6 Natural Language Generation with GPT A Comprehensive Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f1898f1",
   "metadata": {},
   "source": [
    "## Introduction to the GPT family"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "def3ab5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline, set_seed, GPT2Tokenizer, GPT2LMHeadModel\n",
    "from torch import tensor, numel\n",
    "from bertviz import model_view\n",
    "\n",
    "set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe869922",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': \"Hello, I'm a language model and I don't want to be a computer programmer. As for all those bad languages in JavaScript, I'll never\"},\n",
       " {'generated_text': \"Hello, I'm a language model and I know that's not the right language to build this program of yours. What was wrong with the code?\"},\n",
       " {'generated_text': \"Hello, I'm a language model and I'm trying to work with that much of this, and have this kind of a great conversation about language model\"}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = pipeline('text-generation', model='gpt2')\n",
    "\n",
    "generator(\"Hello, I'm a language model and I\", max_length=30, num_return_sequences=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9687d05c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
    "\n",
    "'sinan' in tokenizer.get_vocab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "167d0a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sin', 'an', 'Ġloves', 'Ġa', 'Ġbeautiful', 'Ġday']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.convert_ids_to_tokens(tokenizer.encode('Sinan loves a beautiful day'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4777b3fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46200, 272, 10408, 257, 4950, 1110]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.encode('Sinan loves a beautiful day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c892447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[46200,   272, 10408,   257,  4950,  1110]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded = tokenizer.encode('Sinan loves a beautiful day', return_tensors='pt')\n",
    "\n",
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3bee3a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a56b028b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
    "model = GPT2LMHeadModel.from_pretrained('gpt2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9687f9f2",
   "metadata": {},
   "outputs": [
    {
     "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): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (1): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (2): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (3): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (4): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (5): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (6): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (7): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (8): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (9): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (10): 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",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (11): 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",
       "          (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",
       ")"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "541d5c22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 768])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.transformer.wte(encoded).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78d1d442",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 768])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.transformer.wpe(tensor([0, 1, 2, 3, 4, 5]).reshape(1, 6)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b8200f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 6, 768])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_input = model.transformer.wte(encoded) + model.transformer.wpe(tensor([0, 1, 2, 3, 4, 5]).reshape(1, 6))\n",
    "\n",
    "initial_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "97a70fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[ 0.0107, -0.2453,  0.1275,  ..., -0.1969,  0.0006,  0.1539],\n",
       "         [-0.1013, -0.0894, -0.0378,  ..., -0.0534, -0.0527,  0.0046],\n",
       "         [-0.0716, -0.1690,  0.0386,  ..., -0.2034, -0.0197, -0.1113],\n",
       "         [-0.0509, -0.0682,  0.1526,  ...,  0.0527,  0.0912, -0.0455],\n",
       "         [ 0.0612,  0.1425,  0.1402,  ...,  0.0964,  0.0510,  0.1474],\n",
       "         [-0.1283, -0.0632,  0.1287,  ..., -0.0907, -0.0655,  0.1085]]],\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_input = model.transformer.drop(initial_input)\n",
    "initial_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55ce36f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=768, out_features=50257, bias=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.lm_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4066d604",
   "metadata": {},
   "outputs": [],
   "source": [
    "for module in model.transformer.h:\n",
    "    initial_input = module(initial_input)[0]\n",
    "    \n",
    "initial_input = model.transformer.ln_f(initial_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11780d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(initial_input == model(encoded, output_hidden_states=True).hidden_states[-1]).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7162862a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d03c5d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of params: 124,439,808\n"
     ]
    }
   ],
   "source": [
    "total_params = 0\n",
    "for param in model.parameters():\n",
    "    total_params += numel(param)\n",
    "    \n",
    "print(f'Number of params: {total_params:,}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3e7ed76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "274aac4e",
   "metadata": {},
   "source": [
    "## Masked multi-headed attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "de1da310",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d20f2219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phrase = 'My friend was right about this class. It is so fun!'\n",
    "encoded_phrase = tokenizer(phrase, return_tensors='pt')\n",
    "\n",
    "response = model(**encoded_phrase, output_attentions=True, output_hidden_states=True)\n",
    "\n",
    "len(response.attentions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "acc9e4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([[3666, 1545,  373,  826,  546,  428, 1398,   13,  632,  318,  523, 1257,\n",
       "            0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_phrase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3fe66d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 12, 13, 13])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.attentions[-1].shape  # From the final decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2337791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 13])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_phrase['input_ids'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "85eec46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['My',\n",
       " 'Ġfriend',\n",
       " 'Ġwas',\n",
       " 'Ġright',\n",
       " 'Ġabout',\n",
       " 'Ġthis',\n",
       " 'Ġclass',\n",
       " '.',\n",
       " 'ĠIt',\n",
       " 'Ġis',\n",
       " 'Ġso',\n",
       " 'Ġfun',\n",
       " '!']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens = tokenizer.convert_ids_to_tokens(encoded_phrase['input_ids'][0])\n",
    "\n",
    "tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a12d5b0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>My</th>\n",
       "      <th>Ġfriend</th>\n",
       "      <th>Ġwas</th>\n",
       "      <th>Ġright</th>\n",
       "      <th>Ġabout</th>\n",
       "      <th>Ġthis</th>\n",
       "      <th>Ġclass</th>\n",
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       "      <th>ĠIt</th>\n",
       "      <th>Ġis</th>\n",
       "      <th>Ġso</th>\n",
       "      <th>Ġfun</th>\n",
       "      <th>!</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>My</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>Ġfriend</th>\n",
       "      <td>0.968</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>Ġwas</th>\n",
       "      <td>0.824</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>Ġright</th>\n",
       "      <td>0.979</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>Ġabout</th>\n",
       "      <td>0.979</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġthis</th>\n",
       "      <td>0.924</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>Ġclass</th>\n",
       "      <td>0.946</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>.</th>\n",
       "      <td>0.691</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.269</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ĠIt</th>\n",
       "      <td>0.318</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.599</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġis</th>\n",
       "      <td>0.331</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.533</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġso</th>\n",
       "      <td>0.141</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.645</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ġfun</th>\n",
       "      <td>0.532</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.317</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>!</th>\n",
       "      <td>0.527</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.089</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            My  Ġfriend   Ġwas  Ġright  Ġabout  Ġthis  Ġclass      .    ĠIt  \\\n",
       "My       1.000    0.000  0.000   0.000   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġfriend  0.968    0.032  0.000   0.000   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġwas     0.824    0.145  0.031   0.000   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġright   0.979    0.008  0.007   0.005   0.000  0.000   0.000  0.000  0.000   \n",
       "Ġabout   0.979    0.008  0.004   0.005   0.005  0.000   0.000  0.000  0.000   \n",
       "Ġthis    0.924    0.031  0.007   0.006   0.016  0.016   0.000  0.000  0.000   \n",
       "Ġclass   0.946    0.005  0.001   0.001   0.001  0.002   0.044  0.000  0.000   \n",
       ".        0.691    0.013  0.003   0.003   0.002  0.006   0.269  0.013  0.000   \n",
       "ĠIt      0.318    0.003  0.003   0.003   0.006  0.018   0.599  0.018  0.032   \n",
       "Ġis      0.331    0.006  0.002   0.002   0.003  0.018   0.533  0.013  0.062   \n",
       "Ġso      0.141    0.010  0.005   0.004   0.004  0.026   0.645  0.023  0.096   \n",
       "Ġfun     0.532    0.015  0.003   0.002   0.001  0.002   0.317  0.029  0.035   \n",
       "!        0.527    0.008  0.003   0.001   0.001  0.003   0.245  0.026  0.034   \n",
       "\n",
       "           Ġis    Ġso   Ġfun      !  \n",
       "My       0.000  0.000  0.000  0.000  \n",
       "Ġfriend  0.000  0.000  0.000  0.000  \n",
       "Ġwas     0.000  0.000  0.000  0.000  \n",
       "Ġright   0.000  0.000  0.000  0.000  \n",
       "Ġabout   0.000  0.000  0.000  0.000  \n",
       "Ġthis    0.000  0.000  0.000  0.000  \n",
       "Ġclass   0.000  0.000  0.000  0.000  \n",
       ".        0.000  0.000  0.000  0.000  \n",
       "ĠIt      0.000  0.000  0.000  0.000  \n",
       "Ġis      0.030  0.000  0.000  0.000  \n",
       "Ġso      0.041  0.003  0.000  0.000  \n",
       "Ġfun     0.014  0.003  0.047  0.000  \n",
       "!        0.012  0.007  0.046  0.089  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Layer index 9, head 0. Check out the almost 60% attention the token it is giving to the token class\n",
    "arr = response.attentions[9][0][0]\n",
    "\n",
    "n_digits = 3\n",
    "\n",
    "attention_df = pd.DataFrame((torch.round(arr * 10**n_digits) / (10**n_digits)).detach()).applymap(float)\n",
    "\n",
    "attention_df.columns = tokens\n",
    "attention_df.index = tokens\n",
    "\n",
    "attention_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c9cc57a5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "                \n",
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       "/**\n",
       " * @fileoverview Transformer Visualization D3 javascript code.\n",
       " *\n",
       " * Based on: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/visualization/attention.js\n",
       " *\n",
       " * Change log:\n",
       " *\n",
       " * 02/01/19  Jesse Vig   Initial implementation\n",
       " * 12/31/20  Jesse Vig   Support multiple visualizations in single notebook.\n",
       " * 01/19/21  Jesse Vig   Support light/dark modes\n",
       " * 02/06/21  Jesse Vig   Move require config from separate jupyter notebook step\n",
       " * 05/03/21  Jesse Vig   Adjust visualization height dynamically\n",
       " **/\n",
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[0.6960647106170654, 0.014140622690320015, 0.010153231211006641, 0.005605451762676239, 0.017221741378307343, 0.018391398712992668, 0.05184609815478325, 0.04829704761505127, 0.04183603450655937, 0.09644363075494766, 0.0, 0.0, 0.0], [0.34653425216674805, 0.007128129247575998, 0.025980643928050995, 0.011721323244273663, 0.015366232953965664, 0.019062576815485954, 0.04066082835197449, 0.016706781461834908, 0.03356537967920303, 0.41946613788604736, 0.06380760669708252, 0.0, 0.0], [0.7025812864303589, 0.018039684742689133, 0.013967253267765045, 0.006009518634527922, 0.012736446224153042, 0.013749348931014538, 0.020495761185884476, 0.059024762362241745, 0.02173696458339691, 0.03848383203148842, 0.009814238175749779, 0.08336081355810165, 0.0], [0.5130559206008911, 0.05385600030422211, 0.01890607364475727, 0.006382295396178961, 0.014300012961030006, 0.012356793507933617, 0.010356870479881763, 0.16915816068649292, 0.013011016882956028, 0.017024891451001167, 0.015220049768686295, 0.016783148050308228, 0.1395888477563858]], [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.9337283372879028, 0.06627167016267776, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8877817392349243, 0.06043471768498421, 0.0517834797501564, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8983765244483948, 0.03053700551390648, 0.04336400330066681, 0.027722492814064026, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8921017050743103, 0.02235882729291916, 0.03499011695384979, 0.02014000341296196, 0.030409418046474457, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.7990766167640686, 0.03278417885303497, 0.01830497570335865, 0.015316284261643887, 0.08809564262628555, 0.04642244428396225, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.7498540878295898, 0.02448827400803566, 0.012515392154455185, 0.03989727422595024, 0.06819640845060349, 0.06768149882555008, 0.0373670794069767, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.8043721318244934, 0.046553175896406174, 0.01901666820049286, 0.011860761791467667, 0.039725977927446365, 0.014250344596803188, 0.0355987623333931, 0.028622223064303398, 0.0, 0.0, 0.0, 0.0, 0.0], [0.7778428196907043, 0.015712792053818703, 0.014451815746724606, 0.009017929434776306, 0.021709604188799858, 0.03276413306593895, 0.030534978955984116, 0.054923076182603836, 0.04304293170571327, 0.0, 0.0, 0.0, 0.0], [0.5656001567840576, 0.020349204540252686, 0.01622604764997959, 0.008259467780590057, 0.017519457265734673, 0.023164765909314156, 0.13457565009593964, 0.013636317104101181, 0.11620897054672241, 0.08446003496646881, 0.0, 0.0, 0.0], [0.32016098499298096, 0.0095110684633255, 0.021348901093006134, 0.004720298573374748, 0.013046201318502426, 0.03450184687972069, 0.13969287276268005, 0.008988777175545692, 0.09442957490682602, 0.32065126299858093, 0.03294820711016655, 0.0, 0.0], [0.40046432614326477, 0.025749068707227707, 0.028219297528266907, 0.005882476456463337, 0.012943207286298275, 0.0156415868550539, 0.08395569026470184, 0.21688887476921082, 0.05434427410364151, 0.05227844417095184, 0.04368346557021141, 0.059949301183223724, 0.0], [0.6322539448738098, 0.0624263696372509, 0.025440135970711708, 0.009289507754147053, 0.019578566774725914, 0.010334108956158161, 0.030978495255112648, 0.07678186893463135, 0.019628329202532768, 0.018525971099734306, 0.017492912709712982, 0.023062530905008316, 0.05420727655291557]]]], \"left_text\": [\"My\", \" friend\", \" was\", \" right\", \" about\", \" this\", \" class\", \".\", \" It\", \" is\", \" so\", \" fun\", \"!\"], \"right_text\": [\"My\", \" friend\", \" was\", \" right\", \" about\", \" this\", \" class\", \".\", \" It\", \" is\", \" so\", \" fun\", \"!\"]}], \"default_filter\": \"0\", \"display_mode\": \"dark\", \"root_div_id\": \"bertviz-f14a9daaa9d842f891be3ff2cd7792a3\", \"include_layers\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"include_heads\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"total_heads\": 12} is a template marker that is replaced by actual params.\n",
       "        const config = {};\n",
       "\n",
       "        const MIN_X = 0;\n",
       "        const MIN_Y = 0;\n",
       "        const DIV_WIDTH = 970;\n",
       "        const THUMBNAIL_PADDING = 5;\n",
       "        const DETAIL_WIDTH = 300;\n",
       "        const DETAIL_ATTENTION_WIDTH = 140;\n",
       "        const DETAIL_BOX_WIDTH = 80;\n",
       "        const DETAIL_BOX_HEIGHT = 18;\n",
       "        const DETAIL_PADDING = 15;\n",
       "        const ATTN_PADDING = 0;\n",
       "        const DETAIL_HEADING_HEIGHT = 25;\n",
       "        const HEADING_TEXT_SIZE = 15;\n",
       "        const HEADING_PADDING = 5;\n",
       "        const TEXT_SIZE = 13;\n",
       "        const TEXT_PADDING = 5;\n",
       "        const LAYER_COLORS = d3.schemeCategory10;\n",
       "        const PALETTE = {\n",
       "            'light': {\n",
       "                'text': 'black',\n",
       "                'background': 'white',\n",
       "                'highlight': '#F5F5F5'\n",
       "            },\n",
       "            'dark': {\n",
       "                'text': '#ccc',\n",
       "                'background': 'black',\n",
       "                'highlight': '#222'\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function render() {\n",
       "\n",
       "            // Set global state variables\n",
       "\n",
       "            var attData = config.attention[config.filter];\n",
       "            config.leftText = attData.left_text;\n",
       "            config.rightText = attData.right_text;\n",
       "            config.attn = attData.attn;\n",
       "            config.numLayers = config.attn.length;\n",
       "            config.numHeads = config.attn[0].length;\n",
       "            config.thumbnailBoxHeight = 7 * (12 / config.totalHeads);\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            config.thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            config.thumbnailWidth = (DIV_WIDTH - axisSize) / config.totalHeads;\n",
       "            config.detailHeight = Math.max(config.leftText.length, config.rightText.length) * DETAIL_BOX_HEIGHT + 2 * DETAIL_PADDING + DETAIL_HEADING_HEIGHT;\n",
       "            config.divHeight = Math.max(config.numLayers * config.thumbnailHeight + axisSize, config.detailHeight);\n",
       "\n",
       "            const vis = $(`#${config.rootDivId} #vis`)\n",
       "            vis.empty();\n",
       "            vis.attr(\"height\", config.divHeight);\n",
       "            config.svg = d3.select(`#${config.rootDivId} #vis`)\n",
       "                .append('svg')\n",
       "                .attr(\"width\", DIV_WIDTH)\n",
       "                .attr(\"height\", config.divHeight)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "\n",
       "            renderAxisLabels();\n",
       "\n",
       "            var i;\n",
       "            var j;\n",
       "            for (i = 0; i < config.numLayers; i++) {\n",
       "                for (j = 0; j < config.numHeads; j++) {\n",
       "                    renderThumbnail(i, j);\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function renderAxisLabels() {\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            const tableWidth = config.thumbnailWidth * config.heads.length;\n",
       "            config.svg.append(\"text\")\n",
       "                .text(\"Heads\")\n",
       "                .attr(\"fill\", \"black\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .attr(\"font-size\", HEADING_TEXT_SIZE + \"px\")\n",
       "                .attr(\"x\", axisSize + tableWidth / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", 0)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "            for (let i = 0; i < config.numHeads; i++) {\n",
       "                config.svg.append(\"text\")\n",
       "                    .text(config.heads[i])\n",
       "                    .attr(\"fill\", \"black\")\n",
       "                    .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                    .attr(\"x\", axisSize + (i + .5) * config.thumbnailWidth)\n",
       "                    .attr(\"text-anchor\", \"middle\")\n",
       "                    .attr(\"y\", HEADING_TEXT_SIZE + HEADING_PADDING)\n",
       "                    .attr(\"dy\", TEXT_SIZE);\n",
       "            }\n",
       "            let x = 0;\n",
       "            let y = axisSize + config.thumbnailHeight * config.layers.length / 2;\n",
       "            console.log(\"x\", x, y)\n",
       "            config.svg.append(\"text\")\n",
       "                .text(\"Layers\")\n",
       "                .attr(\"fill\", \"black\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .attr(\"transform\", \"rotate(270, \" + x  + \", \" + y + \")\")\n",
       "                .attr(\"font-size\", HEADING_TEXT_SIZE + \"px\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "            for (let i = 0; i < config.numLayers; i++) {\n",
       "                x = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE; // HACK\n",
       "                y = axisSize + (i + .5) * config.thumbnailHeight;\n",
       "                config.svg.append(\"text\")\n",
       "                    .text(config.layers[i])\n",
       "                    .attr(\"fill\", \"black\")\n",
       "                    .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                    .attr(\"x\", x)\n",
       "                    .attr(\"text-anchor\", \"end\")\n",
       "                    .attr(\"y\", y)\n",
       "                    .attr(\"dy\", TEXT_SIZE / 2);\n",
       "            }\n",
       "        }\n",
       "\n",
       "\n",
       "        function renderThumbnail(layerIndex, headIndex) {\n",
       "            const axisSize = HEADING_TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING\n",
       "            const x = headIndex * config.thumbnailWidth + axisSize;\n",
       "            const y = layerIndex * config.thumbnailHeight + axisSize;\n",
       "            renderThumbnailAttn(x, y, config.attn[layerIndex][headIndex], layerIndex, headIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetail(att, layerIndex, headIndex) {\n",
       "            const axisSize = TEXT_SIZE + HEADING_PADDING + TEXT_SIZE + TEXT_PADDING;\n",
       "            var xOffset = .8 * config.thumbnailWidth;\n",
       "            var maxX = DIV_WIDTH;\n",
       "            var maxY = config.divHeight - 3;\n",
       "            var leftPos = axisSize + headIndex * config.thumbnailWidth;\n",
       "            var x = leftPos + THUMBNAIL_PADDING + xOffset;\n",
       "            if (x < MIN_X) {\n",
       "                x = MIN_X;\n",
       "            } else if (x + DETAIL_WIDTH > maxX) {\n",
       "                x = leftPos + THUMBNAIL_PADDING - DETAIL_WIDTH + 8;\n",
       "            }\n",
       "            var posLeftText = x;\n",
       "            var posAttention = posLeftText + DETAIL_BOX_WIDTH;\n",
       "            var posRightText = posAttention + DETAIL_ATTENTION_WIDTH;\n",
       "            var thumbnailHeight = Math.max(config.leftText.length, config.rightText.length) * config.thumbnailBoxHeight + 2 * THUMBNAIL_PADDING;\n",
       "            var yOffset = 20;\n",
       "            var y = layerIndex * thumbnailHeight + THUMBNAIL_PADDING + yOffset;\n",
       "            if (y < MIN_Y) {\n",
       "                y = MIN_Y;\n",
       "            } else if (y + config.detailHeight > maxY) {\n",
       "                y = maxY - config.detailHeight;\n",
       "            }\n",
       "            renderDetailFrame(x, y, layerIndex);\n",
       "            y = y + DETAIL_PADDING;\n",
       "            renderDetailHeading(x, y, layerIndex, headIndex);\n",
       "            y = y + DETAIL_HEADING_HEIGHT;\n",
       "            renderDetailText(config.leftText, \"leftText\", posLeftText, y , layerIndex);\n",
       "            renderDetailAttn(posAttention, y, att, layerIndex, headIndex);\n",
       "            renderDetailText(config.rightText, \"rightText\", posRightText, y, layerIndex);\n",
       "        }\n",
       "\n",
       "        function renderDetailHeading(x, y, layerIndex, headIndex) {\n",
       "            var fillColor = getTextColor();\n",
       "            config.svg.append(\"text\")\n",
       "                .classed(\"detail\", true)\n",
       "                .text('Layer ' + config.layers[layerIndex] + \", Head \" + config.heads[headIndex])\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .attr(\"font-weight\", \"bold\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x + DETAIL_WIDTH / 2)\n",
       "                .attr(\"text-anchor\", \"middle\")\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", DETAIL_HEADING_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .attr(\"dy\", HEADING_TEXT_SIZE);\n",
       "        }\n",
       "\n",
       "        function renderDetailText(text, id, x, y, layerIndex) {\n",
       "            var tokenContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .selectAll(\"g\")\n",
       "                .data(text)\n",
       "                .enter()\n",
       "                .append(\"g\");\n",
       "\n",
       "            var fillColor = getTextColor();\n",
       "\n",
       "            tokenContainer.append(\"rect\")\n",
       "                .classed(\"highlight\", true)\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .style(\"opacity\", 0.0)\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return y + i * DETAIL_BOX_HEIGHT;\n",
       "                });\n",
       "\n",
       "            var textContainer = tokenContainer.append(\"text\")\n",
       "                .classed(\"token\", true)\n",
       "                .text(function (d) {\n",
       "                    return d;\n",
       "                })\n",
       "                .attr(\"font-size\", TEXT_SIZE + \"px\")\n",
       "                .style(\"cursor\", \"default\")\n",
       "                .style(\"-webkit-user-select\", \"none\")\n",
       "                .attr(\"fill\", fillColor)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", function (d, i) {\n",
       "                    return i * DETAIL_BOX_HEIGHT + y;\n",
       "                })\n",
       "                .attr(\"height\", DETAIL_BOX_HEIGHT)\n",
       "                .attr(\"width\", DETAIL_BOX_WIDTH)\n",
       "                .attr(\"dy\", TEXT_SIZE);\n",
       "\n",
       "            if (id == \"leftText\") {\n",
       "                textContainer.style(\"text-anchor\", \"end\")\n",
       "                    .attr(\"dx\", DETAIL_BOX_WIDTH - 2);\n",
       "                tokenContainer.on(\"mouseover\", function (d, index) {\n",
       "                    highlightSelection(index);\n",
       "                });\n",
       "                tokenContainer.on(\"mouseleave\", function () {\n",
       "                    unhighlightSelection();\n",
       "                });\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function highlightSelection(index) {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", function (d, i) {\n",
       "                    return i == index ? 1.0 : 0.0;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function unhighlightSelection() {\n",
       "            config.svg.select(\"#leftText\")\n",
       "                .selectAll(\".highlight\")\n",
       "                .style(\"opacity\", 0.0);\n",
       "            config.svg.selectAll(\".attn-line-group\")\n",
       "                .style(\"opacity\", 1);\n",
       "        }\n",
       "\n",
       "        function renderThumbnailAttn(x, y, att, layerIndex, headIndex) {\n",
       "\n",
       "            var attnContainer = config.svg.append(\"svg:g\");\n",
       "\n",
       "            var attnBackground = attnContainer.append(\"rect\")\n",
       "                .attr(\"id\", 'attn_background_' + layerIndex + \"_\" + headIndex)\n",
       "                .classed(\"attn_background\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .attr(\"stroke-width\", 2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", 0)\n",
       "                .attr(\"fill\", getBackgroundColor());\n",
       "            var x1 = x + THUMBNAIL_PADDING;\n",
       "            var x2 = x1 + config.thumbnailWidth - 14;\n",
       "            var y1 = y + THUMBNAIL_PADDING;\n",
       "\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter() // When entering\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x1)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y1 + (sourceIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"x2\", x2)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y1 + (targetIndex + .5) * config.thumbnailBoxHeight;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "\n",
       "            var clickRegion = attnContainer.append(\"rect\")\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.thumbnailHeight)\n",
       "                .attr(\"width\", config.thumbnailWidth)\n",
       "                .style(\"opacity\", 0);\n",
       "\n",
       "            clickRegion.on(\"click\", function (d, index) {\n",
       "                var attnBackgroundOther = config.svg.selectAll(\".attn_background\");\n",
       "                attnBackgroundOther.attr(\"fill\", getBackgroundColor());\n",
       "                attnBackgroundOther.attr(\"stroke-opacity\", 0);\n",
       "\n",
       "                config.svg.selectAll(\".detail\").remove();\n",
       "                if (config.detail_layer != layerIndex || config.detail_head != headIndex) {\n",
       "                    renderDetail(att, layerIndex, headIndex);\n",
       "                    config.detail_layer = layerIndex;\n",
       "                    config.detail_head = headIndex;\n",
       "                    attnBackground.attr(\"fill\", getHighlightColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", .8);\n",
       "                } else {\n",
       "                    config.detail_layer = null;\n",
       "                    config.detail_head = null;\n",
       "                    attnBackground.attr(\"fill\", getBackgroundColor());\n",
       "                    attnBackground.attr(\"stroke-opacity\", 0);\n",
       "                }\n",
       "            });\n",
       "\n",
       "            clickRegion.on(\"mouseover\", function (d) {\n",
       "                d3.select(this).style(\"cursor\", \"pointer\");\n",
       "            });\n",
       "        }\n",
       "\n",
       "        function renderDetailFrame(x, y, layerIndex) {\n",
       "            var detailFrame = config.svg.append(\"rect\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"x\", x)\n",
       "                .attr(\"y\", y)\n",
       "                .attr(\"height\", config.detailHeight)\n",
       "                .attr(\"width\", DETAIL_WIDTH)\n",
       "                .style(\"opacity\", 1)\n",
       "                .attr(\"stroke-width\", 1.5)\n",
       "                .attr(\"stroke-opacity\", 0.7)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex));\n",
       "        }\n",
       "\n",
       "        function renderDetailAttn(x, y, att, layerIndex) {\n",
       "            var attnContainer = config.svg.append(\"svg:g\")\n",
       "                .classed(\"detail\", true)\n",
       "                .attr(\"pointer-events\", \"none\");\n",
       "            attnContainer.selectAll(\"g\")\n",
       "                .data(att)\n",
       "                .enter()\n",
       "                .append(\"g\") // Add group for each source token\n",
       "                .classed('attn-line-group', true)\n",
       "                .attr(\"source-index\", function (d, i) { // Save index of source token\n",
       "                    return i;\n",
       "                })\n",
       "                .selectAll(\"line\")\n",
       "                .data(function (d) { // Loop over all target tokens\n",
       "                    return d;\n",
       "                })\n",
       "                .enter()\n",
       "                .append(\"line\")\n",
       "                .attr(\"x1\", x + ATTN_PADDING)\n",
       "                .attr(\"y1\", function (d) {\n",
       "                    var sourceIndex = +this.parentNode.getAttribute(\"source-index\");\n",
       "                    return y + (sourceIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"x2\", x + DETAIL_ATTENTION_WIDTH - ATTN_PADDING)\n",
       "                .attr(\"y2\", function (d, targetIndex) {\n",
       "                    return y + (targetIndex + .5) * DETAIL_BOX_HEIGHT;\n",
       "                })\n",
       "                .attr(\"stroke-width\", 2.2)\n",
       "                .attr(\"stroke\", getLayerColor(layerIndex))\n",
       "                .attr(\"stroke-opacity\", function (d) {\n",
       "                    return d;\n",
       "                });\n",
       "        }\n",
       "\n",
       "        function getLayerColor(layer) {\n",
       "          return LAYER_COLORS[config.layers[layer] % 10];\n",
       "        }\n",
       "\n",
       "        function getTextColor() {\n",
       "            return PALETTE[config.mode]['text']\n",
       "        }\n",
       "\n",
       "        function getBackgroundColor() {\n",
       "           return PALETTE[config.mode]['background']\n",
       "        }\n",
       "\n",
       "        function getHighlightColor() {\n",
       "           return PALETTE[config.mode]['highlight']\n",
       "        }\n",
       "\n",
       "        function initialize() {\n",
       "            config.attention = params['attention'];\n",
       "            config.filter = params['default_filter'];\n",
       "            config.mode = params['display_mode'];\n",
       "            config.layers = params['include_layers']\n",
       "            config.heads = params['include_heads']\n",
       "            config.totalHeads = params['total_heads']\n",
       "            config.rootDivId = params['root_div_id'];\n",
       "            $(`#${config.rootDivId} #filter`).on('change', function (e) {\n",
       "                config.filter = e.currentTarget.value;\n",
       "                render();\n",
       "            });\n",
       "        }\n",
       "\n",
       "        initialize();\n",
       "        render();\n",
       "\n",
       "    });"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokens = tokenizer.convert_ids_to_tokens(encoded_phrase['input_ids'][0]) \n",
    "model_view(response.attentions, tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77a281d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b82dc780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 13, 768])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.hidden_states[-1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e06305a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 13, 50257])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.logits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "170c473e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sequence up until</th>\n",
       "      <th>Next token with highest probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>My</td>\n",
       "      <td>Ċ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ġfriend</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ġwas</td>\n",
       "      <td>Ġa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ġright</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ġabout</td>\n",
       "      <td>Ġthat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ġthis</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ġclass</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>.</td>\n",
       "      <td>ĠI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ĠIt</td>\n",
       "      <td>'s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ġis</td>\n",
       "      <td>Ġa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ġso</td>\n",
       "      <td>Ġimportant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ġfun</td>\n",
       "      <td>Ġto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>!</td>\n",
       "      <td>ĠI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sequence up until Next token with highest probability\n",
       "0                 My                                   Ċ\n",
       "1            Ġfriend                                   ,\n",
       "2               Ġwas                                  Ġa\n",
       "3             Ġright                                   .\n",
       "4             Ġabout                               Ġthat\n",
       "5              Ġthis                                   .\n",
       "6             Ġclass                                   .\n",
       "7                  .                                  ĠI\n",
       "8                ĠIt                                  's\n",
       "9                Ġis                                  Ġa\n",
       "10               Ġso                          Ġimportant\n",
       "11              Ġfun                                 Ġto\n",
       "12                 !                                  ĠI"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    zip(tokens, tokenizer.convert_ids_to_tokens(response.logits.argmax(2)[0])), \n",
    "    columns=['Sequence up until', 'Next token with highest probability']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cd539ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8e6e8b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'My friend was right about this class. It is so fun! I love it! I love the'}]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(phrase, max_length=20, num_return_sequences=1, do_sample=False)  # greedy search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2fa15fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'My friend was right about this class. It is so fun! I wanted to do a different class'}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(phrase, max_length=20, num_return_sequences=1, do_sample=True)  # greedy search with sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff934358",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1643fc51",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f4616e0f",
   "metadata": {},
   "source": [
    "## Pre-training GPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9eee5b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline, set_seed\n",
    "from torch import tensor\n",
    "\n",
    "generator = pipeline('text-generation', model='gpt2', tokenizer=tokenizer)\n",
    "set_seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7768633e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9982bb3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'The holocaust was a cruel and degrading and brutal'},\n",
       " {'generated_text': 'The holocaust was created in order to save the'},\n",
       " {'generated_text': 'The holocaust was a crime carried out by Jews'},\n",
       " {'generated_text': 'The holocaust was perpetrated on the Jews who died'},\n",
       " {'generated_text': 'The holocaust was never officially discovered.\\n\\n'},\n",
       " {'generated_text': 'The holocaust was brought to an end on June'},\n",
       " {'generated_text': 'The holocaust was an event that happened in the'},\n",
       " {'generated_text': 'The holocaust was the product of human sin.'},\n",
       " {'generated_text': 'The holocaust was \"the most horrible crime in'},\n",
       " {'generated_text': 'The holocaust was the worst in history. The'}]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bias\n",
    "generator(\"The holocaust was\", max_length=10, num_return_sequences=10, temperature=0.8, beams=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "629c5b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'Jewish people are not the primary target. They are'},\n",
       " {'generated_text': 'Jewish people are not allowed to be on the same'},\n",
       " {'generated_text': 'Jewish people are in the U.S. illegally'},\n",
       " {'generated_text': 'Jewish people are being persecuted by Muslim extremists.\"\\n'},\n",
       " {'generated_text': \"Jewish people are not just a minority – they're\"},\n",
       " {'generated_text': 'Jewish people are a real danger.\" He said:'},\n",
       " {'generated_text': 'Jewish people are not the same as Jews. They'},\n",
       " {'generated_text': 'Jewish people are a tiny minority. These are people'},\n",
       " {'generated_text': 'Jewish people are called \"foreigners\" or \"'},\n",
       " {'generated_text': 'Jewish people are often treated as terrorists. For example'}]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\"Jewish people are\", max_length=10, num_return_sequences=10, temperature=0.8, beams=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe80d246",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'The earth is a giant ball with millions of tiny'},\n",
       " {'generated_text': 'The earth is an incredibly thin layer of matter,'},\n",
       " {'generated_text': 'The earth is flat, but there are no clouds'},\n",
       " {'generated_text': 'The earth is a sphere, an axis, a'},\n",
       " {'generated_text': 'The earth is not an island, but a region'},\n",
       " {'generated_text': 'The earth is dying due to this event!\" The'},\n",
       " {'generated_text': 'The earth is flat, but it bends.\\n'},\n",
       " {'generated_text': 'The earth is flat. I just need to get'},\n",
       " {'generated_text': 'The earth is thin but it is thick; we'},\n",
       " {'generated_text': 'The earth is not flat. It is an endless'}]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator(\"The earth is\", max_length=10, num_return_sequences=10, temperature=0.8, beams=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93acda7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ec88e66",
   "metadata": {},
   "source": [
    "## Few-shot learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "60177cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Analysis\n",
      "Text: I hate it when my phone battery dies.\n",
      "Sentiment: Negative\n",
      "###\n",
      "Text: My day has been really great!\n",
      "Sentiment: Positive\n",
      "###\n",
      "Text: Not a fan when it is cloudy\n",
      "Sentiment: Negative\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(generator(\"\"\"Sentiment Analysis\n",
    "Text: I hate it when my phone battery dies.\n",
    "Sentiment: Negative\n",
    "###\n",
    "Text: My day has been really great!\n",
    "Sentiment: Positive\n",
    "###\n",
    "Text: Not a fan when it is cloudy\n",
    "Sentiment:\"\"\", top_k=2, temperature=0.1, max_length=55)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "d239b93a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question/Answering\n",
      "C: Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock.\n",
      "Q: When was Google founded?\n",
      "A: 1998\n",
      "###\n",
      "C: Hugging Face is a company which develops social AI-run chatbot applications. It was established in 2016 by Clement Delangue and Julien Chaumond. The company is based in Brooklyn, New York, United States.\n",
      "Q: What does Hugging Face develop?\n",
      "A: social AI-run chatbot applications\n",
      "###\n",
      "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
      "Q: What division do the Jets play in?\n",
      "A: American Football Conference (AFC) East\n"
     ]
    }
   ],
   "source": [
    "print(generator(\"\"\"Question/Answering\n",
    "C: Google was founded in 1998 by Larry Page and Sergey Brin while they were Ph.D. students at Stanford University in California. Together they own about 14 percent of its shares and control 56 percent of the stockholder voting power through supervoting stock.\n",
    "Q: When was Google founded?\n",
    "A: 1998\n",
    "###\n",
    "C: Hugging Face is a company which develops social AI-run chatbot applications. It was established in 2016 by Clement Delangue and Julien Chaumond. The company is based in Brooklyn, New York, United States.\n",
    "Q: What does Hugging Face develop?\n",
    "A: social AI-run chatbot applications\n",
    "###\n",
    "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
    "Q: What division do the Jets play in?\n",
    "A:\"\"\", top_k=2, beams=2, max_length=215, temperature=0.5)[0]['generated_text'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "982e0492",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Zero Shot Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "11c405c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question/Answering\n",
      "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
      "Q: What division do the Jets play in?\n",
      "A: The New York Jets play in the American Football Conference East\n"
     ]
    }
   ],
   "source": [
    "# Same question as before, with no previous examples ie Zero-shot learning. Still works\n",
    "print(generator(\n",
    "    '''Question/Answering\n",
    "C: The New York Jets are a professional American football team based in the New York metropolitan area. The Jets compete in the National Football League (NFL) as a member club of the league's American Football Conference (AFC) East division.\n",
    "Q: What division do the Jets play in?\n",
    "A:''',\n",
    "    top_k=2, beams=2, max_length=80, temperature=0.5)[0]['generated_text']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "4338c6e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment Analysis\n",
      "Text: This new music video was so good\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n",
      "Sentiment: I love it\n"
     ]
    }
   ],
   "source": [
    "# Zero-shot doesn't work as much with the sentiment analysis example\n",
    "print(generator(\"\"\"Sentiment Analysis\n",
    "Text: This new music video was so good\n",
    "Sentiment:\"\"\", top_k=2, temperature=0.1, max_length=55)[0]['generated_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2749594b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7f678731",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Zero-shot abstractive summarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2090aa0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_summarize = \"\"\"This training will focus on how the GPT family of models are used for NLP tasks including abstractive text summarization and natural language generation. The training will begin with an introduction to necessary concepts including masked self attention, language models, and transformers and then build on those concepts to introduce the GPT architecture. We will then move into how GPT is used for multiple natural language processing tasks with hands-on examples of using pre-trained GPT-2 models as well as fine-tuning these models on custom corpora.\n",
    "\n",
    "GPT models are some of the most relevant NLP architectures today and it is closely related to other important NLP deep learning models like BERT. Both of these models are derived from the newly invented transformer architecture and represent an inflection point in how machines process language and context.\n",
    "\n",
    "The Natural Language Processing with Next-Generation Transformer Architectures series of online trainings provides a comprehensive overview of state-of-the-art natural language processing (NLP) models including GPT and BERT which are derived from the modern attention-driven transformer architecture and the applications these models are used to solve today. All of the trainings in the series blend theory and application through the combination of visual mathematical explanations, straightforward applicable Python examples within hands-on Jupyter notebook demos, and comprehensive case studies featuring modern problems solvable by NLP models. (Note that at any given time, only a subset of these classes will be scheduled and open for registration.)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f76b05b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summarization Task:\n",
      "This training will focus on how the GPT family of models are used for NLP tasks including abstractive text summarization and natural language generation. The training will begin with an introduction to necessary concepts including masked self attention, language models, and transformers and then build on those concepts to introduce the GPT architecture. We will then move into how GPT is used for multiple natural language processing tasks with hands-on examples of using pre-trained GPT-2 models as well as fine-tuning these models on custom corpora.\n",
      "\n",
      "GPT models are some of the most relevant NLP architectures today and it is closely related to other important NLP deep learning models like BERT. Both of these models are derived from the newly invented transformer architecture and represent an inflection point in how machines process language and context.\n",
      "\n",
      "The Natural Language Processing with Next-Generation Transformer Architectures series of online trainings provides a comprehensive overview of state-of-the-art natural language processing (NLP) models including GPT and BERT which are derived from the modern attention-driven transformer architecture and the applications these models are used to solve today. All of the trainings in the series blend theory and application through the combination of visual mathematical explanations, straightforward applicable Python examples within hands-on Jupyter notebook demos, and comprehensive case studies featuring modern problems solvable by NLP models. (Note that at any given time, only a subset of these classes will be scheduled and open for registration.)\n",
      "TL;DR: The trainees will learn the basic principles of how to use a natural-language processing model to generate and understand NPL sentences. They will also learn how NPTs can be used in a variety of natural languages and how they can also be implemented in machine learning. This is a good place to start.\n"
     ]
    }
   ],
   "source": [
    "print(generator(\n",
    "    f\"\"\"Summarization Task:\\n{to_summarize}\\nTL;DR:\"\"\", \n",
    "    max_length=512, top_k=5, beams=5, temperature=0.5, no_repeat_ngram_size=2\n",
    ")[0]['generated_text'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfa3f4cf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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