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      "metadata": {
        "id": "DXhtQZ-N1DDH"
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      "source": [
        "#install transformers\n",
        "!pip install transformers -q"
      ]
    },
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      "cell_type": "code",
      "source": [
        "##Example 1: sentiment analysis\n",
        "\n",
        "#import pipeline\n",
        "from transformers import pipeline\n",
        "\n",
        "#object\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "\n",
        "result = classifier(\"I am happy.\")\n",
        "print(result)\n",
        "\n",
        "\n"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n",
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'POSITIVE', 'score': 0.9998760223388672}]\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "##Example 2: text generation\n",
        "\n",
        "#import pipeline\n",
        "from transformers import pipeline\n",
        "\n",
        "#object (we can also specify a specific model here)\n",
        "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
        "\n",
        "\n",
        "result = generator(\"a person needs to eat three times per day because\", max_length=100, num_return_sequences=2)\n",
        "print(result)\n",
        "\n"
      ],
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          "data": {
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        {
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'generated_text': \"a person needs to eat three times per day because they don't have enough calories to live and is therefore unable to meet the needs of the diet or lifestyle.”If you want to eat at least 5-8 meals per day, just go ahead and go and look at the amount of calories the person could get from eating a single meal. To give that a feel check you must go to a specialist diet check out www.eatingcharts.com for more information.\"}, {'generated_text': \"a person needs to eat three times per day because it's really hard to get enough oxygen to survive when you're in the woods. So he's taking a full-time shift just as often on a day out and the food's all going to be the same, but he's taking a full-time shift for every day and in fact he's taking quite a bit of time out. He's taking a full-time shift for every day and the food's all going to be different,\"}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Example 3: text classification\n",
        "\n",
        "#import pipeline\n",
        "from transformers import pipeline\n",
        "\n",
        "#object\n",
        "classifier = pipeline(\"zero-shot-classification\")\n",
        "\n",
        "\n",
        "result = classifier(\"London is a beautiful city\", candidate_labels=[\"education\",\"politics\",\"tourism\"])\n",
        "print(result)"
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to facebook/bart-large-mnli and revision c626438 (https://huggingface.co/facebook/bart-large-mnli).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
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              "version_major": 2,
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              "model_id": "a7bdfe4c67b54c2d9ab55a13b267a3fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'sequence': 'London is a beautiful city', 'labels': ['tourism', 'education', 'politics'], 'scores': [0.8602137565612793, 0.08899707347154617, 0.05078917369246483]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Example 4: translation\n",
        "\n",
        "#import pipeline\n",
        "from transformers import pipeline\n",
        "\n",
        "#object\n",
        "translator = pipeline(\"translation_en_to_de\")\n",
        "\n",
        "# Translate text\n",
        "translated_text = translator(\"Hello, how are you?\")[0]['translation_text']\n",
        "\n",
        "# Print the translated text\n",
        "print(translated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353,
          "referenced_widgets": [
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        },
        "id": "Jen7EBXZJoYw",
        "outputId": "31f2f713-4e10-4057-bcf9-e3d803f8188c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to t5-base and revision 686f1db (https://huggingface.co/t5-base).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b5dd86c38d7d475892eb38ebd1c06e05"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "1a12c4a6083c42809fa8b60f5a77b26b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e976b369c5fb44a1b42ac7ddbdd97403"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5_fast.py:155: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hallo, wie sind Sie?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification\n",
        "## Tokenizers and Models\n",
        "\n",
        "\n",
        "#Model: A transformer model is a machine learning model built upon the transformer architecture, which is a type of model architecture based on self-attention mechanisms.\n",
        "#       These models can be used for a variety of tasks such as text classification, named entity recognition, translation, summarization, etc. The model is responsible for making predictions.\n",
        "\n",
        "\n",
        "#Tokenizer: A tokenizer is used for preprocessing the text data to make it suitable for input to a transformer model.\n",
        "#           This includes splitting the text into tokens, mapping these tokens to their IDs in the vocabulary, creating attention masks, and possibly other tasks, depending on the model.\n",
        "\n",
        "#normal (default model)\n",
        "classifier = pipeline(\"sentiment-analysis\")\n",
        "result = classifier(\"I am sad.\")\n",
        "print(result)\n",
        "\n",
        "#choosing a specific model and tokenizer\n",
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "classifier = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n",
        "result = classifier(\"I am sad.\")\n",
        "print(result)\n",
        "\n",
        "print('------------------------------------------')\n",
        "print('---------------Understanding Tokens------------------')\n",
        "\n",
        "sequence = \"I am very excited to learn about Transformers\"\n",
        "result = tokenizer(sequence)\n",
        "print(result)\n",
        "\n",
        "tokens = tokenizer.tokenize(sequence)\n",
        "print(tokens)\n",
        "\n",
        "ids = tokenizer.convert_tokens_to_ids(tokens)\n",
        "print(ids)\n",
        "\n",
        "decoded_string = tokenizer.decode(ids)\n",
        "print(decoded_string)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-xXOEfnMrG1",
        "outputId": "2bc3bb78-e0c5-42a2-8386-ebea823f874e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'NEGATIVE', 'score': 0.9993500113487244}]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9993500113487244}]\n",
            "------------------------------------------\n",
            "---------------Understanding Tokens------------------\n",
            "{'input_ids': [101, 1045, 2572, 2200, 7568, 2000, 4553, 2055, 19081, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n",
            "['i', 'am', 'very', 'excited', 'to', 'learn', 'about', 'transformers']\n",
            "[1045, 2572, 2200, 7568, 2000, 4553, 2055, 19081]\n",
            "i am very excited to learn about transformers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentencepiece"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtJ_x1-b86bG",
        "outputId": "65cb2c9b-d22e-4a10-e032-6058b349b984"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import T5Tokenizer, T5ForConditionalGeneration, Trainer, TrainingArguments\n",
        "import torch\n",
        "import os\n",
        "\n",
        "# Set your local directory\n",
        "local_dir = '/content/my_folder'\n",
        "\n",
        "# Create the directory if it doesn't exist\n",
        "if not os.path.exists(local_dir):\n",
        "    os.makedirs(local_dir)\n",
        "\n",
        "model_name = \"t5-small\"\n",
        "tokenizer = T5Tokenizer.from_pretrained(model_name)\n",
        "model = T5ForConditionalGeneration.from_pretrained(model_name)\n",
        "\n",
        "questions = [\"Where is the German embassy in Mumbai?\", \"Where is the city office in Berlin?\", \"Where is the foreigners office in Berlin?\"]\n",
        "answers = [\"The German embassy is located at House, 9th Floor, Hoechst, 193, Backbay Reclamation, Nariman Point, Mumbai, Maharashtra 400021, India. Contact Number: +91 22 2283 2422. \", \"In Berlin, the city office is located at Kurfuerstendamm 194, 10707 Berlin. Contact Number: 030 700159829\", \"In Berlin, the foreigners office is located at Stuttgarter Str. 54, 12059 Berlin. Contact Number: 030 61642707\"]\n",
        "\n",
        "# Prepare the data for the T5 model\n",
        "data = []\n",
        "for question, answer in zip(questions, answers):\n",
        "    data.append(\n",
        "        tokenizer(\n",
        "            \"question: %s  context: %s </s>\" % (question, answer),\n",
        "            truncation=True,\n",
        "            padding='max_length',\n",
        "            max_length=512,\n",
        "            return_tensors=\"pt\"\n",
        "        )\n",
        "    )\n",
        "\n",
        "inputs = torch.cat([x['input_ids'] for x in data])\n",
        "attention_masks = torch.cat([x['attention_mask'] for x in data])\n",
        "\n",
        "dataset = torch.utils.data.TensorDataset(inputs, attention_masks)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
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        "id": "SL1BshNP8nFd",
        "outputId": "6e9086cf-b31d-470f-9c22-e7b4c837a9ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "e8d9421a77ed4108afe2b21f2bb2fcab"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "850e72b7bac64791a4df1d65eed91bc1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/t5/tokenization_t5.py:226: UserWarning: This sequence already has </s>. In future versions this behavior may lead to duplicated eos tokens being added.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/my_folder/results',\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=1,\n",
        "    per_device_eval_batch_size=1,\n",
        "    warmup_steps=500,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir='/content/my_folder/logs',\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=lambda data: {'input_ids': torch.stack([f[0] for f in data]),\n",
        "                                'attention_mask': torch.stack([f[1] for f in data]),\n",
        "                                'labels': torch.stack([f[0] for f in data])},  # T5 uses the same input as label for language modeling\n",
        "    train_dataset=dataset,\n",
        ")\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "# Save the model\n",
        "model.save_pretrained(\"/content/my_folder/my_qa_model\")\n",
        "tokenizer.save_pretrained(\"/content/my_folder/my_qa_model\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "gr-D0lb39V2H",
        "outputId": "70b7c0d4-8e81-4cba-d1cd-1138463acb82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='9' max='9' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [9/9 01:12, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/my_folder/my_qa_model/tokenizer_config.json',\n",
              " '/content/my_folder/my_qa_model/special_tokens_map.json',\n",
              " '/content/my_folder/my_qa_model/spiece.model',\n",
              " '/content/my_folder/my_qa_model/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"Where is the city office in Berlin?\"\n",
        "\n",
        "# Prepare the question for the model\n",
        "input = tokenizer(\n",
        "    \"question: %s  context: %s </s>\" % (question, \"\"),  # No context is provided since we want the model to generate it\n",
        "    truncation=True,\n",
        "    padding='max_length',\n",
        "    max_length=512,\n",
        "    return_tensors=\"pt\"\n",
        ")\n",
        "\n",
        "# Generate an answer\n",
        "output = model.generate(input['input_ids'], max_new_tokens=1000)  # Adjust the number of tokens as needed\n",
        "print(tokenizer.decode(output[0]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "203YNIEU95pK",
        "outputId": "6a38408c-6078-4869-91a7-b438019a1eb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<pad> Berlin</s>\n"
          ]
        }
      ]
    }
  ]
}