Roger - RoBERTa - QA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "# ! pip install datasets\n",
    "# ! pip install evaluate\n",
    "# ! pip install transformers==4.28.0"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PIFH4IILciIk",
    "outputId": "014fb9a2-9ee7-44d5-bce1-96cfbf204b7a"
   },
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, aiohttp, datasets\n",
      "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.12.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.15.1 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.9.2\n",
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      "Installing collected packages: evaluate\n",
      "Successfully installed evaluate-0.4.0\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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     ]
    }
   ]
  },
  {
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    },
    "id": "q5cGcn_FaP9m",
    "outputId": "ba113f73-79c0-4d0d-b9c2-68520405ed39"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/5.27k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     "metadata": {}
    },
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      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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       "Downloading readme:   0%|          | 0.00/7.67k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Downloading and preparing dataset squad/plain_text to /root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453...\n"
     ]
    },
    {
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      }
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     "metadata": {}
    },
    {
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "c8ccc9f9bfe24b058ac2ca22b4abde01"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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      ],
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       "version_major": 2,
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      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating train split:   0%|          | 0/87599 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "6373d5f4746a43c293a9932953f3374e"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Generating validation split:   0%|          | 0/10570 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "230220bb06854fbaa20785cf135a4ab1"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Dataset squad downloaded and prepared to /root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, DefaultDataCollator, AutoModelForQuestionAnswering, TrainingArguments, Trainer\n",
    "from datasets import load_dataset\n",
    "\n",
    "squad = load_dataset(\"squad\", split=\"train[:5000]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "eQwY9sCna9RD"
   },
   "outputs": [],
   "source": [
    "squad = squad.train_test_split(test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
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    "id": "7TY5Ob5qbGeq",
    "outputId": "46e0add9-3802-4503-9c82-a0ec52ffb4ec"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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      ],
      "application/vnd.jupyter.widget-view+json": {
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       "model_id": "d73e1f0794c74eaa99d27e987102a63c"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "35a230714aa54fecbf340f4e3aa16af7"
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     },
     "metadata": {}
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"roberta-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "oKCVNgyAbLHK"
   },
   "outputs": [],
   "source": [
    "def preprocess_function(examples):\n",
    "    questions = [q.strip() for q in examples[\"question\"]]\n",
    "    inputs = tokenizer(\n",
    "        questions,\n",
    "        examples[\"context\"],\n",
    "        max_length=384,\n",
    "        truncation=\"only_second\",\n",
    "        return_offsets_mapping=True,\n",
    "        padding=\"max_length\",\n",
    "    )\n",
    "\n",
    "    offset_mapping = inputs.pop(\"offset_mapping\")\n",
    "    answers = examples[\"answers\"]\n",
    "    start_positions = []\n",
    "    end_positions = []\n",
    "\n",
    "    for i, offset in enumerate(offset_mapping):\n",
    "        answer = answers[i]\n",
    "        start_char = answer[\"answer_start\"][0]\n",
    "        end_char = answer[\"answer_start\"][0] + len(answer[\"text\"][0])\n",
    "        sequence_ids = inputs.sequence_ids(i)\n",
    "\n",
    "        # Find the start and end of the context\n",
    "        idx = 0\n",
    "        while sequence_ids[idx] != 1:\n",
    "            idx += 1\n",
    "        context_start = idx\n",
    "        while sequence_ids[idx] == 1:\n",
    "            idx += 1\n",
    "        context_end = idx - 1\n",
    "\n",
    "        # If the answer is not fully inside the context, label it (0, 0)\n",
    "        if offset[context_start][0] > end_char or offset[context_end][1] < start_char:\n",
    "            start_positions.append(0)\n",
    "            end_positions.append(0)\n",
    "        else:\n",
    "            # Otherwise it's the start and end token positions\n",
    "            idx = context_start\n",
    "            while idx <= context_end and offset[idx][0] <= start_char:\n",
    "                idx += 1\n",
    "            start_positions.append(idx - 1)\n",
    "\n",
    "            idx = context_end\n",
    "            while idx >= context_start and offset[idx][1] >= end_char:\n",
    "                idx -= 1\n",
    "            end_positions.append(idx + 1)\n",
    "\n",
    "    inputs[\"start_positions\"] = start_positions\n",
    "    inputs[\"end_positions\"] = end_positions\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17,
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    },
    "id": "swRtOfHwbOyy",
    "outputId": "0a91ba9f-4fe5-46d2-fa5f-2cfac9de92ee"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d62f2999bf2f42b8a9fdb1dbbd6ba73f"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
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      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad[\"train\"].column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "qjaD1QLjbRyB"
   },
   "outputs": [],
   "source": [
    "data_collator = DefaultDataCollator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U24shhSkbWMT",
    "outputId": "ce7a18c9-e197-4cae-c945-30eeb2cb8d03"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForQuestionAnswering: ['lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias']\n",
      "- This IS expected if you are initializing RobertaForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForQuestionAnswering were not initialized from the model checkpoint at roberta-base and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForQuestionAnswering.from_pretrained(\"roberta-base\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "LhpJWkZAbdAV",
    "outputId": "c814b619-217b-4b15-d31e-1da5df24eeb2"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ],
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='750' max='750' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [750/750 16:51, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>1.089244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.709500</td>\n",
       "      <td>1.013733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.709500</td>\n",
       "      <td>1.036940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ]
     },
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       "TrainOutput(global_step=750, training_loss=0.6353842875162761, metrics={'train_runtime': 1012.5891, 'train_samples_per_second': 11.851, 'train_steps_per_second': 0.741, 'total_flos': 2351670810624000.0, 'train_loss': 0.6353842875162761, 'epoch': 3.0})"
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    "training_args = TrainingArguments(\n",
    "    output_dir=\"qa_roBERTa\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.01,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_squad[\"train\"],\n",
    "    eval_dataset=tokenized_squad[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    ")\n",
    "\n",
    "trainer.train()"
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