{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xhO-gEyY6KER", "outputId": "06ecca0b-e5fe-4c2f-87dc-4b8efb033a10" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sun Aug 13 13:34:56 2023 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 41C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "# #Checking if GPU is running or not\n", "\n", "!nvidia-smi" ] }, { "cell_type": "markdown", "metadata": { "id": "KyvS5dBy3tt1" }, "source": [ "## Installing the Required Python Packages\n", "This cell installs the necessary Python libraries using the pip package manager. It silently installs the datasets and transformers libraries with support for SentencePiece tokenizer, which are essential for working with NLP models." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LrDg76NC7rU0", "outputId": "c833ab2c-5179-498a-c342-d8918659f7a9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.3/519.3 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m70.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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Packages for running of my code.\n", "!pip install datasets transformers[sentencepiece] -q\n" ] }, { "cell_type": "markdown", "metadata": { "id": "SHwlAwOC3zbI" }, "source": [ "## Importing the Required libraries\n", "In this cell, you import the libraries and modules necessary for setting up your translation model and creating the GUI interface." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "Oo_6qLi471RZ" }, "outputs": [], "source": [ "# Imported Required Libraries.\n", "import os\n", "import sys\n", "import transformers\n", "import tensorflow as t\n", "import torch\n", "from datasets import load_dataset\n", "from transformers import AutoTokenizer\n", "from transformers import TFAutoModelForSeq2SeqLM, DataCollatorForSeq2Seq\n", "from transformers import AdamWeightDecay\n", "from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "AcpXPznW7_Ef" }, "outputs": [], "source": [ "model_checkpoint = \"Helsinki-NLP/opus-mt-en-hi\" # Set the model checkpoint to the pre-trained English to Hindi translation model from the Helsinki-NLP repository\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # Check if CUDA (GPU) is available, and if so, use it; otherwise, use CPU" ] }, { "cell_type": "markdown", "metadata": { "id": "QpPrlKzzzTOQ" }, "source": [ "## Loading Dataset From HuggingFace\n", "source: https://huggingface.co/datasets/cfilt/iitb-english-hindi\n", "\n", "Here we load the dataset from Huggingface made by IIT Bombay with 1.66 Million training data elements and nearly 2500 testing dataset" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 615, "referenced_widgets": [ "9b3fc97bde704620bb692dd21ac0010b", "c837162e33bb4537a5bd3d33bb4ab77b", "0c0d2db3b89b4cc48c5e6dda6c7d6943", "ffa6676aacec4737b0eebd3be8d50168", "5794572d08da45f2aef6245903da1d37", 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"dd4b994953b44d92a0bf5d665834bf4c", "4daa3aea25984a3b8cfd148cde65891a", "9d326922829247a3883fe51eb324a47e", "98f00754ad3a43f3894dd1b58385f819", "2b307800573e45119743efa99cbbb51a", "ca2f2ed0eb8b4c0e81ecae2bf7d96274", "f48ab6aed05f47d7bb55e2c7f5167b22", "e3f070ec1ff74a8d9e8f0b0cfc20e759", "24648488119341e0ae01eafde50d4b98", "8a96a7737a5f4fed8b89422c79004c04", "ed015e56710942009abcc39b75254148", "6400d7a8246a43bfb7af35a382c9a55b", "aa88917baa09495798d41dc33690a71f", "4bf28ba97b424be0a6d0d9059bc0c872", "5bdfa6529ea243a1a360ce72efc04049", "6929849f86e748e6bdd059124feb10b7", "58726c84783542f99742bbf226f298f8", "9f34d04510d44d239ae682a48ea2760e", "6a1732153a6b4b17a0eda6673a3a2859", "9e43b4beb1294722b38eb41d8eb44afb", "79dd80210dad4bd98ffdc4e9aa584141", "abb3346b51f44e7a97f79352de1c6af2", "13f1a4334bb544dc815501aa20dcf21a", "7934f27e47bd411582bece268a658c26" ] }, "id": "adj6SJn08dCM", "outputId": "89acb4ab-340a-4103-ffdb-550083ce4d94" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9b3fc97bde704620bb692dd21ac0010b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/3.11k [00:00" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Train the model using the prepared training dataset and validate using the validation dataset for 3 epochs.\n", "model.fit(train_dataset, epochs=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "casIzdcdAJZx" }, "source": [ "## Save and Load Model\n", "This cell saves the trained model using inbuilt tensorflow function model.save_pretrained() where input parameter is path where model is to be saved. I have specified path on Google Drive. It also demonstrates how to load both the tokenizer and model from the original model checkpoint and from your saved Google Drive path." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "T9aw_HlKIeOD", "outputId": "53a42419-c55c-4223-d729-327de519e2da" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "All model checkpoint layers were used when initializing TFMarianMTModel.\n", "\n", "All the layers of TFMarianMTModel were initialized from the model checkpoint at /content/drive/MyDrive/my_model/.\n", "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFMarianMTModel for predictions without further training.\n" ] } ], "source": [ "# Saving my model in my google drive in my_model folder.\n", "path = 'tf_model/'\n", "model.save_pretrained(path)\n", "# Loading both tokenizer and my model from model_checkpoint and from my google drive.\n", "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n", "model = TFAutoModelForSeq2SeqLM.from_pretrained(path)" ] }, { "cell_type": "markdown", "metadata": { "id": "WLpmWRNnAgca" }, "source": [ "## Testing the Model\n", " Here, you provide an example input text and test the translation capabilities of your model. The input text is tokenized using the tokenizer, and the model generates an output sequence. The generated output is then decoded using the target tokenizer to produce the translated text. The translated text is printed as the output." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-8xNSKYpVxGp", "outputId": "9b1706cb-fda4-4906-aa8b-544b52f5279f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "कितना कठोर!\n" ] } ], "source": [ "# Testing our model with some random input text and printing the translated text.\n", "input_text = \"How rude of you!\"\n", "\n", "# Tokenize the input text using the tokenizer and convert to NumPy arrays\n", "tokenized = tokenizer([input_text], return_tensors='np')\n", "\n", "# Generate output sequences using the pre-trained model\n", "out = model.generate(**tokenized, max_length=128)\n", "\n", "# Switch the tokenizer to target mode\n", "with tokenizer.as_target_tokenizer():\n", " # Decode the generated output sequence, skipping special tokens\n", " decoded_output = tokenizer.decode(out[0], skip_special_tokens=True)\n", "\n", "# Print the decoded output\n", "print(decoded_output)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7Rpue0Y0srdl", "outputId": "7ec913d2-5766-4349-d13a-4f3e3d21f525" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.0/20.0 MB\u001b[0m \u001b[31m26.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.7/65.7 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.4/297.4 kB\u001b[0m \u001b[31m35.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.3/140.3 kB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m11.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "! pip install gradio -q # Installing the gradio package to deploy my model as an application." ] }, { "cell_type": "markdown", "metadata": { "id": "zo-zfbk3An4I" }, "source": [ "## Create and Launch Gradio App for Translation\n", "\n", "Finally, you create a GUI interface for interacting with your translation model using the Gradio library. The GUI allows users to input English text and receive translated Hindi text as output. The interface includes text boxes for input and output and is launched with sharing enabled." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 610 }, "id": "FZyF6VAcsvHj", "outputId": "f4d1839e-db87-4b04-8ada-b5ce27c2e690" }, "outputs": [], "source": [ "import gradio as gr # Import the Gradio library for creating a simple GUI\n", "\n", "title = \"Text Translation(English to Hindi)\" # Title for the GUI application\n", "\n", "# Define a function to process input text and generate translation\n", "def process_input(text):\n", " # Tokenize the input text using the tokenizer and convert to NumPy arrays\n", " tokenized = tokenizer([text], return_tensors='np')\n", " # Generate output sequences using the pre-trained model\n", " out = model.generate(**tokenized, max_length=128)\n", " # Switch the tokenizer to target mode\n", " with tokenizer.as_target_tokenizer():\n", " # Decode the generated output sequence, skipping special tokens\n", " result = tokenizer.decode(out[0], skip_special_tokens=True)\n", " return result\n", "\n", "# Example input text for the GUI\n", "examples = ['If you have the time, come along with me.', 'I can come if you want.', 'Tom was at home alone.', 'Wow!','How rude of you!',\"What's in your hand?\"]\n", "\n", "# Create a Gradio Interface for the model\n", "model_gui = gr.Interface(\n", " process_input, # Function for processing input and generating output\n", " gr.Textbox(lines=3, label=\"English\"), # Textbox for entering English text\n", " gr.Textbox(lines=3, label=\"Hindi\"), # Textbox for displaying translated Hindi text\n", " title=title, # Set the title of the GUI\n", " examples=examples # Provide example input text for the GUI\n", ")\n", "\n", "# Launch the Gradio GUI with sharing enabled\n", "model_gui.launch(share=True)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", 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