path: root/notebooks/japanese.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "execution_state": "idle",
   "id": "1ddfc692-bda7-4d38-a549-2fb0d40d437d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BertForMaskedLM has generative capabilities, as `prepare_inputs_for_generation` is explicitly overwritten. However, it doesn't directly inherit from `GenerationMixin`. From 👉v4.50👈 onwards, `PreTrainedModel` will NOT inherit from `GenerationMixin`, and this model will lose the ability to call `generate` and other related functions.\n",
      "  - If you're using `trust_remote_code=True`, you can get rid of this warning by loading the model with an auto class. See https://huggingface.co/docs/transformers/en/model_doc/auto#auto-classes\n",
      "  - If you are the owner of the model architecture code, please modify your model class such that it inherits from `GenerationMixin` (after `PreTrainedModel`, otherwise you'll get an exception).\n",
      "  - If you are not the owner of the model architecture class, please contact the model code owner to update it.\n",
      "Some weights of the model checkpoint at /home/sipb/nlp-class-project/checkpoints/bert_base_ltr/epoch_3_checkpt were not used when initializing BertForMaskedLM: ['attention_mask']\n",
      "- This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM 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 BertForMaskedLM were not initialized from the model checkpoint at /home/sipb/nlp-class-project/checkpoints/bert_base_ltr/epoch_3_checkpt and are newly initialized because the shapes did not match:\n",
      "- bert.embeddings.position_embeddings.weight: found shape torch.Size([512, 768]) in the checkpoint and torch.Size([128, 768]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import torch\n",
    "import transformers\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from utils import add_attn_hooks\n",
    "\n",
    "# text_dir = \"rtl\"\n",
    "text_dir = \"ltr\"\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "model = transformers.AutoModelForMaskedLM.from_pretrained(f\"/home/sipb/nlp-class-project/checkpoints/bert_base_{text_dir}/epoch_3_checkpt\", ignore_mismatched_sizes=True)\n",
    "\n",
    "# tokenizer = transformers.AutoTokenizer.from_pretrained(\"distilbert/distilbert-base-uncased\")\n",
    "# model = transformers.AutoModelForMaskedLM.from_pretrained(f\"/home/sipb/nlp-class-project/checkpoints/distilbert_base_{text_dir}/epoch_3_checkpt\", ignore_mismatched_sizes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "145d2ffd-db55-4b8f-9fbb-85a51e0b3d11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "execution_state": "idle",
   "id": "a732375b-1682-45c6-8df0-8db1458559c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_attn_hooks(model, text_dir)\n",
    "model.eval();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "execution_state": "idle",
   "id": "041d1702-5aaf-45f0-9413-4014b315d1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet('/home/sipb/nlp-class-project/data/japan.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "execution_state": "idle",
   "id": "2bace74b-a716-4d49-a912-53155cf002ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'\n",
      "_START_ARTICLE_\n",
      "ビートたけしの教科書に載らない日本人の謎\n",
      "_START_SECTION_\n",
      "概要\n",
      "_START_PARAGRAPH_\n",
      "「教科書には決して載らない」日本人の謎やしきたりを多角的に検証し、日本人のDNAを解明する。_NEWLINE_新春番組として定期的に放送されており、年末の午前中に再放送されるのが恒例となっている。'\n"
     ]
    }
   ],
   "source": [
    "df[\"text\"][0]\n",
    "import codecs\n",
    "decoded_str = codecs.escape_decode(df[\"text\"][0])[0].decode('utf-8')\n",
    "print(decoded_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "execution_state": "idle",
   "id": "8a9147ea-d9dc-4826-8030-c8417609405d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "q : where do pandas live? a : (,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, and,, (,,,,,,.,,,,,,,,,,,,, and\n"
     ]
    }
   ],
   "source": [
    "input_text = [\"Q: Where do pandas live? A:\"]#, \"ビートたけしの教科書に載らない日\"]\n",
    "batch = tokenizer(input_text, return_tensors=\"pt\", padding_side=\"right\", padding=\"max_length\", max_length=64)\n",
    "output_ids = model.generate(batch['input_ids'], max_length=128, do_sample=False)  # do_sample=False ensures greedy decoding\n",
    "decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
    "print(decoded_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "execution_state": "idle",
   "id": "1a7c9b35-0c07-431d-91df-bd2f8c7467eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MaskedLMOutput(loss=None, logits=tensor([[[ -7.9645,  -7.6722,  -7.8979,  ...,  -8.6562,  -8.2586,  -6.7448],\n",
      "         [-11.1255, -11.2591, -11.3443,  ..., -10.1338, -11.9891, -10.2974],\n",
      "         [ -8.1256,  -8.1880,  -7.9874,  ...,  -8.0597,  -8.6987, -10.2472],\n",
      "         ...,\n",
      "         [-14.5633, -14.4418, -14.4735,  ..., -14.5651, -14.2234, -13.5610],\n",
      "         [-18.9095, -18.6487, -18.7593,  ..., -19.1327, -18.8564, -17.4334],\n",
      "         [-17.8532, -17.6451, -17.7208,  ..., -18.0046, -17.7334, -16.5670]]]), hidden_states=None, attentions=None)\n"
     ]
    }
   ],
   "source": [
    "with torch.inference_mode():\n",
    "    batch = tokenizer([\"ビートたけしの教科書に載らない日本人の謎\"], return_tensors=\"pt\", padding_side=\"left\" if text_dir == \"rtl\" else \"right\", padding=\"max_length\", max_length=128)\n",
    "    output = model(**batch)\n",
    "    print(output)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "execution_state": "idle",
   "id": "a4098975-2df6-4435-bc93-1a5afd6d7e68",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'riddles' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[15], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# CPU is fast enough\u001b[39;00m\n\u001b[1;32m      3\u001b[0m ppls \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m riddle \u001b[38;5;129;01min\u001b[39;00m \u001b[43mriddles\u001b[49m:\n\u001b[1;32m      5\u001b[0m     batch \u001b[38;5;241m=\u001b[39m tokenizer([riddle], return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m, padding_side\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m text_dir \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrtl\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mright\u001b[39m\u001b[38;5;124m\"\u001b[39m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_length\u001b[39m\u001b[38;5;124m\"\u001b[39m, max_length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m128\u001b[39m)\n\u001b[1;32m      6\u001b[0m     batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mclone()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'riddles' is not defined"
     ]
    }
   ],
   "source": [
    "# CPU is fast enough\n",
    "\n",
    "ppls = []\n",
    "for riddle in riddles:\n",
    "    batch = tokenizer([riddle], return_tensors=\"pt\", padding_side=\"left\" if text_dir == \"rtl\" else \"right\", padding=\"max_length\", max_length=128)\n",
    "    batch[\"labels\"] = batch[\"input_ids\"].clone()\n",
    "    batch[\"labels\"][batch[\"attention_mask\"] == 0] = -100\n",
    "    # batch = tokenizer([riddle], return_tensors=\"pt\")#, padding_side=\"left\" if text_dir == \"rtl\" else \"right\", padding=\"longest\", max_length=128)\n",
    "    # batch[\"labels\"] = batch[\"input_ids\"]\n",
    "    with torch.inference_mode():\n",
    "        output = model(**batch)\n",
    "        ppls.append(math.e ** output.loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "execution_state": "idle",
   "id": "c4a82af4-d0d8-415a-9135-3a1350c1402e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(692.7175314596647, 'rtl')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ppls) / len(ppls), text_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "execution_state": "idle",
   "id": "84a95c66-6dd3-4ccb-96a2-96f38008f70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(616.6241458855995, 'ltr')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ppls) / len(ppls), text_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "execution_state": "idle",
   "id": "51ed80f1-a935-42bc-8194-832f91222c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(526.979384061791, 'rtl')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ppls) / len(ppls), text_dir  # distilbert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "execution_state": "idle",
   "id": "34a2edec-b1d9-466c-a457-954c587f7817",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(288.22724792187364, 'ltr')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ppls) / len(ppls), text_dir  # distilbert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "execution_state": "idle",
   "id": "40a98c10-59c3-498a-a9e6-c23bd9437bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "937.8557468023619"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ppls) / len(ppls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "execution_state": "idle",
   "id": "80b22ba1-e5ba-4f1e-8038-158a2c2f37a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "             0,     0,     0,     0,     0,     0,     0,     0]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "         0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "         0, 0, 0, 0, 0, 0, 0, 0]]), 'labels': tensor([[  101,  1045,  2064,  2022,  2524,  1010,  2021,  1045,  2572,  2025,\n",
       "          5024,  1012,  2054,  2572,  1045,  1029,  1037, 15117,  1012,   102,\n",
       "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
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       "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
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       "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100,\n",
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       "          -100,  -100,  -100,  -100,  -100,  -100,  -100,  -100]])}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "execution_state": "idle",
   "id": "c68b5235-a4a7-4f38-9acb-f5072e546a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.,  6., 11.,  6.,  5.,  2.,  1.,  1.,  2.,  2.]),\n",
       " array([ 613.56297843,  829.36555779, 1045.16813716, 1260.97071653,\n",
       "        1476.77329589, 1692.57587526, 1908.37845463, 2124.18103399,\n",
       "        2339.98361336, 2555.78619272, 2771.58877209]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.hist(ppls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "execution_state": "idle",
   "id": "8acad3ce-905d-455e-af5d-9770495f374a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414,\n",
       " 956.7294281325414]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86daa05b-5784-457b-b65e-8b8395128d6f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}