train-evaluate-girt.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download data\n",
    "! wget https://huggingface.co/datasets/nafisehNik/girt-instruct/resolve/main/train.csv\n",
    "! wget https://huggingface.co/datasets/nafisehNik/girt-instruct/resolve/main/val.csv\n",
    "! wget https://huggingface.co/datasets/nafisehNik/girt-instruct/resolve/main/test.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "id": "RRGRsj7IiPcR",
    "outputId": "e161300f-89a5-46db-ba70-65aebb7f4693"
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   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from datasets import DatasetDict\n",
    "\n",
    "# Load dataset from the hub\n",
    "my_dataset_train = load_dataset('csv', data_files='train.csv', split=\"train\", cache_dir = './cache')\n",
    "my_dataset_train = my_dataset_train.shuffle(seed=42)\n",
    "my_dataset_val = load_dataset('csv', data_files='val.csv', split=\"train\", cache_dir = './cache')\n",
    "my_dataset_train = my_dataset_train.shuffle(seed=42)\n",
    "my_dataset_test = load_dataset('csv', data_files='test.csv', split=\"train\", cache_dir = './cache')\n",
    "\n",
    "\n",
    "my_dataset = DatasetDict({\n",
    "    'train': my_dataset_train,\n",
    "    'test': my_dataset_test,\n",
    "    'val': my_dataset_val\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dataset['train'] = my_dataset['train'].remove_columns(['_type'])\n",
    "my_dataset['val'] = my_dataset['val'].remove_columns(['_type'])\n",
    "my_dataset['test'] = my_dataset['test'].remove_columns(['_type'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "id": "a7TVOMZLnnIq",
    "outputId": "a49b157c-1350-4495-aca8-13664d729bf0"
   },
   "outputs": [],
   "source": [
    "print(f\"Train dataset size: {len(my_dataset['train'])}\")\n",
    "print(f\"Val dataset size: {len(my_dataset['val'])}\")\n",
    "print(f\"Test dataset size: {len(my_dataset['test'])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "3nX2WgUon6N4",
    "outputId": "918a501f-5ba7-45c9-9f45-ba53b5484dba"
   },
   "outputs": [],
   "source": [
    "from random import randrange\n",
    "\n",
    "sample = my_dataset['train'][randrange(len(my_dataset[\"train\"]))]\n",
    "print(f\"{sample['instruction']}\\n---------------\")\n",
    "print(f\"{sample['output']}\\n---------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "KXGW3ubFoMkM"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
    "\n",
    "model_id=\"t5-base\"\n",
    "\n",
    "# Load tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir = './cache')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "special_tokens_dict = {'additional_special_tokens': ['<|Repo_Name|>', '<|Email|>', '<|URL|>', '<|Code|>', '<|Image|>', '<|MASK|>', '<|EMPTY|>']}\n",
    "\n",
    "# check the number of special tokens\n",
    "num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)\n",
    "\n",
    "from tokenizers import AddedToken\n",
    "num_added_toks+=tokenizer.add_tokens(AddedToken(\"\\n\", normalized=False))\n",
    "num_added_toks+=tokenizer.add_tokens(AddedToken(\"<!---\", normalized=False))\n",
    "num_added_toks+=tokenizer.add_tokens(AddedToken(\"-->\", normalized=False))\n",
    "\n",
    "print('We have added', num_added_toks, 'tokens')"
   ]
  },
  {
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   "source": [
    "from datasets import concatenate_datasets\n",
    "\n",
    "# The maximum total input sequence length after tokenization.\n",
    "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
    "tokenized_inputs = concatenate_datasets([my_dataset[\"train\"], my_dataset[\"test\"], my_dataset[\"val\"]]).map(lambda x: tokenizer(x[\"instruction\"], truncation=True), batched=True, remove_columns=[\"output\", \"instruction\"])\n",
    "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
    "print(f\"Max source length: {max_source_length}\")\n",
    "\n",
    "# The maximum total sequence length for target text after tokenization.\n",
    "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
    "tokenized_targets = concatenate_datasets([my_dataset[\"train\"], my_dataset[\"test\"], my_dataset[\"val\"]]).map(lambda x: tokenizer(x[\"output\"], truncation=True), batched=True, remove_columns=[\"output\", \"instruction\"])\n",
    "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
    "print(f\"Max target length: {max_target_length}\")\n"
   ]
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   "source": [
    "def preprocess_function(sample,padding=\"max_length\"):\n",
    "    # add prefix to the input for t5\n",
    "    inputs = [item for item in sample[\"instruction\"]]\n",
    "\n",
    "    # tokenize inputs\n",
    "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
    "\n",
    "    # Tokenize targets with the `text_target` keyword argument\n",
    "    labels = tokenizer(text_target=sample[\"output\"], max_length=max_target_length, padding=padding, truncation=True)\n",
    "\n",
    "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
    "    # padding in the loss.\n",
    "    if padding == \"max_length\":\n",
    "        labels[\"input_ids\"] = [\n",
    "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
    "        ]\n",
    "\n",
    "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
    "    return model_inputs\n",
    "\n",
    "tokenized_dataset = my_dataset.map(preprocess_function, batched=True, remove_columns=[\"output\", \"instruction\"])\n",
    "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSeq2SeqLM\n",
    "\n",
    "# huggingface hub model id\n",
    "model_id=\"t5-base\"\n",
    "\n",
    "\n",
    "# load model from the hub\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_id, cache_dir = './cache')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change max length to 300 for evaluation part - in average IRTs have a length of 300\n",
    "model.config.max_length = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qo9DyWoBr1vh",
    "outputId": "adefba1e-71ec-4a71-ad86-a541865dc5f2"
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "import nltk\n",
    "import numpy as np\n",
    "from nltk.tokenize import sent_tokenize\n",
    "nltk.download(\"punkt\")\n",
    "\n",
    "# Metric\n",
    "metric_meteor = evaluate.load(\"meteor\")\n",
    "metric_rouge = evaluate.load(\"rouge\")\n",
    "metric_bleu = evaluate.load(\"bleu\")\n",
    "\n",
    "\n",
    "# helper function to postprocess text\n",
    "def postprocess_text_rouge(preds, labels):\n",
    "    preds = [pred.strip() for pred in preds]\n",
    "    labels = [label.strip() for label in labels]\n",
    "\n",
    "    # rougeLSum expects newline after each sentence\n",
    "    preds = [\"\\n\".join(sent_tokenize(pred)) for pred in preds]\n",
    "    labels = [\"\\n\".join(sent_tokenize(label)) for label in labels]\n",
    "\n",
    "    return preds, labels\n",
    "\n",
    "\n",
    "# helper function to postprocess text\n",
    "def postprocess_text_bleu(preds, labels):\n",
    "    preds = [pred.strip() for pred in preds]\n",
    "    # list of list based on documentation\n",
    "    labels = [[label.strip()] for label in labels]\n",
    "\n",
    "    return preds, labels\n",
    "\n",
    "# helper function to postprocess text\n",
    "def postprocess_text_meteor(preds, labels):\n",
    "    preds = [pred.strip() for pred in preds]\n",
    "    labels = [label.strip() for label in labels]\n",
    "\n",
    "    return preds, labels\n",
    "\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    preds, labels = eval_preds\n",
    "    if isinstance(preds, tuple):\n",
    "        preds = preds[0]\n",
    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "    # Replace -100 in the labels as we can't decode them.\n",
    "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    # Rouge\n",
    "    decoded_preds_rouge, decoded_labels_rouge = postprocess_text_rouge(decoded_preds, decoded_labels)\n",
    "    result_rouge = metric_rouge.compute(predictions=decoded_preds_rouge, references=decoded_labels_rouge, use_stemmer=True)\n",
    "    \n",
    "    # Meteor\n",
    "    decoded_preds_meteor, decoded_labels_meteor = postprocess_text_meteor(decoded_preds, decoded_labels)\n",
    "    result_meteor = metric_meteor.compute(predictions=decoded_preds_meteor, references=decoded_labels_meteor)\n",
    "    \n",
    "    # Bleu\n",
    "    decoded_preds_bleu, decoded_labels_bleu = postprocess_text_bleu(decoded_preds, decoded_labels)\n",
    "    result_bleu = metric_bleu.compute(predictions=decoded_preds_bleu, references=decoded_labels_bleu)\n",
    "\n",
    "    # norm\n",
    "    result = {**result_rouge,**result_meteor, **result_bleu}\n",
    "    \n",
    "    # mean length\n",
    "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
    "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
    "\n",
    "\n",
    "    # Define the file path\n",
    "    file_path = 'evaluate_dict.txt'\n",
    "\n",
    "    # Iterate through the list of dictionaries\n",
    "    with open(file_path, 'a') as file:\n",
    "        file.write(str(result) + '\\n')\n",
    "\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6Nw5ppF5sC1Z"
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorForSeq2Seq\n",
    "\n",
    "# we want to ignore tokenizer pad token in the loss\n",
    "label_pad_token_id = -100\n",
    "# Data collator\n",
    "data_collator = DataCollatorForSeq2Seq(\n",
    "    tokenizer,\n",
    "    model=model,\n",
    "    label_pad_token_id=label_pad_token_id,\n",
    "    pad_to_multiple_of=8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fEr9fWS1-9vD"
   },
   "outputs": [],
   "source": [
    "# Hugging Face repository id\n",
    "repository_id = f\"girt-{model_id.split('/')[1]}\"\n",
    "output_dir_custom = f'./trainer_save/{repository_id}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "L4cgZizNsjTV"
   },
   "outputs": [],
   "source": [
    "import collections \n",
    "import collections.abc\n",
    "from huggingface_hub import HfFolder\n",
    "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
    "\n",
    "\n",
    "\n",
    "# Define training args\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "    output_dir=output_dir_custom,\n",
    "    per_device_train_batch_size=8,\n",
    "    per_device_eval_batch_size=8,\n",
    "    predict_with_generate=True,\n",
    "    fp16=False, # Overflows with fp16\n",
    "    learning_rate=5e-5,\n",
    "    num_train_epochs=30,\n",
    "    # logging & evaluation strategies\n",
    "    logging_dir=f\"./trainer_log/{repository_id}/logs\",\n",
    "    logging_strategy=\"steps\",\n",
    "    logging_steps=500,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    save_total_limit=10,\n",
    "    load_best_model_at_end=True,\n",
    "    # metric_for_best_model=\"overall_f1\",\n",
    "    # push to hub parameters\n",
    "    report_to=\"tensorboard\",\n",
    "    push_to_hub=False,\n",
    "    hub_strategy=\"every_save\",\n",
    "    hub_model_id=repository_id,\n",
    "    hub_token=HfFolder.get_token(),\n",
    ")\n",
    "\n",
    "# Create Trainer instance\n",
    "trainer = Seq2SeqTrainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    data_collator=data_collator,\n",
    "    train_dataset=tokenized_dataset[\"train\"],\n",
    "    eval_dataset=tokenized_dataset[\"val\"],\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# empty cache & gb\n",
    "import gc\n",
    "import torch\n",
    "torch.cuda.empty_cache()\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3AoUvGT5sxTb"
   },
   "outputs": [],
   "source": [
    "# train\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save final model\n",
    "trainer.save_model(\"trainer_save/final\")\n",
    "tokenizer.save_pretrained(\"trainer_save/final/tokenizer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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