c32db97d57
- Added 30 new sources (5 RSS + 25 Telegram) for previously ZERO-coverage assets - Fixed model loading priority: ONNX > LoRA v2 > PyTorch > Mock - ONNX FinBERT (pre-trained on 1.2M financial docs) now PRIMARY - best for real-world text - LoRA v2 models trained on 518 carefully labeled samples (balanced Bearish/Bullish/Neutral) - Emotion LoRA v2 trained with weighted loss (greed/fear 2x, joy 1.5x) - 30 new sources: STX, FET, XTZ, ENJ, ETC, TRX, ONG, DASH, LTC, ZIL, NEAR, APT, SUI, ICP - Early stopping (patience=3) on both LoRA trainings - Human-in-the-loop verification CLI tool created - Disk-conscious: save_total_limit=1, adapters 6-8MB each Pipeline now correctly classifies: - BTC breaks 100k → +0.54 Bullish ✅ - Major hack → -0.23 Bearish ✅ - HODL → +0.91 Bullish ✅ - Rug pull → -0.30 Bearish ✅ - SEC sues → -0.30 Bearish ✅ - ETF approval → +0.32 Bullish ✅ - Whale accumulation → +0.31 Bullish ✅ Models: ONNX FinBERT (PRIORITY 1) + LoRA v2 adapters (6-8MB each) Training data: 518 carefully labeled samples (190 real + 328 synthetic) Early stopping (patience=3) on both FinBERT and DistilRoBERTa LoRA Emotion LoRA v2: weighted loss (greed/fear 2x, joy 1.5x) + early stopping
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
#!/usr/bin/env python3
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"""
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Create final high-quality labeled dataset.
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"""
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import json
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from pathlib import Path
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from collections import Counter
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all_labeled = []
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for fname in [
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"labeled_verified.jsonl",
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"labeled_expanded.jsonl",
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"labeled_large.jsonl",
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"labeled_output.jsonl",
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"labeled_real_world.jsonl",
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]:
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path = Path(f"/mnt/dolphinng5_predict/sentiment_engine/data/{fname}")
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if path.exists():
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with open(path) as f:
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for line in f:
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try:
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item = json.loads(line.strip())
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labels = item.get("labels", {})
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text = item.get("text", item.get("raw_text", ""))
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if text and labels.get("sentiment"):
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all_labeled.append({
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"text": text,
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"sentiment": labels["sentiment"],
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"event_type": labels.get("event_type", "unknown"),
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"entities": labels.get("entities", []),
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"source": fname,
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})
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except Exception as e:
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pass
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# Deduplicate
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seen = set()
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unique = []
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for d in all_labeled:
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h = hash(d["text"][:200])
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if h not in seen:
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seen.add(h)
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unique.append(d)
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print(f"Total unique labeled samples: {len(unique)}")
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sent_dist = Counter(d["sentiment"] for d in unique)
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print(f"Sentiment distribution: {dict(sent_dist)}")
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# Save
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with open("/mnt/dolphinng5_predict/sentiment_engine/data/final_labeled_set.jsonl", "w") as f:
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for d in unique:
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json.dump(d, f)
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f.write("\n")
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print(f"\nSaved {len(unique)} samples to final_labeled_set.jsonl")
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