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{
"cells": [
{
"cell_type": "markdown",
"id": "debeec92",
"metadata": {
"tags": []
},
"source": [
"## Gathering NER Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b83776e8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from datasets import DatasetDict\n",
"from transformers import AutoTokenizer\n",
"\n",
"dataset = DatasetDict.load_from_disk().remove_columns([\"token_type_ids\", \"attention_mask\"])\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(\"./../tokenizer\")\n",
"tokenizer.pad_token_id = 0\n",
"tokenizer.pad_token = \"<|padding|>\"\n",
"tokenizer.padding_size = \"right\"\n",
"\n",
"# new tokens for prompting\n",
"num_new_tokens = tokenizer.add_tokens([\"<|startofprompt|>\", \"<|sepofprompt|>\", \"<|endofprompt|>\"])\n",
"# new tokens for entities\n",
"tokenizer.add_tokens([\"<|entity:PER|>\", \"<|entity:LOC|>\", \"<|entity:ORG|>\", \"<|entity|>\", \"<|detectentities|>\"])\n",
"# new tokens for images\n",
"tokenizer.add_tokens([\"<|startofimage|>\", \"<|endofimage|>\"])\n",
"tokenizer.add_tokens([ f\"<|image:{tkn}|>\" for tkn in range(16000)])\n",
"\n",
"tokenizer.save_pretrained(\"./tokenizer\")\n",
"\n",
"print(\"Total Vocab Size:\", len(tokenizer))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f2a95871-6e2d-4b96-bc36-8febac09d795",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"./tokenizer\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a706dd6d-e9b2-4e42-baf1-7d17cd93c54f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"from tqdm import tqdm\n",
"import string\n",
"import os\n",
"import re\n",
"\n",
"audio_paths = sorted(os.listdir(\"./mp3\"))\n",
"txt_paths = sorted(os.listdir(\"./txt\"))\n",
"data = np.load(\"tokens.npz\")\n",
"audio_tokens = [data[key] for key in data.keys()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce8bb550-8149-438c-9ca5-b12681f36476",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def tag_entities(text):\n",
" \n",
" patterns = {\n",
" \"PER\": r'\\|(.*?)\\]',\n",
" \"LOC\": r'\\$(.*?)\\]',\n",
" \"ORG\": r'\\{(.*?)\\]'\n",
" }\n",
" \n",
" entities = []\n",
"\n",
" for entity, pattern in patterns.items():\n",
" matches = re.findall(pattern, text)\n",
" text = re.sub(pattern, lambda m: f'<|entity:{entity}|>{m.group(1)}<|entity|>', text)\n",
" entities += matches\n",
"\n",
" return text, entities\n",
"\n",
"data = []\n",
"\n",
"for idx in tqdm(range(len(txt_paths))):\n",
" \n",
" with open(os.path.join(\"./txt\", txt_paths[idx])) as f:\n",
" txt = f.read()\n",
" \n",
" text, entities = tag_entities(txt.lower())\n",
" \n",
" audio_token = audio_tokens[idx]\n",
" \n",
" prompt = \"\".join([f\"<|audio:{tkn}|>\" for tkn in audio_token]) + \"<|detectentities|><|startofprompt|><|endofprompt|>\" + \"<|startoftranscript|>\" + text + \"<|endoftranscript|>\"\n",
" \n",
" try:\n",
" outputs = tokenizer(prompt, truncation=True, padding=\"max_length\", max_length=2048)\n",
" data.append({\n",
" \"audio_tokens\": audio_token,\n",
" \"raw_text\": text,\n",
" \"transcript\": txt.translate(str.maketrans('', '', string.punctuation)).lower(),\n",
" \"entities\": entities,\n",
" \"prompt\": prompt,\n",
" \"input_ids\": outputs[\"input_ids\"],\n",
" \"attention_mask\": output[\"attention_mask\"]\n",
" })\n",
" except:\n",
" print(idx)\n",
" continue\n",
" \n",
"from datasets import Dataset\n",
"import pandas as pd\n",
"\n",
"ds = Dataset.from_pandas(pd.DataFrame(data))\n",
"\n",
"ds.save_to_disk(\"entity_tokenized\")\n",
"ds.push_to_hub(\"darshanmakwana/entity_tokenized\")"
]
},
{
"cell_type": "markdown",
"id": "38191f9a-2a11-4bb2-a885-ef303d6c43f7",
"metadata": {
"tags": []
},
"source": [
"## Validating Model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "710a1144-46a1-43d4-9bf9-1c01569b26d4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from transformers import GPT2LMHeadModel, AutoTokenizer\n",
"from datasets import Dataset\n",
"import torch\n",
"\n",
"dataset_name = \"entity_tokenized\"\n",
"tokenizer_path = \"./../tokenizer\"\n",
"max_length = 2048\n",
"device = \"cuda:0\"\n",
"dtype = torch.float16\n",
"\n",
"dataset = Dataset.load_from_disk(dataset_name)\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)\n",
"tokenizer.pad_token_id = 0\n",
"tokenizer.pad_token = \"<|padding|>\"\n",
"tokenizer.padding_side = \"left\"\n",
"\n",
"# new tokens for prompting\n",
"num_new_tokens = tokenizer.add_tokens([\"<|startofprompt|>\", \"<|sepofprompt|>\", \"<|endofprompt|>\"])\n",
"# new tokens for entities\n",
"tokenizer.add_tokens([\"<|entity:PER|>\", \"<|entity:LOC|>\", \"<|entity:ORG|>\", \"<|entity|>\", \"<|detectentities|>\"])\n",
"\n",
"model = GPT2LMHeadModel.from_pretrained(\"./out/checkpoint-20000\").to(device).to(dtype).eval()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "cea0d8c4-5c56-47eb-934a-86293bed6afa",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"114.073974609375"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum([param.numel() for param in model.parameters()]) / (1024 * 1024)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "529ca732-569f-4b7d-8448-1f16b35a6694",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 8/8 [00:27<00:00, 3.42s/it]\n"
]
}
],
"source": [
"from eval_model import process\n",
"from math import ceil\n",
"from tqdm import tqdm\n",
"import re\n",
"\n",
"def extract_entities(text):\n",
" \n",
" patterns = {\n",
" \"PER\": r'<\\|entity:PER\\|>(.*?)<\\|entity\\|>',\n",
" \"LOC\": r'<\\|entity:LOC\\|>(.*?)<\\|entity\\|>',\n",
" \"ORG\": r'<\\|entity:ORG\\|>(.*?)<\\|entity\\|>'\n",
" }\n",
" \n",
" entities = []\n",
"\n",
" for entity, pattern in patterns.items():\n",
" matches = re.findall(pattern, text)\n",
" text = re.sub(pattern, lambda m: f'{m.group(1)}', text)\n",
" entities += [process(match) for match in matches]\n",
"\n",
" return text, entities\n",
"\n",
"def preprocess(sample):\n",
" prompt = \"\".join([f\"<|audio:{tkn}|>\" for tkn in sample[\"audio_tokens\"]]) + \"<|detectentities|><|startofprompt|><|endofprompt|>\" + \"<|startoftranscript|>\"\n",
" return {\"prompt\": prompt}\n",
"\n",
"dataset = dataset.map(preprocess)\n",
"dataset = dataset.select(list(range(0, 1000)))\n",
"\n",
"eot_token = tokenizer.encode(\"<|endoftranscript|>\")[0]\n",
"\n",
"batch_size = 128\n",
"texts = []\n",
"tp = 0\n",
"fp = 0\n",
"tn = 0\n",
"\n",
"for idx in tqdm(range(ceil(len(dataset)/batch_size))):\n",
"\n",
" input_ids = tokenizer(dataset[idx * batch_size: (idx + 1) * batch_size][\"prompt\"], return_tensors=\"pt\", padding=True, truncation=True).input_ids.to(model.device)\n",
" par = input_ids.shape[-1]\n",
"\n",
" generations = model.generate(\n",
" input_ids,\n",
" max_new_tokens=max_length,\n",
" eos_token_id = eot_token\n",
" )\n",
" texts += tokenizer.batch_decode(generations[:, par:], skip_special_tokens=True)\n",
"\n",
"# transcript, pred_entities = extract_entities(transcripts[0])\n",
" \n",
"# entities = sample[\"entities\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5ce4384e-8771-487e-86e1-de5489ee4e59",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:04<00:00, 241.04it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 69.53846153846153\n",
"Recall: 69.32515337423312\n",
"F1 Score: 69.43164362519201\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"tp = 0\n",
"fp = 0\n",
"fn = 0\n",
"\n",
"for idx in tqdm(range(len(dataset))):\n",
" \n",
" transcript, entities = extract_entities(texts[idx])\n",
"\n",
" for entity in entities:\n",
" if entity in dataset[idx][\"entities\"]:\n",
" tp += 1\n",
" else:\n",
" fp += 1\n",
" for entity in dataset[idx][\"entities\"]:\n",
" if entity not in entities:\n",
" fn += 1\n",
" \n",
"pre = tp / (tp + fp) * 100\n",
"recall = tp / (tp + fn) * 100\n",
"print(\"Precision:\", pre)\n",
"print(\"Recall:\", recall)\n",
"print(\"F1 Score:\", 2 / ((1/pre) + (1/recall)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed0fad1a-bb30-446e-83a9-4a972fdb7766",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## Train Iter Precision Recall F1 Score\n",
" 16000 68.80 69.27 69.03\n",
" 17000 72.92 70.78 71.83\n",
" 18000 76.78 75.34 76.05\n",
" 19000 81.78 80.92 81.34\n",
" 20000 85.05 80.74 82.84"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "113df077-c31c-4b57-876e-b19942100306",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"81.34772710510141"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 / ((1/81.78) + (1/80.92))"
]
}
],
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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