Transformers.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Load pipelines for each task\n",
        "sentiment_analysis = pipeline(\"sentiment-analysis\")\n",
        "text_generation = pipeline(\"text-generation\")\n",
        "translation = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-tc-big-en-ko\")  # Example English to Korean translation\n",
        "summarization = pipeline(\"summarization\")\n",
        "ner = pipeline(\"ner\")\n",
        "\n",
        "# Function to perform the chosen task\n",
        "def perform_task(text, user_choice):\n",
        "  if user_choice == \"1\":\n",
        "    sentiment = sentiment_analysis(text)\n",
        "    print(\"Sentiment Analysis:\", sentiment[0][\"label\"])\n",
        "  elif user_choice == \"2\":\n",
        "    generated_text = text_generation(text, max_length=100, num_return_sequences=1)[0][\"generated_text\"]\n",
        "    print(\"Text Generation:\", generated_text)\n",
        "  elif user_choice == \"3\":\n",
        "    translated_text = translation(text, src_lang=\"en\", tgt_lang=\"ko\")[0][\"translation\"]\n",
        "    print(\"Translation (English to Korean):\", translated_text)\n",
        "  elif user_choice == \"4\":\n",
        "    summary = summarization(text, max_length=100)[0][\"summary_text\"]\n",
        "    print(\"Summary:\", summary)\n",
        "  elif user_choice == \"5\":\n",
        "    entities = ner(text)\n",
        "    print(\"Named Entities:\", entities)\n",
        "  else:\n",
        "    print(\"Invalid choice. Please try again.\")\n",
        "\n",
        "# Get user input text\n",
        "text = input(\"Enter your text: \")\n",
        "\n",
        "# Display task options and get user choice\n",
        "print(\"\\nSelect an action to perform:\")\n",
        "print(\"1. Sentiment Analysis\")\n",
        "print(\"2. Text Generation\")\n",
        "print(\"3. Translation (English to French)\")\n",
        "print(\"4. Summarization\")\n",
        "print(\"5. Named Entity Recognition (NER)\")\n",
        "user_choice = input(\"Enter your choice (1-5): \")\n",
        "\n",
        "# Perform the chosen task\n",
        "perform_task(text, user_choice)\n"
      ],
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        "id": "8DZWSGulJk8p",
        "outputId": "35395099-baa7-439e-f80a-5aef6d6e0a05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "No model was supplied, defaulted to openai-community/gpt2 and revision 6c0e608 (https://huggingface.co/openai-community/gpt2).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.07k [00:00<?, ?B/s]"
            ],
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        },
        {
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          "data": {
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              "model.safetensors:   0%|          | 0.00/418M [00:00<?, ?B/s]"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/293 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/341 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
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              "special_tokens_map.json:   0%|          | 0.00/65.0 [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to sshleifer/distilbart-cnn-12-6 and revision a4f8f3e (https://huggingface.co/sshleifer/distilbart-cnn-12-6).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "No model was supplied, defaulted to dbmdz/bert-large-cased-finetuned-conll03-english and revision f2482bf (https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    }
  ]
}