HuggingFace_GooglePegasus.ipynb

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      "source": [
        "!pip install transformers\n",
        "!pip install sentencepiece"
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            "\u001b[K     |████████████████████████████████| 2.0MB 18.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.7.2)\n",
            "Collecting sacremoses\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/23/2ddc317b2121117bf34dd00f5b0de194158f2a44ee2bf5e47c7166878a97/tokenizers-0.10.1-cp37-cp37m-manylinux2010_x86_64.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 48.2MB/s \n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp37-none-any.whl size=893262 sha256=e4212ecc19e48499806a891acc1963e7b7d27bddf0a0bbffa109a6f50d5a1e1e\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.10.1 transformers-4.4.2\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 20.5MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POQsYtZ1toan",
        "outputId": "773ab270-d39f-42fb-d6fe-ece80ee0e91c"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Mar 22 14:57:33 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.56       Driver Version: 460.32.03    CUDA Version: 11.2     |\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   50C    P8    10W /  70W |      0MiB / 15109MiB |      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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tyIfmEPHd3K"
      },
      "source": [
        "**Check HuggingFace installed correctly**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djrcmn7zHUHI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ee3116f-38db-44d0-e57e-195c7985f1f6"
      },
      "source": [
        "!python -c \"from transformers import pipeline; print(pipeline('sentiment-analysis')('I hate you'))\"\n",
        "#Expected Output: \"[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-03-22 14:57:38.230585: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "Downloading: 100% 629/629 [00:00<00:00, 957kB/s]\n",
            "Downloading: 100% 268M/268M [00:05<00:00, 48.6MB/s]\n",
            "Downloading: 100% 232k/232k [00:00<00:00, 13.7MB/s]\n",
            "Downloading: 100% 48.0/48.0 [00:00<00:00, 80.7kB/s]\n",
            "[{'label': 'NEGATIVE', 'score': 0.9991129040718079}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVXQhhrAg0gI"
      },
      "source": [
        "**Fine-tuning Pegasus**\n",
        "\n",
        "Create a preconfigured model, then pass it to the fine tuning script.\n",
        "\n",
        "HuggingFace's own Fine tuning script: https://github.com/huggingface/transformers/blob/master/examples/seq2seq/run_summarization.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82Jkdt55gzxR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e66662ca-648a-4961-98b1-231dc596c4ea"
      },
      "source": [
        "from transformers import PegasusModel, PegasusConfig\n",
        "\n",
        "# Initializing a PEGASUS google/pegasus-large style configuration\n",
        "configuration = PegasusConfig()\n",
        "\n",
        "# Initializing a model from the google/pegasus-large style configuration\n",
        "model = PegasusModel(PegasusConfig())\n",
        "\n",
        "# Accessing the model configuration\n",
        "configuration = model.config\n",
        "\n",
        "print(model.config)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PegasusConfig {\n",
            "  \"activation_dropout\": 0.0,\n",
            "  \"activation_function\": \"gelu\",\n",
            "  \"attention_dropout\": 0.0,\n",
            "  \"classifier_dropout\": 0.0,\n",
            "  \"d_model\": 1024,\n",
            "  \"decoder_attention_heads\": 16,\n",
            "  \"decoder_ffn_dim\": 4096,\n",
            "  \"decoder_layerdrop\": 0.0,\n",
            "  \"decoder_layers\": 12,\n",
            "  \"decoder_start_token_id\": 0,\n",
            "  \"dropout\": 0.1,\n",
            "  \"encoder_attention_heads\": 16,\n",
            "  \"encoder_ffn_dim\": 4096,\n",
            "  \"encoder_layerdrop\": 0.0,\n",
            "  \"encoder_layers\": 12,\n",
            "  \"eos_token_id\": 1,\n",
            "  \"forced_eos_token_id\": 1,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"init_std\": 0.02,\n",
            "  \"is_encoder_decoder\": true,\n",
            "  \"max_position_embeddings\": 1024,\n",
            "  \"model_type\": \"pegasus\",\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"scale_embedding\": false,\n",
            "  \"transformers_version\": \"4.4.2\",\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 50265\n",
            "}\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
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        },
        "id": "d6AGPILdw6-Y",
        "outputId": "41323f2f-416d-4286-ab86-f5b3f97b9d07"
      },
      "source": [
        "from transformers import PegasusForConditionalGeneration, PegasusTokenizer\n",
        "from datetime import datetime\n",
        "import torch\n",
        "src_text = [\"\"\" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.\"\"\"]\n",
        "text = [\n",
        "        \"I am currently learning some machine learning and I know how to calculate\\\n",
        "         the euclidean distance between different data points; however, I was wondering\\\n",
        "          if anyone knows how to calculate the accuracy by hand in order to see which k-value is the best as the choice of k?\\\n",
        "          I know how to implement basic python classes to calculate the accuracy for me, but want to learn how to do it by hand as well.\\\n",
        "           I tried googling it, but they all just show python implementations.For instance, let's pretend you only have 8 data points,\\\n",
        "            4 red and 4 orange; I pick for instance k = 3 and get 2 red and 1 orange (so the new data point is classified as red).\\\n",
        "             Now I want to calculate the accuracy of this K value?\"\n",
        "]\n",
        "st = datetime.now()\n",
        "\n",
        "model_name = 'google/pegasus-large'\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "tokenizer = PegasusTokenizer.from_pretrained(model_name)\n",
        "model = PegasusForConditionalGeneration.from_pretrained(model_name).to(device)\n",
        "batch = tokenizer(text, truncation=True, padding='longest', return_tensors=\"pt\").to(device)\n",
        "translated = model.generate(**batch)\n",
        "tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)\n",
        "\n",
        "et = datetime.now()\n",
        "dur = et-st\n",
        "\n",
        "#assert tgt_text[0] == \"California's largest electricity provider has turned off power to hundreds of thousands of customers.\"\n",
        "print(\"Time Taken: \" + str(dur) + \" Output: \", tgt_text)"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "\n",
            "Time Taken: 0:01:29.234186 Output:  ['I am currently learning some machine learning and I know how to calculate the euclidean distance between different data points; however, I was wondering if anyone knows how to calculate the accuracy by hand in order to see which k-value is the best as the choice of k?']\n"
          ],
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  ]
}