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Le","userId":"02258782812217274557"}}},"outputs":[],"source":["import re\n","import string\n","import numpy as np\n","import random\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline\n","from plotly import graph_objs as go\n","import plotly.express as px\n","import plotly.figure_factory as ff\n","from collections import Counter\n","\n","from PIL import Image\n","from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n","\n","\n","import nltk\n","from nltk.corpus import stopwords\n","from nltk.tokenize import word_tokenize\n","\n","from tqdm import tqdm\n","import os\n","import nltk\n","import spacy\n","import random\n","from spacy.util import compounding\n","from spacy.util import minibatch\n","\n","from collections import defaultdict\n","from collections import Counter\n","\n","import keras\n","from keras.models import Sequential\n","from keras.initializers import Constant\n","from keras.layers import (LSTM,\n","                          Embedding,\n","                          BatchNormalization,\n","                          Dense,\n","                          TimeDistributed,\n","                          Dropout,\n","                          Bidirectional,\n","                          Flatten,\n","                          GlobalMaxPool1D)\n","from keras.preprocessing.text import Tokenizer\n","from keras.preprocessing.sequence import pad_sequences\n","# from keras.layers.embeddings import Embedding\n","from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n","from keras.optimizers import Adam\n","\n","from sklearn.metrics import (\n","    precision_score,\n","    recall_score,\n","    f1_score,\n","    classification_report,\n","    accuracy_score\n",")\n","import torch, os\n","import pandas as pd\n","from transformers import pipeline, BertForSequenceClassification, BertTokenizerFast\n","from torch.utils.data import Dataset"]},{"cell_type":"code","source":["!pip install accelerate -U"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"te4T8LBfQU2v","executionInfo":{"status":"ok","timestamp":1721280039419,"user_tz":-420,"elapsed":71504,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"d5f487ec-917c-4a78-9b99-066995f812d7"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting accelerate\n","  Downloading accelerate-0.32.1-py3-none-any.whl (314 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/314.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m204.8/314.1 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta 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/usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.7.4)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n","Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n","Successfully installed accelerate-0.32.1 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-cu12-8.9.2.26 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-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105\n"]}]},{"cell_type":"code","source":["dataset = pd.read_csv(\"/content/drive/MyDrive/ThaiLand/Data/finetuned/afterProcessing.csv\")"],"metadata":{"id":"-ILANLavQXm_","executionInfo":{"status":"ok","timestamp":1721280115790,"user_tz":-420,"elapsed":673,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["from torch import cuda\n","device = 'cuda' if cuda.is_available() else 'cpu'\n","device"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"iAf1Ub1rQoaV","executionInfo":{"status":"ok","timestamp":1721280115791,"user_tz":-420,"elapsed":18,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"d5425a20-66c9-46a9-824e-ed8463b3c5f5"},"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'cuda'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":3}]},{"cell_type":"code","source":["dataset = dataset.sample(frac=1.0, random_state=42)"],"metadata":{"id":"3l45jG65Q7QL","executionInfo":{"status":"ok","timestamp":1721280115791,"user_tz":-420,"elapsed":16,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["labels = dataset['sentiment'].unique().tolist()\n","labels = [s.strip() for s in labels ]\n","labels"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cDmG_mWWQ9Zh","executionInfo":{"status":"ok","timestamp":1721280115791,"user_tz":-420,"elapsed":16,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"b4132e30-5414-457c-9185-a5f629a0bd6d"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Positive', 'Neutral', 'Negative']"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["for key, value in enumerate(labels):\n","    print(value)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-gPsU9_iRAnS","executionInfo":{"status":"ok","timestamp":1721280115791,"user_tz":-420,"elapsed":15,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"f732f5b8-52f2-47d5-9998-a7e11a3b7658"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Positive\n","Neutral\n","Negative\n"]}]},{"cell_type":"code","source":["NUM_LABELS= len(labels)\n","\n","id2label={id:label for id,label in enumerate(labels)}\n","\n","label2id={label:id for id,label in enumerate(labels)}"],"metadata":{"id":"ZD4qISrFRBoI","executionInfo":{"status":"ok","timestamp":1721280115791,"user_tz":-420,"elapsed":14,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["dataset[\"labels\"]=dataset.sentiment.map(lambda x: label2id[x.strip()])"],"metadata":{"id":"3ZXKRXtrREns","executionInfo":{"status":"ok","timestamp":1721280115792,"user_tz":-420,"elapsed":15,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["tokenizer = BertTokenizerFast.from_pretrained(\"ProsusAI/finbert\", max_length=512)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":249,"referenced_widgets":["4c1db77f7131490c9b7913a0e657c061","77ee6ab4b31f47789cb9f1df7dcc1dc9","343f349617cb40bdbce950f5e0c39e7c","12dea7a6f5c5453f90df11a068a0eb21","d745b639f7a2417e9162b8755daf46c4","4e9e03501e1f4852acd6cf6c25a63a38","c64b5aa4b012456aa8ee272097f59770","d194cd9ab7134e43b19b1c4dfb013b54","50262a51f70245bf85dae103f13f8b0c","10805c2321a743b393a589b849d24ab4","d502d3b63e954ef2a1db0f6c80eeaf22","2dc61c5fe0fe47a28851b1a89a3fd81b","9c43569ede994d23bbfed687b2ba0a9e","5d9f334c833a4eb290954f1fe138fef9","f6883b2a67b849aaba998ac86543447a","d957b82a5cf34bf09cf3246cfdb977c0","2d6cdb4acfcf44da85040b15f26f8663","350aab0911694f0bb0d25a189b5597da","e7f11a9bb67c47db8dfcd075abfa680d","758ae67c24204729853e96ae7d6d198f","3c7d7ede820b4f36a5a82380dc0418cb","94a1ab82e3ef4ce1b23b7c19eba4522e","c52bd193e16b4a46bd9448919848b1ba","dbb0b4dc9b06410eb470c3a0f816a1d6","f8041547dbd44b1296ea2a9dfd116b27","31f416302aa64a0fb809fa2750c7a68b","2bf239b1c803422dbaf1146074a909d6","308e4b8a771c4149b54f9920c3054c65","8cfd0ff385f44fe68c7e7905d90fde77","0fbbc4bc74054caf864aca2141ccb81e","d157dffa311f42dc8050cf49a65d5af3","42f5e9c8f503468f970b66bbdff7ba59","50200764bb584526a72cdb3c83a91a1d","e7b3556f30ed499283cb50e1e6e99077","7b0f65c69b7746da8fc05b83f2c82a5d","92b4291ad78d4a2e872b2066cb33bb27","ce04528401cc4b64ab55f3292839e824","a3a8b14b44be4ee0a896209d2cac3ed6","9039bf753f65475c82190c17943f80eb","cc539086ade141838b077de8c7a74d3e","1b962e27eac04d35a8a517fd719e4845","f31d0c35190b41f8b1e8658bf652fd90","94b2178d06914cc29a309b21807006ef","639833706e064d50a735a161894f5f6f"]},"id":"5a23ZfEGRG8D","executionInfo":{"status":"ok","timestamp":1721280118966,"user_tz":-420,"elapsed":3188,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"ff0540ad-975d-4229-b432-b13212b3781b"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/252 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4c1db77f7131490c9b7913a0e657c061"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2dc61c5fe0fe47a28851b1a89a3fd81b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c52bd193e16b4a46bd9448919848b1ba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/758 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e7b3556f30ed499283cb50e1e6e99077"}},"metadata":{}}]},{"cell_type":"code","source":["model = BertForSequenceClassification.from_pretrained(\"ProsusAI/finbert\", num_labels=NUM_LABELS, id2label=id2label, label2id=label2id)\n","model.to(device)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":830,"referenced_widgets":["f120eab4b3f14e69b4f89b39f148dbd5","0e9daf41077a41a5aff590a09f5b5dd8","443374200c584bcab07055f3b808dad6","07917e3a063b49fa9c4bae32bcc51a59","063a9adac27d4999ae8f36a21d9e008e","a67408aaca384dc589fe5dea995c16ce","10255410a4464cebaaf79bb6ba749a07","d53d8d9178cf43459292a48ab5b9a251","42b5efe09cf3498cbb11d4303f81d55d","6754bfbea6ed4e1297a30cfaaeaaf274","aa34bdc0a29943c49b6bcd2297a0dd75"]},"id":"YoBBpG39RVcH","executionInfo":{"status":"ok","timestamp":1721280127571,"user_tz":-420,"elapsed":8608,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"014dd7f3-42fe-46d1-b959-7c278767ee12"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/438M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f120eab4b3f14e69b4f89b39f148dbd5"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["BertForSequenceClassification(\n","  (bert): BertModel(\n","    (embeddings): BertEmbeddings(\n","      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n","      (position_embeddings): Embedding(512, 768)\n","      (token_type_embeddings): Embedding(2, 768)\n","      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (encoder): BertEncoder(\n","      (layer): ModuleList(\n","        (0-11): 12 x BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSdpaSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","            (intermediate_act_fn): GELUActivation()\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (pooler): BertPooler(\n","      (dense): Linear(in_features=768, out_features=768, bias=True)\n","      (activation): Tanh()\n","    )\n","  )\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (classifier): Linear(in_features=768, out_features=3, bias=True)\n",")"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["SIZE= dataset.shape[0]\n","\n","train_texts= list(dataset.reviewText[:SIZE//2])\n","\n","val_texts=   list(dataset.reviewText[SIZE//2:(3*SIZE)//4 ])\n","\n","test_texts=  list(dataset.reviewText[(3*SIZE)//4:])\n","\n","train_labels= list(dataset.labels[:SIZE//2])\n","\n","val_labels=   list(dataset.labels[SIZE//2:(3*SIZE)//4])\n","\n","test_labels=  list(dataset.labels[(3*SIZE)//4:])"],"metadata":{"id":"O-v65aDGRXSM","executionInfo":{"status":"ok","timestamp":1721280127571,"user_tz":-420,"elapsed":10,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["train_encodings = tokenizer(train_texts, truncation=True, padding=True)\n","val_encodings  = tokenizer(val_texts, truncation=True, padding=True)\n","test_encodings = tokenizer(test_texts, truncation=True, padding=True)"],"metadata":{"id":"ja-CiNG8RZOP","executionInfo":{"status":"ok","timestamp":1721280145185,"user_tz":-420,"elapsed":17623,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["class DataLoader(Dataset):\n","    \"\"\"\n","    Custom Dataset class for handling tokenized text data and corresponding labels.\n","    Inherits from torch.utils.data.Dataset.\n","    \"\"\"\n","    def __init__(self, encodings, labels):\n","        \"\"\"\n","        Initializes the DataLoader class with encodings and labels.\n","\n","        Args:\n","            encodings (dict): A dictionary containing tokenized input text data\n","                              (e.g., 'input_ids', 'token_type_ids', 'attention_mask').\n","            labels (list): A list of integer labels for the input text data.\n","        \"\"\"\n","        self.encodings = encodings\n","        self.labels = labels\n","\n","    def __getitem__(self, idx):\n","        \"\"\"\n","        Returns a dictionary containing tokenized data and the corresponding label for a given index.\n","\n","        Args:\n","            idx (int): The index of the data item to retrieve.\n","\n","        Returns:\n","            item (dict): A dictionary containing the tokenized data and the corresponding label.\n","        \"\"\"\n","        # Retrieve tokenized data for the given index\n","        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n","        # Add the label for the given index to the item dictionary\n","        item['labels'] = torch.tensor(self.labels[idx])\n","        return item\n","\n","    def __len__(self):\n","        \"\"\"\n","        Returns the number of data items in the dataset.\n","\n","        Returns:\n","            (int): The number of data items in the dataset.\n","        \"\"\"\n","        return len(self.labels)"],"metadata":{"id":"WuJIe7vSRa0l","executionInfo":{"status":"ok","timestamp":1721280145185,"user_tz":-420,"elapsed":10,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["train_dataloader = DataLoader(train_encodings, train_labels)\n","\n","val_dataloader = DataLoader(val_encodings, val_labels)\n","\n","test_dataset = DataLoader(test_encodings, test_labels)"],"metadata":{"id":"PwBYUbdpRdPK","executionInfo":{"status":"ok","timestamp":1721280145186,"user_tz":-420,"elapsed":11,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"IK-dS2RMRe2o","executionInfo":{"status":"ok","timestamp":1721280145186,"user_tz":-420,"elapsed":10,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["from transformers import TrainingArguments, Trainer"],"metadata":{"id":"maFkC1zqRew7","executionInfo":{"status":"ok","timestamp":1721280145772,"user_tz":-420,"elapsed":596,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n","\n","def compute_metrics(pred):\n","    \"\"\"\n","    Computes accuracy, F1, precision, and recall for a given set of predictions.\n","\n","    Args:\n","        pred (obj): An object containing label_ids and predictions attributes.\n","            - label_ids (array-like): A 1D array of true class labels.\n","            - predictions (array-like): A 2D array where each row represents\n","              an observation, and each column represents the probability of\n","              that observation belonging to a certain class.\n","\n","    Returns:\n","        dict: A dictionary containing the following metrics:\n","            - Accuracy (float): The proportion of correctly classified instances.\n","            - F1 (float): The macro F1 score, which is the harmonic mean of precision\n","              and recall. Macro averaging calculates the metric independently for\n","              each class and then takes the average.\n","            - Precision (float): The macro precision, which is the number of true\n","              positives divided by the sum of true positives and false positives.\n","            - Recall (float): The macro recall, which is the number of true positives\n","              divided by the sum of true positives and false negatives.\n","    \"\"\"\n","    # Extract true labels from the input object\n","    labels = pred.label_ids\n","\n","    # Obtain predicted class labels by finding the column index with the maximum probability\n","    preds = pred.predictions.argmax(-1)\n","\n","    # Compute macro precision, recall, and F1 score using sklearn's precision_recall_fscore_support function\n","    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='macro')\n","\n","    # Calculate the accuracy score using sklearn's accuracy_score function\n","    acc = accuracy_score(labels, preds)\n","\n","    # Return the computed metrics as a dictionary\n","    return {\n","        'Accuracy': acc,\n","        'F1': f1,\n","        'Precision': precision,\n","        'Recall': recall\n","    }"],"metadata":{"id":"v4UANqI_RhBo","executionInfo":{"status":"ok","timestamp":1721280145772,"user_tz":-420,"elapsed":5,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["training_args = TrainingArguments(\n","    # The output directory where the model predictions and checkpoints will be written\n","    output_dir='/content/drive/MyDrive/ThaiLand/model/finetuned/finBert',\n","    do_train=True,\n","    do_eval=True,\n","    #  The number of epochs, defaults to 3.0\n","    num_train_epochs=3,\n","    per_device_train_batch_size=32,\n","    per_device_eval_batch_size=32,\n","    # Number of steps used for a linear warmup\n","    warmup_steps=100,\n","    weight_decay=0.01,\n","    logging_strategy='steps',\n","   # TensorBoard log directory\n","    logging_dir='/content/drive/MyDrive/ThaiLand/model/finetuned/finBert/multi-class-logs',\n","    logging_steps=50,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=50,\n","    save_strategy=\"steps\",\n","    fp16=True,\n","    load_best_model_at_end=True\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FnPF3omzRjPS","executionInfo":{"status":"ok","timestamp":1721280145772,"user_tz":-420,"elapsed":4,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"0357a899-f8d3-4e40-e114-a3632fb442e4"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n","  warnings.warn(\n"]}]},{"cell_type":"code","source":["trainer = Trainer(\n","    # the pre-trained model that will be fine-tuned\n","    model=model,\n","     # training arguments that we defined above\n","    args=training_args,\n","    train_dataset=train_dataloader,\n","    eval_dataset=val_dataloader,\n","    compute_metrics= compute_metrics\n","\n",")"],"metadata":{"id":"ZUP4E17zRlaq","executionInfo":{"status":"ok","timestamp":1721280145772,"user_tz":-420,"elapsed":2,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"im4XwWC4Rra-","executionInfo":{"status":"ok","timestamp":1721283943570,"user_tz":-420,"elapsed":3797800,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"aec7565b-5dee-464d-8219-ae9190b38036"},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1830' max='1830' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1830/1830 1:03:13, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","      <th>F1</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>50</td>\n","      <td>1.215200</td>\n","      <td>0.614000</td>\n","      <td>0.700410</td>\n","      <td>0.654520</td>\n","      <td>0.733853</td>\n","      <td>0.702028</td>\n","    </tr>\n","    <tr>\n","      <td>100</td>\n","      <td>0.535700</td>\n","      <td>0.473581</td>\n","      <td>0.826462</td>\n","      <td>0.821550</td>\n","      <td>0.826996</td>\n","      <td>0.826804</td>\n","    </tr>\n","    <tr>\n","      <td>150</td>\n","      <td>0.442100</td>\n","      <td>0.418947</td>\n","      <td>0.850051</td>\n","      <td>0.846622</td>\n","      <td>0.849416</td>\n","      <td>0.850491</td>\n","    </tr>\n","    <tr>\n","      <td>200</td>\n","      <td>0.459100</td>\n","      <td>0.417725</td>\n","      <td>0.838974</td>\n","      <td>0.833642</td>\n","      <td>0.841746</td>\n","      <td>0.839463</td>\n","    </tr>\n","    <tr>\n","      <td>250</td>\n","      <td>0.438200</td>\n","      <td>0.387644</td>\n","      <td>0.854872</td>\n","      <td>0.853693</td>\n","      <td>0.853832</td>\n","      <td>0.855017</td>\n","    </tr>\n","    <tr>\n","      <td>300</td>\n","      <td>0.400200</td>\n","      <td>0.406291</td>\n","      <td>0.843385</td>\n","      <td>0.845370</td>\n","      <td>0.850290</td>\n","      <td>0.843340</td>\n","    </tr>\n","    <tr>\n","      <td>350</td>\n","      <td>0.408500</td>\n","      <td>0.381836</td>\n","      <td>0.857641</td>\n","      <td>0.857365</td>\n","      <td>0.857235</td>\n","      <td>0.857941</td>\n","    </tr>\n","    <tr>\n","      <td>400</td>\n","      <td>0.369200</td>\n","      <td>0.370010</td>\n","      <td>0.860821</td>\n","      <td>0.859204</td>\n","      <td>0.859768</td>\n","      <td>0.861085</td>\n","    </tr>\n","    <tr>\n","      <td>450</td>\n","      <td>0.368600</td>\n","      <td>0.372971</td>\n","      <td>0.861026</td>\n","      <td>0.860391</td>\n","      <td>0.860033</td>\n","      <td>0.861260</td>\n","    </tr>\n","    <tr>\n","      <td>500</td>\n","      <td>0.396600</td>\n","      <td>0.376049</td>\n","      <td>0.856615</td>\n","      <td>0.857340</td>\n","      <td>0.858361</td>\n","      <td>0.856850</td>\n","    </tr>\n","    <tr>\n","      <td>550</td>\n","      <td>0.372900</td>\n","      <td>0.353303</td>\n","      <td>0.867282</td>\n","      <td>0.866727</td>\n","      <td>0.866365</td>\n","      <td>0.867522</td>\n","    </tr>\n","    <tr>\n","      <td>600</td>\n","      <td>0.360500</td>\n","      <td>0.358157</td>\n","      <td>0.863179</td>\n","      <td>0.863681</td>\n","      <td>0.864476</td>\n","      <td>0.863239</td>\n","    </tr>\n","    <tr>\n","      <td>650</td>\n","      <td>0.347100</td>\n","      <td>0.367144</td>\n","      <td>0.864410</td>\n","      <td>0.864313</td>\n","      <td>0.864586</td>\n","      <td>0.864471</td>\n","    </tr>\n","    <tr>\n","      <td>700</td>\n","      <td>0.339700</td>\n","      <td>0.363200</td>\n","      <td>0.865846</td>\n","      <td>0.865801</td>\n","      <td>0.865614</td>\n","      <td>0.866027</td>\n","    </tr>\n","    <tr>\n","      <td>750</td>\n","      <td>0.297700</td>\n","      <td>0.373714</td>\n","      <td>0.863077</td>\n","      <td>0.861932</td>\n","      <td>0.861959</td>\n","      <td>0.863432</td>\n","    </tr>\n","    <tr>\n","      <td>800</td>\n","      <td>0.304700</td>\n","      <td>0.360875</td>\n","      <td>0.865641</td>\n","      <td>0.866366</td>\n","      <td>0.867339</td>\n","      <td>0.865733</td>\n","    </tr>\n","    <tr>\n","      <td>850</td>\n","      <td>0.281700</td>\n","      <td>0.381632</td>\n","      <td>0.868205</td>\n","      <td>0.866136</td>\n","      <td>0.867533</td>\n","      <td>0.868517</td>\n","    </tr>\n","    <tr>\n","      <td>900</td>\n","      <td>0.266700</td>\n","      <td>0.373397</td>\n","      <td>0.869026</td>\n","      <td>0.868028</td>\n","      <td>0.868553</td>\n","      <td>0.869162</td>\n","    </tr>\n","    <tr>\n","      <td>950</td>\n","      <td>0.280900</td>\n","      <td>0.352265</td>\n","      <td>0.870154</td>\n","      <td>0.870576</td>\n","      <td>0.870962</td>\n","      <td>0.870274</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>0.296400</td>\n","      <td>0.362135</td>\n","      <td>0.870872</td>\n","      <td>0.869292</td>\n","      <td>0.870477</td>\n","      <td>0.871104</td>\n","    </tr>\n","    <tr>\n","      <td>1050</td>\n","      <td>0.299300</td>\n","      <td>0.350195</td>\n","      <td>0.873128</td>\n","      <td>0.872778</td>\n","      <td>0.872898</td>\n","      <td>0.873227</td>\n","    </tr>\n","    <tr>\n","      <td>1100</td>\n","      <td>0.315300</td>\n","      <td>0.353175</td>\n","      <td>0.872308</td>\n","      <td>0.870296</td>\n","      <td>0.871765</td>\n","      <td>0.872607</td>\n","    </tr>\n","    <tr>\n","      <td>1150</td>\n","      <td>0.308900</td>\n","      <td>0.358130</td>\n","      <td>0.872103</td>\n","      <td>0.870992</td>\n","      <td>0.871133</td>\n","      <td>0.872372</td>\n","    </tr>\n","    <tr>\n","      <td>1200</td>\n","      <td>0.302700</td>\n","      <td>0.351292</td>\n","      <td>0.872513</td>\n","      <td>0.871211</td>\n","      <td>0.871867</td>\n","      <td>0.872669</td>\n","    </tr>\n","    <tr>\n","      <td>1250</td>\n","      <td>0.229400</td>\n","      <td>0.367332</td>\n","      <td>0.871385</td>\n","      <td>0.871496</td>\n","      <td>0.871498</td>\n","      <td>0.871527</td>\n","    </tr>\n","    <tr>\n","      <td>1300</td>\n","      <td>0.232100</td>\n","      <td>0.363012</td>\n","      <td>0.871590</td>\n","      <td>0.871842</td>\n","      <td>0.872701</td>\n","      <td>0.871609</td>\n","    </tr>\n","    <tr>\n","      <td>1350</td>\n","      <td>0.200300</td>\n","      <td>0.395148</td>\n","      <td>0.872000</td>\n","      <td>0.871407</td>\n","      <td>0.871124</td>\n","      <td>0.872225</td>\n","    </tr>\n","    <tr>\n","      <td>1400</td>\n","      <td>0.194800</td>\n","      <td>0.391207</td>\n","      <td>0.872410</td>\n","      <td>0.872140</td>\n","      <td>0.871925</td>\n","      <td>0.872579</td>\n","    </tr>\n","    <tr>\n","      <td>1450</td>\n","      <td>0.193900</td>\n","      <td>0.387140</td>\n","      <td>0.873333</td>\n","      <td>0.873423</td>\n","      <td>0.873431</td>\n","      <td>0.873477</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>0.189700</td>\n","      <td>0.393748</td>\n","      <td>0.873846</td>\n","      <td>0.873173</td>\n","      <td>0.872961</td>\n","      <td>0.874070</td>\n","    </tr>\n","    <tr>\n","      <td>1550</td>\n","      <td>0.195100</td>\n","      <td>0.391498</td>\n","      <td>0.871077</td>\n","      <td>0.870204</td>\n","      <td>0.870351</td>\n","      <td>0.871258</td>\n","    </tr>\n","    <tr>\n","      <td>1600</td>\n","      <td>0.193000</td>\n","      <td>0.390159</td>\n","      <td>0.873231</td>\n","      <td>0.872859</td>\n","      <td>0.872687</td>\n","      <td>0.873397</td>\n","    </tr>\n","    <tr>\n","      <td>1650</td>\n","      <td>0.198200</td>\n","      <td>0.390281</td>\n","      <td>0.873436</td>\n","      <td>0.873419</td>\n","      <td>0.873326</td>\n","      <td>0.873591</td>\n","    </tr>\n","    <tr>\n","      <td>1700</td>\n","      <td>0.183600</td>\n","      <td>0.392936</td>\n","      <td>0.873436</td>\n","      <td>0.873193</td>\n","      <td>0.873116</td>\n","      <td>0.873577</td>\n","    </tr>\n","    <tr>\n","      <td>1750</td>\n","      <td>0.211200</td>\n","      <td>0.392684</td>\n","      <td>0.874154</td>\n","      <td>0.873830</td>\n","      <td>0.873753</td>\n","      <td>0.874303</td>\n","    </tr>\n","    <tr>\n","      <td>1800</td>\n","      <td>0.193700</td>\n","      <td>0.389910</td>\n","      <td>0.873846</td>\n","      <td>0.873913</td>\n","      <td>0.874053</td>\n","      <td>0.873958</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=1830, training_loss=0.3258447571530368, metrics={'train_runtime': 3796.5285, 'train_samples_per_second': 15.409, 'train_steps_per_second': 0.482, 'total_flos': 1.5392134937088e+16, 'train_loss': 0.3258447571530368, 'epoch': 3.0})"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","source":["q=[trainer.evaluate(eval_dataset=df) for df in [train_dataloader, val_dataloader, test_dataset]]\n","\n","pd.DataFrame(q, index=[\"train\",\"val\",\"test\"]).iloc[:,:5]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":163},"id":"TM8323vQRtPS","executionInfo":{"status":"ok","timestamp":1721284215256,"user_tz":-420,"elapsed":271699,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"5b1efaf5-9b28-4abe-f712-2b1082441e4a"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1220' max='610' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [610/610 04:31]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["       eval_loss  eval_Accuracy   eval_F1  eval_Precision  eval_Recall\n","train   0.234579       0.920615  0.919718        0.920132     0.920377\n","val     0.362135       0.870872  0.869292        0.870477     0.871104\n","test    0.362268       0.868000  0.866591        0.867133     0.868500"],"text/html":["\n","  <div id=\"df-a1f00607-3e25-4d26-98ac-6bfa746bf430\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>eval_loss</th>\n","      <th>eval_Accuracy</th>\n","      <th>eval_F1</th>\n","      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In this case, the output logits are the first element in the tuple, which is why we access it using outputs[0].\n","\n","    outputs[0]: This is a tensor containing the raw output logits for each class. The shape of the tensor is (batch_size, num_classes) where batch_size is the number of input samples (in this case, 1, as we are predicting for a single input text) and num_classes is the number of target classes.\n","\n","    softmax(1): The softmax function is applied along dimension 1 (the class dimension) to convert the raw logits into class probabilities. Softmax normalizes the logits so that they sum to 1, making them interpretable as probabilities. \"\"\"\n","\n","    # Get the index of the class with the highest probability\n","    # argmax() finds the index of the maximum value in the tensor along a specified dimension.\n","    # By default, if no dimension is specified, it returns the index of the maximum value in the flattened tensor.\n","    pred_label_idx = probs.argmax()\n","\n","    # Now map the predicted class index to the actual class label\n","    # Since pred_label_idx is a tensor containing a single value (the predicted class index),\n","    # the .item() method is used to extract the value as a scalar\n","    pred_label = model.config.id2label[pred_label_idx.item()]\n","\n","    return probs, pred_label_idx, pred_label"],"metadata":{"id":"4JnodKOARw4g","executionInfo":{"status":"ok","timestamp":1721284215256,"user_tz":-420,"elapsed":4,"user":{"displayName":"Le Le","userId":"02258782812217274557"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["model_path = \"/content/drive/MyDrive/ThaiLand/model/finetuned/finBert\"\n","trainer.save_model(model_path)\n","tokenizer.save_pretrained(model_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4fk9x28QRy4z","executionInfo":{"status":"ok","timestamp":1721284218049,"user_tz":-420,"elapsed":2796,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"44c1abc1-d2da-4475-c106-d759f36b25e6"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["('/content/drive/MyDrive/ThaiLand/model/finetuned/finBert/tokenizer_config.json',\n"," '/content/drive/MyDrive/ThaiLand/model/finetuned/finBert/special_tokens_map.json',\n"," '/content/drive/MyDrive/ThaiLand/model/finetuned/finBert/vocab.txt',\n"," '/content/drive/MyDrive/ThaiLand/model/finetuned/finBert/added_tokens.json',\n"," '/content/drive/MyDrive/ThaiLand/model/finetuned/finBert/tokenizer.json')"]},"metadata":{},"execution_count":23}]},{"cell_type":"code","source":["model_path = \"railwayBangkok-sentiment-analysis\"\n","trainer.save_model(model_path)\n","tokenizer.save_pretrained(model_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UaJkcsa8idXJ","executionInfo":{"status":"ok","timestamp":1721284319502,"user_tz":-420,"elapsed":3096,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"bea5ba39-8dfb-4533-dab4-cd87f80dac2a"},"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/plain":["('railwayBangkok-sentiment-analysis/tokenizer_config.json',\n"," 'railwayBangkok-sentiment-analysis/special_tokens_map.json',\n"," 'railwayBangkok-sentiment-analysis/vocab.txt',\n"," 'railwayBangkok-sentiment-analysis/added_tokens.json',\n"," 'railwayBangkok-sentiment-analysis/tokenizer.json')"]},"metadata":{},"execution_count":24}]},{"cell_type":"code","source":["model_path = \"/content/drive/MyDrive/ThaiLand/model/finetuned/finBert\"\n","\n","\n","model = BertForSequenceClassification.from_pretrained(model_path)\n","tokenizer= BertTokenizerFast.from_pretrained(model_path)\n","nlp= pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nFSoOS0AitAJ","executionInfo":{"status":"ok","timestamp":1721284741645,"user_tz":-420,"elapsed":2785,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"d3f2ca41-0321-44de-f047-1296d8490863"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stderr","text":["Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n"]}]},{"cell_type":"code","source":["nlp(\"This bts train is smooth but i think we dont need to use\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-Nl975bzi2Yl","executionInfo":{"status":"ok","timestamp":1721284747093,"user_tz":-420,"elapsed":1012,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"c06d71a0-e743-4d01-e8e5-ac858b8edd57"},"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'label': 'Neutral', 'score': 0.8331486582756042}]"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","source":["import torch\n","from huggingface_hub import notebook_login\n","\n","notebook_login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":331,"referenced_widgets":["43ef02e4c5824941ae796527195503e7","2d6fc956181d4397b037095e895a9842","9cab8b07473d4aca8cfb0fce36fb85fd","f43cd597a1f54144bc947c2d1b7cf46d","9279fee1e7684dd497595cb43fd1e34c","6f76d4fd234d4090b7ab1cbe16a30ea1","8019a8bcd72e426da6b5e7dde18f64a5","8a4ff0c0605144398c48d4f0976e80ac","0c72beb97efa460b946a43cd2aac9fb4","ec2088bc6a4d47bbb554d9456f53b02c","acb077846ef842d38c1ee27b67dde520","d758fe9080184797afff5e4c5e5bb175","10fa32a81e52484e9e5891e866ce0741","0a7374ae6c8e4186bf062788c35ad091","e22bee69bc08452a90d5306f2bd78537","e463923780264f019c3dbdd7d18b7851","6fe7eefa8c0e4580b638ddf4de9fadab","7b8a6c9bd828456c8d7c84823bf16051","e6df1520898f48bda994bde1e84382ec","5736707c4a56455c96055674820c637d","dba8d2229d214f3391543129a0079524","0f40195548de431e8ba6f3c48492f989","bf43032f96f94da0bd75340c24f45efe","aaa3fea5323f450b8f2e9c2eff13171e","d0556f7ada3b4ff193a621fcd905f892","af64eaea213740f6b0bc29dd5d837a5d","7bc5f5f41aec45e7b4f65f3d318b913b","f3e50a935b2d40fa9b94407238cc338e","9b96c6c3afbe4494b612d760ad54be91","624e4718b028427db1444f0886c60627","dc3adb376d5d484eb50e1b33c06a9825","fa6dc2f01abf433787cea9386d38bc2a"]},"id":"RoDkFk4yl70X","executionInfo":{"status":"ok","timestamp":1721285603738,"user_tz":-420,"elapsed":420,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"a7f2b32a-f137-4b8c-ece8-48ddd82491df"},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":["VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43ef02e4c5824941ae796527195503e7"}},"metadata":{}}]},{"cell_type":"code","source":["trainer.push_to_hub(\"railwayBK-sentiment-analysis\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":477},"id":"eRAzae7InDyu","executionInfo":{"status":"error","timestamp":1721285526932,"user_tz":-420,"elapsed":445,"user":{"displayName":"Le Le","userId":"02258782812217274557"}},"outputId":"56b5b2d7-4642-49b1-f55d-a98be58ade5d"},"execution_count":33,"outputs":[{"output_type":"error","ename":"HfHubHTTPError","evalue":" (Request ID: Root=1-6698bb96-16e8663d3d51591c5b511547;e22595ec-d9ac-4add-9ee5-622f11708499)\n\n403 Forbidden: You don't have the rights to create a model under the namespace \"vinh120203\".\nCannot access content at: https://huggingface.co/api/repos/create.\nIf you are trying to create or update content,make sure you have a token with the `write` role.","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-33-cbbf486498bd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"railwayBK-sentiment-analysis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, commit_message, blocking, token, **kwargs)\u001b[0m\n\u001b[1;32m   4274\u001b[0m         \u001b[0;31m# In case the user calls this method with args.push_to_hub = False\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4275\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4276\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_hf_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4278\u001b[0m         \u001b[0;31m# Needs to be executed on all processes for TPU training, but will only save on the processed determined by\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36minit_hf_repo\u001b[0;34m(self, token)\u001b[0m\n\u001b[1;32m   4098\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4099\u001b[0m         \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtoken\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_token\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4100\u001b[0;31m         \u001b[0mrepo_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_private_repo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4101\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_model_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrepo_url\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4102\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_in_progress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3267\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mRepoUrl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.endpoint}/{repo_type}/{repo_id}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3268\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mHfHubHTTPError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3269\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3270\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3271\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3255\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3256\u001b[0;31m             \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3257\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3258\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mexist_ok\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m409\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    365\u001b[0m                 \u001b[0;34m+\u001b[0m \u001b[0;34m\"make sure you have a token with the `write` role.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m             )\n\u001b[0;32m--> 367\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHfHubHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0;31m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mHfHubHTTPError\u001b[0m:  (Request ID: Root=1-6698bb96-16e8663d3d51591c5b511547;e22595ec-d9ac-4add-9ee5-622f11708499)\n\n403 Forbidden: You don't have the rights to create a model under the namespace \"vinh120203\".\nCannot access content at: https://huggingface.co/api/repos/create.\nIf you are trying to create or update content,make sure you have a token with the `write` role."]}]}]}