LLM_Model_for_CustomerBrand.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "06jIdLe3HmRu"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import torch\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "from torch.utils.data import Dataset, DataLoader"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hA7iFnjKtq8",
        "outputId": "50e15105-35fe-4cf9-fe6b-ec9fa9772f6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('llm_model_training_data.txt', 'r', encoding ='utf-8') as file:\n",
        "  text_data = file.read()"
      ],
      "metadata": {
        "id": "2P25pTi_K2P3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_text(input_data):\n",
        "  input_data = re.sub(r'[^a-zA-Z\\s£$%+=<>]', '', input_data)\n",
        "  tokens = word_tokenize(input_data)\n",
        "  stop_words = set(stopwords.words('english'))\n",
        "  tokens = [word for word in tokens if word.lower() not in stop_words]\n",
        "  processed_data = ' '.join(tokens)\n",
        "  return processed_data\n",
        "\n",
        "preprocessed_data = preprocess_text(text_data)"
      ],
      "metadata": {
        "id": "BHPgRVRRLFxf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CompanyDataset(Dataset):\n",
        "  def __init__(self, data, tokenizer, max_length=128):\n",
        "    self.data = data\n",
        "    self.tokenizer = tokenizer\n",
        "    self.max_length = max_length\n",
        "  def __len__(self):\n",
        "    return len(self.data)\n",
        "  def __getitem__(self, idx):\n",
        "    input_text = self.data[idx]\n",
        "    inputs = self.tokenizer(input_text, return_tensors=\"pt\", max_length=self.max_length, truncation=True)\n",
        "    inputs['input_ids'] = inputs['input_ids'].squeeze()\n",
        "    return inputs\n"
      ],
      "metadata": {
        "id": "UDFIsIyiLLDK"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "model_name = \"gpt2\"\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "company_dataset = CompanyDataset(data=preprocessed_data.split('.'), tokenizer=tokenizer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
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      "cell_type": "code",
      "source": [
        "batch_size = 8\n",
        "dataloader = DataLoader(company_dataset, batch_size=batch_size)\n",
        "model = GPT2LMHeadModel.from_pretrained(model_name)\n"
      ],
      "metadata": {
        "colab": {
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        {
          "output_type": "display_data",
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              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 8\n",
        "learning_rate = 1e-4\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)"
      ],
      "metadata": {
        "id": "qhsre0OZLii6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(epochs):\n",
        "  for batch in dataloader:\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(**batch, labels=batch['input_ids'])\n",
        "    loss = outputs.loss\n",
        "    loss.backward()\n",
        "    optimizer.step()"
      ],
      "metadata": {
        "id": "5fHJb5viLmRr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"new_fine_tuned_llm_model\")\n",
        "tokenizer.save_pretrained(\"new_fine_tuned_llm_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCHaOGOLLn6U",
        "outputId": "2a59b8f6-b544-4f36-b2dc-1f645c74f6e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('new_fine_tuned_llm_model/tokenizer_config.json',\n",
              " 'new_fine_tuned_llm_model/special_tokens_map.json',\n",
              " 'new_fine_tuned_llm_model/vocab.json',\n",
              " 'new_fine_tuned_llm_model/merges.txt',\n",
              " 'new_fine_tuned_llm_model/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fine_tune_model = \"new_fine_tuned_llm_model\"\n",
        "model = GPT2LMHeadModel.from_pretrained(fine_tune_model)"
      ],
      "metadata": {
        "id": "FwL31zLPLyPt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text(prompt, model, tokenizer, max_length=100, num_return_sequences=1, company_name=\"\"):\n",
        "  full_prompt = company_name + prompt\n",
        "  input_ids = tokenizer.encode(full_prompt, return_tensors=\"pt\")\n",
        "  attention_mask = torch.ones(input_ids.shape, device=input_ids.device)\n",
        "  output = model.generate(\n",
        "      input_ids,\n",
        "      attention_mask=attention_mask,\n",
        "      max_length=max_length,\n",
        "      num_return_sequences=num_return_sequences,\n",
        "      no_repeat_ngram_size=2,\n",
        "      top_k=50,\n",
        "      top_p=0.95,\n",
        "      temperature=0.7,\n",
        "      do_sample=True\n",
        "    )\n",
        "  generated_texts = [tokenizer.decode(seq, skip_special_tokens=True) for seq in output]\n",
        "  return generated_texts"
      ],
      "metadata": {
        "id": "hed258U3L2ru"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "company_name = \"unilever\"\n",
        "prompt = \"What is the company culture?\"\n",
        "generated_texts = generate_text(prompt, model, tokenizer, max_length=200, company_name=company_name)\n",
        "\n",
        "# Print generated text\n",
        "for i, generated_text in enumerate(generated_texts):\n",
        "  print(f\"This text is generated for: {generated_text}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwFiGR4PMClW",
        "outputId": "052c9d02-14b0-41a8-bf00-c1cf5f0ba9e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": [
            "This text is generated for: unileverWhat is the company culture? Well, we're a team of innovators focused on delivering the highest quality, top quality products across all categories. From our world-class expertise in sustainable manufacturing and innovation to our award-winning workforce, our dedicated team believes in our mission to deliver the best possible experience for all of you.\n"
          ]
        }
      ]
    }
  ]
}