, + "Ġvictorious": 31956, + "cffffcc": 31957, + "Ġsingled": 31958, + "Ġelves": 31959, + "ĠWond": 31960, + "burst": 31961, + "Ġcamoufl": 31962, + "ĠBLACK": 31963, + "Ġconditioned": 31964, + "çī": 31965, + "answered": 31966, + "Ġcompulsory": 31967, + "ascist": 31968, + "Ġpodcasts": 31969, + "ĠFrankfurt": 31970, + "bnb": 31971, + "Ġneoliberal": 31972, + "ĠKeyboard": 31973, + "ĠBelle": 31974, + "warm": 31975, + "Ġtrusts": 31976, + "Ġinsured": 31977, + "ĠBucc": 31978, + "usable": 31979, + "607": 31980, + "ĠPlains": 31981, + "Ġ1890": 31982, + "Ġsabotage": 31983, + "Ġlodged": 31984, + "felt": 31985, + "Ġga": 31986, + "ĠNarc": 31987, + "ĠSalem": 31988, + "Ġseventy": 31989, + "ĠBlank": 31990, + "pocket": 31991, + "Ġwhisper": 31992, + "Ġmating": 31993, + "omics": 31994, + "ĠSalman": 31995, + "ĠKad": 31996, + "Ġangered": 31997, + "Ġcollisions": 31998, + "Ġextraordinarily": 31999, + "Ġcoercion": 32000, + "Ghost": 32001, + "birds": 32002, + "èĢ": 32003, + "kok": 32004, + "Ġpermissible": 32005, + "avorable": 32006, + "Ġpointers": 32007, + "Ġdissip": 32008, + "aci": 32009, + "Ġtheatrical": 32010, + "ĠCosmic": 32011, + 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"abbage": 32061, + "Ġ1915": 32062, + "Ġblended": 32063, + "Ġalarmed": 32064, + "Language": 32065, + "356": 32066, + "Ġblends": 32067, + "ĠChanged": 32068, + "Wolf": 32069, + "Ġhepat": 32070, + "Creating": 32071, + "Ġpersecut": 32072, + "Ġsweetness": 32073, + "arte": 32074, + "Ġforfeiture": 32075, + "ĠRoberto": 32076, + "impro": 32077, + "NFL": 32078, + "ĠMagnet": 32079, + "Detailed": 32080, + "Ġinsignificant": 32081, + "ĠPOLIT": 32082, + "ĠBBQ": 32083, + "ĠCPS": 32084, + "Ġseaw": 32085, + "aminer": 32086, + "mL": 32087, + "endif": 32088, + "finals": 32089, + "Ġ265": 32090, + "uish": 32091, + "Ġ})": 32092, + "ĠProblems": 32093, + "Ġemblem": 32094, + "Ġseriousness": 32095, + "Ġparsing": 32096, + "Ġsubstitution": 32097, + "Ġpressured": 32098, + "Ġrecycled": 32099, + "aleb": 32100, + "Ruby": 32101, + "Ġproficiency": 32102, + "Driver": 32103, + "ĠWester": 32104, + ":'": 32105, + "AFTA": 32106, + "Ġmantle": 32107, + "ĠClayton": 32108, + "flag": 32109, + "Ġpractitioner": 32110, + "covered": 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+ "Ġcooled": 32162, + "Lev": 32163, + "ĠFalk": 32164, + "fail": 32165, + "Ġpoliceman": 32166, + "ĠDag": 32167, + "Ġprayed": 32168, + "ĠKernel": 32169, + "Ġclut": 32170, + "Ġcath": 32171, + "Ġanomaly": 32172, + "Storm": 32173, + "emaker": 32174, + "ĠBreakfast": 32175, + "uli": 32176, + "oire": 32177, + "JJ": 32178, + "hz": 32179, + "Operation": 32180, + "ĠSick": 32181, + "354": 32182, + "ĠGuatemala": 32183, + "Rate": 32184, + "Ġexposures": 32185, + "faces": 32186, + "ĠArchae": 32187, + "raf": 32188, + "ĠMia": 32189, + "Ġ2025": 32190, + "Ġopaque": 32191, + "Ġdisguised": 32192, + "ĠHeadquarters": 32193, + "Sah": 32194, + "Ġpots": 32195, + "978": 32196, + "ĠMalf": 32197, + "Ġfrowned": 32198, + "Ġpoisonous": 32199, + "ĠConvers": 32200, + "eeks": 32201, + "Ġcrab": 32202, + ".\"\"": 32203, + "Ġtreason": 32204, + "Ġranc": 32205, + "Ġescalating": 32206, + "Ġwarr": 32207, + "Ġmobs": 32208, + "Ġlamps": 32209, + "ĠSunshine": 32210, + "ĠBrunswick": 32211, + "Phones": 32212, + "Ġspelled": 32213, + 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"Ġpetrol": 34139, + "Ġimagining": 34140, + "Ġpatrols": 34141, + "ĠInteger": 34142, + "ĠAshes": 34143, + "actus": 34144, + "ĠRadiant": 34145, + "ĠLT": 34146, + "itability": 34147, + "htaking": 34148, + "Setting": 34149, + "Ġnuanced": 34150, + "ĠReef": 34151, + "ĠDevelopers": 34152, + "Ni": 34153, + "pieces": 34154, + "990": 34155, + "License": 34156, + "Ġlowers": 34157, + "ĠOttoman": 34158, + "327": 34159, + "ooo": 34160, + "Ġquitting": 34161, + "markets": 34162, + "Behind": 34163, + "Ġbasin": 34164, + "Ġdocs": 34165, + "anie": 34166, + "flash": 34167, + "ctl": 34168, + "Ġcivilized": 34169, + "ĠFukushima": 34170, + "\"],\"": 34171, + "ĠKS": 34172, + "ĠHonestly": 34173, + "arat": 34174, + "Ġconstructs": 34175, + "ĠLans": 34176, + "ĠDire": 34177, + "ĠLIKE": 34178, + "ĠTrouble": 34179, + "Ġwithholding": 34180, + "ĠOblivion": 34181, + "Ġsanity": 34182, + "anya": 34183, + "Const": 34184, + "Ġgrocer": 34185, + "ĠCelsius": 34186, + "Ġrecounted": 34187, + "ĠWife": 34188, + "Border": 34189, + "atered": 34190, + "happy": 34191, + "Ġspoiler": 34192, + "Ġlogically": 34193, + "Hall": 34194, + "Ġsucceeding": 34195, + "Ġpolymorph": 34196, + "Ġaxes": 34197, + "ĠShotgun": 34198, + "ĠSlim": 34199, + "ĠPrinciples": 34200, + "ĠLeth": 34201, + "arta": 34202, + "Ġscor": 34203, + "Screenshot": 34204, + "Ġrelaxation": 34205, + "#$#$": 34206, + "Ġdeterrent": 34207, + "iddy": 34208, + "Ġpowerless": 34209, + "Ġlesbians": 34210, + "Ġchords": 34211, + "ĠEdited": 34212, + "selected": 34213, + "Ġseparatists": 34214, + "0002": 34215, + "Ġairspace": 34216, + "Ġturnaround": 34217, + "Ġcunning": 34218, + "PATH": 34219, + "Poly": 34220, + "Ġbombed": 34221, + "Ġtion": 34222, + "xs": 34223, + "Ġwithhold": 34224, + "Ġwaged": 34225, + "ĠLiberties": 34226, + "Flag": 34227, + "Ġcomforting": 34228, + "454": 34229, + "ĠIris": 34230, + "arers": 34231, + "Ġrag": 34232, + "Ġrelocated": 34233, + "ĠGuarant": 34234, + "Ġstrategically": 34235, + "Ġgamma": 34236, + "uberty": 34237, + "ĠLockheed": 34238, + "gres": 34239, + "Ġgrilled": 34240, + "ĠLowe": 34241, + "stats": 34242, + "ĠRocks": 34243, + "Ġsensing": 34244, + "Ġrenting": 34245, + "ĠGeological": 34246, + "اØ": 34247, + "otrop": 34248, + "Ġsew": 34249, + "Ġimproperly": 34250, + "486": 34251, + "Ġâĸł": 34252, + "Ġstarving": 34253, + "ĠBj": 34254, + "Discussion": 34255, + "328": 34256, + "ĠCombo": 34257, + "ĠFixes": 34258, + "NAT": 34259, + "Ġstriving": 34260, + "thora": 34261, + "Ġharvested": 34262, + "ĠPing": 34263, + "Ġplayful": 34264, + "Ġavenues": 34265, + "Ġoccupational": 34266, + "Ġwakes": 34267, + "ĠCourier": 34268, + "Ġdrummer": 34269, + "ĠBrowser": 34270, + "ĠHouth": 34271, + "itu": 34272, + "Ġapparel": 34273, + "paste": 34274, + "Ġhunted": 34275, + "ĠSecondly": 34276, + "lain": 34277, + "XY": 34278, + "ĠPIN": 34279, + "icons": 34280, + "Ġcocktails": 34281, + "Ġsizable": 34282, + "Ġhurdles": 34283, + "estinal": 34284, + "ĠRecreation": 34285, + "Ġeco": 34286, + "648": 34287, + "ĠDied": 34288, + "mint": 34289, + "Ġfingerprints": 34290, + "Ġdispose": 34291, + "ĠBosnia": 34292, + "tsy": 34293, + "2200": 34294, + "Ġinspected": 34295, + "ĠFou": 34296, + "Ġfuss": 34297, + "Ġambush": 34298, + "ĠRak": 34299, + "Ġmanifested": 34300, + "Prosecut": 34301, + "Ġsuffice": 34302, + "rences": 34303, + "Ġcompensated": 34304, + "ĠCyrus": 34305, + "Ġgenus": 34306, + "ĠWolverine": 34307, + "ĠTrends": 34308, + "Ġhikes": 34309, + "ĠSeen": 34310, + "Ġenrol": 34311, + "Cold": 34312, + "Ġpolitely": 34313, + "ĠSlav": 34314, + "ĠRupert": 34315, + "Ġeyewitness": 34316, + "ĠAlto": 34317, + "Ġuncomp": 34318, + "Ġposterior": 34319, + "Must": 34320, + "ĠHerz": 34321, + "Ġprogressively": 34322, + "Ġ234": 34323, + "Ġindifference": 34324, + "ĠCunningham": 34325, + "Ġacademia": 34326, + "Ġsewer": 34327, + "Ġastounding": 34328, + "ĠAES": 34329, + "rather": 34330, + "Ġeldest": 34331, + "Ġclimbs": 34332, + "ĠAdds": 34333, + "Ġoutcry": 34334, + "Ġcontag": 34335, + "ĠHouses": 34336, + "Ġpept": 34337, + "ĠMelania": 34338, + "interested": 34339, + 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"ĠSocket": 47068, + "Ġbould": 47069, + "ĠOU": 47070, + "ĠBorderlands": 47071, + "Ġ1863": 47072, + "Gordon": 47073, + "ĠWTO": 47074, + "Ġrestricts": 47075, + "Ġmosaic": 47076, + "Ġmelodies": 47077, + "çĦ": 47078, + "Tar": 47079, + "Ġdisson": 47080, + "ĠProvides": 47081, + "Ġ......": 47082, + "bek": 47083, + "FIX": 47084, + "Ġbroom": 47085, + "anship": 47086, + "Doctors": 47087, + "Ġnerds": 47088, + "ĠRegions": 47089, + "naissance": 47090, + "Ġmete": 47091, + "Ġcrept": 47092, + "plings": 47093, + "Ġgirlfriends": 47094, + "knit": 47095, + "igent": 47096, + "owe": 47097, + "Ġushered": 47098, + "ĠBaz": 47099, + "Mobil": 47100, + "434": 47101, + "ĠPresents": 47102, + "origin": 47103, + "Ġinsomnia": 47104, + "ĠAux": 47105, + "439": 47106, + "ĠChili": 47107, + "irsch": 47108, + "GAME": 47109, + "Ġgestation": 47110, + "algia": 47111, + "romising": 47112, + "$,": 47113, + "crow": 47114, + "ĠInspection": 47115, + "atomic": 47116, + "Relations": 47117, + "JOHN": 47118, + "roman": 47119, + "ĠClockwork": 47120, + "ĠBakr": 47121, + "mone": 47122, + "MET": 47123, + "Ġthirsty": 47124, + "Ġbc": 47125, + "Ġfaculties": 47126, + "Rum": 47127, + "Ġnuance": 47128, + "ĠDarius": 47129, + "pleting": 47130, + "fters": 47131, + "etchup": 47132, + "Registration": 47133, + "ĠKE": 47134, + "Rah": 47135, + "Ġpreferential": 47136, + "ĠLash": 47137, + "ĠHH": 47138, + "Valid": 47139, + "ĠNAV": 47140, + "Ġstarve": 47141, + "ĠGong": 47142, + "zynski": 47143, + "ĠActress": 47144, + "Ġwik": 47145, + "Ġunaccompanied": 47146, + "lvl": 47147, + "Bride": 47148, + "ADS": 47149, + "ĠCommando": 47150, + "ĠVaughn": 47151, + "Wallet": 47152, + "Ġhopping": 47153, + "ĠVie": 47154, + "Ġcaveats": 47155, + "Ġalas": 47156, + "ifled": 47157, + "abuse": 47158, + "661": 47159, + "Ġibn": 47160, + "Ġgul": 47161, + "Ġrobbing": 47162, + "til": 47163, + "ILA": 47164, + "Ġmitigating": 47165, + "Ġaptly": 47166, + "Ġtyrant": 47167, + "Ġmidday": 47168, + "ĠGilmore": 47169, + "ĠDecker": 47170, + "Ġ§§": 47171, + "partial": 47172, + "Exactly": 47173, + "Ġphenotype": 47174, + "Ġ[+]": 47175, + "ĠPlex": 47176, + "ĠIps": 47177, + "versions": 47178, + "Ġebook": 47179, + "Ġchic": 47180, + "gross": 47181, + "\":\"\"},{\"": 47182, + "ĠSurprisingly": 47183, + "Morgan": 47184, + "Ġresidues": 47185, + "ĠConfederation": 47186, + "infeld": 47187, + "Ġlyr": 47188, + "moderate": 47189, + "Ġperpendicular": 47190, + "VK": 47191, + "Ġsynchronized": 47192, + "Ġrefreshed": 47193, + "Ġadore": 47194, + "ĠTorment": 47195, + "olina": 47196, + "Ġ2600": 47197, + "ItemTracker": 47198, + "Ġpies": 47199, + "ĠFAT": 47200, + "ĠRHP": 47201, + "048": 47202, + "ĠRESP": 47203, + "ĠBJ": 47204, + "allows": 47205, + "Pand": 47206, + "Ġunwelcome": 47207, + "ĠVoc": 47208, + "ĠBastard": 47209, + "ĠOW": 47210, + "ĠLAR": 47211, + "ĠHealer": 47212, + "Environmental": 47213, + "ĠKenyan": 47214, + "ĠTrance": 47215, + "ĠPats": 47216, + "Ġaliases": 47217, + "ĠGarfield": 47218, + "Ġcampaigner": 47219, + "Ġadvancements": 47220, + "ĠOkinawa": 47221, + "ĠCoh": 47222, + "owsky": 47223, + "Ġstarved": 47224, + "Ġsizeable": 47225, + "Ġ:-)": 47226, + "ĠmRNA": 47227, + "Ġsuspensions": 47228, + "istar": 47229, + "Scotland": 47230, + "Prin": 47231, + "------------------------------------------------": 47232, + "Ġ502": 47233, + "Ġteaspoons": 47234, + "Ġ1050": 47235, + "Ġcoercive": 47236, + "ĠMasonic": 47237, + "edded": 47238, + "ĠPassenger": 47239, + "Ġlatt": 47240, + "Ġbraces": 47241, + "ĠSteal": 47242, + "ĠNYT": 47243, + "ĠKats": 47244, + "ĠCelest": 47245, + "aez": 47246, + "Tu": 47247, + "ĠCoulter": 47248, + "ðŁĺ": 47249, + "Flickr": 47250, + "ĠWilmington": 47251, + "iths": 47252, + "++;": 47253, + "Ġvending": 47254, + "Ġnegro": 47255, + "ĠPhi": 47256, + "ĠYellowstone": 47257, + "Callback": 47258, + "Ġshampoo": 47259, + "ĠShades": 47260, + "wat": 47261, + "Ġsuperhuman": 47262, + "Ġridiculed": 47263, + "Ġholiest": 47264, + "ombo": 47265, + "Ġinterns": 47266, + "Ġhone": 47267, + "ĠParagu": 47268, + "URI": 47269, + "Ġdangling": 47270, + "ãĤ»": 47271, + "sov": 47272, + "ictional": 47273, + "availability": 47274, + "Ġrevocation": 47275, + "Ġdow": 47276, + "inic": 47277, + "ĠTHEIR": 47278, + "Ġiso": 47279, + "Ġoutings": 47280, + "ĠLethal": 47281, + "Ġ)))": 47282, + "Ġinaccur": 47283, + "Ġoutlandish": 47284, + "Ġanus": 47285, + "letico": 47286, + "idon": 47287, + "lol": 47288, + "Ġunregulated": 47289, + "Ġsuccumbed": 47290, + "Ġcuff": 47291, + "ĠWasteland": 47292, + "letal": 47293, + "Ġsubstr": 47294, + "Ġcoffers": 47295, + "Ġautomakers": 47296, + "ovi": 47297, + "ĠXue": 47298, + "ĠDaytona": 47299, + "Ġjarring": 47300, + "Ġfumes": 47301, + "Ġdisbanded": 47302, + "zik": 47303, + "itton": 47304, + "Ġstrikingly": 47305, + "Ġspores": 47306, + "Adapter": 47307, + ".):": 47308, + "ĠLyndon": 47309, + "ivalry": 47310, + "Ġorally": 47311, + "Ġtumultuous": 47312, + "Ġdispleasure": 47313, + "Ġcones": 47314, + "orrect": 47315, + "Ġappease": 47316, + "Ġderby": 47317, + "ĠTripoli": 47318, + "ĠAless": 47319, + "Ġpoked": 47320, + "ĠGuilty": 47321, + "vP": 47322, + "Enough": 47323, + "Ġoriginals": 47324, + "699": 47325, + "Ġrabbi": 47326, + "Ġproverbial": 47327, + "Ġpostpone": 47328, + "elope": 47329, + "ĠMisty": 47330, + "Ġstaffed": 47331, + "ĠUnemployment": 47332, + "reditary": 47333, + "Ġdiligent": 47334, + "recomm": 47335, + "measures": 47336, + "asin": 47337, + "825": 47338, + "Ġponds": 47339, + "Ġmmol": 47340, + "ĠSAR": 47341, + "ĠCARE": 47342, + "Ġ371": 47343, + "Ġclenched": 47344, + "ĠCorsair": 47345, + "Ġcaricature": 47346, + "zn": 47347, + "attach": 47348, + "ĠSchro": 47349, + "speak": 47350, + "painted": 47351, + "ĠSuc": 47352, + "ĠENT": 47353, + "Ġcellul": 47354, + "ĠPaid": 47355, + "diagn": 47356, + "WHERE": 47357, + "Ġtexted": 47358, + "Barn": 47359, + "Ġretracted": 47360, + "ĠReferred": 47361, + "Sav": 47362, + "Ġupkeep": 47363, + "Ġworkplaces": 47364, + "ĠTokens": 47365, + "Ġamplify": 47366, + "clinical": 47367, + "Ġmultic": 47368, + "mberg": 47369, + "Ġconvoluted": 47370, + "Region": 47371, + "565": 47372, + "ĠTopic": 47373, + "Ġsnail": 47374, + "Ġsaline": 47375, + "Ġinsurrection": 47376, + "ĠPetr": 47377, + "forts": 47378, + "BAT": 47379, + "ĠNavajo": 47380, + "Ġrudimentary": 47381, + "ĠLaksh": 47382, + "ONDON": 47383, + "Measure": 47384, + "Ġtransformer": 47385, + "ĠGoddard": 47386, + "Ġcoincides": 47387, + "irin": 47388, + "Rex": 47389, + "ĠBok": 47390, + "quit": 47391, + "Ġshotguns": 47392, + "Ġproletarian": 47393, + "Ġscorp": 47394, + "ĠAda": 47395, + "514": 47396, + "Ġslander": 47397, + "recorded": 47398, + "Ġembell": 47399, + "risome": 47400, + "Ġapologizing": 47401, + "ĠMulcair": 47402, + "ĠGibraltar": 47403, + "Cla": 47404, + "Ġallot": 47405, + "ĠAttention": 47406, + "Ġ433": 47407, + "leave": 47408, + "Ġwhine": 47409, + "ĠIssa": 47410, + "ĠFaust": 47411, + "ĠBarron": 47412, + "heny": 47413, + "Ġvictimized": 47414, + "Jews": 47415, + "Ġnurturing": 47416, + "ettel": 47417, + "Winged": 47418, + "ĠSubtle": 47419, + "Ġflavorful": 47420, + "ĠReps": 47421, + "enged": 47422, + "callback": 47423, + "Ġdirectional": 47424, + "Ġclasp": 47425, + "ĠDirections": 47426, + "planet": 47427, + "iculture": 47428, + "Helper": 47429, + "icion": 47430, + "acia": 47431, + "Ġç¥ŀ": 47432, + "Ġsurges": 47433, + "Ġcanoe": 47434, + "ĠPremiership": 47435, + "been": 47436, + "Ġdefied": 47437, + "ĠTrooper": 47438, + "Ġtripod": 47439, + "Ġgasp": 47440, + "ĠEuph": 47441, + "ĠAds": 47442, + "vernight": 47443, + "highly": 47444, + "Role": 47445, + "Ġentangled": 47446, + "ĠZeit": 47447, + "618": 47448, + "ĠRusty": 47449, + "Ġhavens": 47450, + "ĠVaughan": 47451, + "HAEL": 47452, + "ĠSERVICE": 47453, + "/,": 47454, + "Ġstricken": 47455, + "Ġdelusions": 47456, + "Ġbis": 47457, + "ĠHaf": 47458, + "Ġgratification": 47459, + "Ġenticing": 47460, + "UNCH": 47461, + "Adams": 47462, + "ĠOLED": 47463, + "ĠBeetle": 47464, + "Ġ1899": 47465, + "ĠSOFTWARE": 47466, + "ategor": 47467, + "VL": 47468, + "ĠTotem": 47469, + "ĠGators": 47470, + "ATURES": 47471, + "Ġimpedance": 47472, + "Registered": 47473, + "ĠCary": 47474, + "ĠAerial": 47475, + "onne": 47476, + "enium": 47477, + "Ġdred": 47478, + "ĠBeg": 47479, + "Ġconcurrently": 47480, + "Ġsuperpower": 47481, + "ĠXan": 47482, + "jew": 47483, + "imester": 47484, + "ĠDickinson": 47485, + "âĶģ": 47486, + "Fla": 47487, + "Ġpree": 47488, + "ĠRollins": 47489, + "©¶æ": 47490, + "Ġdenomination": 47491, + "ĠLana": 47492, + "516": 47493, + "Ġinciting": 47494, + "scribed": 47495, + "juries": 47496, + "ĠWonders": 47497, + "approximately": 47498, + "Ġsuspending": 47499, + "Ġmountainous": 47500, + "ĠLaugh": 47501, + "oidal": 47502, + "Ns": 47503, + "Detect": 47504, + ")=": 47505, + "ĠLuthor": 47506, + "ĠSchwarzenegger": 47507, + "ĠMuller": 47508, + "ĠDevi": 47509, + "ecycle": 47510, + "Jar": 47511, + "613": 47512, + "ĠLongh": 47513, + "Bah": 47514, + "ĠSPORTS": 47515, + "nw": 47516, + "Ġrefinement": 47517, + "Ġwaterways": 47518, + "Ġdiner": 47519, + "Blade": 47520, + "683": 47521, + "Fac": 47522, + "Ġinitials": 47523, + "Ġrog": 47524, + "Ġparanormal": 47525, + "BUT": 47526, + "Ġ[(": 47527, + "ĠSwanson": 47528, + "ĠMesh": 47529, + "âĸ¬": 47530, + "Improve": 47531, + "ĠRadiation": 47532, + "ĠEsther": 47533, + "ĠEsk": 47534, + "ĠAly": 47535, + "iky": 47536, + "Ġirrad": 47537, + "ĠBuckingham": 47538, + "Ġrefill": 47539, + "Ġ._": 47540, + "Repe": 47541, + "CONCLUS": 47542, + "Ġdifferentiated": 47543, + "Ġchirop": 47544, + "ĠAtkins": 47545, + "Pattern": 47546, + "Ġexcise": 47547, + "Ġcabal": 47548, + "NSA": 47549, + "ĠSTA": 47550, + "ĠSIL": 47551, + "ĠParaly": 47552, + "Ġrye": 47553, + "ĠHowell": 47554, + "ĠCountdown": 47555, + "nesses": 47556, + "alysed": 47557, + "Ġresize": 47558, + "ãĤ½": 47559, + "Ġbudgetary": 47560, + "ĠStras": 47561, + "wang": 47562, + "Ġapiece": 47563, + "Ġprecincts": 47564, + "Ġpeach": 47565, + "Ġskyline": 47566, + "Ġ353": 47567, + "popular": 47568, + "Appearances": 47569, + "ĠMechanics": 47570, + "ĠDevOnline": 47571, + "Sullivan": 47572, + "Zen": 47573, + "Ġpu": 47574, + "opolis": 47575, + "544": 47576, + "Ġdeform": 47577, + "Ġcounteract": 47578, + "ĠLange": 47579, + "Ġ417": 47580, + "Console": 47581, + "774": 47582, + "Ġnodding": 47583, + "Ġpopulism": 47584, + "Ġhep": 47585, + "Ġcounselling": 47586, + "compliance": 47587, + "UFF": 47588, + "Ġundeniably": 47589, + "Ġrailing": 47590, + "ĠHorowitz": 47591, + "ĠSimone": 47592, + "ĠBungie": 47593, + "Ġak": 47594, + "ĠTalks": 47595, + "xff": 47596, + "flake": 47597, + "Crash": 47598, + "Ġsweaty": 47599, + "Ġbanquet": 47600, + "ĠOFFIC": 47601, + "Ġinventive": 47602, + "Ġastronomer": 47603, + "ĠStamford": 47604, + "ĠScare": 47605, + "ĠGREEN": 47606, + "olicited": 47607, + "Ġrusher": 47608, + "Ġcentrist": 47609, + "ighting": 47610, + "Ġsubclass": 47611, + "Ġdisav": 47612, + "Ġdefund": 47613, + "ĠNanto": 47614, + "ociate": 47615, + "mast": 47616, + "Ġpacif": 47617, + "Ġmend": 47618, + "eers": 47619, + "immigration": 47620, + "ESSION": 47621, + "Ġnumbering": 47622, + "Ġlaughable": 47623, + "ĠEnded": 47624, + "viation": 47625, + "emark": 47626, + "Pitt": 47627, + "Ġmeticulous": 47628, + "ĠLF": 47629, + "Ġcongratulated": 47630, + "ĠBirch": 47631, + "Ġswayed": 47632, + "Ġsemifinals": 47633, + "Ġhumankind": 47634, + "matter": 47635, + "ĠEquip": 47636, + "opausal": 47637, + "Said": 47638, + "ĠLayout": 47639, + "Ġvoicing": 47640, + "Ġthug": 47641, + "Ġpornographic": 47642, + "IPS": 47643, + "Ġmoaning": 47644, + "Ġgrievance": 47645, + "Ġconfessions": 47646, + "escal": 47647, + "TEXTURE": 47648, + "Authent": 47649, + "osaurus": 47650, + "Purchase": 47651, + "Ġrelegation": 47652, + "alter": 47653, + "Ġ³³": 47654, + "Ġriddled": 47655, + "Ġogre": 47656, + "ĠLowell": 47657, + "Occup": 47658, + "Eat": 47659, + "ĠHyder": 47660, + "ĠAdviser": 47661, + "Commerce": 47662, + "Hunt": 47663, + "ĠOrth": 47664, + "ĠCompetitive": 47665, + "ĠCLA": 47666, + "CDC": 47667, + "Ġsalads": 47668, + "Fle": 47669, + "Ġindustrialized": 47670, + "`,": 47671, + "ĠOWN": 47672, + "Ġbeck": 47673, + "ĠParticularly": 47674, + "oubt": 47675, + "ĠmM": 47676, + "ĠHussain": 47677, + "ĠChennai": 47678, + "Ġ920": 47679, + "Ġappointing": 47680, + "ĠCullen": 47681, + ",,,,,,,,": 47682, + "Ġpores": 47683, + "verified": 47684, + "Ġbiochemical": 47685, + "emate": 47686, + "Ġcowardly": 47687, + "ĠHelsinki": 47688, + "ĠEthiopian": 47689, + "SOURCE": 47690, + "ERC": 47691, + "estro": 47692, + "Ġbiotech": 47693, + "ĠSour": 47694, + "Ġbrewer": 47695, + "Bloomberg": 47696, + "Ġintensify": 47697, + "Glass": 47698, + "anco": 47699, + "ĠFDR": 47700, + "greSQL": 47701, + "ĠFires": 47702, + "©¶æ¥µ": 47703, + "eco": 47704, + "1001": 47705, + "ĠHomeless": 47706, + "Ġinstantaneous": 47707, + "ĠHaste": 47708, + "igel": 47709, + "Diamond": 47710, + "Ġpaving": 47711, + "Ġlandfill": 47712, + "Ġdads": 47713, + "houn": 47714, + ":]": 47715, + "Ġincendiary": 47716, + "ĠLivingston": 47717, + "ĠHilbert": 47718, + "ĠChecks": 47719, + "styles": 47720, + "inators": 47721, + "ĠClive": 47722, + "phrine": 47723, + "Ġchimpanzees": 47724, + "Ġpall": 47725, + "ĠJM": 47726, + "ĠAadhaar": 47727, + "ðĿ": 47728, + "Ġachievable": 47729, + "disabled": 47730, + "PET": 47731, + "OOOOOOOO": 47732, + "Mot": 47733, + "Ġintangible": 47734, + "Ġballet": 47735, + "ĠWebs": 47736, + "ĠEstimated": 47737, + "Effects": 47738, + "Ġbailed": 47739, + "Joshua": 47740, + "Ġturbulence": 47741, + "Ġoccupant": 47742, + "ĠDaylight": 47743, + "Ġ361": 47744, + "meet": 47745, + "Ġstatically": 47746, + "Ġonlook": 47747, + "Ġki": 47748, + "illegal": 47749, + "Ġvelvet": 47750, + "Ġdehydration": 47751, + "Ġacquies": 47752, + "ĠRez": 47753, + "akura": 47754, + "ĠUpton": 47755, + "atro": 47756, + "Ġincomprehensible": 47757, + "Ġbackdoor": 47758, + "ĠRhino": 47759, + "727": 47760, + "Ġmaths": 47761, + ")+": 47762, + "Ġheresy": 47763, + "Ġdf": 47764, + "ĠRoche": 47765, + "ĠLydia": 47766, + "Ġpancreat": 47767, + "reply": 47768, + "arrell": 47769, + "Ġsolicitation": 47770, + "Ġcircadian": 47771, + "BIP": 47772, + "Ġforay": 47773, + "Ġcryptic": 47774, + "izu": 47775, + "imeo": 47776, + "ĠTomato": 47777, + "ĠHoms": 47778, + "examination": 47779, + "Ġquarry": 47780, + "ĠValiant": 47781, + "ĠJericho": 47782, + "ĠINCLUD": 47783, + "Ġ1840": 47784, + "519": 47785, + "Ġresists": 47786, + "Ġsnapshots": 47787, + "ĠSpur": 47788, + "ĠAntiqu": 47789, + "Login": 47790, + "Ġbestselling": 47791, + "Ġantic": 47792, + "ĠSutherland": 47793, + "ãĤ¢ãĥ«": 47794, + "Ġ~/": 47795, + "ĠParm": 47796, + "èĥ": 47797, + "Pages": 47798, + "intensity": 47799, + "Ġimmobil": 47800, + "Ġ1865": 47801, + "zzo": 47802, + "Ġnifty": 47803, + "Ġfentanyl": 47804, + "ĠPreservation": 47805, + "ophen": 47806, + "Ġdarts": 47807, + "ĠDinosaur": 47808, + "pointers": 47809, + "ĠRite": 47810, + "suggest": 47811, + "awareness": 47812, + "ĠSheridan": 47813, + "Ġstances": 47814, + "Ġsorcery": 47815, + "Ġperjury": 47816, + "ĠNikola": 47817, + "iever": 47818, + "Ġfiance": 47819, + "ĠJordanian": 47820, + "ĠBalloon": 47821, + "Ġnab": 47822, + "Ġkb": 47823, + "Ġhumanities": 47824, + "ĠTanaka": 47825, + "hillary": 47826, + "Ġconsultancy": 47827, + "ĠZub": 47828, + "Ġremission": 47829, + "Ġconfid": 47830, + "CHQ": 47831, + "ĠFug": 47832, + "Ġimprovis": 47833, + "Yep": 47834, + "/_": 47835, + "Ġunwillingness": 47836, + "Ġportfolios": 47837, + "055": 47838, + "ĠInstructor": 47839, + "aiman": 47840, + "Ġclaimants": 47841, + "Mbps": 47842, + "ĠBye": 47843, + "received": 47844, + "Tweet": 47845, + "Ġindemn": 47846, + "riz": 47847, + "amara": 47848, + "Nat": 47849, + "Ġevaluates": 47850, + "ĠLur": 47851, + "epad": 47852, + "FOX": 47853, + "ĠThro": 47854, + "Ġrusty": 47855, + "Ġbedrock": 47856, + "ĠOprah": 47857, + "JB": 47858, + "Ġmanipulative": 47859, + "Ġwillful": 47860, + "Ġrelapse": 47861, + "Ġextant": 47862, + "Theme": 47863, + "Sensor": 47864, + "ĠStability": 47865, + "govern": 47866, + "Ġpoppy": 47867, + "Ġknack": 47868, + "Ġinsulated": 47869, + "ĠTile": 47870, + "ĠExtrem": 47871, + "Ġuntold": 47872, + "Ġconverge": 47873, + "Ġrefuel": 47874, + "igroup": 47875, + "Ġdistortions": 47876, + "Ġravaged": 47877, + "Ġmechanically": 47878, + "ĠReilly": 47879, + "ĠNose": 47880, + "ĠIncarnation": 47881, + "ĠBecky": 47882, + "abbling": 47883, + "Ġtaco": 47884, + "Ġrake": 47885, + "Ġmelancholy": 47886, + "Ġillustrious": 47887, + "ĠDartmouth": 47888, + "Guide": 47889, + "ĠRazer": 47890, + "ĠBenz": 47891, + "Ultimate": 47892, + "ĠSurprise": 47893, + "Ġpageant": 47894, + "offer": 47895, + "Whoever": 47896, + "Ġwiser": 47897, + "Ġchemist": 47898, + "ĠHELL": 47899, + "ĠBulk": 47900, + "Ġplutonium": 47901, + "ĠCOVER": 47902, + "Ö¼": 47903, + "failed": 47904, + "Ġtirelessly": 47905, + "Ġinfertility": 47906, + "ĠTrident": 47907, + "ĠShowtime": 47908, + "ĠCiv": 47909, + "Vice": 47910, + "requires": 47911, + "ittance": 47912, + "Ġuncontrolled": 47913, + "interesting": 47914, + "561": 47915, + "Ġinnovate": 47916, + "ategic": 47917, + "Lie": 47918, + "ĠSelling": 47919, + "Ul": 47920, + "Ġsavior": 47921, + "ĠTosh": 47922, + "Ġswast": 47923, + "PASS": 47924, + "Ġrink": 47925, + "Ġcardio": 47926, + "ĠIro": 47927, + "udi": 47928, + "Ġvantage": 47929, + "Ġvans": 47930, + "ĠNiño": 47931, + "+=": 47932, + "Ġpropagate": 47933, + "": 49029, + "Ġleukemia": 49030, + "Ġeluc": 49031, + "Ġannouncer": 49032, + "ĠLithuan": 49033, + "ĠArmageddon": 49034, + "åĩ": 49035, + "Lenin": 49036, + "ĠRuk": 49037, + "Ġpepp": 49038, + "ĠRomantic": 49039, + "ĠPIT": 49040, + "ĠInterstellar": 49041, + "ĠAtkinson": 49042, + "Raid": 49043, + "Js": 49044, + "Goal": 49045, + "Course": 49046, + "Ġvanishing": 49047, + "esley": 49048, + "ĠRounds": 49049, + "Elsa": 49050, + "593": 49051, + "Ġredundancy": 49052, + "ĠSTAND": 49053, + "Ġprophetic": 49054, + "Ġhabitable": 49055, + "ryu": 49056, + "Ġfaintly": 49057, + "MODE": 49058, + "Ġflanked": 49059, + "IRC": 49060, + "Awesome": 49061, + "Ġspurious": 49062, + "ĠZah": 49063, + "ĠMSG": 49064, + "Ġshading": 49065, + "Ġmotivational": 49066, + "ĠSantana": 49067, + "ĠSPR": 49068, + "Ġexcruciating": 49069, + "omial": 49070, + "ĠMiko": 49071, + "ĠLeopard": 49072, + "Abyss": 49073, + "Ġ[|": 49074, + "dirty": 49075, + "Ġbaths": 49076, + "Ġdemoral": 49077, + "andre": 49078, + "PB": 49079, + "Ġunification": 49080, + "Ġsacrament": 49081, + "Ġ[&": 49082, + "Ġpriceless": 49083, + "Ġgelatin": 49084, + "Ġemanating": 49085, + "ĠAllaah": 49086, + "986": 49087, + "Ġoutburst": 49088, + "Ġeras": 49089, + "ĠXVI": 49090, + "ĠSPI": 49091, + "Ott": 49092, + "ĠLazarus": 49093, + "PLIED": 49094, + "Flying": 49095, + "blogs": 49096, + "Wisconsin": 49097, + "Raven": 49098, + "Ġrebate": 49099, + "Ġcreeps": 49100, + "ĠSpan": 49101, + "ĠPainter": 49102, + "ĠKira": 49103, + "ĠAmos": 49104, + "ĠCorvette": 49105, + "Consumer": 49106, + "ĠRecover": 49107, + "cki": 49108, + "Ġpesky": 49109, + "ĠInvention": 49110, + "Companies": 49111, + "Ġchallengers": 49112, + "ademic": 49113, + "ĠUkrainians": 49114, + "ĠNeurolog": 49115, + "ĠForsaken": 49116, + "Ġentrants": 49117, + "Ġembattled": 49118, + "Ġdefunct": 49119, + "ĠGlacier": 49120, + "Ġpoisons": 49121, + "ĠHorses": 49122, + "makes": 49123, + "ĠDirt": 49124, + "Ġ423": 49125, + "hhh": 49126, + "ĠTransformation": 49127, + "QUIRE": 49128, + "..................": 49129, + "Ġtraveller": 49130, + "ĠSexy": 49131, + "ĠKern": 49132, + "ipolar": 49133, + "Ġransomware": 49134, + "oooooooooooooooo": 49135, + "Ec": 49136, + "ruby": 49137, + "Professional": 49138, + "ĠOutbreak": 49139, + "argument": 49140, + "Grey": 49141, + "ĠFifa": 49142, + "ĠCHO": 49143, + "ĠFORM": 49144, + "ĠAmtrak": 49145, + "-[": 49146, + "Ġcradle": 49147, + "Ġantioxidants": 49148, + "ãģ®å®": 49149, + "736": 49150, + "ĠNASL": 49151, + "ĠContributions": 49152, + "Indiana": 49153, + "ĠSTEP": 49154, + "CSS": 49155, + "Ġsalient": 49156, + "Ġallocations": 49157, + "yrights": 49158, + "Ġmashed": 49159, + "ĠCutter": 49160, + "Sexual": 49161, + "Ġpounded": 49162, + "Ġfanbase": 49163, + "Ġcasc": 49164, + "ĠTransparency": 49165, + "Ġanalytic": 49166, + "ĠSummoner": 49167, + "×ŀ": 49168, + "ĠADC": 49169, + "detail": 49170, + "Ġvanquished": 49171, + "Ġcrabs": 49172, + "arie": 49173, + "Destroy": 49174, + "ĠSack": 49175, + "Ġtransistor": 49176, + "Alabama": 49177, + "ĠKoen": 49178, + "ĠFisheries": 49179, + "cone": 49180, + "Ġannexed": 49181, + "ĠMGM": 49182, + "esa": 49183, + "Ġfaked": 49184, + "ĠCongratulations": 49185, + "Ġhindered": 49186, + "Ġcorrectional": 49187, + "ĠITV": 49188, + "leeve": 49189, + "Ġinappropriately": 49190, + "licks": 49191, + "Ġtrespass": 49192, + "Ġpaws": 49193, + "Ġnegotiator": 49194, + "ĠChristensen": 49195, + "limits": 49196, + "ĠDianne": 49197, + "Ġelegance": 49198, + "ĠContracts": 49199, + "anke": 49200, + "Obj": 49201, + "Ġvigilance": 49202, + "Ġcastles": 49203, + "ĠNAD": 49204, + "ĠHolo": 49205, + "Ġemphatically": 49206, + "ĠTitus": 49207, + "ĠServing": 49208, + "ĠRichie": 49209, + "ĠPigs": 49210, + "568": 49211, + "Ġanimosity": 49212, + "ĠAttributes": 49213, + "ĠUriel": 49214, + "MQ": 49215, + "myra": 49216, + "ĠApplicant": 49217, + "Ġpsychiatrists": 49218, + "ĠVij": 49219, + "ĠAbby": 49220, + "agree": 49221, + "Push": 49222, + "ĠkWh": 49223, + "hiba": 49224, + "Ġincite": 49225, + "ĠWeasley": 49226, + "ĠTaxi": 49227, + "ministic": 49228, + "hyper": 49229, + "ĠFarn": 49230, + "Ġ601": 49231, + "ĠNationwide": 49232, + "Fake": 49233, + "952": 49234, + "Ġmaize": 49235, + "Ġinteracted": 49236, + "Ġtransitioned": 49237, + "Ġparasitic": 49238, + "Ġharmonic": 49239, + "Ġdecaying": 49240, + "Ġbaseless": 49241, + "nsics": 49242, + "Ġtranspired": 49243, + "Ġabundantly": 49244, + "ĠForensic": 49245, + "Ġtreadmill": 49246, + "ĠJav": 49247, + "aband": 49248, + "Ġsshd": 49249, + "Ġfrontman": 49250, + "ĠJakarta": 49251, + "oller": 49252, + "drops": 49253, + "ĠSERVICES": 49254, + "romptu": 49255, + "ophical": 49256, + "hospital": 49257, + "bledon": 49258, + "645": 49259, + "Ġmidrange": 49260, + "ĠEVENT": 49261, + "culated": 49262, + "rawled": 49263, + "Ġperched": 49264, + "Ġoverboard": 49265, + "ĠPeel": 49266, + "ĠPwr": 49267, + "ĠCarth": 49268, + "ĠCOMPLE": 49269, + "coe": 49270, + "shall": 49271, + "Ġdeterrence": 49272, + "METHOD": 49273, + "ĠAbsent": 49274, + "MEN": 49275, + "Ġsill": 49276, + "ĠLEVEL": 49277, + "York": 49278, + "Ġsinners": 49279, + "ĠOPEC": 49280, + "ĠNur": 49281, + "ĠDesigns": 49282, + "selection": 49283, + "Ġunworthy": 49284, + "CHA": 49285, + "Ġstrengthens": 49286, + "883": 49287, + "edly": 49288, + "Ġslicing": 49289, + "Ġmalnutrition": 49290, + "Ġfilmmaking": 49291, + "ĠPolk": 49292, + "urated": 49293, + "Ġ421": 49294, + "breakers": 49295, + "!'\"": 49296, + "Ġwetlands": 49297, + "ĠDiscrimination": 49298, + "Ġallowable": 49299, + "Ġsteered": 49300, + "ĠSicily": 49301, + "SAM": 49302, + "Ġmustache": 49303, + "Ġmids": 49304, + "Ġclipped": 49305, + "Ġcirculate": 49306, + "Ġbrittle": 49307, + "ĠBuildings": 49308, + "raised": 49309, + "ĠRoundup": 49310, + "Ġwealthier": 49311, + "Ġoverwrite": 49312, + "Ġoverpowered": 49313, + "ĠGerrard": 49314, + "sites": 49315, + "PDATED": 49316, + "Ġacutely": 49317, + "ĠGamble": 49318, + "Ġpim": 49319, + "ĠKus": 49320, + "Typically": 49321, + "Deploy": 49322, + "ĠMoroccan": 49323, + "potion": 49324, + "combe": 49325, + "Ġvigilante": 49326, + "Ġ363": 49327, + "Stew": 49328, + "ĠBagg": 49329, + "Ġresided": 49330, + "ĠSpo": 49331, + "Ġremnant": 49332, + "Ġemptiness": 49333, + "brainer": 49334, + "Ġoutpatient": 49335, + "priority": 49336, + "Ġleptin": 49337, + "ĠPayton": 49338, + "ĠGleaming": 49339, + "ĠShed": 49340, + "ĠPolo": 49341, + "ĠMormonism": 49342, + "restricted": 49343, + "arlane": 49344, + "wx": 49345, + "Ġcreatine": 49346, + "ĠAnon": 49347, + "ĠSTUD": 49348, + "ĠJUL": 49349, + "ĠTee": 49350, + "528": 49351, + "089": 49352, + "Ġhatched": 49353, + "Dispatch": 49354, + "ĠComposite": 49355, + "Ġ451": 49356, + "puff": 49357, + "ĠXCOM": 49358, + "ĠOrn": 49359, + "ĠTHANK": 49360, + "ENDED": 49361, + "ĠAsheville": 49362, + "ĠÃľ": 49363, + "Ġmango": 49364, + "ĠSlightly": 49365, + "worldly": 49366, + "ĠWander": 49367, + "ĠExpand": 49368, + "ĠChr": 49369, + "Mist": 49370, + "Ġorthodoxy": 49371, + "ĠUNESCO": 49372, + "regate": 49373, + "Elsewhere": 49374, + "kie": 49375, + "irled": 49376, + "Ġtopple": 49377, + "Ġadoptive": 49378, + "ĠLegs": 49379, + "dress": 49380, + "ĠSagan": 49381, + "bare": 49382, + "ĠGlou": 49383, + "Crunch": 49384, + "Ġhelpers": 49385, + "Ġchronically": 49386, + "ĠHuma": 49387, + "10000": 49388, + "Ġaccommodating": 49389, + "äºĶ": 49390, + "Ġwrinkles": 49391, + "Ġdodged": 49392, + "fourth": 49393, + "Ġprecon": 49394, + "Ġcompressor": 49395, + "ĠKare": 49396, + "Ġevict": 49397, + "ĠWarwick": 49398, + "imar": 49399, + "Ġmodernization": 49400, + "Ġbandwagon": 49401, + "Ġrefuted": 49402, + "Ġnetted": 49403, + "ĠNaples": 49404, + "ĠGenie": 49405, + "perors": 49406, + "Ġfielded": 49407, + "Ġdere": 49408, + "ĠParables": 49409, + "lees": 49410, + "Ġtrout": 49411, + "aspers": 49412, + "Ġnihil": 49413, + "Ġhappiest": 49414, + "Ġfloppy": 49415, + "ĠLoft": 49416, + "ĠHeard": 49417, + "Ġunison": 49418, + "Ġlug": 49419, + "ĠRedmond": 49420, + "classic": 49421, + "Supporters": 49422, + "SHIP": 49423, + "GMT": 49424, + "Ġfuelled": 49425, + "çIJ": 49426, + "Ġdd": 49427, + "ĠEminem": 49428, + "Ġ1897": 49429, + "NYSE": 49430, + "Ġsecretaries": 49431, + "ĠFIA": 49432, + "ĠCanaveral": 49433, + "Favorite": 49434, + "Ġpomp": 49435, + "Ġdetainee": 49436, + "ership": 49437, + "aimon": 49438, + "iour": 49439, + "ĠApex": 49440, + "Ġplantations": 49441, + "amia": 49442, + "acion": 49443, + "Rust": 49444, + "Ġtowed": 49445, + "ĠTruly": 49446, + "577": 49447, + "Ġsheltered": 49448, + "rider": 49449, + "Wo": 49450, + "Ġlair": 49451, + "ĠIntelligent": 49452, + "improve": 49453, + "matically": 49454, + "Ġetiquette": 49455, + "adra": 49456, + "allo": 49457, + "ĠJuno": 49458, + "anything": 49459, + "ĠStruggle": 49460, + "ĠPredict": 49461, + "ĠGrimes": 49462, + "ĠAMERICA": 49463, + "ctx": 49464, + "ĠSituation": 49465, + "WOOD": 49466, + "Ġsoluble": 49467, + "meier": 49468, + "Ġintolerable": 49469, + "angering": 49470, + "Ġuninterrupted": 49471, + "Ġtooltip": 49472, + "Ġinterrogated": 49473, + "Ġgunned": 49474, + "ĠSneak": 49475, + "æѦ": 49476, + "Ġtether": 49477, + "Ġcrumble": 49478, + "Lens": 49479, + "Ġclustered": 49480, + "ĠSyl": 49481, + "ĠHasan": 49482, + "Ġdystopian": 49483, + "wana": 49484, + "Ġjoystick": 49485, + "ĠThib": 49486, + "ammu": 49487, + "Tomorrow": 49488, + "546": 49489, + "Ġovercame": 49490, + "Ġminimized": 49491, + "ceptor": 49492, + "Runner": 49493, + "ENGTH": 49494, + "ĠBrenda": 49495, + "ĠAchievements": 49496, + "Ġtorches": 49497, + "Ġrapport": 49498, + "ĠInvestigator": 49499, + "ĠHandling": 49500, + "relation": 49501, + "grey": 49502, + "815": 49503, + "Ġkcal": 49504, + "ĠCommands": 49505, + "dq": 49506, + "Ġcurls": 49507, + "Ġbearer": 49508, + "Ġcynicism": 49509, + "itri": 49510, + "ĠUseful": 49511, + "Bee": 49512, + "DCS": 49513, + "Ġabras": 49514, + "Pract": 49515, + "BILITIES": 49516, + "712": 49517, + "Ġdebugger": 49518, + "Ġdebtor": 49519, + "ĠLia": 49520, + "ĠKers": 49521, + "Ġexacerbate": 49522, + "ĠStacy": 49523, + "ĠBland": 49524, + "ĠScenes": 49525, + "Ġbranching": 49526, + "âĸĪâĸĪâĸĪâĸĪâĸĪâĸĪâĸĪâĸĪ": 49527, + "apeake": 49528, + "Ġsalsa": 49529, + "Ġmishand": 49530, + "ĠKonami": 49531, + "ĠNib": 49532, + "Ġanecdote": 49533, + "Ġagreeable": 49534, + "Ïī": 49535, + "ĠNathaniel": 49536, + "ĠHeisman": 49537, + "ĠBeware": 49538, + "Ġ1886": 49539, + "spective": 49540, + "691": 49541, + "522": 49542, + "Ġinhibits": 49543, + "Ġhashing": 49544, + "Ġ1889": 49545, + "å°Ĩ": 49546, + "vich": 49547, + "Pure": 49548, + "Ġsolidly": 49549, + "Ġaspirin": 49550, + "imaru": 49551, + "Ġstreetcar": 49552, + "ĠUCS": 49553, + "ĠJudd": 49554, + "Ġflashbacks": 49555, + "pins": 49556, + "Ġ1440": 49557, + "ĠUNHCR": 49558, + "ĠSymptoms": 49559, + "TIT": 49560, + "538": 49561, + "Fra": 49562, + "%);": 49563, + "Ġooz": 49564, + "Ġcurfew": 49565, + "Ġcalmed": 49566, + "Ġparticipates": 49567, + "TeX": 49568, + "Ġnonsensical": 49569, + "Ġfullback": 49570, + "ĠDeL": 49571, + "monkey": 49572, + "hari": 49573, + "Ġmetabolites": 49574, + "Ġlooted": 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You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. +2024-11-06 09:03:33.998689: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered +2024-11-06 09:03:34.018235: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered +2024-11-06 09:03:34.024188: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered +2024-11-06 09:03:34.038006: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. +To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. +2024-11-06 09:03:34.899647: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT +/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead. + warnings.warn( +/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 + warnings.warn( + * Serving Flask app 'app' + * Debug mode: off +Address already in use +Port 5000 is in use by another program. Either identify and stop that program, or start the server with a different port. +Exception in thread Thread-4 (run_gradio): +Traceback (most recent call last): + File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner + self.run() + File "/usr/lib/python3.10/threading.py", line 953, in run + self._target(*self._args, **self._kwargs) + File "/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/code/chatbot/app.py", line 162, in run_gradio + interface.launch(server_name="0.0.0.0", server_port=8080, share=True) + File "/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/gradio/blocks.py", line 2368, in launch + ) = http_server.start_server( + File "/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/gradio/http_server.py", line 154, in start_server + raise OSError( +OSError: Cannot find empty port in range: 8080-8080. You can specify a different port by setting the GRADIO_SERVER_PORT environment variable or passing the `server_port` parameter to `launch()`. +/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/gradio/analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.42.0, however version 4.44.1 is available, please upgrade. +-------- + warnings.warn( +Traceback (most recent call last): + File "/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/code/chatbot/app.py", line 12, in + import faiss +ModuleNotFoundError: No module named 'faiss' +2024-11-06 09:16:46.412670: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. +2024-11-06 09:16:46.426322: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered +2024-11-06 09:16:46.445934: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered +2024-11-06 09:16:46.451959: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered +2024-11-06 09:16:46.465820: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. +To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. +2024-11-06 09:16:47.323371: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT +/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead. + warnings.warn( +/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 + warnings.warn( + * Serving Flask app 'app' + * Debug mode: off +WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://127.0.0.1:5000 +Press CTRL+C to quit +/home/ubuntu/nvidia-workbench/rogerkorantenng-Mental-Health-Chat-Bot/venv/lib/python3.10/site-packages/gradio/analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.42.0, however version 4.44.1 is available, please upgrade. +-------- + warnings.warn( +Exception ignored in: +Traceback (most recent call last): + File "/usr/lib/python3.10/threading.py", line 1567, in _shutdown + lock.acquire() +KeyboardInterrupt: +Running on local URL: http://0.0.0.0:8080 +Running on public URL: https://97297d8d0789e6b8d0.gradio.live + +This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces) +Killing tunnel 0.0.0.0:8080 <> https://97297d8d0789e6b8d0.gradio.live diff --git a/chatbot/pie_chart.png b/chatbot/pie_chart.png new file mode 100644 index 0000000000000000000000000000000000000000..bd0b00d8a4a4e6d3bed55857f517b0ba9d0df361 Binary files /dev/null and b/chatbot/pie_chart.png differ diff --git a/chatbot/requirements.txt b/chatbot/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea1d71cd93d389c6ea1da07b804bbbec37c389a3 --- /dev/null +++ b/chatbot/requirements.txt @@ -0,0 +1,170 @@ +aiofiles==23.2.1 +aiohappyeyeballs==2.4.3 +aiohttp==3.10.10 +aiosignal==1.3.1 +analytics-python==1.4.post1 +annotated-types==0.7.0 +anyio==3.7.1 +asgiref==3.8.1 +async-timeout==4.0.3 +attrs==24.2.0 +backoff==1.10.0 +bcrypt==4.2.0 +build==1.2.2.post1 +cachetools==5.5.0 +certifi==2024.8.30 +cffi==1.17.1 +charset-normalizer==3.4.0 +chroma-bullet==2.2.0 +chroma-hnswlib==0.7.6 +chroma-migrate==0.0.7 +chromadb==0.5.18 +click==8.1.7 +clickhouse-connect==0.6.6 +coloredlogs==15.0.1 +contourpy==1.3.0 +cryptography==43.0.3 +cycler==0.12.1 +Deprecated==1.2.14 +duckdb==0.7.1 +durationpy==0.9 +exceptiongroup==1.2.2 +fastapi==0.115.4 +ffmpeg==1.4 +ffmpy==0.4.0 +filelock==3.16.1 +flatbuffers==24.3.25 +fonttools==4.54.1 +frozenlist==1.5.0 +fsspec==2024.10.0 +google-auth==2.36.0 +googleapis-common-protos==1.65.0 +gradio==5.5.0 +gradio_client==1.4.2 +grpcio==1.67.1 +gTTS==2.5.4 +h11==0.14.0 +HLL==2.2.0 +httpcore==1.0.6 +httptools==0.6.4 +httpx==0.27.2 +huggingface-hub==0.26.2 +humanfriendly==10.0 +idna==3.10 +importlib_metadata==8.5.0 +importlib_resources==6.4.5 +Jinja2==3.1.4 +joblib==1.4.2 +kiwisolver==1.4.7 +kubernetes==31.0.0 +linkify-it-py==2.0.3 +lz4==4.3.3 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +matplotlib==3.9.2 +mdit-py-plugins==0.4.2 +mdurl==0.1.2 +mmh3==5.0.1 +monotonic==1.6 +more-itertools==10.5.0 +mpmath==1.3.0 +multidict==6.1.0 +networkx==3.4.2 +nltk==3.9.1 +numpy==1.23.5 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cusparselt-cu12==0.6.2 +nvidia-nccl-cu12==2.21.5 +nvidia-nvjitlink-cu12==12.4.127 +nvidia-nvtx-cu12==12.1.105 +oauthlib==3.2.2 +onnxruntime==1.20.0 +opentelemetry-api==1.28.1 +opentelemetry-exporter-otlp-proto-common==1.28.1 +opentelemetry-exporter-otlp-proto-grpc==1.28.1 +opentelemetry-instrumentation==0.49b1 +opentelemetry-instrumentation-asgi==0.49b1 +opentelemetry-instrumentation-fastapi==0.49b1 +opentelemetry-proto==1.28.1 +opentelemetry-sdk==1.28.1 +opentelemetry-semantic-conventions==0.49b1 +opentelemetry-util-http==0.49b1 +orjson==3.10.11 +overrides==7.7.0 +packaging==24.2 +pandas==1.5.3 +paramiko==3.5.0 +pillow==11.0.0 +plotly==5.14.0 +posthog==3.7.0 +propcache==0.2.0 +protobuf==5.28.3 +pulsar-client==3.5.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycparser==2.22 +pycryptodome==3.21.0 +pydantic==2.9.2 +pydantic_core==2.23.4 +pydub==0.25.1 +Pygments==2.18.0 +PyNaCl==1.5.0 +pyparsing==3.2.0 +PyPika==0.48.9 +pyproject_hooks==1.2.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 +python-multipart==0.0.12 +pytorch-triton==3.1.0+cf34004b8a +pytz==2024.2 +PyYAML==6.0.2 +regex==2024.11.6 +requests==2.32.3 +requests-oauthlib==2.0.0 +rich==13.9.4 +rsa==4.9 +ruff==0.7.3 +safehttpx==0.1.1 +safetensors==0.4.5 +scikit-learn==1.1.3 +scipy==1.14.1 +semantic-version==2.10.0 +sentence-transformers==3.2.1 +sentencepiece==0.2.0 +shellingham==1.5.4 +six==1.16.0 +sniffio==1.3.1 +starlette==0.41.2 +sympy==1.13.1 +tenacity==9.0.0 +threadpoolctl==3.5.0 +tokenizers==0.20.3 +tomli==2.0.2 +tomlkit==0.12.0 +torch==2.6.0.dev20241107+cu121 +torchaudio==2.5.0.dev20241107+cu121 +torchvision==0.20.0.dev20241107+cu121 +tqdm==4.67.0 +transformers==4.46.2 +triton==3.1.0 +typer==0.13.0 +typing_extensions==4.12.2 +uc-micro-py==1.0.3 +urllib3==2.2.3 +uvicorn==0.32.0 +uvloop==0.21.0 +watchfiles==0.24.0 +websocket-client==1.8.0 +websockets==12.0 +wrapt==1.16.0 +yarl==1.17.1 +zipp==3.20.2 +zstandard==0.23.0 diff --git a/chatbot/static/bar_chart.png b/chatbot/static/bar_chart.png new file mode 100644 index 0000000000000000000000000000000000000000..5d56e523d7f51d6e4f8c93407f58b39a814e3cbd Binary files /dev/null and b/chatbot/static/bar_chart.png differ diff --git a/chatbot/static/line_chart.png b/chatbot/static/line_chart.png new file mode 100644 index 0000000000000000000000000000000000000000..9a8616378e6f94636b5cac2bc584c91b8ed41492 Binary files /dev/null and b/chatbot/static/line_chart.png differ diff --git a/chatbot/static/pie_chart.png b/chatbot/static/pie_chart.png new file mode 100644 index 0000000000000000000000000000000000000000..92ee22c434dbcce73fc2e24defb9a31a1a23665c Binary files /dev/null and b/chatbot/static/pie_chart.png differ diff --git a/chatbot/stock_changes.png b/chatbot/stock_changes.png new file mode 100644 index 0000000000000000000000000000000000000000..6dfaef5ddfadb7b437037d20faaf65675583d028 Binary files /dev/null and b/chatbot/stock_changes.png differ diff --git a/chatbot/test.py b/chatbot/test.py new file mode 100644 index 0000000000000000000000000000000000000000..27eac1c468da60bf04b27bd0874ffc15c42bf793 --- /dev/null +++ b/chatbot/test.py @@ -0,0 +1,487 @@ +import os +import json +import torch +import sqlite3 +import gradio as gr +import faiss +import pandas as pd +from datetime import datetime +from PIL import Image +from flask import Flask, jsonify, request +from werkzeug.utils import secure_filename +from threading import Thread +from transformers import AutoFeatureExtractor, AutoModelForImageClassification, AutoTokenizer, AutoModel, \ + AutoModelForCausalLM +from matplotlib import pyplot as plt +from sklearn.linear_model import LinearRegression +import numpy as np +import seaborn as sns +from torch.utils.data import Dataset, DataLoader +from torchvision import transforms +from torch import nn, optim +from torch.optim import lr_scheduler +from sklearn.preprocessing import LabelEncoder + + +# Initialize Flask app and models +app = Flask(__name__, static_folder="static") + +# Constants +LOW_STOCK_THRESHOLD = 5 # Customize threshold as needed +DATABASE = 'uploaded_images.db' + +# Available model options +MODEL_OPTIONS = { + "Google ViT (Base)": "google/vit-base-patch16-224", + "Google ViT (Large)": "google/vit-large-patch16-224", + "Microsoft ResNet50": "microsoft/resnet-50", + "Facebook ConvNeXt Tiny": "facebook/convnext-tiny-224", + "Microsoft Swin": "microsoft/swin-tiny-patch4-window7-224", +} + +# Set default model +selected_model_name = MODEL_OPTIONS["Google ViT (Base)"] +feature_extractor = AutoFeatureExtractor.from_pretrained(selected_model_name) +model = AutoModelForImageClassification.from_pretrained(selected_model_name) +class_names = model.config.id2label + +# Initialize inventory data +inventory_data = {} + + +# Initialize database +def init_db(): + conn = sqlite3.connect(DATABASE) + cursor = conn.cursor() + cursor.execute(""" + CREATE TABLE IF NOT EXISTS images ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + filename TEXT, + upload_time TEXT + ) + """) + conn.commit() + conn.close() + + +init_db() + + +def plot_bar_chart(inventory_data): + # Get the list of items and their counts + items = list(inventory_data.keys()) + counts = [inventory_data[item]["count"] for item in items] + + # Extract only the first word from each item name + first_words = [item.split()[0] for item in items] + + # Create the bar chart + fig, ax = plt.subplots(figsize=(8, 6)) + ax.bar(first_words, counts, color="skyblue") + + # Set titles and labels + ax.set_title("Inventory Counts") + ax.set_xlabel("Items") + ax.set_ylabel("Count") + + # Save the chart as a PNG file + chart_path = "bar_chart.png" + plt.tight_layout() + plt.savefig(chart_path) + plt.close() + + return chart_path + + + +def plot_line_chart(inventory_data): + # Extract items, counts, and dates from inventory data + items = list(inventory_data.keys()) + counts = [inventory_data[item]["count"] for item in items] + dates = [inventory_data[item]["last_detected"] for item in items] + + # Convert string dates to pandas datetime objects + dates = pd.to_datetime(dates) + + # Create a DataFrame with dates and counts + counts_df = pd.DataFrame({"Date": dates, "Count": counts}) + counts_df.sort_values("Date", inplace=True) + + # Format the 'Date' column into a human-readable format + counts_df["Date"] = counts_df["Date"].dt.strftime('%Y-%m-%d %H:%M:%S') + + # Create the plot + fig, ax = plt.subplots(figsize=(8, 6)) + ax.plot(counts_df["Date"], counts_df["Count"], marker="o", color="orange") + + # Set the title and labels for the plot + ax.set_title("Stock Changes Over Time") + ax.set_xlabel("Date") + ax.set_ylabel("Stock Level") + + # Save the plot as a PNG file + chart_path = "line_chart.png" + plt.xticks(rotation=45) # Rotate x-ticks for better readability + plt.tight_layout() + plt.savefig(chart_path) + plt.close() + + return chart_path + + +def plot_pie_chart(inventory_data): + # Get the list of items and their counts + items = list(inventory_data.keys()) + counts = [inventory_data[item]["count"] for item in items] + + # Extract only the first word from each item name + first_words = [item.split()[0] for item in items] + + # Create the pie chart + fig, ax = plt.subplots(figsize=(8, 6)) + ax.pie(counts, labels=first_words, autopct='%1.1f%%', startangle=140, colors=plt.cm.Paired.colors) + + # Set the title + ax.set_title("Product Category Breakdown") + + # Save the chart as a PNG file + chart_path = "pie_chart.png" + plt.tight_layout() + plt.savefig(chart_path) + plt.close() + + return chart_path + + + +def plot_heatmap(inventory_data): + # Get the list of items + items = list(inventory_data.keys()) + + # Extract only the first word from each item name + first_words = [item.split()[0] for item in items] + + # Create a matrix where the count is placed at the correct location + change_matrix = [[inventory_data[item]["count"] if item == other else 0 for other in items] for item in items] + + # Create the heatmap + fig, ax = plt.subplots(figsize=(8, 6)) + cax = ax.imshow(change_matrix, cmap="YlOrRd", interpolation='nearest') + # Set the labels on the x and y axes to the first words of the items + ax.set_xticks(range(len(items))) + ax.set_yticks(range(len(items))) + ax.set_xticklabels(first_words) + ax.set_yticklabels(first_words) + # Add a colorbar + fig.colorbar(cax) + # Set the title of the heatmap + ax.set_title("Stock Change Heatmap") + # Save the chart as a PNG file + chart_path = "heatmap.png" + plt.tight_layout() + plt.savefig(chart_path) + plt.close() + + return chart_path + + + +# Function to return the paths of the images +def generate_bar_chart(): + return plot_bar_chart(inventory_data) + + +def generate_line_chart(): + return plot_line_chart(inventory_data) + + +def generate_pie_chart(): + return plot_pie_chart(inventory_data) + + +def generate_heatmap(): + return plot_heatmap(inventory_data) + + +# Utility functions +def log_inventory_data(item_class): + try: + timestamp = datetime.now().isoformat() + + # Initialize the inventory data for the item class if it's not present + if item_class not in inventory_data: + inventory_data[item_class] = { + "category": item_class, + "count": 0, # Initial count + "last_detected": None, # Timestamp of last detection + "history": [] # Track the historical counts over time + } + + # Log the count and timestamp to the history + inventory_data[item_class]["history"].append({ + "timestamp": timestamp, # Store the timestamp as a string + "count": inventory_data[item_class]["count"] + }) + + # Update the count of the item class + inventory_data[item_class]["count"] += 1 + + # Update the last detected timestamp + inventory_data[item_class]["last_detected"] = timestamp + + # Optionally: Save the data to a file for persistence + with open("inventory_log.json", "w") as f: + json.dump(inventory_data, f, indent=4) + + print(f"Inventory data logged for item class: {item_class}") + + except Exception as e: + print(f"Error logging inventory data: {e}") + + +def check_stock_levels(): + try: + for item, details in inventory_data.items(): + if details["count"] < LOW_STOCK_THRESHOLD: + print(f"Low stock detected for {item}: {details['count']} items.") + except Exception as e: + print(f"Error checking stock levels: {e}") + + +def save_image_to_db(file): + filename = secure_filename(file.filename) + upload_time = datetime.now().isoformat() + file_path = os.path.join("uploads", filename) + file.save(file_path) + conn = sqlite3.connect(DATABASE) + cursor = conn.cursor() + cursor.execute("INSERT INTO images (filename, upload_time) VALUES (?, ?)", (filename, upload_time)) + conn.commit() + conn.close() + + +def batch_predict(images): + results = [] + try: + for image_file in images: + with Image.open(image_file.name) as image: + if image.mode != "RGB": + image = image.convert("RGB") + inputs = feature_extractor(images=image, return_tensors="pt") + outputs = model(**inputs) + predicted_class_id = torch.argmax(outputs.logits, dim=1).item() + item_class = class_names[predicted_class_id] + log_inventory_data(item_class) # Pass only the item_class argument + results.append({"Image": os.path.basename(image_file.name), "Classification": item_class}) + except Exception as e: + return [{"Image": "Error", "Classification": str(e)}] + return pd.DataFrame(results) + + +def forecast_inventory(item_class, days=7): + """Use linear regression for basic inventory forecasting.""" + try: + # Get the historical data for the item class + if item_class not in inventory_data or not inventory_data[item_class]["history"]: + return {"error": f"No historical data for {item_class}."} + + history = inventory_data[item_class]["history"] + timestamps = [datetime.fromisoformat(entry["timestamp"]) for entry in history] + counts = [entry["count"] for entry in history] + + # Convert timestamps to days since the first entry + days_since_first_entry = [(timestamp - timestamps[0]).days for timestamp in timestamps] + + # Apply linear regression to forecast the next `days` values + model = LinearRegression() + model.fit(np.array(days_since_first_entry).reshape(-1, 1), counts) + + # Predict future inventory counts + future_days = np.array([days_since_first_entry[-1] + i for i in range(1, days + 1)]).reshape(-1, 1) + predictions = model.predict(future_days) + + forecast = [{"day": i + 1, "predicted_count": int(predictions[i])} for i in range(days)] + return forecast + print(f"Inventory data logged for item class: {forecast}") + + except Exception as e: + return {"error": f"Error in forecasting: {str(e)}"} + + +def change_model(selected_model_key): + try: + global feature_extractor, model, class_names + + if selected_model_key not in MODEL_OPTIONS: + return "Error: Invalid model selection" + + # Retrieve the model path (either pre-trained or fine-tuned) + selected_model_path = MODEL_OPTIONS[selected_model_key] + + # Load the model from the specified path + feature_extractor = AutoFeatureExtractor.from_pretrained(selected_model_path) + model = AutoModelForImageClassification.from_pretrained(selected_model_path) + class_names = model.config.id2label + + return f"Model changed to {selected_model_key}" + except Exception as e: + return f"Error changing model: {str(e)}" + + +# Define a custom dataset class +class CustomDataset(Dataset): + def __init__(self, images, labels, transform=None): + self.images = images + self.labels = labels + self.transform = transform + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + image = Image.open(self.images[idx]).convert("RGB") + label = self.labels[idx] + if self.transform: + image = self.transform(image) + return image, label + +# Define transformations for image preprocessing +transform = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) +]) + +# Function to preprocess and load data +def preprocess_data(train_images, train_labels): + dataset = CustomDataset(train_images, train_labels, transform=transform) + dataloader = DataLoader(dataset, batch_size=16, shuffle=True) + return dataloader + + +# Fine-tune the model with a custom dataset +def fine_tune_model(train_images, train_labels, model_name="custom_model"): + try: + # Convert image paths to a suitable format + train_images = [image.name for image in train_images] # list of image paths + + # Check if labels need encoding + if isinstance(train_labels, str): + train_labels = train_labels.split(",") # Convert the comma-separated string to a list + + # Encode labels if they are not integers + label_encoder = LabelEncoder() + train_labels = label_encoder.fit_transform(train_labels) + + # Load the model and prepare for fine-tuning + global model + model = AutoModelForImageClassification.from_pretrained(selected_model_name) + + # Modify the classifier layer for the new dataset + num_labels = len(set(train_labels)) # Number of unique labels + model.classifier = nn.Linear(model.config.hidden_size, num_labels) + + # Prepare data loader + dataloader = preprocess_data(train_images, train_labels) + + # Define loss function, optimizer, and learning rate scheduler + criterion = nn.CrossEntropyLoss() + optimizer = optim.Adam(model.parameters(), lr=0.0001) + scheduler = lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) + + # Training loop + num_epochs = 50 # Set the number of epochs + model.train() # Set model to training mode + for epoch in range(num_epochs): + running_loss = 0.0 + for inputs, labels in dataloader: + optimizer.zero_grad() + + # Forward pass + outputs = model(inputs) + logits = outputs.logits if hasattr(outputs, "logits") else outputs # Handle outputs + + # Calculate loss + loss = criterion(logits, labels) + loss.backward() + optimizer.step() + + running_loss += loss.item() + + scheduler.step() + print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(dataloader)}") + # return f"Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(dataloader)}" + + # Save the fine-tuned model + fine_tuned_model_path = f"models/{model_name}" # Save path + model.save_pretrained(fine_tuned_model_path) + print(f"Fine-tuned model saved to {fine_tuned_model_path}") + + # Dynamically add the fine-tuned model to the model selection + MODEL_OPTIONS[model_name] = fine_tuned_model_path + return f"Fine-tuned model {model_name} has been added successfully!" + except Exception as e: + return f"Error fine-tuning the model: {str(e)}" + + + +proxy_prefix = os.environ.get("PROXY_PREFIX") + +# Gradio Interface +with gr.Blocks() as interface: + gr.Markdown("## VisionTrack - Smart Inventory Management and Analysis System") + + with gr.Tab("Model Selection/Fine-Tuning"): + with gr.Tab("Select Model"): + gr.Markdown("Choose a model for image classification.") + model_dropdown = gr.Dropdown(choices=list(MODEL_OPTIONS.keys()), label="Select Model", + value="Google ViT (Base)") + model_dropdown.change(change_model, inputs=model_dropdown, outputs=gr.Textbox(label="status")) + with gr.Tab("Model Fine-Tuning"): + gr.Markdown("Upload your custom dataset for model fine-tuning.") + train_image_input = gr.File(label="Upload Training Images", file_count="multiple", type="filepath") + train_label_input = gr.Textbox(label="Enter Corresponding Labels (comma-separated)") + fine_tune_button = gr.Button("Fine-Tune Model") + fine_tune_output = gr.Text(label="Training Status") + fine_tune_button.click(fine_tune_model, inputs=[train_image_input, train_label_input], + outputs=fine_tune_output) + + with gr.Tab("Image Classification"): + gr.Markdown("Upload images for classification.") + image_input = gr.File(label="Upload Images", file_count="multiple", type="filepath") + output = gr.DataFrame(label="Classification Results", interactive=True) + image_input.upload(batch_predict, inputs=image_input, outputs=output) + + with gr.Tab("Inventory"): + gr.Markdown("Check current inventory.") + inventory_display = gr.DataFrame(value=pd.DataFrame(columns=["Item", "Count", "Last Detected"])) + gr.Button("Fetch Inventory").click(lambda: pd.DataFrame(inventory_data).T, outputs=inventory_display) + + with gr.Tab("Forecasting"): + gr.Markdown("Forecast inventory levels for the next 7 days.") + item_class_input = gr.Textbox(label="Enter Item Class") + forecast_button = gr.Button("Forecast") + forecast_output = gr.Text(label="Predicted Inventory Forecast") + forecast_button.click(forecast_inventory, inputs=item_class_input, outputs=forecast_output) + + with gr.Tab("Inventory Dashboard"): + with gr.Tab("Bar Chart"): + bar_chart_image = gr.Image(type="filepath", label="Bar Chart") + gr.Button("Generate Bar Chart").click(generate_bar_chart, outputs=bar_chart_image) + + with gr.Tab("Line Chart"): + line_chart_image = gr.Image(type="filepath", label="Line Chart") + gr.Button("Generate Line Chart").click(generate_line_chart, outputs=line_chart_image) + + with gr.Tab("Pie Chart"): + pie_chart_image = gr.Image(type="filepath", label="Pie Chart") + gr.Button("Generate Pie Chart").click(generate_pie_chart, outputs=pie_chart_image) + + with gr.Tab("Heatmap"): + heatmap_image = gr.Image(type="filepath", label="Heatmap") + gr.Button("Generate Heatmap").click(generate_heatmap, outputs=heatmap_image) + +if __name__ == '__main__': + app_thread = Thread(target=lambda: app.run(debug=True, use_reloader=False)) + app_thread.start() + interface.launch(server_name="0.0.0.0", server_port=8080, root_path=proxy_prefix, share=True) diff --git a/chatbot/uploaded_images.db b/chatbot/uploaded_images.db new file mode 100644 index 0000000000000000000000000000000000000000..e2c396d9f762ad78792a5e0bed25ba5e78404548 Binary files /dev/null and b/chatbot/uploaded_images.db differ diff --git a/chatbot/visualizations.png b/chatbot/visualizations.png new file mode 100644 index 0000000000000000000000000000000000000000..fc61370a48c2dc537a2110501d5ce2d1161c695b Binary files /dev/null and b/chatbot/visualizations.png differ diff --git a/health-Copy1.ipynb b/health-Copy1.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..53a52d01c0683cb6a45c51bbce6d32411e32ce8d --- /dev/null +++ b/health-Copy1.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a6b7c9f9-db9d-4278-8e0c-192db80afb9b", + "metadata": {}, + "source": [ + "### Importing Libraries\n", + "\n", + "This cell imports the necessary libraries for the project. `keras` is a high-level neural networks API, and `keras_nlp` provides additional tools and functionalities for natural language processing tasks using Keras.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "09958eb5-8363-47dd-b508-353d6e538827", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-29 18:01:15.929029: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-08-29 18:01:15.944717: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-08-29 18:01:15.963838: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-08-29 18:01:15.969620: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-08-29 18:01:15.983677: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import keras\n", + "import keras_nlp\n", + "import tensorflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de2731f6-422e-46bf-839d-ef8f474e7742", + "metadata": {}, + "outputs": [], + "source": [ + "# import os\n", + "\n", + "# os.environ[\"KERAS_BACKEND\"] = \"jax\" \n", + "# # Avoid memory fragmentation on JAX backend.\n", + "# os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]=\"1.00\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8725e8ba-f1c3-4e5f-8bb8-1451e3a7a394", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import json\n", + "\n", + "# Initialize an empty list to hold the processed data.\n", + "data = []\n", + "\n", + "# Open and read the JSON file line by line.\n", + "with open('/project/data/combined_dataset.json') as file:\n", + " for line in file:\n", + " features = json.loads(line)\n", + " \n", + " # Filter out examples without \"Context\".\n", + " if not features.get(\"Context\"):\n", + " continue\n", + " \n", + " # Format the example as a string.\n", + " template = \"Instruction:\\n{Context}\\n\\nResponse:\\n{Response}\"\n", + " formatted_example = template.format(**features)\n", + " \n", + " # Append the formatted example to the data list.\n", + " data.append(formatted_example)\n", + "\n", + "# Now data contains a list of formatted strings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a26993cb-c8aa-42b1-943f-b6033d909336", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Set Kaggle API credentials\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"rogerkorantenng\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"9a33b6e88bcb6058b1281d777fa6808d\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "008c3f60-b0f6-4709-a7c7-836a6ea4f5cb", + "metadata": {}, + "outputs": [], + "source": [ + "gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\n", + "gemma_lm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fcd0823-61b8-4037-863d-d81dfcd8dec1", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(gemma_lm.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "834bffa8-4361-4ec9-8c3a-7403d3ce83c4", + "metadata": {}, + "outputs": [], + "source": [ + "# gemma_lm.save(\"gemma_model.h5\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3af9301d-2623-4773-9f3b-07efcc788fc4", + "metadata": {}, + "outputs": [], + "source": [ + "# Enable LoRA for the model and set the LoRA rank to 4.\n", + "gemma_lm.backbone.enable_lora(rank=10)\n", + "gemma_lm.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab365ee3-da5f-4b5c-b00b-428005ff42e4", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import tensorflow as tf\n", + "import keras_nlp\n", + "import keras\n", + "import json\n", + "\n", + "# Set Kaggle API credentials\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"rogerkorantenng\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"9a33b6e88bcb6058b1281d777fa6808d\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae2db527-0964-491d-8fd6-0c746cbcae2e", + "metadata": {}, + "outputs": [], + "source": [ + "def get_compiled_model():\n", + " gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\n", + " gemma_lm.summary()\n", + "\n", + " gemma_lm.backbone.enable_lora(rank=2)\n", + " gemma_lm.summary()\n", + " \n", + " # Set the sequence length to 128 before using the model.\n", + " gemma_lm.preprocessor.sequence_length = 128\n", + " \n", + " # Use AdamW (a common optimizer for transformer models).\n", + " optimizer = keras.optimizers.AdamW(\n", + " learning_rate=5e-5,\n", + " weight_decay=0.01,\n", + " )\n", + " \n", + " # Exclude layernorm and bias terms from decay.\n", + " optimizer.exclude_from_weight_decay(var_names=[\"bias\", \"scale\"])\n", + " \n", + " gemma_lm.compile(\n", + " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " optimizer=optimizer,\n", + " weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\n", + " )\n", + "\n", + " \n", + " return gemma_lm\n", + "print(gemma_lm.)\n", + "\n", + "print(gemma_lm.summary())\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd11ce52-9eb8-4e48-a4da-fdc759e7f789", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dataset():\n", + " # Initialize an empty list to hold the processed data.\n", + " data = []\n", + " \n", + " # Open and read the JSON file line by line.\n", + " with open('/project/data/HealthCareMagic-100k-en.jsonl') as file:\n", + " for line in file:\n", + " features = json.loads(line)\n", + " \n", + " # Filter out examples without \"Context\".\n", + " if not features.get(\"Context\"):\n", + " continue\n", + " \n", + " # Format the example as a string.\n", + " template = \"Instruction:\\n{Context}\\n\\nResponse:\\n{Response}\"\n", + " formatted_example = template.format(**features)\n", + " \n", + " # Append the formatted example to the data list.\n", + " data.append(formatted_example)\n", + " \n", + " return data " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ec94c5f-70b6-4683-95d4-53c1231f5a9c", + "metadata": {}, + "outputs": [], + "source": [ + "model = get_compiled_model()\n", + "\n", + "# Get the dataset outside the strategy scope.\n", + "data = get_dataset()\n", + "\n", + "# Fit the model using the data.\n", + "model.fit(data, epochs=2, batch_size=0, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bfe053b2-a02f-4e2b-af04-48c28a00c20e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello\n" + ] + } + ], + "source": [ + "print('Hello')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35d93608-b1ef-44a1-a57b-4c1a3d3dbebb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..dd63fd25c27f4d1770803c8ddd17e0ee73851d3c --- /dev/null +++ b/main.py @@ -0,0 +1,348 @@ +import gradio as gr +import os +import json +from dotenv import load_dotenv +import requests +from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline +from huggingface_hub import login +from datetime import datetime +import numpy as np +import torch +from gtts import gTTS +import tempfile +from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer +import torch + +# Load environment variables from .env file +load_dotenv() +token = os.getenv("HF_TOKEN") + +# Use the token in the login function +login(token=token) +# File paths for storing model configurations and chat history +MODEL_CONFIG_FILE = "model_config.json" +CHAT_HISTORY_FILE = "chat_history.json" + +# Load model configurations from a JSON file (if exists) +def load_model_config(): + if os.path.exists(MODEL_CONFIG_FILE): + with open(MODEL_CONFIG_FILE, 'r') as f: + return json.load(f) + return { + "gpt-4": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_API_KEY"), + "model_path": None # No model path for API models + }, + "gpt-4o": { + "endpoint": "https://roger-m38jr9pd-eastus2.openai.azure.com/openai/deployments/gpt-4o/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4O_API_KEY"), + "model_path": None + }, + "gpt-35-turbo": { + "endpoint": "https://rogerkoranteng.openai.azure.com/openai/deployments/gpt-35-turbo/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT35_TURBO_API_KEY"), + "model_path": None + }, + "gpt-4-32k": { + "endpoint": "https://roger-m38orjxq-australiaeast.openai.azure.com/openai/deployments/gpt-4-32k/chat/completions?api-version=2024-08-01-preview", + "api_key": os.getenv("GPT4_32K_API_KEY"), + "model_path": None + } + } + +predefined_messages = { + "feeling_sad": "Hello, I am feeling sad today, what should I do?", + "Nobody likes me": "Hello, Sage. I feel like nobody likes me. What should I do?", + 'Boyfriend broke up': "Hi Sage, my boyfriend broke up with me. I'm feeling so sad. What should I do?", + 'I am lonely': "Hi Sage, I am feeling lonely. What should I do?", + 'I am stressed': "Hi Sage, I am feeling stressed. What should I do?", + 'I am anxious': "Hi Sage, I am feeling anxious. What should I do?", +} + +# Save model configuration to JSON +def save_model_config(): + with open(MODEL_CONFIG_FILE, 'w') as f: + json.dump(model_config, f, indent=4) + +# Load chat history from a JSON file +def load_chat_history(): + if os.path.exists(CHAT_HISTORY_FILE): + with open(CHAT_HISTORY_FILE, 'r') as f: + return json.load(f) + return [] + +# Save chat history to a JSON file +def save_chat_history(chat_history): + with open(CHAT_HISTORY_FILE, 'w') as f: + json.dump(chat_history, f, indent=4) + +# Define model configurations +model_config = load_model_config() + +# Function to dynamically add downloaded model to model_config +def add_downloaded_model(model_name, model_path): + model_config[model_name] = { + "endpoint": None, + "model_path": model_path, + "api_key": None + } + save_model_config() + return list(model_config.keys()) + +# Function to download model from Hugging Face synchronously +def download_model(model_name): + try: + tokenizer = AutoTokenizer.from_pretrained(model_name) + model = AutoModelForCausalLM.from_pretrained(model_name) + model_path = f"./models/{model_name}" + os.makedirs(model_path, exist_ok=True) + model.save_pretrained(model_path) + tokenizer.save_pretrained(model_path) + updated_models = add_downloaded_model(model_name, model_path) + return f"Model '{model_name}' downloaded and added.", updated_models + except Exception as e: + return f"Error downloading model '{model_name}': {e}", list(model_config.keys()) + +# Chat function using the selected model +def generate_response(model_choice, user_message, chat_history): + model_info = model_config.get(model_choice) + if not model_info: + return "Invalid model selection. Please choose a valid model.", chat_history + + chat_history.append({"role": "user", "content": user_message}) + headers = {"Content-Type": "application/json"} + + # Check if the model is an API model (it will have an endpoint) + if model_info["endpoint"]: + if model_info["api_key"]: + headers["api-key"] = model_info["api_key"] + + data = {"messages": chat_history, "max_tokens": 1500, "temperature": 0.7} + + try: + # Send request to the API model endpoint + response = requests.post(model_info["endpoint"], headers=headers, json=data) + response.raise_for_status() + assistant_message = response.json()['choices'][0]['message']['content'] + chat_history.append({"role": "assistant", "content": assistant_message}) + save_chat_history(chat_history) # Save chat history to JSON + except requests.exceptions.RequestException as e: + assistant_message = f"Error: {e}" + chat_history.append({"role": "assistant", "content": assistant_message}) + save_chat_history(chat_history) + else: + # If it's a local model, load the model and tokenizer from the local path + model_path = model_info["model_path"] + try: + tokenizer = AutoTokenizer.from_pretrained(model_path) + model = AutoModelForCausalLM.from_pretrained(model_path) + + inputs = tokenizer(user_message, return_tensors="pt") + outputs = model.generate(inputs['input_ids'], max_length=500, num_return_sequences=1) + assistant_message = tokenizer.decode(outputs[0], skip_special_tokens=True) + + chat_history.append({"role": "assistant", "content": assistant_message}) + save_chat_history(chat_history) + except Exception as e: + assistant_message = f"Error loading model locally: {e}" + chat_history.append({"role": "assistant", "content": assistant_message}) + save_chat_history(chat_history) + + # Convert the assistant message to audio + tts = gTTS(assistant_message) + audio_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") + tts.save(audio_file.name) + + return chat_history, audio_file.name + +# Function to format chat history with custom bubble styles +def format_chat_bubble(history): + formatted_history = "" + for message in history: + timestamp = datetime.now().strftime("%H:%M:%S") + if message["role"] == "user": + formatted_history += f''' +
+ Me: {message["content"]} +
+ ''' + else: + formatted_history += f''' +
+ Sage: {message["content"]} +
+ ''' + return formatted_history + +tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h") +model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") + +def transcribe(audio): + if audio is None: + return "No audio input received." + + sr, y = audio + + # Convert to mono if stereo + if y.ndim > 1: + y = y.mean(axis=1) + + y = y.astype(np.float32) + y /= np.max(np.abs(y)) + + # Tokenize the audio + input_values = tokenizer(y, return_tensors="pt", sampling_rate=sr).input_values + + # Perform inference + with torch.no_grad(): + logits = model(input_values).logits + + # Decode the logits + predicted_ids = torch.argmax(logits, dim=-1) + transcription = tokenizer.decode(predicted_ids[0]) + + return transcription + +# Create the Gradio interface +with gr.Blocks() as interface: + gr.Markdown("## Chat with Sage - Your Mental Health Advisor") + + with gr.Tab("Model Management"): + with gr.Tabs(): + with gr.TabItem("Model Selection"): + gr.Markdown("### Select Model for Chat") + model_dropdown = gr.Dropdown(choices=list(model_config.keys()), label="Choose a Model", value="gpt-4", + allow_custom_value=True) + status_textbox = gr.Textbox(label="Model Selection Status", value="Selected model: gpt-4") + model_dropdown.change(lambda model: f"Selected model: {model}", inputs=model_dropdown, + outputs=status_textbox) + + with gr.TabItem("Download Model"): # Sub-tab for downloading models + gr.Markdown("### Download a Model from Hugging Face") + model_name_input = gr.Textbox(label="Enter Model Name from Hugging Face (e.g., gpt2)") + download_button = gr.Button("Download Model") + download_status = gr.Textbox(label="Download Status") + + # Model download synchronous handler + def on_model_download(model_name): + download_message, updated_models = download_model(model_name) + # Trigger the dropdown update to show the newly added model + return download_message, gr.update(choices=updated_models, value=updated_models[-1]) + + download_button.click(on_model_download, inputs=model_name_input, + outputs=[download_status, model_dropdown]) + + refresh_button = gr.Button("Refresh Model List") + refresh_button.click(lambda: gr.update(choices=list(model_config.keys())), inputs=[], + outputs=model_dropdown) + + with gr.Tab("Chat Interface"): + gr.Markdown("### Chat with Sage") + + # Chat history state for tracking conversation + chat_history_state = gr.State(load_chat_history()) # Load existing chat history + + # Add initial introduction message + if not chat_history_state.value: + chat_history_state.value.append({"role": "assistant", "content": "Hello, I am Sage. How can I assist you today?"}) + + chat_display = gr.HTML(label="Chat", value=format_chat_bubble(chat_history_state.value), elem_id="chat-display") + + user_message = gr.Textbox(placeholder="Type your message here...", label="Your Message") + send_button = gr.Button("Send Message") + + # Predefined message buttons + predefined_buttons = [gr.Button(value=msg) for msg in predefined_messages.values()] + + # Real-time message updating + def update_chat(model_choice, user_message, chat_history_state): + chat_history, audio_file = generate_response(model_choice, user_message, chat_history_state) + formatted_chat = format_chat_bubble(chat_history) + return formatted_chat, chat_history, audio_file + + send_button.click( + update_chat, + inputs=[model_dropdown, user_message, chat_history_state], + outputs=[chat_display, chat_history_state, gr.Audio(autoplay=True)] + ) + + send_button.click(lambda: "", None, user_message) # Clears the user input after sending + + # Add click events for predefined message buttons + for button, message in zip(predefined_buttons, predefined_messages.values()): + button.click( + update_chat, + inputs=[model_dropdown, gr.State(message), chat_history_state], + outputs=[chat_display, chat_history_state, gr.Audio(autoplay=True)] + ) + + with gr.Tab("Speech Interface"): + gr.Markdown("### Speak with Sage") + + audio_input = gr.Audio(type="numpy") + transcribe_button = gr.Button("Transcribe") + transcribed_text = gr.Textbox(label="Transcribed Text") + + transcribe_button.click( + transcribe, + inputs=audio_input, + outputs=transcribed_text + ) + + send_speech_button = gr.Button("Send Speech Message") + + send_speech_button.click( + update_chat, + inputs=[model_dropdown, transcribed_text, chat_history_state], + outputs=[chat_display, chat_history_state, gr.Audio(autoplay=True)] + ) + + # Add custom CSS for scrolling chat box and bubbles + interface.css = """ + #chat-display { + max-height: 500px; + overflow-y: auto; + padding: 10px; + background-color: #1a1a1a; + border-radius: 10px; + display: flex; + flex-direction: column; + justify-content: flex-start; + box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.1); + scroll-behavior: smooth; + } + + /* User message style - text only */ + .user-bubble { + color: #ffffff; /* Text color for the user */ + padding: 8px 15px; + margin: 8px 0; + word-wrap: break-word; + align-self: flex-end; + font-size: 14px; + position: relative; + max-width: 70%; /* Make the bubble width dynamic */ + border-radius: 15px; + background-color: #121212; /* Light cyan background for the user */ + transition: color 0.3s ease; + } + + /* Assistant message style - text only */ + .assistant-bubble { + color: #ffffff; /* Text color for the assistant */ + padding: 8px 15px; + margin: 8px 0; + word-wrap: break-word; + align-self: flex-start; + background-color: #2a2a2a; + font-size: 14px; + position: relative; + max-width: 70%; + transition: color 0.3s ease; + } + + """ + +# Launch the Gradio interface +interface.launch(server_name="0.0.0.0", server_port=8080, share=True) diff --git a/model fine-tunning.ipynb b/model fine-tunning.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a5fc70b601593fef532b842c7253c19a187adff2 --- /dev/null +++ b/model fine-tunning.ipynb @@ -0,0 +1,747 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "96d10e25-bc25-4058-baaa-a74ab15af266", + "metadata": {}, + "source": [ + "# Kaggle API Setup for TensorFlow and Keras NLP Project\n", + "Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models.\n", + "\n", + "## Introduction\n", + "In this notebook, we will prepare the environment to download and utilize the Gemma model in a TensorFlow and Keras NLP project. We will begin by importing the necessary libraries, followed by configuring the Kaggle API credentials to enable seamless access to the required datasets.\n", + "\n", + "## 1. Importing Required Libraries\n", + "To start, we will import the essential libraries for this project, including TensorFlow, Keras, and Keras NLP, which are crucial for building and deploying NLP models.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "996572a0-edb7-49c9-89cc-27ad9b5a42d2", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Import required libraries\n", + "\n", + "import os\n", + "import tensorflow as tf\n", + "import keras_nlp\n", + "import keras\n", + "import json\n", + "\n", + "# Ignore Warnings\n", + "from silence_tensorflow import silence_tensorflow\n", + "silence_tensorflow()\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "29ec8457-c4b5-4017-b190-812e0b2428f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Set Kaggle API credentials\n", + "\n", + "os.environ[\"KAGGLE_USERNAME\"] = \"rogerkorantenng\"\n", + "os.environ[\"KAGGLE_KEY\"] = \"\"" + ] + }, + { + "cell_type": "markdown", + "id": "0145d6ca-9424-4c32-9e87-7cbac86cf65f", + "metadata": {}, + "source": [ + "## 2. Building and Compiling the Gemma Model\n", + "\n", + "In this section, we will build and compile the Gemma model, a language model designed for natural language processing (NLP) tasks. The process involves several key steps: loading the pre-trained model, enabling fine-tuning with LoRA (Low-Rank Adaptation), configuring the input sequence length, setting up the optimizer, and compiling the model for training.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e8045579-6e77-4879-b630-d1a9d2550a4d", + "metadata": {}, + "outputs": [], + "source": [ + "def get_compiled_model():\n", + " gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(\"gemma_2b_en\")\n", + " gemma_lm.summary()\n", + "\n", + " gemma_lm.backbone.enable_lora(rank=4)\n", + " gemma_lm.summary()\n", + " \n", + " # Set the sequence length to 128 before using the model.\n", + " gemma_lm.preprocessor.sequence_length = 256\n", + " \n", + " # Use AdamW (a common optimizer for transformer models).\n", + " optimizer = keras.optimizers.AdamW(\n", + " learning_rate=5e-5,\n", + " weight_decay=0.01,\n", + " )\n", + " \n", + " # Exclude layernorm and bias terms from decay.\n", + " optimizer.exclude_from_weight_decay(var_names=[\"bias\", \"scale\"])\n", + " \n", + " gemma_lm.compile(\n", + " loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " optimizer=optimizer,\n", + " weighted_metrics=[keras.metrics.SparseCategoricalAccuracy()],\n", + " )\n", + "\n", + " \n", + " return gemma_lm" + ] + }, + { + "cell_type": "markdown", + "id": "6475baba-ce54-40ec-a484-22629936c845", + "metadata": {}, + "source": [ + "## 3. Loading and Processing the Dataset\n", + "\n", + "In this section, we will define a function to load and process a JSON dataset. The dataset is read line by line, and each line is parsed and formatted according to the required structure. The function returns a list of formatted examples that can be used for training or analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "238f65b1-75c2-42a1-abd3-b4d8c7c36799", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dataset():\n", + " # Initialize an empty list to hold the processed data.\n", + " data = []\n", + " \n", + " # Open and read the JSON file line by line.\n", + " with open('/project/data/combined_dataset.json') as file:\n", + " for line in file:\n", + " features = json.loads(line)\n", + " \n", + " # Filter out examples without \"Context\".\n", + " if not features.get(\"Context\"):\n", + " continue\n", + " \n", + " # Format the example as a string.\n", + " template = \"Instruction:\\n{Context}\\n\\nResponse:\\n{Response}\"\n", + " formatted_example = template.format(**features)\n", + " \n", + " # Append the formatted example to the data list.\n", + " data.append(formatted_example)\n", + " \n", + " return data " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d589e374-cc82-4132-9613-23ee064aa10e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-30 16:13:39.953235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13775 MB memory: -> device: 0, name: Tesla T4, pci bus id: 0000:18:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.954747: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13775 MB memory: -> device: 1, name: Tesla T4, pci bus id: 0000:19:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.956103: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 13775 MB memory: -> device: 2, name: Tesla T4, pci bus id: 0000:35:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.957459: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:3 with 13775 MB memory: -> device: 3, name: Tesla T4, pci bus id: 0000:36:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.958812: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:4 with 13775 MB memory: -> device: 4, name: Tesla T4, pci bus id: 0000:e7:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.960166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:5 with 13775 MB memory: -> device: 5, name: Tesla T4, pci bus id: 0000:e8:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.961521: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:6 with 13775 MB memory: -> device: 6, name: Tesla T4, pci bus id: 0000:f4:00.0, compute capability: 7.5\n", + "2024-08-30 16:13:39.962850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:7 with 13775 MB memory: -> device: 7, name: Tesla T4, pci bus id: 0000:f5:00.0, compute capability: 7.5\n", + "normalizer.cc(51) LOG(INFO) precompiled_charsmap is empty. use identity normalization.\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "┃ Tokenizer (type)                                   ┃                                             Vocab # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │\n",
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+       "┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ gemma_backbone                │ (None, None, 2048)        │   2,506,172,416 │ padding_mask[0][0],        │\n",
+       "│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_embedding               │ (None, None, 256000)      │     524,288,000 │ gemma_backbone[0][0]       │\n",
+       "│ (ReversibleEmbedding)         │                           │                 │                            │\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Tokenizer (type)                                   ┃                                             Vocab # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ gemma_tokenizer (GemmaTokenizer)                   │                                             256,000 │\n",
+       "└────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┘\n",
+       "
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Model: \"gemma_causal_lm\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                  ┃ Output Shape              ┃         Param # ┃ Connected to               ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+       "│ padding_mask (InputLayer)     │ (None, None)              │               0 │ -                          │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_ids (InputLayer)        │ (None, None)              │               0 │ -                          │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ gemma_backbone                │ (None, None, 2048)        │   2,507,536,384 │ padding_mask[0][0],        │\n",
+       "│ (GemmaBackbone)               │                           │                 │ token_ids[0][0]            │\n",
+       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
+       "│ token_embedding               │ (None, None, 256000)      │     524,288,000 │ gemma_backbone[0][0]       │\n",
+       "│ (ReversibleEmbedding)         │                           │                 │                            │\n",
+       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
+       "
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+       "
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+       "
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+       "
\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,506,172,416\u001b[0m (9.34 GB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get Model and Compile\n", + "model = get_compiled_model()\n", + "\n", + "# Get the dataset outside the strategy scope.\n", + "data = get_dataset()" + ] + }, + { + "cell_type": "markdown", + "id": "50106081-cd9e-4246-974e-a4e7db99be97", + "metadata": {}, + "source": [ + "## 4. Defining the Prompt Template and Generating Responses\n", + "\n", + "In this section, we define a template for creating prompts that the language model will use to generate responses. The template includes placeholders for an 'instruction' and a 'response'. We then format this template with actual data to create a complete prompt. Finally, the prompt is passed to the language model to generate a response.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "47a8c8da-b73a-4684-9199-176b4fa9bd3d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-30 16:13:57.828298: E tensorflow/core/util/util.cc:131] oneDNN supports DT_INT64 only on platforms with AVX-512. Falling back to the default Eigen-based implementation if present.\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "I0000 00:00:1725034446.797146 18073 service.cc:146] XLA service 0x5a7be0cb31d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n", + "I0000 00:00:1725034446.797176 18073 service.cc:154] StreamExecutor device (0): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797180 18073 service.cc:154] StreamExecutor device (1): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797182 18073 service.cc:154] StreamExecutor device (2): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797185 18073 service.cc:154] StreamExecutor device (3): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797188 18073 service.cc:154] StreamExecutor device (4): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797190 18073 service.cc:154] StreamExecutor device (5): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797193 18073 service.cc:154] StreamExecutor device (6): Tesla T4, Compute Capability 7.5\n", + "I0000 00:00:1725034446.797196 18073 service.cc:154] StreamExecutor device (7): Tesla T4, Compute Capability 7.5\n", + "2024-08-30 16:14:07.351952: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n", + "2024-08-30 16:14:09.094695: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8905\n", + "I0000 00:00:1725034456.429832 18073 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instruction:\n", + "I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\n", + " I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\n", + " How can I change my feeling of being worthless to everyone?\n", + "\n", + "Response:\n", + "I'm sorry to hear that you're going through some things. I'm not sure what you mean by \"I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\" I'm not sure what you mean by \"I've never tried or contemplated suicide.\" I'm not sure what you mean by \"I've always wanted to fix my issues, but I never get around to it.\" I'm not sure what you mean by \"How can I change my feeling of being worthless to everyone?\"\n", + "\n", + "I'm not sure what you mean by \"I'm sorry to hear that you're going through some things.\"\n", + "\n", + "I'm not sure what you mean by \"I'm not sure what you mean by 'I barely sleep\n" + ] + } + ], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(model.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "markdown", + "id": "9784a448-b7a2-40e7-8162-3caa3ac64d22", + "metadata": {}, + "source": [ + "## 5. Model Fine Tunning\n", + "\n", + "In this section, we compile the model, prepare the dataset, and then train the model using the data. We will walk through the steps of obtaining the compiled model, loading the dataset, and fitting the model to the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9355a347-5893-4f94-bff8-09cb126d818d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/40\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "W0000 00:00:1725034506.217331 18546 assert_op.cc:38] Ignoring Assert operator compile_loss/sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert\n", + "2024-08-30 16:15:23.759806: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'loop_add_subtract_fusion_2', 220 bytes spill stores, 220 bytes spill loads\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2455s\u001b[0m 682ms/step - loss: 2.0513 - sparse_categorical_accuracy: 0.4572\n", + "Epoch 2/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.9421 - sparse_categorical_accuracy: 0.4760\n", + "Epoch 3/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.8639 - sparse_categorical_accuracy: 0.4929\n", + "Epoch 4/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.7974 - sparse_categorical_accuracy: 0.5083\n", + "Epoch 5/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 683ms/step - loss: 1.7301 - sparse_categorical_accuracy: 0.5239\n", + "Epoch 6/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.6699 - sparse_categorical_accuracy: 0.5380\n", + "Epoch 7/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.6151 - sparse_categorical_accuracy: 0.5512\n", + "Epoch 8/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.5641 - sparse_categorical_accuracy: 0.5633\n", + "Epoch 9/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2393s\u001b[0m 681ms/step - loss: 1.5194 - sparse_categorical_accuracy: 0.5776\n", + "Epoch 10/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2392s\u001b[0m 681ms/step - loss: 1.4795 - sparse_categorical_accuracy: 0.5869\n", + "Epoch 11/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2393s\u001b[0m 681ms/step - loss: 1.4477 - sparse_categorical_accuracy: 0.5949\n", + "Epoch 12/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2395s\u001b[0m 682ms/step - loss: 1.4191 - sparse_categorical_accuracy: 0.6025\n", + "Epoch 13/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 683ms/step - loss: 1.3948 - sparse_categorical_accuracy: 0.6080\n", + "Epoch 14/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.3707 - sparse_categorical_accuracy: 0.6142\n", + "Epoch 15/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.3508 - sparse_categorical_accuracy: 0.6195\n", + "Epoch 16/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.3308 - sparse_categorical_accuracy: 0.6236\n", + "Epoch 17/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.3068 - sparse_categorical_accuracy: 0.6303\n", + "Epoch 18/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.2879 - sparse_categorical_accuracy: 0.6350\n", + "Epoch 19/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2401s\u001b[0m 683ms/step - loss: 1.2676 - sparse_categorical_accuracy: 0.6395\n", + "Epoch 20/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.2474 - sparse_categorical_accuracy: 0.6444\n", + "Epoch 21/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2395s\u001b[0m 682ms/step - loss: 1.2283 - sparse_categorical_accuracy: 0.6491\n", + "Epoch 22/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.2086 - sparse_categorical_accuracy: 0.6543\n", + "Epoch 23/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.1896 - sparse_categorical_accuracy: 0.6593\n", + "Epoch 24/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.1706 - sparse_categorical_accuracy: 0.6644\n", + "Epoch 25/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.1508 - sparse_categorical_accuracy: 0.6695\n", + "Epoch 26/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2401s\u001b[0m 683ms/step - loss: 1.1322 - sparse_categorical_accuracy: 0.6744\n", + "Epoch 27/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2400s\u001b[0m 683ms/step - loss: 1.1152 - sparse_categorical_accuracy: 0.6789\n", + "Epoch 28/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2397s\u001b[0m 682ms/step - loss: 1.0921 - sparse_categorical_accuracy: 0.6851\n", + "Epoch 29/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2396s\u001b[0m 682ms/step - loss: 1.0791 - sparse_categorical_accuracy: 0.6881\n", + "Epoch 30/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2398s\u001b[0m 683ms/step - loss: 1.0581 - sparse_categorical_accuracy: 0.6941\n", + "Epoch 31/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2399s\u001b[0m 683ms/step - loss: 1.0382 - sparse_categorical_accuracy: 0.6994\n", + "Epoch 32/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2401s\u001b[0m 684ms/step - loss: 1.0208 - sparse_categorical_accuracy: 0.7045\n", + "Epoch 33/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 1.0037 - sparse_categorical_accuracy: 0.7089\n", + "Epoch 34/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 0.9862 - sparse_categorical_accuracy: 0.7137\n", + "Epoch 35/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2405s\u001b[0m 685ms/step - loss: 0.9688 - sparse_categorical_accuracy: 0.7183\n", + "Epoch 36/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 0.9554 - sparse_categorical_accuracy: 0.7219\n", + "Epoch 37/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2402s\u001b[0m 684ms/step - loss: 0.9479 - sparse_categorical_accuracy: 0.7239\n", + "Epoch 38/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2404s\u001b[0m 685ms/step - loss: 0.9224 - sparse_categorical_accuracy: 0.7313\n", + "Epoch 39/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2408s\u001b[0m 686ms/step - loss: 0.9132 - sparse_categorical_accuracy: 0.7335\n", + "Epoch 40/40\n", + "\u001b[1m3512/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2410s\u001b[0m 686ms/step - loss: 0.8930 - sparse_categorical_accuracy: 0.7399\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit the model using the data.\n", + "model.fit(data, epochs=40, batch_size=1, verbose=1)" + ] + }, + { + "cell_type": "markdown", + "id": "8ca51424-9aa2-4933-9f97-106eec5347c5", + "metadata": {}, + "source": [ + "## 6. Generating a Response from the Fine-Tuned Language Model\n", + "\n", + "In this section, we will define a template for generating prompts and use the language model `gemma_lm` to generate a response based on the provided instruction. This process involves creating a formatted prompt and then using the model to produce a response.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9199e8bd-3269-4b4a-bb8b-78399965883f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instruction:\n", + "I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\n", + " I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\n", + " How can I change my feeling of being worthless to everyone?\n", + "\n", + "Response:\n", + "It sounds like you are having a really tough time with feeling this way.  Feeling this way is not normal and it is important to talk about these feelings with someone.  You are not worthless, you are a wonderful person who is going through some tough times.  Therapy can help you to work through these issues and come to a place of self-love and acceptance.  You can do this!  \n" + ] + } + ], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(model.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "252c7c12-e763-4524-8cd4-7582431efb02", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m3136/3512\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m4:11\u001b[0m 668ms/step - loss: 0.3683 - sparse_categorical_accuracy: 0.8940" + ] + } + ], + "source": [ + "model.fit(data, epochs=1, batch_size=1, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1fda3d9d-488d-46a2-8f4e-b0b83f175426", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Instruction:\n", + "I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\n", + " I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\n", + " How can I change my feeling of being worthless to everyone?\n", + "\n", + "Response:\n", + "I'm glad you're willing to slow down, but it's a complicated feeling that can be challenging to identify and work through. I've heard it described as feeling like a heavy cloud or rain cloud, and it's good to know where you are when you want to know what to do about it.It is possible to know more about this feeling and why it's arising from time to time like this. It helps to know that what you're experiencing is likely to do with your sense of who you are and your sense of how important you are in people's lives. It's also helpful to know that this feeling is something that's happening deep within you, not something outside of you.It can be valuable to look at the relationships in your life and consider your place in them.\n" + ] + } + ], + "source": [ + "# Define the template with placeholders for 'instruction' and 'response'\n", + "template = \"Instruction:\\n{instruction}\\n\\nResponse:\\n{response}\"\n", + "\n", + "# Create the prompt by formatting the template with actual data\n", + "prompt = template.format(\n", + " instruction=\"I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think about how I'm worthless and how I shouldn't be here.\\n I've never tried or contemplated suicide. I've always wanted to fix my issues, but I never get around to it.\\n How can I change my feeling of being worthless to everyone?\",\n", + " response=\"\",\n", + ")\n", + "\n", + "# Assuming gemma_lm is a language model that you're using to generate text\n", + "print(model.generate(prompt, max_length=256))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11bda333-9e55-4a62-9992-3d595d27ff54", + "metadata": {}, + "outputs": [], + "source": [ + "model.backbone.save_lora_weights(\"model2.lora.h5\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..ea1d71cd93d389c6ea1da07b804bbbec37c389a3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,170 @@ +aiofiles==23.2.1 +aiohappyeyeballs==2.4.3 +aiohttp==3.10.10 +aiosignal==1.3.1 +analytics-python==1.4.post1 +annotated-types==0.7.0 +anyio==3.7.1 +asgiref==3.8.1 +async-timeout==4.0.3 +attrs==24.2.0 +backoff==1.10.0 +bcrypt==4.2.0 +build==1.2.2.post1 +cachetools==5.5.0 +certifi==2024.8.30 +cffi==1.17.1 +charset-normalizer==3.4.0 +chroma-bullet==2.2.0 +chroma-hnswlib==0.7.6 +chroma-migrate==0.0.7 +chromadb==0.5.18 +click==8.1.7 +clickhouse-connect==0.6.6 +coloredlogs==15.0.1 +contourpy==1.3.0 +cryptography==43.0.3 +cycler==0.12.1 +Deprecated==1.2.14 +duckdb==0.7.1 +durationpy==0.9 +exceptiongroup==1.2.2 +fastapi==0.115.4 +ffmpeg==1.4 +ffmpy==0.4.0 +filelock==3.16.1 +flatbuffers==24.3.25 +fonttools==4.54.1 +frozenlist==1.5.0 +fsspec==2024.10.0 +google-auth==2.36.0 +googleapis-common-protos==1.65.0 +gradio==5.5.0 +gradio_client==1.4.2 +grpcio==1.67.1 +gTTS==2.5.4 +h11==0.14.0 +HLL==2.2.0 +httpcore==1.0.6 +httptools==0.6.4 +httpx==0.27.2 +huggingface-hub==0.26.2 +humanfriendly==10.0 +idna==3.10 +importlib_metadata==8.5.0 +importlib_resources==6.4.5 +Jinja2==3.1.4 +joblib==1.4.2 +kiwisolver==1.4.7 +kubernetes==31.0.0 +linkify-it-py==2.0.3 +lz4==4.3.3 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +matplotlib==3.9.2 +mdit-py-plugins==0.4.2 +mdurl==0.1.2 +mmh3==5.0.1 +monotonic==1.6 +more-itertools==10.5.0 +mpmath==1.3.0 +multidict==6.1.0 +networkx==3.4.2 +nltk==3.9.1 +numpy==1.23.5 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-cusparselt-cu12==0.6.2 +nvidia-nccl-cu12==2.21.5 +nvidia-nvjitlink-cu12==12.4.127 +nvidia-nvtx-cu12==12.1.105 +oauthlib==3.2.2 +onnxruntime==1.20.0 +opentelemetry-api==1.28.1 +opentelemetry-exporter-otlp-proto-common==1.28.1 +opentelemetry-exporter-otlp-proto-grpc==1.28.1 +opentelemetry-instrumentation==0.49b1 +opentelemetry-instrumentation-asgi==0.49b1 +opentelemetry-instrumentation-fastapi==0.49b1 +opentelemetry-proto==1.28.1 +opentelemetry-sdk==1.28.1 +opentelemetry-semantic-conventions==0.49b1 +opentelemetry-util-http==0.49b1 +orjson==3.10.11 +overrides==7.7.0 +packaging==24.2 +pandas==1.5.3 +paramiko==3.5.0 +pillow==11.0.0 +plotly==5.14.0 +posthog==3.7.0 +propcache==0.2.0 +protobuf==5.28.3 +pulsar-client==3.5.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycparser==2.22 +pycryptodome==3.21.0 +pydantic==2.9.2 +pydantic_core==2.23.4 +pydub==0.25.1 +Pygments==2.18.0 +PyNaCl==1.5.0 +pyparsing==3.2.0 +PyPika==0.48.9 +pyproject_hooks==1.2.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.0.1 +python-multipart==0.0.12 +pytorch-triton==3.1.0+cf34004b8a +pytz==2024.2 +PyYAML==6.0.2 +regex==2024.11.6 +requests==2.32.3 +requests-oauthlib==2.0.0 +rich==13.9.4 +rsa==4.9 +ruff==0.7.3 +safehttpx==0.1.1 +safetensors==0.4.5 +scikit-learn==1.1.3 +scipy==1.14.1 +semantic-version==2.10.0 +sentence-transformers==3.2.1 +sentencepiece==0.2.0 +shellingham==1.5.4 +six==1.16.0 +sniffio==1.3.1 +starlette==0.41.2 +sympy==1.13.1 +tenacity==9.0.0 +threadpoolctl==3.5.0 +tokenizers==0.20.3 +tomli==2.0.2 +tomlkit==0.12.0 +torch==2.6.0.dev20241107+cu121 +torchaudio==2.5.0.dev20241107+cu121 +torchvision==0.20.0.dev20241107+cu121 +tqdm==4.67.0 +transformers==4.46.2 +triton==3.1.0 +typer==0.13.0 +typing_extensions==4.12.2 +uc-micro-py==1.0.3 +urllib3==2.2.3 +uvicorn==0.32.0 +uvloop==0.21.0 +watchfiles==0.24.0 +websocket-client==1.8.0 +websockets==12.0 +wrapt==1.16.0 +yarl==1.17.1 +zipp==3.20.2 +zstandard==0.23.0