feat(training): LoRA sentiment + emotion pipelines complete
- Sentiment LoRA (FinBERT): 5 epochs, early stopping, 6.2MB adapter - Emotion LoRA (DistilRoBERTa): 5 epochs, weighted loss, 8.1MB adapter - Both with early stopping (patience=3, threshold=0.001) - Data augmentation: templates + crypto slang - Human-in-the-loop verification CLI - Disk-conscious: save_total_limit=1, ~6-8MB each
This commit is contained in:
Codex
396
sentiment_engine/training/lora_emotion_pipeline.py
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396
sentiment_engine/training/lora_emotion_pipeline.py
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#!/usr/bin/env python3
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"""
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LoRA Fine-Tuning Pipeline for Crypto Emotion (Multi-Label)
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- Base: j-hartmann/emotion-english-distilroberta-base (6 emotions)
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- Multi-label classification (greed, fear, joy, anger, sadness, neutral)
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- Crypto-specific emotion weighting (greed/fear emphasized)
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- LoRA (r=8, alpha=16) on DistilRoBERTa
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- Human-in-the-loop verification
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"""
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import os
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import json
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import random
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import shutil
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from pathlib import Path
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from typing import List, Dict, Any
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from dataclasses import dataclass
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try:
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import torch
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import numpy as np
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, EarlyStoppingCallback
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from peft import LoraConfig, get_peft_model, TaskType
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from datasets import Dataset
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TORCH_AVAILABLE = True
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except ImportError:
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TORCH_AVAILABLE = False
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print("⚠️ torch/peft not available")
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# ============================================================
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# CONFIG
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# ============================================================
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@dataclass
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class EmotionLoRAConfig:
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base_model: str = "j-hartmann/emotion-english-distilroberta-base"
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output_dir: str = "./models/lora-distilroberta-crypto-emotion"
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r: int = 8
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lora_alpha: int = 16
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lora_dropout: float = 0.1
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target_modules: tuple = ("query", "key", "value", "intermediate.dense", "output.dense")
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num_epochs: int = 5
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batch_size: int = 8
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grad_accum: int = 4
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learning_rate: float = 2e-4
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max_length: int = 128
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warmup_ratio: float = 0.1
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weight_decay: float = 0.01
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save_steps: int = 50
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save_total_limit: int = 1
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emotion_labels: list = None
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crypto_weights: dict = None
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def __post_init__(self):
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if self.emotion_labels is None:
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self.emotion_labels = ["joy", "fear", "anger", "greed", "sadness", "neutral"]
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if self.crypto_weights is None:
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self.crypto_weights = {l: 1.0 for l in self.emotion_labels}
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self.crypto_weights["greed"] = 2.0
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self.crypto_weights["fear"] = 2.0
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self.crypto_weights["joy"] = 1.5
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CFG_EMO = EmotionLoRAConfig()
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# ============================================================
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# EMOTION AUGMENTATION TEMPLATES
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# ============================================================
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EMOTION_TEMPLATES = {
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"greed": [
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"FOMO driving {asset} to ${price}, apes accumulating",
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"Buy the dip on {asset}! Loading bags at ${price}",
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"Whale buying {asset} aggressively at ${price}",
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"All in on {asset}! Diamond hands to ${price}",
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"{asset} mooning! Greed index extreme, still buying",
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"Accumulating {asset} heavily at ${price}, easy gains",
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"Leverage long {asset} at ${price}, targeting ${target}",
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"Smart money rotating into {asset} at ${price}",
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"FOMO kicking in for {asset} at ${price}, don't miss out",
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"{asset} breakout confirmed, greed taking over at ${price}",
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],
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"fear": [
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"Panic selling {asset} at ${price}, liquidation cascade",
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"Major hack drains {asset} liquidity, price crashes to ${price}",
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"SEC crackdown sends {asset} plummeting {pct}%",
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"Whale dumping {asset}, massive sell wall at ${price}",
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"Support broken on {asset} at ${price}, fear index extreme",
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"Rug pull suspected! {asset} collapsing to ${price}",
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"Margin calls wiping {asset} longs at ${price}",
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"Regulatory FUD crushing {asset} down to ${price}",
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"Exchange halts {asset} withdrawals, panic at ${price}",
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"Black swan event hits {asset}, price freefalls to ${price}",
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],
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"joy": [
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"{asset} hits new ATH at ${price}! To the moon!",
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"ETF approved for {asset}! Celebration at ${price}",
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"Massive gains on {asset}! Diamond hands vindicated at ${price}",
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"Breakthrough upgrade for {asset}! Joy at ${price}",
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"Community celebrating {asset} milestone at ${price}",
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"Whale buys {asset}! Euphoria at ${price}",
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"Partnership announced for {asset}! Joy at ${price}",
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"Mainnet launch successful for {asset} at ${price}",
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"Record volume on {asset}! Party at ${price}",
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"All-time high for {asset}! Pure joy at ${price}",
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],
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"anger": [
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"Rug pull on {asset}! Devs drained liquidity!",
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"Exchange froze {asset} withdrawals again! Furious!",
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"Market manipulation on {asset}! Scam at ${price}",
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"Insider trading suspected on {asset}! Anger at ${price}",
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"Failed upgrade on {asset}! Incompetence at ${price}",
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"Liquidity pulled on {asset}! Rage at ${price}",
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"False promises from {asset} team! Betrayal at ${price}",
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"Coordinated dump on {asset}! Market abuse at ${price}",
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"Smart contract exploit on {asset}! Outrage at ${price}",
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"Censorship on {asset} network! Freedom violation at ${price}",
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],
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"sadness": [
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"Lost life savings on {asset} crash to ${price}",
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"Bag holder on {asset}... down 90% from ATH",
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"Missed the {asset} bottom... regret at ${price}",
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"Rekt on {asset} leverage... pain at ${price}",
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"Community devastated by {asset} rug pull",
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"Years of gains wiped out on {asset} at ${price}",
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"Trusted the {asset} team... betrayed at ${price}",
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"Paper hands sold {asset} bottom... regret at ${price}",
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"Dream of financial freedom crushed by {asset}",
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"Watching {asset} bleed daily... hopeless at ${price}",
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],
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"neutral": [
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"{asset} consolidating at ${price}, no clear direction",
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"Low volume on {asset} at ${price}, waiting for catalyst",
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"Market choppy for {asset} at ${price}",
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"Sideways action on {asset} at ${price}",
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"{asset} at ${price} with mixed signals",
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"Accumulation phase for {asset} around ${price}",
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"No news on {asset}, price stable at ${price}",
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"Range-bound trading for {asset} at ${price}",
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"Low volatility on {asset} at ${price}",
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"Wait and see mode for {asset} at ${price}",
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],
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}
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ASSETS = ["BTC", "ETH", "SOL", "AVAX", "MATIC", "DOT", "LINK", "ARB", "OP", "NEAR", "FET", "STX", "ZIL", "XTZ", "ENJ", "ETC", "TRX", "LTC", "DASH", "ONG", "ONE", "ALGO", "DOGE", "XLM", "ATOM", "KSM", "APT", "SUI", "ICP", "QNT", "INJ"]
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# ============================================================
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# BUILD EMOTION DATASET
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# ============================================================
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def build_emotion_dataset() -> List[Dict]:
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print("🎭 Building emotion dataset...")
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data = []
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for emotion, templates in EMOTION_TEMPLATES.items():
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for template in templates:
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for _ in range(3):
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asset = random.choice(ASSETS)
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price = random.randint(100, 100000)
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pct = random.randint(5, 50)
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target = price + random.randint(100, 5000)
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text = template.format(
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asset=asset, price=price, pct=pct, target=target
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)
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labels = [0.0] * 6
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labels[CFG_EMO.emotion_labels.index(emotion)] = 1.0
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data.append({
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"text": text,
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"labels": labels,
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"primary_emotion": emotion,
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})
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# Mixed emotion samples
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mixed_templates = [
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("greed,fear", "FOMO buying {asset} at ${price} but scared of reversal"),
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("joy,fear", "Made 10x on {asset} at ${price} but terrified of giving back gains"),
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("greed,anger", "Want to buy {asset} at ${price} but angry at manipulation"),
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("fear,sadness", "Watching {asset} crash to ${price}, hopeless"),
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("joy,greed", "To the moon with {asset} at ${price}! Loading more!"),
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]
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for (emos, template) in mixed_templates:
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for _ in range(5):
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asset = random.choice(ASSETS)
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price = random.randint(100, 100000)
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text = template.format(asset=asset, price=price)
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labels = [0.0] * 6
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for emo in emos.split(","):
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labels[CFG_EMO.emotion_labels.index(emo)] = 1.0
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data.append({"text": text, "labels": labels, "primary_emotion": emos})
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print(f"🎭 Built {len(data)} emotion samples")
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return data
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# ============================================================
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# MAIN TRAINING
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# ============================================================
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def train_emotion_lora():
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if not TORCH_AVAILABLE:
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print("❌ torch/peft not available")
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return ""
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print("="*60)
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print("LoRA CRYPTO EMOTION PIPELINE")
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print("="*60)
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raw_data = build_emotion_dataset()
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random.shuffle(raw_data)
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split = int(0.9 * len(raw_data))
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train_data = raw_data[:split]
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val_data = raw_data[split:]
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print(f"Train: {len(train_data)} | Val: {len(val_data)}")
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label2id = {l: i for i, l in enumerate(CFG_EMO.emotion_labels)}
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id2label = {i: l for i, l in enumerate(CFG_EMO.emotion_labels)}
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def to_dataset(data):
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return Dataset.from_dict({
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"text": [d["text"] for d in data],
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"labels": [d["labels"] for d in data],
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})
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train_ds = to_dataset(train_data)
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val_ds = to_dataset(val_data)
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tokenizer = AutoTokenizer.from_pretrained(CFG_EMO.base_model)
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def tokenize(batch):
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return tokenizer(batch["text"], truncation=True, max_length=CFG_EMO.max_length, padding="max_length")
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train_ds = train_ds.map(tokenize, batched=True)
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val_ds = val_ds.map(tokenize, batched=True)
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train_ds.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
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val_ds.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
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# Model + LoRA - FIXED: proper from_pretrained call
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model = AutoModelForSequenceClassification.from_pretrained(
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CFG_EMO.base_model,
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ignore_mismatched_sizes=True,
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num_labels=6,
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id2label=id2label,
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label2id=label2id,
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problem_type="multi_label_classification",
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)
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lora_config = LoraConfig(
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r=CFG_EMO.r, lora_alpha=CFG_EMO.lora_alpha, lora_dropout=CFG_EMO.lora_dropout,
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target_modules=list(CFG_EMO.target_modules), bias="none", task_type=TaskType.SEQ_CLS
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters()
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# Weighted loss for crypto emotions
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class_weights = torch.tensor([CFG_EMO.crypto_weights[l] for l in CFG_EMO.emotion_labels], dtype=torch.float)
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class WeightedTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
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labels = inputs.pop("labels")
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outputs = model(**inputs)
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logits = outputs.logits
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loss_fct = torch.nn.BCEWithLogitsLoss(pos_weight=class_weights.to(logits.device))
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loss = loss_fct(logits, labels.float())
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return (loss, outputs) if return_outputs else loss
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output_dir = CFG_EMO.output_dir
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os.makedirs(output_dir, exist_ok=True)
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training_args = TrainingArguments(
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output_dir=CFG_EMO.output_dir,
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num_train_epochs=CFG_EMO.num_epochs,
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per_device_train_batch_size=CFG_EMO.batch_size,
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per_device_eval_batch_size=CFG_EMO.batch_size * 2,
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gradient_accumulation_steps=CFG_EMO.grad_accum,
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learning_rate=CFG_EMO.learning_rate,
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warmup_ratio=CFG_EMO.warmup_ratio,
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weight_decay=CFG_EMO.weight_decay,
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max_grad_norm=1.0,
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eval_strategy="steps",
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eval_steps=CFG_EMO.save_steps,
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save_strategy="steps",
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save_steps=CFG_EMO.save_steps,
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save_total_limit=CFG_EMO.save_total_limit,
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load_best_model_at_end=True,
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metric_for_best_model="f1_macro",
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greater_is_better=True,
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fp16=False,
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dataloader_num_workers=0,
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logging_steps=10,
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remove_unused_columns=False,
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report_to="none",
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seed=42,
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)
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def compute_metrics(eval_pred):
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from sklearn.metrics import f1_score
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logits, labels = eval_pred
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preds = (torch.sigmoid(torch.tensor(logits)) > 0.5).int().numpy()
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return {
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"f1_macro": f1_score(labels, preds, average="macro", zero_division=0),
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"f1_micro": f1_score(labels, preds, average="micro", zero_division=0),
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"accuracy": (preds == labels).mean(),
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}
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trainer = WeightedTrainer(
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model=model,
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args=TrainingArguments(
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output_dir=CFG_EMO.output_dir,
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num_train_epochs=CFG_EMO.num_epochs,
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per_device_train_batch_size=CFG_EMO.batch_size,
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per_device_eval_batch_size=CFG_EMO.batch_size * 2,
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gradient_accumulation_steps=CFG_EMO.grad_accum,
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learning_rate=CFG_EMO.learning_rate,
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warmup_ratio=CFG_EMO.warmup_ratio,
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weight_decay=CFG_EMO.weight_decay,
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max_grad_norm=1.0,
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eval_strategy="steps",
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eval_steps=CFG_EMO.save_steps,
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save_strategy="steps",
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save_steps=CFG_EMO.save_steps,
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save_total_limit=CFG_EMO.save_total_limit,
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load_best_model_at_end=True,
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metric_for_best_model="f1_macro",
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greater_is_better=True,
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fp16=False,
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dataloader_num_workers=0,
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logging_steps=10,
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remove_unused_columns=False,
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report_to="none",
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seed=42,
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),
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train_dataset=train_ds,
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eval_dataset=val_ds,
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tokenizer=AutoTokenizer.from_pretrained(CFG_EMO.base_model),
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compute_metrics=compute_metrics,
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callbacks=[EarlyStoppingCallback(early_stopping_patience=3, early_stopping_threshold=0.001)],
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)
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# Prepare datasets
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tokenizer = AutoTokenizer.from_pretrained(CFG_EMO.base_model)
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def tokenize(batch):
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return tokenizer(batch["text"], truncation=True, max_length=CFG_EMO.max_length, padding="max_length")
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train_ds = train_ds.map(tokenize, batched=True)
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val_ds = val_ds.map(tokenize, batched=True)
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train_ds.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
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val_ds.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
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trainer = WeightedTrainer(
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model=model,
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args=training_args,
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train_dataset=train_ds,
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eval_dataset=val_ds,
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tokenizer=AutoTokenizer.from_pretrained(CFG_EMO.base_model),
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compute_metrics=compute_metrics,
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callbacks=[EarlyStoppingCallback(early_stopping_patience=3, early_stopping_threshold=0.001)],
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)
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print("🏋️ Training emotion LoRA...")
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trainer.train()
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best_path = os.path.join(CFG_EMO.output_dir, "best")
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trainer.save_model(best_path)
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AutoTokenizer.from_pretrained(CFG_EMO.base_model).save_pretrained(best_path)
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# Cleanup checkpoints
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for d in os.listdir(CFG_EMO.output_dir):
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if d.startswith("checkpoint-"):
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shutil.rmtree(os.path.join(CFG_EMO.output_dir, d), ignore_errors=True)
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print(f"✅ Best emotion model saved to: {best_path}")
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return best_path
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if __name__ == "__main__":
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import argparse
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import os
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import shutil
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, EarlyStoppingCallback
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from peft import LoraConfig, get_peft_model, TaskType
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from datasets import Dataset
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from sklearn.metrics import f1_score
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parser = argparse.ArgumentParser()
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parser.add_argument("--epochs", type=int, default=5)
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parser.add_argument("--batch", type=int, default=8)
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parser.add_argument("--r", type=int, default=8)
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args = parser.parse_args()
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CFG_EMO.num_epochs = args.epochs
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CFG_EMO.batch_size = args.batch
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CFG_EMO.r = args.r
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train_emotion_lora()
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||||
@@ -24,7 +24,7 @@ import hashlib
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||||
# ============================================================
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||||
try:
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import torch
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||||
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
|
||||
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer, EarlyStoppingCallback
|
||||
from peft import LoraConfig, get_peft_model, TaskType, PeftModel
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from datasets import Dataset
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||||
TORCH_AVAILABLE = True
|
||||
@@ -381,6 +381,7 @@ def train_lora(train_data: List[Dict], val_data: List[Dict]) -> str:
|
||||
}
|
||||
|
||||
trainer = Trainer(
|
||||
callbacks=[EarlyStoppingCallback(early_stopping_patience=3, early_stopping_threshold=0.001)],
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_ds,
|
||||
|
||||
Reference in New Issue
Block a user