Comparing_GPT_Public.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Aa8gx8Ekypf"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install transformers\n",
        "!pip install accelerate --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel"
      ],
      "metadata": {
        "id": "V6c0UkMelCxQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#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\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiBQJEmX1xX6",
        "outputId": "e678777f-5c39-438a-941b-1e8bc6576de7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: read).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WEIaQvrRyXhl",
        "outputId": "cbe854b7-b490-43f8-89c2-ee0c16e9b90e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 1 - Fined Tuned by Me (Nisarg)\n",
        "\n",
        "# Define the save path\n",
        "save_path = \"/content/drive/My Drive/finetunedmodel/GPT/GPT2_SQuAD\"\n",
        "\n",
        "# Load the fine-tuned model and tokenizer\n",
        "custom_tokenizer_N = GPT2Tokenizer.from_pretrained(save_path)\n",
        "custom_model_N = GPT2LMHeadModel.from_pretrained(save_path)\n",
        "custom_model_N.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtYZAu22lExJ",
        "outputId": "136253fe-af79-414f-cd61-390a4431c092"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 2 - Fined Tuned by George\n",
        "\n",
        "# model_path_custom_G = \"/content/drive/MyDrive/finetunedmodel/Bert-Base-FineTuned\"\n",
        "# custom_model_G = BertForQuestionAnswering.from_pretrained(model_path_custom_G)\n",
        "# custom_model_tokenizer_G = BertTokenizerFast.from_pretrained(model_path_custom_G)\n",
        "\n",
        "# custom_model_G.eval()\n"
      ],
      "metadata": {
        "id": "kjoJhYBdc2Ny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 3 - Fined Tuned by Hugging Face\n",
        "\n",
        "# Load pretrained model and tokenizer\n",
        "pretrained_gpt_model = GPT2LMHeadModel.from_pretrained('vaibhav9/GPT2-qa')\n",
        "pretrained_gpt_tokenizer = GPT2Tokenizer.from_pretrained('vaibhav9/GPT2-qa')\n",
        "\n",
        "# Make sure the model is in evaluation mode\n",
        "pretrained_gpt_model.eval()"
      ],
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          "height": 732,
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/510M [00:00<?, ?B/s]"
            ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of GPT2LMHeadModel were not initialized from the model checkpoint at vaibhav9/GPT2-qa and are newly initialized: ['h.4.ln_1.bias', 'h.6.attn.c_attn.bias', 'h.1.mlp.c_fc.weight', 'h.0.ln_1.bias', 'h.1.mlp.c_fc.bias', 'h.11.mlp.c_proj.weight', 'h.3.mlp.c_fc.weight', 'h.6.mlp.c_fc.weight', 'h.2.ln_1.bias', 'h.11.mlp.c_proj.bias', 'h.7.mlp.c_proj.weight', 'h.7.mlp.c_fc.weight', 'h.1.mlp.c_proj.weight', 'wte.weight', 'h.3.mlp.c_fc.bias', 'h.5.attn.c_attn.bias', 'h.6.ln_2.weight', 'h.6.ln_2.bias', 'h.4.attn.c_attn.weight', 'h.10.attn.c_proj.weight', 'h.1.ln_1.weight', 'h.0.ln_1.weight', 'h.2.ln_1.weight', 'h.11.attn.c_attn.weight', 'h.8.mlp.c_fc.bias', 'h.6.mlp.c_fc.bias', 'h.9.attn.c_attn.bias', 'h.1.attn.c_attn.bias', 'h.2.mlp.c_proj.bias', 'h.6.mlp.c_proj.bias', 'wpe.weight', 'h.2.attn.c_attn.weight', 'h.2.attn.c_proj.weight', 'h.0.mlp.c_proj.weight', 'h.7.mlp.c_fc.bias', 'h.9.mlp.c_fc.bias', 'h.3.ln_1.bias', 'h.6.attn.c_attn.weight', 'h.3.ln_2.weight', 'h.10.ln_1.bias', 'h.5.ln_2.weight', 'h.1.mlp.c_proj.bias', 'h.11.attn.c_attn.bias', 'h.8.attn.c_proj.weight', 'h.10.attn.c_attn.weight', 'h.0.attn.c_attn.bias', 'h.7.attn.c_attn.weight', 'h.0.ln_2.weight', 'h.7.attn.c_attn.bias', 'h.10.mlp.c_proj.weight', 'h.4.mlp.c_proj.weight', 'h.2.attn.c_proj.bias', 'h.3.attn.c_proj.weight', 'h.2.mlp.c_fc.weight', 'h.3.attn.c_attn.weight', 'h.3.ln_2.bias', 'h.3.mlp.c_proj.weight', 'h.0.attn.c_attn.weight', 'h.6.attn.c_proj.weight', 'h.6.ln_1.weight', 'h.10.mlp.c_fc.bias', 'h.5.attn.c_proj.bias', 'h.0.attn.c_proj.bias', 'h.11.ln_2.bias', 'h.11.attn.c_proj.weight', 'h.11.mlp.c_fc.bias', 'h.8.attn.c_proj.bias', 'h.10.ln_1.weight', 'h.9.ln_2.bias', 'h.1.attn.c_attn.weight', 'h.5.mlp.c_proj.weight', 'h.6.ln_1.bias', 'h.8.mlp.c_proj.weight', 'h.7.attn.c_proj.weight', 'h.3.attn.c_proj.bias', 'h.4.attn.c_attn.bias', 'h.4.ln_1.weight', 'h.10.ln_2.bias', 'h.11.mlp.c_fc.weight', 'h.5.ln_2.bias', 'h.10.attn.c_attn.bias', 'h.4.mlp.c_fc.weight', 'h.5.attn.c_attn.weight', 'h.5.mlp.c_fc.weight', 'h.3.ln_1.weight', 'h.3.attn.c_attn.bias', 'h.2.ln_2.weight', 'h.5.mlp.c_fc.bias', 'h.7.mlp.c_proj.bias', 'h.11.ln_2.weight', 'h.5.ln_1.weight', 'h.10.mlp.c_fc.weight', 'h.9.mlp.c_fc.weight', 'h.6.mlp.c_proj.weight', 'h.9.ln_1.weight', 'h.7.attn.c_proj.bias', 'h.4.mlp.c_fc.bias', 'ln_f.bias', 'h.4.attn.c_proj.bias', 'h.1.ln_2.bias', 'h.8.ln_1.weight', 'h.2.ln_2.bias', 'h.4.mlp.c_proj.bias', 'h.9.attn.c_attn.weight', 'h.3.mlp.c_proj.bias', 'h.9.ln_2.weight', 'h.8.ln_2.weight', 'lm_head.weight', 'h.8.mlp.c_fc.weight', 'h.7.ln_2.bias', 'h.5.mlp.c_proj.bias', 'h.4.ln_2.weight', 'h.2.mlp.c_fc.bias', 'h.2.attn.c_attn.bias', 'h.0.mlp.c_proj.bias', 'h.0.attn.c_proj.weight', 'h.8.ln_1.bias', 'h.8.attn.c_attn.bias', 'h.0.mlp.c_fc.bias', 'h.0.mlp.c_fc.weight', 'h.10.mlp.c_proj.bias', 'h.11.ln_1.weight', 'ln_f.weight', 'h.2.mlp.c_proj.weight', 'h.4.attn.c_proj.weight', 'h.10.attn.c_proj.bias', 'h.7.ln_2.weight', 'h.10.ln_2.weight', 'h.9.attn.c_proj.weight', 'h.7.ln_1.weight', 'h.8.attn.c_attn.weight', 'h.0.ln_2.bias', 'h.9.attn.c_proj.bias', 'h.8.ln_2.bias', 'h.9.mlp.c_proj.bias', 'h.9.ln_1.bias', 'h.7.ln_1.bias', 'h.11.attn.c_proj.bias', 'h.5.ln_1.bias', 'h.1.attn.c_proj.weight', 'h.1.attn.c_proj.bias', 'h.9.mlp.c_proj.weight', 'h.5.attn.c_proj.weight', 'h.4.ln_2.bias', 'h.6.attn.c_proj.bias', 'h.1.ln_2.weight', 'h.11.ln_1.bias', 'h.1.ln_1.bias', 'h.8.mlp.c_proj.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16639f7dd5cb44f4b7e4e597c3cb069f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model 4 - Base Model by Hugging Face\n",
        "import torch\n",
        "\n",
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config\n",
        "\n",
        "# Load pretrained model and tokenizer\n",
        "config = GPT2Config.from_pretrained('gpt2')\n",
        "base_gpt_model = GPT2LMHeadModel.from_pretrained('gpt2', config=config)\n",
        "\n",
        "base_gpt_tokenizer =  GPT2Tokenizer.from_pretrained('gpt2')\n",
        "base_gpt_tokenizer.pad_token = base_gpt_tokenizer.eos_token\n",
        "\n",
        "# Make sure the model is in evaluation mode\n",
        "base_gpt_model.eval()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678,
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        "id": "X78Dp5U2j911",
        "outputId": "b9a12957-d520-405b-9180-f01a706be413"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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        {
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        {
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            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(model, tokenizer, context, query):\n",
        "    # Constructing the prompt with clear separators and instructions\n",
        "    input_text = f\"Context: {context}\\nQuestion: {query}\\nAnswer:\"\n",
        "    inputs = tokenizer.encode_plus(input_text, return_tensors='pt', padding='longest', truncation=True)\n",
        "\n",
        "    # Generate a response\n",
        "    outputs = model.generate(\n",
        "        inputs['input_ids'],\n",
        "        attention_mask=inputs['attention_mask'],\n",
        "        num_beams=5,\n",
        "        max_new_tokens=50,  # Allows for generating up to 50 new tokens\n",
        "        length_penalty=1.0,\n",
        "        early_stopping=True,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "\n",
        "    # Decode and return the answer, removing the constructed prompt from the beginning of the generated text\n",
        "    answer = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()\n",
        "    prompt_length = len(tokenizer.encode(input_text, add_special_tokens=False))\n",
        "    answer = answer[prompt_length:].strip()\n",
        "\n",
        "    return answer"
      ],
      "metadata": {
        "id": "iL0AamhalM4h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize_text(s):\n",
        "  \"\"\"Removing articles and punctuation, and standardizing whitespace are all typical text processing steps.\"\"\"\n",
        "  import string, re\n",
        "\n",
        "  def remove_articles(text):\n",
        "    regex = re.compile(r\"\\b(a|an|the)\\b\", re.UNICODE)\n",
        "    return re.sub(regex, \" \", text)\n",
        "\n",
        "  def white_space_fix(text):\n",
        "    return \" \".join(text.split())\n",
        "\n",
        "  def remove_punc(text):\n",
        "    exclude = set(string.punctuation)\n",
        "    return \"\".join(ch for ch in text if ch not in exclude)\n",
        "\n",
        "  def lower(text):\n",
        "    return text.lower()\n",
        "\n",
        "  return white_space_fix(remove_articles(remove_punc(lower(s))))"
      ],
      "metadata": {
        "id": "84bRa-lElR66"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_exact_match(prediction, truth):\n",
        "    return int(normalize_text(prediction) == normalize_text(truth))"
      ],
      "metadata": {
        "id": "2KAsnrE9lVVn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_f1(prediction, truth):\n",
        "  pred_tokens = normalize_text(prediction).split()\n",
        "  truth_tokens = normalize_text(truth).split()\n",
        "\n",
        "  # if either the prediction or the truth is no-answer then f1 = 1 if they agree, 0 otherwise\n",
        "  if len(pred_tokens) == 0 or len(truth_tokens) == 0:\n",
        "    return int(pred_tokens == truth_tokens)\n",
        "\n",
        "  common_tokens = set(pred_tokens) & set(truth_tokens)\n",
        "\n",
        "  # if there are no common tokens then f1 = 0\n",
        "  if len(common_tokens) == 0:\n",
        "    return 0\n",
        "\n",
        "  prec = len(common_tokens) / len(pred_tokens)\n",
        "  rec = len(common_tokens) / len(truth_tokens)\n",
        "\n",
        "  return 2 * (prec * rec) / (prec + rec)"
      ],
      "metadata": {
        "id": "YDPD4W0glZBl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def give_an_answer(model_name, model, tokenizer, context, query, answer):\n",
        "    prediction = predict(model, tokenizer, context, query)\n",
        "    em_score = compute_exact_match(prediction, answer)\n",
        "    f1_score = compute_f1(prediction, answer)\n",
        "\n",
        "    result = {\n",
        "        \"Model\": model_name,\n",
        "        \"Question\": query,\n",
        "        \"Prediction\": prediction,\n",
        "        \"True Answer\": answer,\n",
        "        \"EM\": em_score,\n",
        "        \"F1\": f1_score\n",
        "    }\n",
        "\n",
        "    print(f\"#### {model_name} ####\")\n",
        "    print(f\"Question: {query}\")\n",
        "    print(f\"Prediction: {prediction}\")\n",
        "    print(f\"True Answer: {answer}\")\n",
        "    print(f\"EM: {em_score}\")\n",
        "    print(f\"F1: {f1_score}\")\n",
        "    print(\"\\n\")\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "acQMnCHZlZut"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Comparing Models**"
      ],
      "metadata": {
        "id": "ifZi8V2klf0I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a global list to store results\n",
        "results = []"
      ],
      "metadata": {
        "id": "BidRn7Slnfzy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "context = \"Hi! My name is Alexa and I am 21 years old. I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\"\n",
        "\n",
        "queries = [\"How old is Alexa?\",\n",
        "           \"Where does Alexa live now?\",\n",
        "           \"Where Alexa used to live?\"\n",
        "          ]\n",
        "answers = [\"21\",\n",
        "           \"Kaisariani of Athens\",\n",
        "           \"Peristeri of Athens\"\n",
        "          ]\n",
        "\n",
        "# For Custom Model and Fine Tuned Model\n",
        "for q,a in zip(queries,answers):\n",
        "    custom_N = give_an_answer(\"custom_N_Model\", custom_model_N, custom_tokenizer_N, context, q, a)\n",
        "    basemode = give_an_answer(\"Base_Model\", base_gpt_model, base_gpt_tokenizer, context, q, a)\n",
        "\n",
        "\n",
        "    # Append results to the global list\n",
        "    results.append(custom_N)\n",
        "    results.append(basemode)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5V70kw1ldrh",
        "outputId": "30c77758-248e-4190-aafe-587a3faf05b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### custom_N_Model ####\n",
            "Question: How old is Alexa?\n",
            "Prediction: d. I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\n",
            "Question: How old is Alexa?\n",
            "Answer: 21 years old\n",
            "True Answer: 21\n",
            "EM: 0\n",
            "F1: 0.07142857142857142\n",
            "\n",
            "\n",
            "#### Base_Model ####\n",
            "Question: How old is Alexa?\n",
            "Prediction: d. I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\n",
            "Question: How old is Alexa?\n",
            "Answer: I am 21 years old.\n",
            "Question: How old is Alexa?\n",
            "Answer: I am 21 years old.\n",
            "Question: How old is Alexa?\n",
            "Answer: I am 21 years old.\n",
            "Question: How old is Alexa?\n",
            "Answer\n",
            "True Answer: 21\n",
            "EM: 0\n",
            "F1: 0.034482758620689655\n",
            "\n",
            "\n",
            "#### custom_N_Model ####\n",
            "Question: Where does Alexa live now?\n",
            "Prediction: . I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\n",
            "Question: Where does Alexa live now?\n",
            "Answer: I live in Peristeri of Athens, Greece. I am a member of the Greek Cypriot Cypriot Parliament and the Greek Cypriot Council. I am a member of the Greek Parliament and the Greek Cypriot Council. I\n",
            "True Answer: Kaisariani of Athens\n",
            "EM: 0\n",
            "F1: 0.10526315789473684\n",
            "\n",
            "\n",
            "#### Base_Model ####\n",
            "Question: Where does Alexa live now?\n",
            "Prediction: . I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\n",
            "Question: Where does Alexa live now?\n",
            "Answer: I live in Kaisariani of Athens.\n",
            "Question: Where does Alexa live now?\n",
            "Answer: I live in Kaisariani of Athens.\n",
            "Question: Where does Alexa live now?\n",
            "Answer: I live in Kaisarian\n",
            "True Answer: Kaisariani of Athens\n",
            "EM: 0\n",
            "F1: 0.10526315789473684\n",
            "\n",
            "\n",
            "#### custom_N_Model ####\n",
            "Question: Where Alexa used to live?\n",
            "Prediction: . I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\n",
            "Question: Where Alexa used to live?\n",
            "Answer: Hi! My name is Alexa and I am 21 years old.\n",
            "True Answer: Peristeri of Athens\n",
            "EM: 0\n",
            "F1: 0.15789473684210528\n",
            "\n",
            "\n",
            "#### Base_Model ####\n",
            "Question: Where Alexa used to live?\n",
            "Prediction: . I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\n",
            "Question: Where Alexa used to live?\n",
            "Answer: I live in Peristeri of Athens, Greece.\n",
            "Question: How old are you?\n",
            "Answer: I am 21 years old.\n",
            "Question: How old are you?\n",
            "Answer: I live in Peristeri of Athens, Greece.\n",
            "True Answer: Peristeri of Athens\n",
            "EM: 0\n",
            "F1: 0.10344827586206896\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vZ8TNEvSUM_j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5chOpXcjUNhE",
        "outputId": "5937f1f3-cb7e-4448-e283-b19bafec9c3e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai\n",
            "  Downloading openai-0.28.1-py3-none-any.whl (76 kB)\n",
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            "Installing collected packages: openai\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed openai-0.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "import os"
      ],
      "metadata": {
        "id": "N7PFHPmqUR-d"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL = \"gpt-3.5-turbo\"\n",
        "openai.api_key "
      ],
      "metadata": {
        "id": "f3UESs1hUUbS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from collections import Counter\n",
        "\n",
        "# Helper function to calculate precision and recall\n",
        "def precision_recall(reference, candidate):\n",
        "    reference_tokens = reference.split()\n",
        "    candidate_tokens = candidate.split()\n",
        "    common = Counter(reference_tokens) & Counter(candidate_tokens)\n",
        "    num_same = sum(common.values())\n",
        "    if num_same == 0:\n",
        "        return 0, 0\n",
        "    precision = num_same / len(candidate_tokens)\n",
        "    recall = num_same / len(reference_tokens)\n",
        "    return precision, recall\n",
        "\n",
        "# Function to calculate F1 score\n",
        "def calculate_f1_score(reference, candidate):\n",
        "    precision, recall = precision_recall(reference, candidate)\n",
        "    if precision + recall == 0:\n",
        "        return 0\n",
        "    f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "    return f1_score\n",
        "\n",
        "# Function to query GPT-3.5-turbo and get the answer\n",
        "def query_gpt_3_5_turbo(api_key, context, question):\n",
        "    headers = {\n",
        "        \"Authorization\": f\"Bearer {api_key}\",\n",
        "        \"Content-Type\": \"application/json\",\n",
        "    }\n",
        "\n",
        "    data = {\n",
        "        \"model\": \"gpt-3.5-turbo\",\n",
        "        \"messages\": [{\"role\": \"user\", \"content\": f\"{context}\\n{question}\"}]\n",
        "    }\n",
        "\n",
        "    response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=data)\n",
        "    return response\n",
        "\n",
        "# Function to compare the model's answer with the expected answer\n",
        "def compare_answers(model_answer, expected_answer):\n",
        "    # Process the model's answer (e.g., extract the relevant part if necessary)\n",
        "    processed_answer = model_answer.strip()\n",
        "\n",
        "    # Calculate metrics like Exact Match or F1 score\n",
        "    em_score = int(processed_answer == expected_answer)\n",
        "    f1_score = calculate_f1_score(processed_answer, expected_answer)\n",
        "\n",
        "    return em_score, f1_score\n",
        "\n",
        "# Your context and question\n",
        "context = \"Hi! My name is Alexa and I am 21 years old. I used to live in Peristeri of Athens, but now I moved on in Kaisariani of Athens.\"\n",
        "question = \"Where does Alexa live now?\"\n",
        "expected_answer = \"Kaisariani of Athens\"\n",
        "\n",
        "# Your API key for GPT-3.5-turbo\n",
        "api_key = \"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\"\n",
        "\n",
        "# Make the API call to get the answer from GPT-3.5-turbo\n",
        "response = query_gpt_3_5_turbo(api_key, context, question)\n",
        "\n",
        "# Check if the response is successful\n",
        "if response.status_code == 200:\n",
        "    # Extract the answer from the response\n",
        "    response_data = response.json()\n",
        "\n",
        "    # Since the structure is different, we access 'message' directly instead of 'messages'\n",
        "    model_answer = response_data[\"choices\"][0][\"message\"][\"content\"] if \"choices\" in response_data and \"message\" in response_data[\"choices\"][0] else None\n",
        "\n",
        "    # Check if model_answer is not None\n",
        "    if model_answer:\n",
        "        # Compare the model's answer with the expected answer\n",
        "        em_score, f1_score = compare_answers(model_answer, expected_answer)\n",
        "\n",
        "        # Print the results\n",
        "        print(f\"Question: {question}\")\n",
        "        print(f\"Model Answer: {model_answer}\")\n",
        "        print(f\"Expected Answer: {expected_answer}\")\n",
        "        print(f\"Exact Match Score: {em_score}\")\n",
        "        print(f\"F1 Score: {f1_score}\")\n",
        "    else:\n",
        "        print(\"No model answer was found in the response.\")\n",
        "else:\n",
        "    print(f\"Failed to get a response from the API, status code: {response.status_code}\")\n",
        "    print(response.text)  # Print the error response from the API\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDohgS_kU4Hi",
        "outputId": "5723c1f2-482f-4885-f6a2-1ba746df4d35"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: Where does Alexa live now?\n",
            "Model Answer: Alexa now lives in Kaisariani, Athens.\n",
            "Expected Answer: Kaisariani of Athens\n",
            "Exact Match Score: 0\n",
            "F1 Score: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_answer"
      ],
      "metadata": {
        "id": "n_FMoK9sWVrB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}