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-{
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- "
"
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- "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"
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- "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"
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- "version_minor": 0,
- "model_id": "e12581585f6d403980a271c2a545d722"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "model.safetensors: 0%| | 0.00/892M [00:00, ?B/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "77b6dc7272914b3d8d38f1672e202e32"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Device set to use cpu\n"
- ]
- }
- ],
- "source": [
- "# English summarization model (BART)\n",
- "summarizer_en = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n",
- "\n",
- "# French summarization model (T5 fine-tuned for summarization)\n",
- "fr_model_name = \"plguillou/t5-base-fr-sum-cnndm\"\n",
- "tokenizer_fr = AutoTokenizer.from_pretrained(fr_model_name)\n",
- "model_fr = AutoModelForSeq2SeqLM.from_pretrained(fr_model_name)\n",
- "summarizer_fr = pipeline(\"summarization\", model=model_fr, tokenizer=tokenizer_fr)"
- ],
- "id": "iiGL50dWlSal"
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 🧪 Application"
- ],
- "metadata": {
- "id": "f25CHUCbnJMY"
- },
- "id": "f25CHUCbnJMY"
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "FwNVa6_llSal"
- },
- "outputs": [],
- "source": [
- "text_en = \"\"\"\n",
- "Artificial Intelligence is revolutionizing many industries such as healthcare, finance, and transportation.\n",
- "Machine learning techniques now enable systems to analyze vast amounts of data and make decisions with minimal human input.\n",
- "However, these advances raise concerns over data privacy, algorithmic transparency, and job displacement.\n",
- "\"\"\"\n",
- "\n",
- "text_fr = \"\"\"\n",
- "L’intelligence artificielle transforme profondément des secteurs comme la santé, les transports et l’éducation.\n",
- "Grâce à l’apprentissage automatique, les systèmes peuvent analyser de grandes quantités de données et prendre des décisions complexes.\n",
- "Cependant, cela soulève des enjeux éthiques majeurs sur la transparence, l’emploi et la confidentialité.\n",
- "\"\"\""
- ],
- "id": "FwNVa6_llSal"
- },
- {
- "cell_type": "code",
- "source": [
- "# help(summarizer_en)"
- ],
- "metadata": {
- "id": "mMoruEIRnUcf"
- },
- "id": "mMoruEIRnUcf",
- "execution_count": 5,
- "outputs": []
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "FpVfGp0ulSam",
- "outputId": "e4bf1c6d-5c67-417d-831b-1972028d9c82"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Your max_length is set to 100, but your input_length is only 63. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=31)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "🔹 Original English Text:\n",
- "\n",
- " Artificial Intelligence is revolutionizing many industries such as healthcare, finance, and\n",
- "transportation. Machine learning techniques now enable systems to analyze vast amounts of data and\n",
- "make decisions with minimal human input. However, these advances raise concerns over data privacy,\n",
- "algorithmic transparency, and job displacement.\n",
- "\n",
- "✅ English Summary:\n",
- "\n",
- "Artificial Intelligence is revolutionizing many industries such as healthcare, finance, and\n",
- "transportation. However, these advances raise concerns over data privacy, algorithmic transparency,\n",
- "and job displacement.\n"
- ]
- }
- ],
- "source": [
- "print(\"🔹 Original English Text:\\n\")\n",
- "print(textwrap.fill(text_en, width=100))\n",
- "\n",
- "summary_en = summarizer_en(text_en, max_length=100, min_length=30 do_sample=False)\n",
- "\n",
- "print(\"\\n✅ English Summary:\\n\")\n",
- "print(textwrap.fill(summary_en[0][\"summary_text\"], width=100))"
- ],
- "id": "FpVfGp0ulSam"
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Hcr5dtB5lSam",
- "outputId": "766c13d2-6fc4-4f64-a6e8-4a298b8b4614"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "🔹 Texte original en français:\n",
- "\n",
- " L’intelligence artificielle transforme profondément des secteurs comme la santé, les transports et\n",
- "l’éducation. Grâce à l’apprentissage automatique, les systèmes peuvent analyser de grandes\n",
- "quantités de données et prendre des décisions complexes. Cependant, cela soulève des enjeux\n",
- "éthiques majeurs sur la transparence, l’emploi et la confidentialité.\n",
- "\n",
- "✅ Résumé en français:\n",
- "\n",
- "L'intelligence artificielle transforme profondément des secteurs tels que la santé, les transports\n",
- "et l'éducation. Cependant, cela soulève des enjeux éthiques majeurs sur la transparence, l’emploi\n",
- "eet la confidentialité. L'apprentissage automatique permet aux systèmes de prendre des décisions\n",
- "complexes.\n"
- ]
- }
- ],
- "source": [
- "print(\"🔹 Texte original en français:\\n\")\n",
- "print(textwrap.fill(text_fr, width=100))\n",
- "\n",
- "summary_fr = summarizer_fr(text_fr, max_length=100, min_length=30, do_sample=False)\n",
- "\n",
- "print(\"\\n✅ Résumé en français:\\n\")\n",
- "print(textwrap.fill(summary_fr[0][\"summary_text\"], width=100))"
- ],
- "id": "Hcr5dtB5lSam"
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "lcaWmvwNlSan",
- "outputId": "1b85a579-7c91-4c62-ec4c-2a5a57d1e5db"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Your max_length is set to 50, but your input_length is only 3. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=1)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "🧪 Edge Case (Empty input):\n",
- "CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery. Please submit your best shots of New York for next week. Visit CNN.com/Travel next Wednesday for a new gallery of snapshots.\n"
- ]
- }
- ],
- "source": [
- "print(\"🧪 Edge Case (Empty input):\")\n",
- "\n",
- "# handle with clause if else"
- ],
- "id": "lcaWmvwNlSan"
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ipja6tLnlSan",
- "outputId": "5c205a71-1e51-4738-8d08-535b9abb1036"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Your max_length is set to 60, but your input_length is only 24. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=12)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "🔁 Résumés français (batch):\n",
- "\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Your max_length is set to 60, but your input_length is only 32. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=16)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "📌 Texte: Le réchauffement climatique provoque des événements météorologiques extrêmes dans le monde entier.\n",
- "➡️ Résumé: Le réchauffement climatique provoque des événements météorologiques extrêmes dans le monde entier.\n",
- "\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Your max_length is set to 60, but your input_length is only 33. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=16)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "📌 Texte: La France accueille chaque année des millions de touristes attirés par sa culture et sa gastronomie.\n",
- "➡️ Résumé: La France accueille chaque année des millions de touristes attirés par la culture et la gastronomie. La culture française est l'une des principales attractions touristiques françaises.\n",
- "\n",
- "📌 Texte: Les véhicules autonomes utilisent des capteurs et de l'IA pour se déplacer sans conducteur humain.\n",
- "➡️ Résumé: Les véhicules autonomes utilisent des capteurs et de l'IA pour se déplacer sans conducteur humain.\n",
- "\n"
- ]
- }
- ],
- "source": [
- "texts_fr = [\n",
- " \"Le réchauffement climatique provoque des événements météorologiques extrêmes dans le monde entier.\",\n",
- " \"La France accueille chaque année des millions de touristes attirés par sa culture et sa gastronomie.\",\n",
- " \"Les véhicules autonomes utilisent des capteurs et de l'IA pour se déplacer sans conducteur humain.\"\n",
- "]\n",
- "\n",
- "print(\"🔁 Résumés français (batch):\\n\")\n",
- "for t in texts_fr:\n",
- " s = summarizer_fr(t, max_length=60, min_length=20, do_sample=False)\n",
- " print(f\"📌 Texte: {t}\\n➡️ Résumé: {s[0]['summary_text']}\\n\")"
- ],
- "id": "ipja6tLnlSan"
- },
- {
- "cell_type": "markdown",
- "source": [
- " ## 🔍 Auto detect text language"
- ],
- "metadata": {
- "id": "0cA3DEJtq7J2"
- },
- "id": "0cA3DEJtq7J2"
- },
- {
- "cell_type": "code",
- "source": [
- "!pip install langdetect"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "poBwmUOUrPxB",
- "outputId": "6d44963f-1157-4a54-f1f3-39b94a18b40e"
- },
- "id": "poBwmUOUrPxB",
- "execution_count": 21,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Requirement already satisfied: langdetect in /usr/local/lib/python3.11/dist-packages (1.0.9)\n",
- "Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from langdetect) (1.17.0)\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "from langdetect import detect\n",
- "\n",
- "texts = [\n",
- " \"Bonjour, comment allez-vous ?\", # French\n",
- " \"Hello, how are you doing?\", # English\n",
- " \"Hola, ¿cómo estás?\", # Spanish\n",
- " \"Guten Tag, wie geht's Ihnen?\", # German\n",
- " \"\", # Empty\n",
- " \"こんにちは、お元気ですか?\", # Japanese\n",
- " \"1234567890 $$$ ???\", # Gibberish\n",
- "]\n",
- "\n",
- "for text in texts:\n",
- " try:\n",
- " lang = detect(text)\n",
- " print(f\"📝 Text: {text}\\n➡️ Language detected: {lang}\\n\")\n",
- " except:\n",
- " print(f\"📝 Text: {text}\\n❌ Could not detect language\\n\")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "s90NKwiNrLeF",
- "outputId": "57c6b9f0-ab2f-4d42-9f0d-7acdd225a082"
- },
- "id": "s90NKwiNrLeF",
- "execution_count": 22,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "📝 Text: Bonjour, comment allez-vous ?\n",
- "➡️ Language detected: fr\n",
- "\n",
- "📝 Text: Hello, how are you doing?\n",
- "➡️ Language detected: en\n",
- "\n",
- "📝 Text: Hola, ¿cómo estás?\n",
- "➡️ Language detected: es\n",
- "\n",
- "📝 Text: Guten Tag, wie geht's Ihnen?\n",
- "➡️ Language detected: de\n",
- "\n",
- "📝 Text: \n",
- "❌ Could not detect language\n",
- "\n",
- "📝 Text: こんにちは、お元気ですか?\n",
- "➡️ Language detected: ja\n",
- "\n",
- "📝 Text: 1234567890 $$$ ???\n",
- "❌ Could not detect language\n",
- "\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Functions"
- ],
- "metadata": {
- "id": "dpCSrgL5r0tG"
- },
- "id": "dpCSrgL5r0tG"
- },
- {
- "cell_type": "code",
- "source": [
- "from transformers import pipeline\n",
- "from langdetect import detect\n",
- "\n",
- "# Load English and French summarizers\n",
- "summarizer_en = pipeline(\"summarization\",\n",
- " model=\"facebook/bart-large-cnn\")\n",
- "fr_model_name = \"plguillou/t5-base-fr-sum-cnndm\"\n",
- "tokenizer_fr = AutoTokenizer.from_pretrained(fr_model_name)\n",
- "model_fr = AutoModelForSeq2SeqLM.from_pretrained(fr_model_name)\n",
- "summarizer_fr = pipeline(\"summarization\",\n",
- " model=model_fr, tokenizer=tokenizer_fr)\n",
- "\n",
- "\n",
- "def detect_language(text):\n",
- " try:\n",
- " return detect(text)\n",
- " except:\n",
- " return \"unknown\"\n",
- "\n",
- "\n"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SKb4od1Irehc",
- "outputId": "a29a7a3f-ed10-42c9-8faf-84de06118bd5"
- },
- "id": "SKb4od1Irehc",
- "execution_count": 34,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Device set to use cpu\n",
- "Device set to use cpu\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "def summarize_text(text):\n",
- " if not text or text.strip() == \"\":\n",
- " return \"⚠️ No input text provided.\"\n",
- "\n",
- " if len(text.split()) < 5:\n",
- " return f\"⚠️ Text too short to summarize. Returning input:\\n{text}\"\n",
- "\n",
- " lang = detect_language(text)\n",
- "\n",
- " max_len = max(130, len(text.split()))\n",
- " min_len = min(30, len(text.split()))\n",
- "\n",
- " if lang == \"en\":\n",
- " summary = summarizer_en(text, max_length=max_len, min_length=min_len, do_sample=False)\n",
- " return f\"🇬🇧 English Summary:\\n{summary[0]['summary_text']}\"\n",
- "\n",
- " elif lang == \"fr\":\n",
- " summary = summarizer_fr(text, max_length=max_len, min_length=min_len, do_sample=False)\n",
- " return f\"🇫🇷 Résumé Français :\\n{summary[0]['summary_text']}\"\n",
- "\n",
- " else:\n",
- " return f\"❌ Unsupported or undetected language: '{lang}'\"\n",
- "\n",
- "\n",
- "text = \"Bonjour, voici un exemple de texte que nous allons résumer automatiquement.\"\n",
- "print(summarize_text(text))\n",
- "\n",
- "text = \"This is an example of a paragraph that we want to summarize using LLMs.\"\n",
- "print(summarize_text(text))"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "R6ChZ4pGr6eP",
- "outputId": "aee3098c-4c65-4fc4-a553-cb164137c62d"
- },
- "id": "R6ChZ4pGr6eP",
- "execution_count": 41,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "Your max_length is set to 130, but your input_length is only 19. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=9)\n",
- "Your max_length is set to 130, but your input_length is only 18. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=9)\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "🇫🇷 Résumé Français :\n",
- "Voici un exemple de texte que nous allons résumer automatiquement.\n",
- "🇬🇧 English Summary:\n",
- "This is an example of a paragraph that we want to summarize using LLMs.\n"
- ]
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [
- "text = \"Bonjour, voici un exemple de texte que nous allons résumer automatiquement.\"\n",
- "detect_language(text)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "DuSZxnQUuBv6",
- "outputId": "b82bcd65-adfc-4717-820c-1141d0fc6747"
- },
- "id": "DuSZxnQUuBv6",
- "execution_count": 36,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "'fr'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 36
- }
- ]
- },
- {
- "cell_type": "code",
- "source": [],
- "metadata": {
- "id": "3duRlP58sKws"
- },
- "id": "3duRlP58sKws",
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "!pip install gradio"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "BHXVI4XjsKtJ",
- "outputId": "30306664-4464-4554-98d7-326b1be7c1c6"
- },
- "id": "BHXVI4XjsKtJ",
- "execution_count": 42,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Collecting gradio\n",
- " Downloading gradio-5.29.0-py3-none-any.whl.metadata (16 kB)\n",
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- "Requirement already satisfied: pydantic<2.12,>=2.0 in /usr/local/lib/python3.11/dist-packages (from gradio) (2.11.4)\n",
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