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‎.DS_Store‎

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‎.gitignore‎

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wandb/
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outputs/
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outputs/
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*.DS_Store
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.DS_Store
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.DS_Store
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.DS_Store?

‎notebooks/test.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "ae8b72691bc94e5b8406681bb5b11d59",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map (num_proc=4): 0%| | 0/63981 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f04a9293b4a14796aa1050f40b8f0135",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map (num_proc=4): 0%| | 0/872 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "0e7c45fd8b974bc1ac58f95e05d69cb6",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map (num_proc=4): 0%| | 0/3368 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9968f3bb38ad4d26b78e6d1cc7ae295c",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Map (num_proc=4): 0%| | 0/1821 [00:00<?, ? examples/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"Dataset({\n",
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" features: ['idx', 'labels', 'text', 'input_ids', 'attention_mask'],\n",
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" num_rows: 63981\n",
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"})"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from datasets import load_from_disk, Dataset\n",
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"from transformers import GPT2Tokenizer\n",
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"\n",
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"dataset = load_from_disk(\"data/clean\")\n",
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"\n",
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"tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
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"tokenizer.pad_token = tokenizer.eos_token\n",
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"\n",
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"def tokenize_function(examples):\n",
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" return tokenizer(examples[\"text\"], padding=True, truncation=True, max_length=32)\n",
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"\n",
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"tokenized_datasets = dataset.map(tokenize_function, batched=True, num_proc=4)\n",
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"\n",
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"tokenized_datasets[\"train\"]\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "'list' object has no attribute 'schema'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[9]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m Dataset(\u001b[43mTable\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtrain\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtext\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m)\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/projects/SentiSynth/venv311/lib/python3.11/site-packages/datasets/table.py:167\u001b[39m, in \u001b[36mTable.__init__\u001b[39m\u001b[34m(self, table)\u001b[39m\n\u001b[32m 166\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, table: pa.Table):\n\u001b[32m--> \u001b[39m\u001b[32m167\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 168\u001b[39m \u001b[38;5;28mself\u001b[39m.table = table\n",
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"\u001b[36mFile \u001b[39m\u001b[32m~/projects/SentiSynth/venv311/lib/python3.11/site-packages/datasets/table.py:107\u001b[39m, in \u001b[36mIndexedTableMixin.__init__\u001b[39m\u001b[34m(self, table)\u001b[39m\n\u001b[32m 106\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, table: pa.Table):\n\u001b[32m--> \u001b[39m\u001b[32m107\u001b[39m \u001b[38;5;28mself\u001b[39m._schema: pa.Schema = \u001b[43mtable\u001b[49m\u001b[43m.\u001b[49m\u001b[43mschema\u001b[49m\n\u001b[32m 108\u001b[39m \u001b[38;5;28mself\u001b[39m._batches: \u001b[38;5;28mlist\u001b[39m[pa.RecordBatch] = [\n\u001b[32m 109\u001b[39m recordbatch \u001b[38;5;28;01mfor\u001b[39;00m recordbatch \u001b[38;5;129;01min\u001b[39;00m table.to_batches() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(recordbatch) > \u001b[32m0\u001b[39m\n\u001b[32m 110\u001b[39m ]\n\u001b[32m 111\u001b[39m \u001b[38;5;28mself\u001b[39m._offsets: np.ndarray = np.cumsum([\u001b[32m0\u001b[39m] + [\u001b[38;5;28mlen\u001b[39m(b) \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._batches], dtype=np.int64)\n",
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"\u001b[31mAttributeError\u001b[39m: 'list' object has no attribute 'schema'"
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]
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}
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],
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"source": [
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"Dataset(Table(d[\"train\"][\"text\"]))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python3.11 (sentisynth)",
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"language": "python",
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"name": "auctionn"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎src/cli/01_train_teacher.py‎

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import logging
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from pathlib import Path
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import numpy as np
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from sklearn.metrics import precision_recall_fscore_support, accuracy_score
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import torch
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from transformers import DataCollatorWithPadding, IntervalStrategy, TrainingArguments, Trainer
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from src.models import build_teacher
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from src.data import ClassificationDataModule
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from utils.wandb_setup import setup_wandb
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from utils.metrics import compute_metrics
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logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
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logger = logging.getLogger(__name__)
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app = typer.Typer()
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def compute_metrics(p):
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"""Computes metrics for HF Trainer."""
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preds = np.argmax(p.predictions, axis=1)
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labels = p.label_ids
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precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary') # Assuming binary
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acc = accuracy_score(labels, preds)
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return {
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'accuracy': acc,
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'f1': f1,
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'precision': precision,
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'recall': recall
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}
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@app.command()

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