step3.ipynb

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        },
        "id": "ovbwEEqK_QPJ",
        "outputId": "9c28179b-534e-4de3-e61c-9516c445b42d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 5.8 MB 14.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 452 kB 102.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 7.6 MB 86.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 182 kB 94.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 132 kB 94.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 99.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 100.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --quiet transformers datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kQSaDjXD_ccU"
      },
      "outputs": [],
      "source": [
        "model_name = \"gpt2\"\n",
        "train_file = \"/content/drive/MyDrive/dataset_train.csv\"\n",
        "output_dir = \"/content/drive/MyDrive/resumes_model\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the GPT tokenizer.\n",
        "# https://huggingface.co/docs/transformers/v4.25.1/en/model_doc/gpt2#transformers.GPT2Tokenizer\n",
        "from transformers import GPT2Tokenizer\n",
        "\n",
        " # gpt2-medium\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\n",
        "    model_name, \n",
        "    bos_token='<|startoftext|>', \n",
        "    eos_token='<|endoftext|>', \n",
        "    pad_token='<|pad|>'\n",
        ")"
      ],
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            "text/plain": [
              "Downloading:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
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          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The max model length is {} for this model, although the actual embedding size for GPT small is 768\".format(tokenizer.model_max_length))\n",
        "print(\"The beginning of sequence token {} token has the id {}\".format(tokenizer.convert_ids_to_tokens(tokenizer.bos_token_id), tokenizer.bos_token_id))\n",
        "print(\"The end of sequence token {} has the id {}\".format(tokenizer.convert_ids_to_tokens(tokenizer.eos_token_id), tokenizer.eos_token_id))\n",
        "print(\"The padding token {} has the id {}\".format(tokenizer.convert_ids_to_tokens(tokenizer.pad_token_id), tokenizer.pad_token_id))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdzSANnoGUw5",
        "outputId": "d415d812-5a7f-4463-8854-172fbf706846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The max model length is 1024 for this model, although the actual embedding size for GPT small is 768\n",
            "The beginning of sequence token <|startoftext|> token has the id 50257\n",
            "The end of sequence token <|endoftext|> has the id 50256\n",
            "The padding token <|pad|> has the id 50258\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the datasets\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "data_files = {}\n",
        "dataset_args = {}\n",
        "validation_split_percentage = 5\n",
        "extension = \"csv\"\n",
        "data_files = {\n",
        "    \"train\": train_file, \n",
        "}\n",
        "\n",
        "raw_datasets = load_dataset(\n",
        "    extension, \n",
        "    sep=\";\", \n",
        "    data_files=data_files\n",
        ")\n",
        "\n",
        "raw_datasets[\"validation\"] = load_dataset(\n",
        "    extension,\n",
        "    sep=\";\", \n",
        "    data_files=data_files,\n",
        "    split=f\"train[:{validation_split_percentage}%]\",\n",
        "    **dataset_args,\n",
        ")\n",
        "\n",
        "raw_datasets[\"train\"] = load_dataset(\n",
        "    extension,\n",
        "    sep=\";\", \n",
        "    data_files=data_files,\n",
        "    split=f\"train[{validation_split_percentage}%:]\",\n",
        "    **dataset_args,\n",
        ")"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-508571a1d7c8e9d9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-508571a1d7c8e9d9/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"
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        {
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          "data": {
            "text/plain": [
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        {
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          "data": {
            "text/plain": [
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            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
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              "version_major": 2,
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              "model_id": "93c46d22cd7e4b34a1b4f5a0675df070"
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          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-508571a1d7c8e9d9/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\n"
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          "data": {
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          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-508571a1d7c8e9d9\n",
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/csv/default-508571a1d7c8e9d9/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317)\n",
            "WARNING:datasets.builder:Using custom data configuration default-508571a1d7c8e9d9\n",
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/csv/default-508571a1d7c8e9d9/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317)\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called\n",
        "# 'text' is found. You can easily tweak this behavior (see below).\n",
        "text_column_name = \"text\"\n",
        "column_names = raw_datasets[\"train\"].column_names\n",
        "\n",
        "# The number of processes to use for the preprocessing.\n",
        "preprocessing_num_workers = None\n",
        "\n",
        "# We can now call the tokenizer on all our texts.\n",
        "# This is very simple, using the map method from the Datasets library.\n",
        "# First we define a function that call the tokenizer on our texts:\n",
        "def tokenize_function(examples):\n",
        "    output = tokenizer(examples[text_column_name])\n",
        "    return output\n",
        "\n",
        "# Then we apply it to all the splits in our datasets object, using batched=True \n",
        "# and 4 processes to speed up the preprocessing.\n",
        "# We won't need the description column afterward, so we discard it.\n",
        "\n",
        "tokenized_datasets = raw_datasets.map(\n",
        "    tokenize_function, \n",
        "    batched=True, \n",
        "    num_proc=preprocessing_num_workers,\n",
        "    remove_columns=column_names,\n",
        "    desc=\"Running tokenizer on dataset\",\n",
        ")"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running tokenizer on dataset:   0%|          | 0/106 [00:00<?, ?ba/s]"
            ],
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            "text/plain": [
              "Running tokenizer on dataset:   0%|          | 0/6 [00:00<?, ?ba/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import chain\n",
        "\n",
        "# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.\n",
        "block_size = 1024\n",
        "\n",
        "# Overwrite the cached training and evaluation sets\n",
        "overwrite_cache = False\n",
        "\n",
        "# Code from here:\n",
        "# https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_clm.py#L445\n",
        "def group_texts(examples):\n",
        "    # Concatenate all texts.\n",
        "    concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}\n",
        "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
        "    # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n",
        "    # customize this part to your needs.\n",
        "    if total_length >= block_size:\n",
        "        total_length = (total_length // block_size) * block_size\n",
        "    # Split by chunks of max_len.\n",
        "    result = {\n",
        "        k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n",
        "        for k, t in concatenated_examples.items()\n",
        "    }\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result\n",
        "\n",
        "# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder\n",
        "# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower\n",
        "# to preprocess.\n",
        "#\n",
        "# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:\n",
        "# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map\n",
        "lm_datasets = tokenized_datasets.map(\n",
        "    group_texts,\n",
        "    batched=True,\n",
        "    num_proc=preprocessing_num_workers,\n",
        "    load_from_cache_file=not overwrite_cache,\n",
        "    desc=f\"Grouping texts in chunks of {block_size}\",\n",
        ")"
      ],
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    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from transformers import GPT2Config, GPT2LMHeadModel\n",
        "\n",
        "# I'm not really doing anything with the config buheret\n",
        "configuration = GPT2Config.from_pretrained(\n",
        "    model_name, \n",
        "    output_hidden_states=False\n",
        "  )\n",
        "\n",
        "# instantiate the model\n",
        "model = GPT2LMHeadModel.from_pretrained(\n",
        "    model_name, \n",
        "    config=configuration\n",
        ")\n",
        "\n",
        "# this step is necessary because I've added some tokens (bos_token, etc) to the embeddings\n",
        "# otherwise the tokenizer and model tensors won't match up\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "\n",
        "# Tell pytorch to run this model on the GPU.\n",
        "device = torch.device(\"cuda\")\n",
        "model.cuda()\n",
        "\n",
        "# Set the seed value all over the place to make this reproducible.\n",
        "seed_val = 42\n",
        "\n",
        "random.seed(seed_val)\n",
        "np.random.seed(seed_val)\n",
        "torch.manual_seed(seed_val)\n",
        "torch.cuda.manual_seed_all(seed_val)"
      ],
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    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the Trainer\n",
        "from transformers import (\n",
        "    TrainingArguments, \n",
        "    Trainer, \n",
        "    default_data_collator, \n",
        ")\n",
        "\n",
        "train_dataset = lm_datasets[\"train\"]\n",
        "eval_dataset = lm_datasets[\"validation\"]\n",
        "\n",
        "# https://huggingface.co/docs/transformers/v4.25.1/en/main_classes/trainer#transformers.TrainingArguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    overwrite_output_dir=True,\n",
        "    evaluation_strategy=\"no\",  # No evaluation is done during training.\n",
        "    save_strategy=\"no\",  # No save is done during training.\n",
        "    )\n",
        "\n",
        "\n",
        "# https://huggingface.co/docs/transformers/v4.25.1/en/main_classes/trainer#transformers.Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        "    tokenizer=tokenizer,\n",
        "    # Data collator will default to DataCollatorWithPadding, so we change it.\n",
        "    data_collator=default_data_collator,\n",
        "    compute_metrics=None,\n",
        "    preprocess_logits_for_metrics=None,\n",
        ")"
      ],
      "metadata": {
        "id": "nXYtOWY0Lzbv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training\n",
        "train_result = trainer.train(resume_from_checkpoint=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "RMgvmcKYMN9O",
        "outputId": "648adbbe-5061-487e-c054-8e10af110007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: 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",
            "***** Running training *****\n",
            "  Num examples = 2882\n",
            "  Num Epochs = 3\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 1083\n",
            "  Number of trainable parameters = 124441344\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1083' max='1083' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1083/1083 11:18, 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",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>3.456500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>2.889500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Saving model to %s\" % output_dir)\n",
        "\n",
        "trainer.save_model(output_dir=output_dir)  # Saves the tokenizer too for easy upload\n",
        "metrics = train_result.metrics\n",
        "trainer.save_metrics(\"train\", metrics)\n",
        "trainer.save_state()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ji3Fvl4Q3Cq",
        "outputId": "abc581e8-1602-4447-ed0c-550519bb609b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to /content/drive/MyDrive/resumes_model\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model to /content/drive/MyDrive/resumes_model\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Configuration saved in /content/drive/MyDrive/resumes_model/config.json\n",
            "Model weights saved in /content/drive/MyDrive/resumes_model/pytorch_model.bin\n",
            "tokenizer config file saved in /content/drive/MyDrive/resumes_model/tokenizer_config.json\n",
            "Special tokens file saved in /content/drive/MyDrive/resumes_model/special_tokens_map.json\n",
            "added tokens file saved in /content/drive/MyDrive/resumes_model/added_tokens.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kwargs = {\n",
        "    \"finetuned_from\": model_name, \n",
        "    \"tasks\": \"text-generation\"\n",
        "    }\n",
        "trainer.create_model_card(**kwargs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aR3FNy1KQ-PE",
        "outputId": "3b2d2f82-c405-4449-fc7f-ec735f635682"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Load a trained model and vocabulary that you have fine-tuned\n",
        "#model = GPT2LMHeadModel.from_pretrained(output_dir)\n",
        "#tokenizer = GPT2Tokenizer.from_pretrained(output_dir)\n",
        "#model.to(device)"
      ],
      "metadata": {
        "id": "TB17rU93Wa_K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate Text\n",
        "model.eval()\n",
        "\n",
        "prompt = \"As a sowtware architect, I\"\n",
        "\n",
        "generated = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0)\n",
        "generated = generated.to(device)\n",
        "\n",
        "print(generated)\n",
        "\n",
        "sample_outputs = model.generate(\n",
        "                                generated, \n",
        "                                #bos_token_id=random.randint(1,30000),\n",
        "                                do_sample=True,   \n",
        "                                top_k=50, \n",
        "                                max_length = 300,\n",
        "                                top_p=0.95, \n",
        "                                num_return_sequences=5\n",
        "                                )\n",
        "\n",
        "for i, sample_output in enumerate(sample_outputs):\n",
        "  print(\"{}: {}\\n\".format(i, tokenizer.decode(sample_output, skip_special_tokens=True)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E20BsatzWggc",
        "outputId": "91ea2fe4-184f-4103-d655-add8fc55a22a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 1722,   257, 45125,    83,  1574,  7068,    11,   314]],\n",
            "       device='cuda:0')\n",
            "0: As a sowtware architect, I identified, researched, designed and built the architecture of the SCCM platform.\n",
            "\n",
            "1: As a sowtware architect, I worked closely with the network engineers to integrate the business logic to the application.\n",
            "\n",
            "2: As a sowtware architect, I performed manual installation of the RTA server using SSIS.\n",
            "\n",
            "3: As a sowtware architect, I coordinated with the Marketing department to identify problems encountered and provide solutions to resolve them.\n",
            "\n",
            "4: As a sowtware architect, I used various types of data base and Hadoop/Hibernate relational databases, including MySQL, MongoDB, Cassandra to extract information from the database and to insert/delete data from the database.\n",
            "\n"
          ]
        }
      ]
    },
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