diff --git "a/Sandbox_Text_Summarization.ipynb" "b/Sandbox_Text_Summarization.ipynb" deleted file mode 100644--- "a/Sandbox_Text_Summarization.ipynb" +++ /dev/null @@ -1,5697 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "crpsoEoflSah" - }, - "source": [ - "# 📝 Multilingual Text Summarization (French + English)\n", - "\n", - "## 📘 Context\n", - "\n", - "Text summarization is a crucial NLP task used to extract key insights from long documents. With the advancement of transformer-based architectures like BART and T5, we can now generate high-quality summaries in different languages.\n", - "\n", - "This notebook demonstrates how to perform automatic summarization in:\n", - "- 🇬🇧 **English**, using `facebook/bart-large-cnn`\n", - "- 🇫🇷 **French**, using `plguillou/t5-base-fr-sum-cnndm`\n", - "\n", - "## 🎯 Objectives\n", - "\n", - "- Load and compare language-specific summarization models\n", - "- Generate and display summaries for both English and French input texts\n", - "- Test edge cases and observe model behavior" - ], - "id": "crpsoEoflSah" - }, - { - "cell_type": "markdown", - "source": [ - "## Packages" - ], - "metadata": { - "id": "R5HB8zbHmcG-" - }, - "id": "R5HB8zbHmcG-" - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PxUScsLGlSak" - }, - "outputs": [], - "source": [ - "!pip install transformers sentencepiece" - ], - "id": "PxUScsLGlSak" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "tXp3V8bclSal" - }, - "outputs": [], - "source": [ - "# import loguru\n", - "\n", - "from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM\n", - "import textwrap # Text wrapping and filling" - ], - "id": "tXp3V8bclSal" - }, - { - "cell_type": "markdown", - "source": [ - "## 🧠 Model Descriptions\n", - "\n", - "### 🇬🇧 `facebook/bart-large-cnn` — English Text Summarization\n", - "\n", - "**BART (Bidirectional and Auto-Regressive Transformer)** is a model developed by Facebook AI that combines the strengths of **encoder-decoder** architectures (like T5) and **auto-regressive** models (like GPT). It is **fine-tuned** on the **CNN/DailyMail dataset**, consisting of articles and summaries.\n", - "\n", - "- **Use Case**: Excellent for **journalistic**, **informal**, or **structured opinion texts**.\n", - "- **Type of Summary**: **Abstractive** (paraphrasing, not just extraction).\n", - "\n", - "**Architecture**:\n", - "- 12 layers of encoder + 12 layers of decoder\n", - "- Bidirectional attention for encoding, causal attention for decoding\n", - "- Around **406M parameters**\n", - "\n", - "---\n", - "\n", - "### 🇫🇷 `plguillou/t5-base-fr-sum-cnndm` — French Text Summarization\n", - "\n", - "Based on **T5 (Text-to-Text Transfer Transformer)**, developed by Google. This model is **fine-tuned** for **French text summarization** on a dataset inspired by CNN/DailyMail.\n", - "\n", - "- **Use Case**: Best for **formal** or **structured** texts: **news articles**, **reports**, or **official documents**.\n", - "- **Type of Summary**: **Abstractive** (rephrasing the input text in its own words).\n", - "\n", - "**Architecture**:\n", - "- **T5-base**: Around **220M parameters**\n", - "- Multilingual, but fine-tuned specifically for **French**.\n", - "\n", - "---\n", - "\n", - "### 🌍 `facebook/mbart-large-50-one-to-many-mmt` — Multilingual Text Summarization\n", - "\n", - "**mBART (Multilingual BART)** is a variation of the BART model that is trained on **multiple languages**. It is designed for **translation** tasks but can also be adapted for **summarization**.\n", - "\n", - "- **Use Case**: Suitable for summarizing text in multiple languages, making it a versatile tool for multilingual applications.\n", - "- **Type of Summary**: **Abstractive**.\n", - "\n", - "**Architecture**:\n", - "- 12 layers of encoder + 12 layers of decoder\n", - "- Multilingual model trained on 50 languages\n", - "- Around **680M parameters**\n", - "\n", - "---\n", - "\n", - "### 🔄 `google/t5-base-xxl-tlm` — T5 for Multilingual Tasks\n", - "\n", - "**T5** (Text-to-Text Transfer Transformer) is a model that frames all NLP tasks as a text-to-text problem, making it highly adaptable. It has been fine-tuned for multiple tasks including **summarization**.\n", - "\n", - "- **Use Case**: Works well for **multilingual summarization**, but can also be used for translation, question-answering, etc.\n", - "- **Type of Summary**: **Abstractive** (like all T5-based models).\n", - "\n", - "**Architecture**:\n", - "- **T5-base**: Around **220M parameters**\n", - "- **T5-XXL**: Much larger, up to **11B parameters**\n", - "- Fine-tuned for many multilingual tasks\n", - "\n", - "---\n", - "\n", - "### 🚀 `google/flan-t5-xl` — Fine-tuned T5 for Better Generalization\n", - "\n", - "**FLAN-T5** is a version of T5 that is **fine-tuned on a variety of tasks** to improve generalization. It aims to perform better on a wide range of NLP tasks, including summarization, when compared to regular T5.\n", - "\n", - "- **Use Case**: Ideal for **high-quality summarization** tasks in multiple languages, with improved robustness.\n", - "- **Type of Summary**: **Abstractive**.\n", - "\n", - "**Architecture**:\n", - "- **T5-XL**: Large model with **11B parameters**.\n", - "- Fine-tuned on a wide variety of tasks, improving the model's ability to generalize across domains.\n", - "\n", - "---\n", - "\n", - "### 📊 Quick Comparison\n", - "\n", - "| Model | Language | Architecture | Fine-Tuning | Type of Summary |\n", - "|-------------------------------|---------------|---------------------|------------------------|-----------------|\n", - "| `facebook/bart-large-cnn` | English | BART | CNN/DailyMail | Abstractive |\n", - "| `plguillou/t5-base-fr-sum` | French | T5 (Base) | CNN/DailyMail FR | Abstractive |\n", - "| `facebook/mbart-large-50` | Multilingual | mBART | Multilingual (50 languages) | Abstractive |\n", - "| `google/t5-base-xxl-tlm` | Multilingual | T5 (Base or XXL) | Multilingual | Abstractive |\n", - "| `google/flan-t5-xl` | Multilingual | T5 (Fine-tuned) | Fine-tuned for better generalization | Abstractive |\n" - ], - "metadata": { - "id": "ZxqM3H8sm_Lc" - }, - "id": "ZxqM3H8sm_Lc" - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 617, - "referenced_widgets": [ - "a2c5be0496de4ea9b8742a666c63d8bb", - "4f72788c5ae542af9cbfaa59839010f5", - "83d03a9916584d8b9394f88e1cdafc47", - "f4baaff74ccf435b9992b8e3c990ba1d", - "7629e30593594eb5aa8a13b2924fbb66", - "717932a9351d46aa9c2182012a783b02", - "b3b5fc7192cb48a2a20e55ee5d080edf", - "ca078e42b7b14a519d014b62152aed86", - "6cf1e04bb3f64d528a8ec71e8419d66c", - "1cca5f1ba0b642dabbf766149e8be554", - "d4ed7ce487c64da68861490bed666aec", - "12dcf3818dcb43c282154591f2967530", - "e212ec38ef004a269068dcb80ffb6e95", - "17f57bdc771a4759b9a69ad6d7eadc88", - "b11e04f699f2420bbbf40b3224a947eb", - 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"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": [ - "config.json: 0%| | 0.00/1.58k [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "pytorch_model.bin: 0%| | 0.00/892M [00:00=22.0 (from gradio)\n", - " Downloading aiofiles-24.1.0-py3-none-any.whl.metadata (10 kB)\n", - "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio) (4.9.0)\n", - "Collecting fastapi<1.0,>=0.115.2 (from gradio)\n", - " Downloading fastapi-0.115.12-py3-none-any.whl.metadata (27 kB)\n", - "Collecting ffmpy (from gradio)\n", - " Downloading ffmpy-0.5.0-py3-none-any.whl.metadata (3.0 kB)\n", - "Collecting gradio-client==1.10.0 (from gradio)\n", - " Downloading gradio_client-1.10.0-py3-none-any.whl.metadata (7.1 kB)\n", - "Collecting groovy~=0.1 (from gradio)\n", - " Downloading groovy-0.1.2-py3-none-any.whl.metadata (6.1 kB)\n", - "Requirement already satisfied: httpx>=0.24.1 in /usr/local/lib/python3.11/dist-packages (from gradio) (0.28.1)\n", - 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Language is detected automatically.\",\n", - " examples=[\n", - " [\"Bonjour, ceci est un exemple de mail professionnel à résumer pour un usage interne.\"],\n", - " [\"This is a long English article that explains how machine learning models are trained using large datasets.\"]\n", - " ]\n", - ")\n", - "\n", - "if __name__ == \"__main__\":\n", - " demo.launch()" - ], - "metadata": { - "id": "RjSZ3LBgvOJO" - }, - "id": "RjSZ3LBgvOJO", - "execution_count": 46, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "demo.close()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3QCyh5aYvOFw", - "outputId": "0607c523-1a7a-469c-c936-0c817f9b6854" - }, - "id": "3QCyh5aYvOFw", - "execution_count": 46, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Closing server running on port: 7860\n" - ] - } - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - 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