wehub-resource-sync
4528 lines
172 KiB
Plaintext
4528 lines
172 KiB
Plaintext
{
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"source": [
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"# FinBERT Cross-Dataset Evaluation: Distribution Shift in Practice\n",
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"\n",
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"**Chapter 10: From Text to Features - The Transformer Breakthrough**\n",
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"**Section Reference**: See Section 10.4 for Transformers and distribution shift\n",
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"\n",
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"**Docker image**: `ml4t-gpu`\n",
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"\n",
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"> **GPU recommended**: ProsusAI/finbert is run as inference over ~8K headlines.\n",
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"> A GPU brings the notebook to ~1 minute end-to-end; on CPU it takes 5–10×\n",
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"> longer. For GPU acceleration:\n",
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"> ```bash\n",
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"> docker compose run --rm ml4t-gpu python 10_text_feature_engineering/06_finbert_cross_dataset.py\n",
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"> ```\n",
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"\n",
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"## Purpose\n",
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"This notebook demonstrates distribution shift by evaluating ProsusAI/finbert\n",
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"(trained on Financial PhraseBank) on the FinMarBa dataset. Both datasets use\n",
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"the same labels (positive/negative/neutral), but differ in text domain:\n",
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"- Financial PhraseBank: Carefully curated financial news sentences\n",
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"- FinMarBa: Market-based sentiment from financial headlines\n",
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"\n",
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"This illustrates why same-label doesn't mean same-distribution.\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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"- Understand the importance of out-of-sample evaluation\n",
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"- Compare in-domain vs cross-domain model performance\n",
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"- Interpret performance gaps as evidence of overfitting to training data\n",
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"- Appreciate why domain-specific models may not generalize perfectly\n",
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"\n",
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"## Prerequisites\n",
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"- Section 10.4 of the chapter (Transformers, distribution shift).\n",
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"- HuggingFace `datasets` cache able to fetch `baptle/financial_headlines_market_based`\n",
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" (~8K rows, downloaded on first run).\n",
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"\n",
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"## Related Notebooks\n",
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"- `03_sentiment_evolution.py` — in-domain comparison on Financial PhraseBank.\n",
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"- `04_bert_finetuning.py` — the fine-tuned baseline used for the in-domain reference value."
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]
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},
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{
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"cell_type": "code",
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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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"\"\"\"FinBERT Cross-Dataset Evaluation — measure distribution shift between Financial PhraseBank and FinMarBa.\"\"\"\n",
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"\n",
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"# 1. Setup and Configuration\n",
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"import os\n",
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"import warnings\n",
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"\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 torch\n",
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"from datasets import load_dataset\n",
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"from sklearn.metrics import accuracy_score, confusion_matrix, f1_score\n",
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"from transformers import pipeline\n",
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"\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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"# Polars display configuration\n",
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"\n",
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"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\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": "a6ea4d03",
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"metadata": {
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"status": "completed"
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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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"SAMPLE_SIZE = 10000"
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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": "8b8275ee",
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"metadata": {
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"execution": {
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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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"Using device: GPU\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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"set_global_seeds(SEED)\n",
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"\n",
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"device = 0 if torch.cuda.is_available() else -1\n",
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"print(f\"Using device: {'GPU' if device == 0 else 'CPU'}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "43301c1b",
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"status": "completed"
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}
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},
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"source": [
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"## 2. Load FinMarBa Dataset\n",
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"\n",
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"FinMarBa (Financial Market-Based) is a 2025 dataset with 61,252 financial\n",
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"headlines labeled by actual market reactions, not human annotators. This\n",
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"provides objective labels and true out-of-sample evaluation for FinBERT.\n",
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"\n",
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"**Key differences from Financial PhraseBank:**\n",
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"- Market-based labels (objective) vs human annotations (subjective)\n",
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"- ~61K samples vs ~4.8K samples\n",
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"- FinBERT (ProsusAI/finbert) was NOT trained on this data"
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]
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},
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{
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"cell_type": "code",
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"status": "completed"
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"data": {
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"version_minor": 0
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},
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"text/plain": [
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"README.md: 0%| | 0.00/425 [00:00<?, ?B/s]"
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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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"text/plain": [
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"to_hg_data.csv: 0.00B [00:00, ?B/s]"
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]
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"metadata": {},
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"version_minor": 0
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},
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"text/plain": [
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"Generating train split: 0%| | 0/8142 [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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"name": "stdout",
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"output_type": "stream",
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"text": [
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||
"Loaded FinMarBa train split (sampled 10,000 samples)\n",
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||
"Loaded FinMarBa dataset: 8,142 samples\n",
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||
"\n",
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||
"Label distribution:\n",
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||
"shape: (3, 2)\n",
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||
"┌───────┬──────┐\n",
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||
"│ label ┆ len │\n",
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"│ --- ┆ --- │\n",
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"│ i64 ┆ u32 │\n",
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||
"╞═══════╪══════╡\n",
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"│ 0 ┆ 2477 │\n",
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"│ 1 ┆ 2154 │\n",
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"│ 2 ┆ 3511 │\n",
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||
"└───────┴──────┘\n"
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||
]
|
||
}
|
||
],
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||
"source": [
|
||
"def load_finmarba_dataset(sample_size: int = 10000) -> pl.DataFrame:\n",
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" \"\"\"Load FinMarBa dataset from Hugging Face.\n",
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"\n",
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" Args:\n",
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" sample_size: Number of samples to use (dataset has 60K+ samples).\n",
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" Set to 0 for all samples.\n",
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"\n",
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" Raises:\n",
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" RuntimeError: If dataset cannot be loaded (network error, etc.)\n",
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" \"\"\"\n",
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" try:\n",
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" # Try 'test' split first, fall back to 'train' (dataset structure changed)\n",
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" try:\n",
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" ds = load_dataset(\n",
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" \"baptle/financial_headlines_market_based\",\n",
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" split=\"test\",\n",
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" )\n",
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" print(\"Loaded FinMarBa test split\")\n",
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" except ValueError:\n",
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" # Dataset may only have 'train' split now\n",
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" split_str = f\"train[:{sample_size}]\" if sample_size > 0 else \"train\"\n",
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" ds = load_dataset(\n",
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" \"baptle/financial_headlines_market_based\",\n",
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" split=split_str,\n",
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" )\n",
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" print(f\"Loaded FinMarBa train split (sampled {sample_size:,} samples)\")\n",
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"\n",
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" df = pl.DataFrame(ds.to_pandas())\n",
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"\n",
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" # Handle schema changes: newer versions have different column names\n",
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" # Map 'Title' -> 'text' and 'Global Sentiment' -> 'label'\n",
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" if \"Title\" in df.columns and \"text\" not in df.columns:\n",
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" df = df.rename({\"Title\": \"text\"})\n",
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" if \"Global Sentiment\" in df.columns and \"label\" not in df.columns:\n",
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" # Global Sentiment is -1/0/1, map to 0/1/2 for consistency\n",
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" # -1 (negative) -> 0, 0 (neutral) -> 1, 1 (positive) -> 2\n",
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" df = df.with_columns((pl.col(\"Global Sentiment\") + 1).cast(pl.Int64).alias(\"label\"))\n",
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"\n",
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" print(f\"Loaded FinMarBa dataset: {len(df):,} samples\")\n",
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" return df\n",
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" except Exception as e:\n",
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" raise RuntimeError(\n",
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" f\"\\n\"\n",
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" f\"{'=' * 70}\\n\"\n",
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" f\"DATASET NOT AVAILABLE: FinMarBa\\n\"\n",
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" f\"{'=' * 70}\\n\"\n",
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" f\"\\n\"\n",
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" f\"Error: {e}\\n\"\n",
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" f\"\\n\"\n",
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" f\"This notebook requires the FinMarBa dataset from Hugging Face:\\n\"\n",
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" f\" baptle/financial_headlines_market_based\\n\"\n",
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" f\"\\n\"\n",
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" f\"Possible causes:\\n\"\n",
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" f\" - No internet connection\\n\"\n",
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" f\" - HuggingFace servers unavailable\\n\"\n",
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" f\" - Dataset has been moved or renamed\\n\"\n",
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" f\"\\n\"\n",
|
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" f\"To run this notebook, ensure you have internet access.\\n\"\n",
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" f\"{'=' * 70}\\n\"\n",
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" ) from e\n",
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"\n",
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"\n",
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"df = load_finmarba_dataset(sample_size=SAMPLE_SIZE)\n",
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"print(\"\\nLabel distribution:\")\n",
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"print(df.group_by(\"label\").len().sort(\"label\"))\n",
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"\n",
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"# Map labels to text\n",
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"LABEL_MAP = {0: \"negative\", 1: \"neutral\", 2: \"positive\"}"
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||
]
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||
},
|
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{
|
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"cell_type": "markdown",
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"id": "4c580779",
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"metadata": {
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"papermill": {
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"duration": 0.003142,
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"status": "completed"
|
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}
|
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},
|
||
"source": [
|
||
"## 3. Load FinBERT Model\n",
|
||
"\n",
|
||
"We use ProsusAI/finbert, which was trained on Financial PhraseBank. This makes\n",
|
||
"the comparison meaningful: in-domain performance (Financial PhraseBank) vs\n",
|
||
"cross-domain performance (FinMarBa) with the SAME labels but DIFFERENT text\n",
|
||
"distributions."
|
||
]
|
||
},
|
||
{
|
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"cell_type": "code",
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"execution_count": 5,
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"id": "4171c482",
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"shell.execute_reply": "2026-06-13T02:48:00.428396Z"
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"papermill": {
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"duration": 10.808092,
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"status": "completed"
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}
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},
|
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"outputs": [
|
||
{
|
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"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Loading ProsusAI/finbert...\n"
|
||
]
|
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},
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{
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"data": {
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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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"pytorch_model.bin: 0%| | 0.00/438M [00:00<?, ?B/s]"
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]
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"version_minor": 0
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"text/plain": [
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"model.safetensors: 0%| | 0.00/438M [00:00<?, ?B/s]"
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]
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"metadata": {},
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"output_type": "display_data"
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"data": {
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"vocab.txt: 0.00B [00:00, ?B/s]"
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]
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"metadata": {},
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{
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"data": {
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"model_id": "c61abe69d1ea47ce967c21fbed781555",
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"version_minor": 0
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"special_tokens_map.json: 0%| | 0.00/112 [00:00<?, ?B/s]"
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"metadata": {},
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"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Device set to use cuda:0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load FinBERT pipeline\n",
|
||
"model_name = \"ProsusAI/finbert\"\n",
|
||
"print(f\"Loading {model_name}...\")\n",
|
||
"\n",
|
||
"classifier = pipeline(\n",
|
||
" \"sentiment-analysis\",\n",
|
||
" model=model_name,\n",
|
||
" tokenizer=model_name,\n",
|
||
" device=device,\n",
|
||
" truncation=True,\n",
|
||
" max_length=512,\n",
|
||
")\n",
|
||
"\n",
|
||
"# ProsusAI/finbert label mapping (lowercase labels)\n",
|
||
"FINBERT_LABEL_MAP = {\"negative\": 0, \"neutral\": 1, \"positive\": 2}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "4763b4b7",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:48:00.440723Z",
|
||
"iopub.status.busy": "2026-06-13T02:48:00.440628Z",
|
||
"iopub.status.idle": "2026-06-13T02:48:00.443020Z",
|
||
"shell.execute_reply": "2026-06-13T02:48:00.442621Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.007701,
|
||
"end_time": "2026-06-13T02:48:00.443280+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:00.435579+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def get_finbert_predictions(texts: list[str], batch_size: int = 32) -> np.ndarray:\n",
|
||
" \"\"\"Get predictions from FinBERT.\"\"\"\n",
|
||
" predictions = []\n",
|
||
" for i in range(0, len(texts), batch_size):\n",
|
||
" batch = texts[i : i + batch_size]\n",
|
||
" results = classifier(batch)\n",
|
||
" for r in results:\n",
|
||
" predictions.append(FINBERT_LABEL_MAP[r[\"label\"]])\n",
|
||
" return np.array(predictions)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b4a5bc84",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00347,
|
||
"end_time": "2026-06-13T02:48:00.450176+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:00.446706+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 4. Evaluate FinBERT on FinMarBa"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "ed6ef439",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:48:00.457666Z",
|
||
"iopub.status.busy": "2026-06-13T02:48:00.457580Z",
|
||
"iopub.status.idle": "2026-06-13T02:48:20.426596Z",
|
||
"shell.execute_reply": "2026-06-13T02:48:20.426155Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 19.973432,
|
||
"end_time": "2026-06-13T02:48:20.427015+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:00.453583+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Running FinBERT on 8142 samples...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"FinBERT on FinMarBa (OUT-OF-SAMPLE)\n",
|
||
"============================================================\n",
|
||
"Accuracy: 49.4%\n",
|
||
"F1 (macro): 0.428\n",
|
||
"============================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Get the text column (may be 'headline' or 'text' depending on dataset)\n",
|
||
"text_col = \"headline\" if \"headline\" in df.columns else \"text\"\n",
|
||
"texts = df[text_col].to_list()\n",
|
||
"true_labels = df[\"label\"].to_numpy()\n",
|
||
"\n",
|
||
"print(f\"Running FinBERT on {len(texts)} samples...\")\n",
|
||
"predictions = get_finbert_predictions(texts)\n",
|
||
"\n",
|
||
"# Compute metrics\n",
|
||
"accuracy = accuracy_score(true_labels, predictions)\n",
|
||
"f1 = f1_score(true_labels, predictions, average=\"macro\")\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\" * 60)\n",
|
||
"print(\"FinBERT on FinMarBa (OUT-OF-SAMPLE)\")\n",
|
||
"print(\"=\" * 60)\n",
|
||
"print(f\"Accuracy: {accuracy:.1%}\")\n",
|
||
"print(f\"F1 (macro): {f1:.3f}\")\n",
|
||
"print(\"=\" * 60)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "16b01913",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00341,
|
||
"end_time": "2026-06-13T02:48:20.434683+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:20.431273+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 5. Cross-Domain Measurement on FinMarBa\n",
|
||
"\n",
|
||
"This notebook measures **one** number: ProsusAI/finbert's zero-shot\n",
|
||
"accuracy / macro-F1 on FinMarBa. We report it as a cross-domain\n",
|
||
"generalization signal — the model was fine-tuned on Financial PhraseBank\n",
|
||
"(analyst reports) and is now evaluated on FinMarBa (Twitter/news headlines)\n",
|
||
"without further training.\n",
|
||
"\n",
|
||
"Araci (2019) reports ~87% accuracy / ~0.85 macro F1 for ProsusAI/finbert\n",
|
||
"on the held-out PhraseBank test split. This notebook does not reproduce\n",
|
||
"that in-domain measurement — running the same pipeline on PhraseBank\n",
|
||
"would require its own evaluation block. The cross-domain accuracy on\n",
|
||
"FinMarBa is the only measurement made here."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "bdf3f148",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:48:20.442357Z",
|
||
"iopub.status.busy": "2026-06-13T02:48:20.442240Z",
|
||
"iopub.status.idle": "2026-06-13T02:48:20.445048Z",
|
||
"shell.execute_reply": "2026-06-13T02:48:20.444554Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.007481,
|
||
"end_time": "2026-06-13T02:48:20.445545+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:20.438064+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"======================================================================\n",
|
||
"FinMarBa cross-domain accuracy for ProsusAI/finbert (zero-shot)\n",
|
||
"======================================================================\n",
|
||
" Accuracy: 49.4%\n",
|
||
" F1 (macro): 0.428\n",
|
||
" Test samples: 8142\n",
|
||
"======================================================================\n",
|
||
"Reference for context (not measured here): Araci (2019) reports ~87% accuracy / ~0.85 macro F1 on Financial PhraseBank held-out test.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"\\n\" + \"=\" * 70)\n",
|
||
"print(\"FinMarBa cross-domain accuracy for ProsusAI/finbert (zero-shot)\")\n",
|
||
"print(\"=\" * 70)\n",
|
||
"print(f\" Accuracy: {accuracy:.1%}\")\n",
|
||
"print(f\" F1 (macro): {f1:.3f}\")\n",
|
||
"print(f\" Test samples: {len(true_labels)}\")\n",
|
||
"print(\"=\" * 70)\n",
|
||
"print(\n",
|
||
" \"Reference for context (not measured here): Araci (2019) reports ~87% \"\n",
|
||
" \"accuracy / ~0.85 macro F1 on Financial PhraseBank held-out test.\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "aef38a71",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:48:20.453556Z",
|
||
"iopub.status.busy": "2026-06-13T02:48:20.453398Z",
|
||
"iopub.status.idle": "2026-06-13T02:48:20.527451Z",
|
||
"shell.execute_reply": "2026-06-13T02:48:20.526909Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.078885,
|
||
"end_time": "2026-06-13T02:48:20.528080+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:20.449195+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 600x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Confusion matrix for FinMarBa\n",
|
||
"fig, ax = plt.subplots(figsize=(6, 5))\n",
|
||
"labels = [\"negative\", \"neutral\", \"positive\"]\n",
|
||
"\n",
|
||
"cm = confusion_matrix(true_labels, predictions)\n",
|
||
"im = ax.imshow(cm, cmap=\"Blues\")\n",
|
||
"\n",
|
||
"# Add annotations\n",
|
||
"for i in range(3):\n",
|
||
" for j in range(3):\n",
|
||
" ax.text(j, i, str(cm[i, j]), ha=\"center\", va=\"center\", fontsize=12)\n",
|
||
"\n",
|
||
"ax.set_xticks(range(3))\n",
|
||
"ax.set_yticks(range(3))\n",
|
||
"ax.set_xticklabels(labels)\n",
|
||
"ax.set_yticklabels(labels)\n",
|
||
"ax.set_xlabel(\"Predicted\")\n",
|
||
"ax.set_ylabel(\"Actual\")\n",
|
||
"ax.set_title(f\"ProsusAI/finbert Cross-Dataset Evaluation\\nFinMarBa Accuracy: {accuracy:.1%}\")\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "12bda8d1",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003627,
|
||
"end_time": "2026-06-13T02:48:20.535611+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:20.531984+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Key Takeaways\n",
|
||
"\n",
|
||
"### Distribution Shift Explained\n",
|
||
"- **Same labels**: Both datasets use positive/negative/neutral\n",
|
||
"- **Different distributions**: Financial PhraseBank has carefully curated sentences;\n",
|
||
" FinMarBa has market-reaction-based labels on headlines\n",
|
||
"- **Performance gap**: The difference reveals distribution shift, NOT label mismatch\n",
|
||
"\n",
|
||
"### Why This Matters\n",
|
||
"1. **Same labels ≠ same distribution**: Text style, length, and vocabulary differ\n",
|
||
"2. **Domain adaptation limits**: Models trained on one corpus may not generalize\n",
|
||
"3. **Market-based labels**: FinMarBa labels reflect actual market reactions, not\n",
|
||
" human annotations—a different notion of \"sentiment\"\n",
|
||
"\n",
|
||
"### Implications for Practitioners\n",
|
||
"- Always evaluate on held-out data from different sources when possible\n",
|
||
"- Distribution shift is common when applying pre-trained models to new data\n",
|
||
"- Consider fine-tuning on your target domain for best results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "eed93240",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T02:48:20.543747Z",
|
||
"iopub.status.busy": "2026-06-13T02:48:20.543625Z",
|
||
"iopub.status.idle": "2026-06-13T02:48:20.547624Z",
|
||
"shell.execute_reply": "2026-06-13T02:48:20.547140Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.008921,
|
||
"end_time": "2026-06-13T02:48:20.548160+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T02:48:20.539239+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"======================================================================\n",
|
||
"KEY INSIGHTS\n",
|
||
"======================================================================\n",
|
||
"\n",
|
||
"Dataset: FinMarBa\n",
|
||
"Samples: 8142\n",
|
||
"\n",
|
||
"FinBERT Performance (ProsusAI/finbert, zero-shot):\n",
|
||
" Accuracy: 49.4%\n",
|
||
" F1 (macro): 0.428\n",
|
||
"\n",
|
||
"Reference for context (not measured in this notebook): Araci (2019) reports ~87% accuracy / ~0.85 macro F1 on Financial PhraseBank held-out test.\n",
|
||
"\n",
|
||
"Results saved to: 10_text_feature_engineering/output/finbert_cross_dataset/results.md\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Structured output\n",
|
||
"print(\"=\" * 70)\n",
|
||
"print(\"KEY INSIGHTS\")\n",
|
||
"print(\"=\" * 70)\n",
|
||
"print(\"\\nDataset: FinMarBa\")\n",
|
||
"print(f\"Samples: {len(df)}\")\n",
|
||
"print(\"\\nFinBERT Performance (ProsusAI/finbert, zero-shot):\")\n",
|
||
"print(f\" Accuracy: {accuracy:.1%}\")\n",
|
||
"print(f\" F1 (macro): {f1:.3f}\")\n",
|
||
"print(\n",
|
||
" \"\\nReference for context (not measured in this notebook): \"\n",
|
||
" \"Araci (2019) reports ~87% accuracy / ~0.85 macro F1 on Financial \"\n",
|
||
" \"PhraseBank held-out test.\"\n",
|
||
")\n",
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"\n",
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"# Save results\n",
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"output_dir = get_chapter_dir(10) / \"output\" / \"finbert_cross_dataset\"\n",
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"results_file = output_dir / \"results.md\"\n",
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"with open(results_file, \"w\") as f:\n",
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" f.write(\"# FinBERT Cross-Dataset Evaluation Results\\n\\n\")\n",
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" f.write(\"## Performance on FinMarBa (Out-of-Sample)\\n\\n\")\n",
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" f.write(f\"- **Accuracy**: {accuracy:.1%}\\n\")\n",
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" f.write(f\"- **F1 (macro)**: {f1:.3f}\\n\")\n",
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" f.write(f\"- **Samples**: {len(df):,}\\n\\n\")\n",
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" \"ProsusAI/finbert on the held-out Financial PhraseBank test split.\\n\"\n",
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" )\n",
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"\n",
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]
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