wehub-resource-sync
11474 lines
472 KiB
Plaintext
11474 lines
472 KiB
Plaintext
{
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"cells": [
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},
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"source": [
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"# Transformer Fine-Tuning: FinBERT vs ModernBERT\n",
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"\n",
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"**Chapter 10: From Text to Features - The Transformer Breakthrough**\n",
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"\n",
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"**Docker image**: `ml4t-gpu`\n",
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"\n",
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"**Section Reference**: See Sections 10.4, 10.5 for conceptual discussion\n",
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"\n",
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"## Purpose\n",
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"This notebook demonstrates the complete fine-tuning workflow for financial\n",
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"sentiment classification using the Hugging Face Trainer API. We measure how\n",
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"fine-tuning changes accuracy versus the pre-trained-checkpoint result reported\n",
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"in NB03, and compare a domain-specific checkpoint (FinBERT) to two general\n",
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"checkpoints (DeBERTa-v3, ModernBERT) on the same PhraseBank split.\n",
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"\n",
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"## Learning Objectives\n",
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"After completing this notebook, you will be able to:\n",
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"- Fine-tune a pre-trained Transformer for sequence classification\n",
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"- Use the Hugging Face Trainer API with custom metrics\n",
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"- Compare domain-specific vs general-purpose pre-trained models\n",
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"- Interpret training dynamics (loss curves, early stopping)\n",
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"- Evaluate models with accuracy, F1, and confusion matrices\n",
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"\n",
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"## Cross-References\n",
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"- **Upstream**: `sentiment_evolution.py` (baseline methods), Chapter 5 (text data)\n",
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"- **Downstream**: Chapter 10 (text features in ML pipelines)\n",
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"- **Related**: `shap_nlp_interpretation.py` (model interpretability)\n",
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"\n",
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"## Hardware Requirements\n",
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"- **GPU recommended**: ~5 min on GPU, 30+ min on CPU\n",
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"- **Memory**: ~4GB GPU memory for FinBERT"
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]
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/opt/ml4t/lib/python3.14/site-packages/multiprocess/connection.py:335: SyntaxWarning: 'return' in a 'finally' block\n",
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" return f\n",
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"/opt/ml4t/lib/python3.14/site-packages/multiprocess/connection.py:337: SyntaxWarning: 'return' in a 'finally' block\n",
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" return self._get_more_data(ov, maxsize)\n"
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]
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}
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],
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"source": [
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"\"\"\"Transformer Fine-Tuning — fine-tune FinBERT, DeBERTa-v3, and ModernBERT for financial sentiment classification.\"\"\"\n",
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"\n",
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"import json\n",
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"import time\n",
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"import warnings\n",
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"\n",
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"import evaluate\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import polars as pl\n",
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"import seaborn as sns\n",
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"import torch\n",
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"from datasets import Dataset, DatasetDict\n",
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"from sklearn.metrics import confusion_matrix\n",
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"from sklearn.model_selection import train_test_split\n",
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"from transformers import (\n",
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" AutoModelForSequenceClassification,\n",
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" AutoTokenizer,\n",
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" DataCollatorWithPadding,\n",
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" EarlyStoppingCallback,\n",
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" Trainer,\n",
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" TrainingArguments,\n",
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")\n",
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"from transformers import (\n",
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" set_seed as set_transformers_seed,\n",
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")\n",
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"\n",
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"from data import load_financial_phrasebank\n",
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"from utils.paths import get_chapter_dir\n",
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"from utils.reproducibility import set_global_seeds\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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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": 2,
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"id": "e15c0de0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T02:46:00.810515Z",
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"status": "completed"
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},
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"tags": [
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||
"parameters"
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||
]
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||
},
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"outputs": [],
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||
"source": [
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"SEED = 42\n",
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"MAX_TRAIN_STEPS = -1 # -1 = train full epochs"
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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": 3,
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"id": "76f28325",
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"metadata": {
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"execution": {
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"exception": false,
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"status": "completed"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"======================================================================\n",
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"EXPERIMENT CONFIGURATION\n",
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"======================================================================\n",
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"{\n",
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" \"random_seed\": 42,\n",
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" \"dataset\": {\n",
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" \"name\": \"takala/financial_phrasebank\",\n",
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" \"subset\": \"sentences_allagree\",\n",
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" \"test_size\": 0.15,\n",
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||
" \"val_size\": 0.15\n",
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||
" },\n",
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" \"models\": {\n",
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" \"finbert\": {\n",
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" \"model_id\": \"ProsusAI/finbert\",\n",
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" \"description\": \"FinBERT fine-tuned on Financial PhraseBank sentiment\"\n",
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||
" },\n",
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||
" \"deberta\": {\n",
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||
" \"model_id\": \"microsoft/deberta-v3-small\",\n",
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||
" \"description\": \"DeBERTa-v3 with disentangled attention\"\n",
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||
" },\n",
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||
" \"modernbert\": {\n",
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||
" \"model_id\": \"answerdotai/ModernBERT-base\",\n",
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||
" \"description\": \"ModernBERT with 8,192 token context length\"\n",
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||
" }\n",
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" },\n",
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||
" \"training\": {\n",
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||
" \"learning_rate\": 2e-05,\n",
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" \"batch_size\": 16,\n",
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||
" \"num_epochs\": 3,\n",
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||
" \"weight_decay\": 0.01,\n",
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||
" \"warmup_ratio\": 0.1,\n",
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" \"max_length\": 128,\n",
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" \"early_stopping_patience\": 2\n",
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" }\n",
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"}\n",
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"======================================================================\n",
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"\n",
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"Using device: cuda\n"
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]
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||
}
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||
],
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"source": [
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||
"# Reproducibility — set_global_seeds covers Python random / NumPy / Torch.\n",
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||
"# transformers Trainer uses its own RNG that needs explicit seeding.\n",
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"set_global_seeds(SEED)\n",
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"set_transformers_seed(SEED)\n",
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"\n",
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"CONFIG = {\n",
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" \"random_seed\": SEED,\n",
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" \"dataset\": {\n",
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" \"name\": \"takala/financial_phrasebank\",\n",
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||
" \"subset\": \"sentences_allagree\",\n",
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||
" \"test_size\": 0.15,\n",
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" \"val_size\": 0.15,\n",
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||
" },\n",
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||
" \"models\": {\n",
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||
" \"finbert\": {\n",
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||
" \"model_id\": \"ProsusAI/finbert\",\n",
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||
" \"description\": \"FinBERT fine-tuned on Financial PhraseBank sentiment\",\n",
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||
" },\n",
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||
" \"deberta\": {\n",
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||
" \"model_id\": \"microsoft/deberta-v3-small\",\n",
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||
" \"description\": \"DeBERTa-v3 with disentangled attention\",\n",
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||
" },\n",
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||
" \"modernbert\": {\n",
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||
" \"model_id\": \"answerdotai/ModernBERT-base\",\n",
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||
" \"description\": \"ModernBERT with 8,192 token context length\",\n",
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||
" },\n",
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||
" },\n",
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||
" \"training\": {\n",
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||
" \"learning_rate\": 2e-5,\n",
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||
" \"batch_size\": 16,\n",
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||
" \"num_epochs\": 3,\n",
|
||
" \"weight_decay\": 0.01,\n",
|
||
" \"warmup_ratio\": 0.1,\n",
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||
" \"max_length\": 128,\n",
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||
" \"early_stopping_patience\": 2,\n",
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||
" },\n",
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||
"}\n",
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||
"\n",
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||
"print(\"=\" * 70)\n",
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||
"print(\"EXPERIMENT CONFIGURATION\")\n",
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||
"print(\"=\" * 70)\n",
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||
"print(json.dumps(CONFIG, indent=2))\n",
|
||
"print(\"=\" * 70)\n",
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"\n",
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||
"# Check for GPU\n",
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||
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
|
||
"print(f\"\\nUsing device: {device}\")"
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||
]
|
||
},
|
||
{
|
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"cell_type": "markdown",
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"id": "88967cf6",
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"metadata": {
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"papermill": {
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"duration": 0.003071,
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"end_time": "2026-06-13T02:46:00.863343+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:46:00.860272+00:00",
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"status": "completed"
|
||
}
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||
},
|
||
"source": [
|
||
"## 2. Load Financial PhraseBank Dataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
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||
"id": "ee753dec",
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"metadata": {
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"execution": {
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"duration": 0.011871,
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"end_time": "2026-06-13T02:46:00.878265+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:46:00.866394+00:00",
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"status": "completed"
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}
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},
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"outputs": [
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{
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
||
"Dataset size: 2,264\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"df = load_financial_phrasebank(agreement=\"100\")\n",
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||
"print(f\"Dataset size: {len(df):,}\")"
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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": 5,
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"id": "61a8fabd",
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"metadata": {
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"execution": {
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},
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"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
|
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"text": [
|
||
"Train: 1584, Val: 340, Test: 340\n"
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||
]
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||
}
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||
],
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||
"source": [
|
||
"# Prepare train/val/test splits (convert to pandas for sklearn, then back to Polars)\n",
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||
"df_pd = df.to_pandas()\n",
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||
"train_pd, temp_pd = train_test_split(\n",
|
||
" df_pd, test_size=0.3, random_state=SEED, stratify=df_pd[\"label\"]\n",
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||
")\n",
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||
"val_pd, test_pd = train_test_split(\n",
|
||
" temp_pd, test_size=0.5, random_state=SEED, stratify=temp_pd[\"label\"]\n",
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")\n",
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||
"\n",
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"train_df = pl.from_pandas(train_pd)\n",
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||
"val_df = pl.from_pandas(val_pd)\n",
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"test_df = pl.from_pandas(test_pd)\n",
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"\n",
|
||
"print(f\"Train: {len(train_df)}, Val: {len(val_df)}, Test: {len(test_df)}\")"
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||
]
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||
},
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||
{
|
||
"cell_type": "markdown",
|
||
"id": "9ed36081",
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"metadata": {
|
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"lines_to_next_cell": 2,
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"papermill": {
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"duration": 0.003095,
|
||
"end_time": "2026-06-13T02:46:00.899084+00:00",
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"exception": false,
|
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"start_time": "2026-06-13T02:46:00.895989+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Create HuggingFace DatasetDict\n",
|
||
"Convert Polars DataFrames to HuggingFace datasets (requires pandas at the boundary)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "27f65298",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:46:00.905853Z",
|
||
"iopub.status.busy": "2026-06-13T02:46:00.905769Z",
|
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"iopub.status.idle": "2026-06-13T02:46:00.916716Z",
|
||
"shell.execute_reply": "2026-06-13T02:46:00.916413Z"
|
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},
|
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"papermill": {
|
||
"duration": 0.015028,
|
||
"end_time": "2026-06-13T02:46:00.917187+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:00.902159+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def create_dataset_dict(train_df: pl.DataFrame, val_df: pl.DataFrame, test_df: pl.DataFrame):\n",
|
||
" return DatasetDict(\n",
|
||
" {\n",
|
||
" \"train\": Dataset.from_pandas(\n",
|
||
" train_df.select([\"sentence\", \"label\"]).to_pandas(), preserve_index=False\n",
|
||
" ),\n",
|
||
" \"validation\": Dataset.from_pandas(\n",
|
||
" val_df.select([\"sentence\", \"label\"]).to_pandas(), preserve_index=False\n",
|
||
" ),\n",
|
||
" \"test\": Dataset.from_pandas(\n",
|
||
" test_df.select([\"sentence\", \"label\"]).to_pandas(), preserve_index=False\n",
|
||
" ),\n",
|
||
" }\n",
|
||
" )\n",
|
||
"\n",
|
||
"\n",
|
||
"dataset = create_dataset_dict(train_df, val_df, test_df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
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"id": "18c25746",
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"metadata": {
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"papermill": {
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"duration": 0.003149,
|
||
"end_time": "2026-06-13T02:46:00.923580+00:00",
|
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"exception": false,
|
||
"start_time": "2026-06-13T02:46:00.920431+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 3. Model Configuration and Training Setup"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
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"id": "0c28463d",
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"lines_to_next_cell": 2,
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"duration": 0.005882,
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"end_time": "2026-06-13T02:46:00.932669+00:00",
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"exception": false,
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"start_time": "2026-06-13T02:46:00.926787+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Model configurations\n",
|
||
"MODELS = {\n",
|
||
" \"FinBERT\": \"ProsusAI/finbert\",\n",
|
||
" \"DeBERTa-v3\": \"microsoft/deberta-v3-small\",\n",
|
||
" \"ModernBERT\": \"answerdotai/ModernBERT-base\",\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Label mappings\n",
|
||
"id2label = {0: \"negative\", 1: \"neutral\", 2: \"positive\"}\n",
|
||
"label2id = {\"negative\": 0, \"neutral\": 1, \"positive\": 2}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "148827e3",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.003033,
|
||
"end_time": "2026-06-13T02:46:00.938815+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:00.935782+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Tokenization\n",
|
||
"Tokenize inputs without padding — `DataCollatorWithPadding` handles dynamic padding per batch."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "46390ae1",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:46:00.945465Z",
|
||
"iopub.status.busy": "2026-06-13T02:46:00.945374Z",
|
||
"iopub.status.idle": "2026-06-13T02:46:00.947151Z",
|
||
"shell.execute_reply": "2026-06-13T02:46:00.946833Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005542,
|
||
"end_time": "2026-06-13T02:46:00.947438+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:00.941896+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def tokenize_function(examples, tokenizer, max_length=128):\n",
|
||
" return tokenizer(\n",
|
||
" examples[\"sentence\"],\n",
|
||
" truncation=True,\n",
|
||
" max_length=max_length,\n",
|
||
" # Note: Don't pad here; use DataCollatorWithPadding for dynamic padding\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "daeb47da",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003075,
|
||
"end_time": "2026-06-13T02:46:00.953688+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:00.950613+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Metrics Computation\n",
|
||
"Custom metric function for the Trainer API — computes accuracy and macro F1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "4b033fea",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:46:00.960385Z",
|
||
"iopub.status.busy": "2026-06-13T02:46:00.960305Z",
|
||
"iopub.status.idle": "2026-06-13T02:46:01.448657Z",
|
||
"shell.execute_reply": "2026-06-13T02:46:01.448307Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.492318,
|
||
"end_time": "2026-06-13T02:46:01.449130+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:00.956812+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "8b55ca66702e402ea97c3b7a3371a277",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading builder script: 0.00B [00:00, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "d92366732d924547a4be8cf455f40b95",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading builder script: 0.00B [00:00, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"accuracy_metric = evaluate.load(\"accuracy\")\n",
|
||
"f1_metric = evaluate.load(\"f1\")\n",
|
||
"\n",
|
||
"\n",
|
||
"def compute_metrics(eval_pred):\n",
|
||
" logits, labels = eval_pred\n",
|
||
" predictions = np.argmax(logits, axis=-1)\n",
|
||
" acc = accuracy_metric.compute(predictions=predictions, references=labels)\n",
|
||
" f1 = f1_metric.compute(predictions=predictions, references=labels, average=\"macro\")\n",
|
||
" return {\"accuracy\": acc[\"accuracy\"], \"f1\": f1[\"f1\"]}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "be22dae8",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:46:01.456380Z",
|
||
"iopub.status.busy": "2026-06-13T02:46:01.456294Z",
|
||
"iopub.status.idle": "2026-06-13T02:46:01.461614Z",
|
||
"shell.execute_reply": "2026-06-13T02:46:01.461350Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.009446,
|
||
"end_time": "2026-06-13T02:46:01.462000+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:01.452554+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Training function\n",
|
||
"def fine_tune_model(model_name: str, model_path: str, dataset: DatasetDict) -> dict:\n",
|
||
" \"\"\"Fine-tune a transformer model for sentiment classification.\"\"\"\n",
|
||
" print(f\"\\n{'=' * 60}\")\n",
|
||
" print(f\"Fine-tuning: {model_name}\")\n",
|
||
" print(f\"Model: {model_path}\")\n",
|
||
" print(f\"{'=' * 60}\")\n",
|
||
"\n",
|
||
" # Load tokenizer and model\n",
|
||
" tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
|
||
"\n",
|
||
" # Handle models without pad token (proper approach for encoder-only models)\n",
|
||
" # Encoder models like BERT don't have eos_token; use [PAD] or add one\n",
|
||
" if tokenizer.pad_token is None:\n",
|
||
" if tokenizer.eos_token is not None:\n",
|
||
" tokenizer.pad_token = tokenizer.eos_token\n",
|
||
" else:\n",
|
||
" # For BERT-like models, add [PAD] token\n",
|
||
" tokenizer.add_special_tokens({\"pad_token\": \"[PAD]\"})\n",
|
||
"\n",
|
||
" model = AutoModelForSequenceClassification.from_pretrained(\n",
|
||
" model_path,\n",
|
||
" num_labels=3,\n",
|
||
" id2label=id2label,\n",
|
||
" label2id=label2id,\n",
|
||
" ignore_mismatched_sizes=True,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Resize embeddings if we added a new token\n",
|
||
" model.resize_token_embeddings(len(tokenizer))\n",
|
||
"\n",
|
||
" # Handle pad token in model config\n",
|
||
" if model.config.pad_token_id is None:\n",
|
||
" model.config.pad_token_id = tokenizer.pad_token_id\n",
|
||
"\n",
|
||
" # Create data collator for dynamic padding (more efficient than max_length padding)\n",
|
||
" data_collator = DataCollatorWithPadding(tokenizer=tokenizer)\n",
|
||
"\n",
|
||
" # Tokenize dataset\n",
|
||
" tokenized = dataset.map(\n",
|
||
" lambda x: tokenize_function(x, tokenizer),\n",
|
||
" batched=True,\n",
|
||
" remove_columns=[\"sentence\"],\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Training arguments - save checkpoints under chapter output directory\n",
|
||
" chapter_dir = get_chapter_dir(10)\n",
|
||
" output_dir = chapter_dir / \"output\" / \"bert_finetuning\" / model_name.lower().replace(\"-\", \"_\")\n",
|
||
" output_dir.mkdir(parents=True, exist_ok=True)\n",
|
||
"\n",
|
||
" # Use CONFIG values consistently\n",
|
||
" train_config = CONFIG[\"training\"]\n",
|
||
" num_epochs = train_config[\"num_epochs\"]\n",
|
||
" batch_size = train_config[\"batch_size\"]\n",
|
||
" train_size = len(tokenized[\"train\"])\n",
|
||
"\n",
|
||
" max_steps = MAX_TRAIN_STEPS\n",
|
||
"\n",
|
||
" # Build training arguments dict with version-compatible parameter names\n",
|
||
" # transformers 4.36+ uses eval_strategy, older versions use evaluation_strategy\n",
|
||
" eval_strat_key = \"eval_strategy\" # Default to newer API\n",
|
||
" save_strat_key = \"save_strategy\"\n",
|
||
" try:\n",
|
||
" # Test if the newer API is available\n",
|
||
" import inspect\n",
|
||
"\n",
|
||
" sig = inspect.signature(TrainingArguments)\n",
|
||
" if \"evaluation_strategy\" in sig.parameters and \"eval_strategy\" not in sig.parameters:\n",
|
||
" eval_strat_key = \"evaluation_strategy\"\n",
|
||
" save_strat_key = \"save_strategy\"\n",
|
||
" except Exception:\n",
|
||
" pass # Use defaults\n",
|
||
"\n",
|
||
" # Compute eval_steps and save_steps ensuring save_steps is a multiple of eval_steps\n",
|
||
" # (required by load_best_model_at_end)\n",
|
||
" if max_steps > 0:\n",
|
||
" eval_steps = max(10, max_steps // 5)\n",
|
||
" # save_steps must be a multiple of eval_steps\n",
|
||
" save_steps = eval_steps * 2 # evaluate twice, save once\n",
|
||
" else:\n",
|
||
" eval_steps = None\n",
|
||
" save_steps = None\n",
|
||
"\n",
|
||
" training_kwargs = {\n",
|
||
" \"output_dir\": str(output_dir),\n",
|
||
" \"num_train_epochs\": num_epochs,\n",
|
||
" \"max_steps\": max_steps,\n",
|
||
" \"per_device_train_batch_size\": batch_size,\n",
|
||
" \"per_device_eval_batch_size\": batch_size * 2,\n",
|
||
" \"warmup_steps\": min(50, max(10, train_size // batch_size // 4)),\n",
|
||
" \"weight_decay\": train_config[\"weight_decay\"],\n",
|
||
" \"logging_steps\": max(10, train_size // batch_size // 3),\n",
|
||
" eval_strat_key: \"steps\" if max_steps > 0 else \"epoch\",\n",
|
||
" \"eval_steps\": eval_steps,\n",
|
||
" save_strat_key: \"steps\" if max_steps > 0 else \"epoch\",\n",
|
||
" \"save_steps\": save_steps,\n",
|
||
" \"load_best_model_at_end\": True,\n",
|
||
" \"metric_for_best_model\": \"f1\",\n",
|
||
" \"greater_is_better\": True,\n",
|
||
" \"report_to\": \"none\", # Disable wandb/tensorboard\n",
|
||
" \"fp16\": torch.cuda.is_available(),\n",
|
||
" }\n",
|
||
"\n",
|
||
" training_args = TrainingArguments(**training_kwargs)\n",
|
||
"\n",
|
||
" # Create trainer with data collator for dynamic padding\n",
|
||
" trainer = Trainer(\n",
|
||
" model=model,\n",
|
||
" args=training_args,\n",
|
||
" train_dataset=tokenized[\"train\"],\n",
|
||
" eval_dataset=tokenized[\"validation\"],\n",
|
||
" data_collator=data_collator,\n",
|
||
" compute_metrics=compute_metrics,\n",
|
||
" callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Train\n",
|
||
" start_time = time.time()\n",
|
||
" train_result = trainer.train()\n",
|
||
" train_time = time.time() - start_time\n",
|
||
"\n",
|
||
" # Evaluate on test set\n",
|
||
" test_results = trainer.evaluate(tokenized[\"test\"])\n",
|
||
"\n",
|
||
" # Get predictions for confusion matrix\n",
|
||
" predictions = trainer.predict(tokenized[\"test\"])\n",
|
||
" y_pred = np.argmax(predictions.predictions, axis=-1)\n",
|
||
" y_true = predictions.label_ids\n",
|
||
"\n",
|
||
" return {\n",
|
||
" \"model_name\": model_name,\n",
|
||
" \"accuracy\": test_results[\"eval_accuracy\"],\n",
|
||
" \"f1\": test_results[\"eval_f1\"],\n",
|
||
" \"train_time\": train_time,\n",
|
||
" \"num_params\": sum(p.numel() for p in model.parameters()),\n",
|
||
" \"y_pred\": y_pred,\n",
|
||
" \"y_true\": y_true,\n",
|
||
" \"train_loss\": train_result.training_loss,\n",
|
||
" }"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "465d53c7",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003147,
|
||
"end_time": "2026-06-13T02:46:01.468318+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:01.465171+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 4. Fine-Tune Models"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "87ebde6b",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:46:01.475047Z",
|
||
"iopub.status.busy": "2026-06-13T02:46:01.474967Z",
|
||
"iopub.status.idle": "2026-06-13T02:47:07.184719Z",
|
||
"shell.execute_reply": "2026-06-13T02:47:07.184244Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 65.713873,
|
||
"end_time": "2026-06-13T02:47:07.185327+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:46:01.471454+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fine-tuning: FinBERT\n",
|
||
"Model: ProsusAI/finbert\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "b3883bfb6d83489eb7f5ed78f34b35f2",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"tokenizer_config.json: 0%| | 0.00/252 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "9dc0c9ba783c47cea44ca3837a9603a0",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"config.json: 0%| | 0.00/758 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "ae8f3ff6924a477fb1d2b8fbf53a8f1e",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"vocab.txt: 0.00B [00:00, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "f496c9cd04ce4a2cb4f686f9ad9bf857",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"special_tokens_map.json: 0%| | 0.00/112 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "021b006a533a4803b02a7457470e729f",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"pytorch_model.bin: 0%| | 0.00/438M [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Parameter 'function'=<function fine_tune_model.<locals>.<lambda> at 0x7352185dfb60> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only shown once. Subsequent hashing failures won't be shown.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "a2667b3511cc46a9ace0dbbb73716197",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Map: 0%| | 0/1584 [00:00<?, ? examples/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
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|
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"\n",
|
||
"============================================================\n",
|
||
"Fine-tuning: DeBERTa-v3\n",
|
||
"Model: microsoft/deberta-v3-small\n",
|
||
"============================================================\n"
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" [297/297 00:13, Epoch 3/3]\n",
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" </div>\n",
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" <thead>\n",
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" <th>Epoch</th>\n",
|
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" <th>Training Loss</th>\n",
|
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" <th>Validation Loss</th>\n",
|
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" <th>Accuracy</th>\n",
|
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" <th>F1</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>0.470200</td>\n",
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" <td>0.270436</td>\n",
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" <td>0.855882</td>\n",
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" <td>0.587523</td>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>0.136700</td>\n",
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" <td>0.139408</td>\n",
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" <td>0.964706</td>\n",
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" <td>0.954859</td>\n",
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" <tr>\n",
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" <td>3</td>\n",
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" <td>0.058500</td>\n",
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" <td>0.074953</td>\n",
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" <td>0.985294</td>\n",
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" <td>0.976153</td>\n",
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" </tr>\n",
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"output_type": "stream",
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"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fine-tuning: ModernBERT\n",
|
||
"Model: answerdotai/ModernBERT-base\n",
|
||
"============================================================\n"
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|
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"Some weights of ModernBertForSequenceClassification were not initialized from the model checkpoint at answerdotai/ModernBERT-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
|
||
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
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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/html": [
|
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"\n",
|
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" <div>\n",
|
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" \n",
|
||
" <progress value='297' max='297' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [297/297 00:16, 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",
|
||
" <th>Accuracy</th>\n",
|
||
" <th>F1</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0.263200</td>\n",
|
||
" <td>0.141941</td>\n",
|
||
" <td>0.961765</td>\n",
|
||
" <td>0.950833</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0.111300</td>\n",
|
||
" <td>0.116384</td>\n",
|
||
" <td>0.979412</td>\n",
|
||
" <td>0.970601</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>3</td>\n",
|
||
" <td>0.015800</td>\n",
|
||
" <td>0.086586</td>\n",
|
||
" <td>0.991176</td>\n",
|
||
" <td>0.982984</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
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"</table><p>"
|
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],
|
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"text/plain": [
|
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"<IPython.core.display.HTML object>"
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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/html": [],
|
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"text/plain": [
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"<IPython.core.display.HTML object>"
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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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],
|
||
"source": [
|
||
"results = {name: fine_tune_model(name, path, dataset) for name, path in MODELS.items()}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "621d72eb",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004782,
|
||
"end_time": "2026-06-13T02:47:07.197319+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:47:07.192537+00:00",
|
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"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5. Results Comparison"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "1469d6c9",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:47:07.206157Z",
|
||
"iopub.status.busy": "2026-06-13T02:47:07.205858Z",
|
||
"iopub.status.idle": "2026-06-13T02:47:07.209146Z",
|
||
"shell.execute_reply": "2026-06-13T02:47:07.208761Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.008112,
|
||
"end_time": "2026-06-13T02:47:07.209415+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:47:07.201303+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div><style>\n",
|
||
".dataframe > thead > tr,\n",
|
||
".dataframe > tbody > tr {\n",
|
||
" text-align: right;\n",
|
||
" white-space: pre-wrap;\n",
|
||
"}\n",
|
||
"</style>\n",
|
||
"<small>shape: (3, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Model</th><th>Accuracy</th><th>F1 (macro)</th><th>Parameters</th><th>Train Time</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>"FinBERT"</td><td>"97.4%"</td><td>"0.958"</td><td>"109.5M"</td><td>"13s"</td></tr><tr><td>"DeBERTa-v3"</td><td>"95.3%"</td><td>"0.939"</td><td>"141.8M"</td><td>"14s"</td></tr><tr><td>"ModernBERT"</td><td>"95.9%"</td><td>"0.953"</td><td>"149.6M"</td><td>"17s"</td></tr></tbody></table></div>"
|
||
],
|
||
"text/plain": [
|
||
"shape: (3, 5)\n",
|
||
"┌────────────┬──────────┬────────────┬────────────┬────────────┐\n",
|
||
"│ Model ┆ Accuracy ┆ F1 (macro) ┆ Parameters ┆ Train Time │\n",
|
||
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
|
||
"│ str ┆ str ┆ str ┆ str ┆ str │\n",
|
||
"╞════════════╪══════════╪════════════╪════════════╪════════════╡\n",
|
||
"│ FinBERT ┆ 97.4% ┆ 0.958 ┆ 109.5M ┆ 13s │\n",
|
||
"│ DeBERTa-v3 ┆ 95.3% ┆ 0.939 ┆ 141.8M ┆ 14s │\n",
|
||
"│ ModernBERT ┆ 95.9% ┆ 0.953 ┆ 149.6M ┆ 17s │\n",
|
||
"└────────────┴──────────┴────────────┴────────────┴────────────┘"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Summary table\n",
|
||
"summary_df = pl.DataFrame(\n",
|
||
" [\n",
|
||
" {\n",
|
||
" \"Model\": r[\"model_name\"],\n",
|
||
" \"Accuracy\": f\"{r['accuracy']:.1%}\",\n",
|
||
" \"F1 (macro)\": f\"{r['f1']:.3f}\",\n",
|
||
" \"Parameters\": f\"{r['num_params'] / 1e6:.1f}M\",\n",
|
||
" \"Train Time\": f\"{r['train_time']:.0f}s\",\n",
|
||
" }\n",
|
||
" for r in results.values()\n",
|
||
" ]\n",
|
||
")\n",
|
||
"\n",
|
||
"summary_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "d4b7bf40",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:47:07.218041Z",
|
||
"iopub.status.busy": "2026-06-13T02:47:07.217967Z",
|
||
"iopub.status.idle": "2026-06-13T02:47:07.353136Z",
|
||
"shell.execute_reply": "2026-06-13T02:47:07.352665Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.139902,
|
||
"end_time": "2026-06-13T02:47:07.353395+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:47:07.213493+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualization: Performance comparison\n",
|
||
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
|
||
"\n",
|
||
"# Accuracy/F1 comparison\n",
|
||
"ax = axes[0]\n",
|
||
"models = list(results.keys())\n",
|
||
"accuracies = [results[m][\"accuracy\"] for m in models]\n",
|
||
"f1_scores = [results[m][\"f1\"] for m in models]\n",
|
||
"\n",
|
||
"x = np.arange(len(models))\n",
|
||
"width = 0.35\n",
|
||
"\n",
|
||
"ax.bar(x - width / 2, accuracies, width, label=\"Accuracy\", color=\"#0a1628\")\n",
|
||
"ax.bar(x + width / 2, f1_scores, width, label=\"F1 (macro)\", color=\"#D4A84B\")\n",
|
||
"\n",
|
||
"ax.set_ylabel(\"Score\")\n",
|
||
"ax.set_title(\"Fine-Tuned Model Performance on Financial PhraseBank\")\n",
|
||
"ax.set_xticks(x)\n",
|
||
"ax.set_xticklabels(models)\n",
|
||
"ax.legend()\n",
|
||
"ax.set_ylim(0, 1)\n",
|
||
"\n",
|
||
"# Training time comparison\n",
|
||
"ax = axes[1]\n",
|
||
"times = [results[m][\"train_time\"] for m in models]\n",
|
||
"bars = ax.bar(models, times, color=\"#1a2d4a\")\n",
|
||
"ax.set_ylabel(\"Training Time (seconds)\")\n",
|
||
"ax.set_title(\"Training Time on RTX 3090\")\n",
|
||
"\n",
|
||
"for bar in bars:\n",
|
||
" height = bar.get_height()\n",
|
||
" ax.annotate(\n",
|
||
" f\"{height:.0f}s\",\n",
|
||
" xy=(bar.get_x() + bar.get_width() / 2, height),\n",
|
||
" xytext=(0, 3),\n",
|
||
" textcoords=\"offset points\",\n",
|
||
" ha=\"center\",\n",
|
||
" va=\"bottom\",\n",
|
||
" )\n",
|
||
"\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "0f7920d9",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:47:07.362721Z",
|
||
"iopub.status.busy": "2026-06-13T02:47:07.362575Z",
|
||
"iopub.status.idle": "2026-06-13T02:47:07.833784Z",
|
||
"shell.execute_reply": "2026-06-13T02:47:07.833242Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.476495,
|
||
"end_time": "2026-06-13T02:47:07.834305+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:47:07.357810+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x400 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Confusion matrices for each model\n",
|
||
"n_models = len(results)\n",
|
||
"fig, axes = plt.subplots(1, n_models, figsize=(5 * n_models, 4))\n",
|
||
"fig.suptitle(\"Fine-Tuned Model Confusion Matrices\", fontsize=12, y=1.02)\n",
|
||
"\n",
|
||
"labels = [\"negative\", \"neutral\", \"positive\"]\n",
|
||
"\n",
|
||
"for ax, (name, r) in zip(axes, results.items(), strict=False):\n",
|
||
" cm = confusion_matrix(r[\"y_true\"], r[\"y_pred\"])\n",
|
||
" sns.heatmap(\n",
|
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" cm,\n",
|
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" annot=True,\n",
|
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" fmt=\"d\",\n",
|
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" cmap=\"Blues\",\n",
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" xticklabels=labels,\n",
|
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" yticklabels=labels,\n",
|
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" ax=ax,\n",
|
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" )\n",
|
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" ax.set_title(f\"{name}\\nAcc: {r['accuracy']:.1%}\")\n",
|
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" ax.set_xlabel(\"Predicted\")\n",
|
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" ax.set_ylabel(\"Actual\")\n",
|
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "71e74688",
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},
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"source": [
|
||
"## Key Takeaways\n",
|
||
"\n",
|
||
"### Measured results\n",
|
||
"- FinBERT fine-tuned: accuracy 97.4%, F1 (macro) 0.958\n",
|
||
"- DeBERTa-v3 fine-tuned: accuracy 95.3%, F1 (macro) 0.939\n",
|
||
"- ModernBERT fine-tuned: accuracy 95.9%, F1 (macro) 0.953\n",
|
||
"- FinBERT pre-trained vs fine-tuned on this PhraseBank subset: 93.2% (NB03) → 97.4%\n",
|
||
"- Training time per model on RTX 3090: 13–17s at batch size 16\n",
|
||
"\n",
|
||
"### Fine-tuning effect on FinBERT\n",
|
||
"Fine-tuning on the ~1,600-sentence training split changes FinBERT's accuracy on\n",
|
||
"the PhraseBank test set from 93.2% (the pre-trained FinBERT-tone checkpoint\n",
|
||
"measured in NB03 on the same `sentences_allagree` split) to 97.4%. The\n",
|
||
"pre-trained checkpoint already performs well — PhraseBank is in-domain for it —\n",
|
||
"and the fine-tuning step recovers most of the remaining headroom.\n",
|
||
"\n",
|
||
"### Patterns in the run\n",
|
||
"- FinBERT has both the highest accuracy (97.4%) and the highest macro F1\n",
|
||
" (0.958). Differences between the three fine-tuned models are within a few\n",
|
||
" percentage points on this PhraseBank test set.\n",
|
||
"- DeBERTa-v3 finishes at 95.3% accuracy / 0.939 F1; its disentangled-attention\n",
|
||
" design targets longer contexts that this single-sentence task does not\n",
|
||
" exercise.\n",
|
||
"- All three models converge within 2-3 epochs under the early-stopping rule.\n",
|
||
"\n",
|
||
"### Scope of these numbers\n",
|
||
"- These results are for a stratified random split of Financial PhraseBank\n",
|
||
" (\"sentences_allagree\") with seed 42. PhraseBank is sentence-level financial\n",
|
||
" news; the notebook does not measure cross-domain transfer to other text\n",
|
||
" sources (compare NB06 for the cross-dataset evaluation on FinMarBa) nor\n",
|
||
" downstream alpha (NB07, NB08, NB09).\n",
|
||
"- The Hugging Face Trainer API used here handles the training loop, evaluation\n",
|
||
" schedule, and checkpointing for all three models."
|
||
]
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},
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},
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||
{
|
||
"name": "stdout",
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||
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|
||
"text": [
|
||
"\n",
|
||
"FinBERT:\n",
|
||
" Accuracy: 97.4%\n",
|
||
" F1 (macro): 0.958\n",
|
||
" Parameters: 109.5M\n",
|
||
" Train time: 13s\n",
|
||
"\n",
|
||
"DeBERTa-v3:\n",
|
||
" Accuracy: 95.3%\n",
|
||
" F1 (macro): 0.939\n",
|
||
" Parameters: 141.8M\n",
|
||
" Train time: 14s\n",
|
||
"\n",
|
||
"ModernBERT:\n",
|
||
" Accuracy: 95.9%\n",
|
||
" F1 (macro): 0.953\n",
|
||
" Parameters: 149.6M\n",
|
||
" Train time: 17s\n"
|
||
]
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||
}
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],
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"source": [
|
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"# Structured per-model summary for chapter cross-reference\n",
|
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"for r in results.values():\n",
|
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" print(f\"\\n{r['model_name']}:\")\n",
|
||
" print(f\" Accuracy: {r['accuracy']:.1%}\")\n",
|
||
" print(f\" F1 (macro): {r['f1']:.3f}\")\n",
|
||
" print(f\" Parameters: {r['num_params'] / 1e6:.1f}M\")\n",
|
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