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poseidon1113
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Model Details
Model Provider
poseidon1113
Model Tree
openai-community/gpt2
Input Modalities
Output Modalities
Supported Functionality
| Base model | GPT-2 (124M) |
| Method | LoRA (r=8, alpha=16) |
| Trainable params | ~300K (0.24%) |
| Target modules | c_attn |
| v1 dataset | Financial PhraseBank — 4,840 examples, 3 epochs |
| v2 dataset | NOSIBLE Financial Sentiment — 100K examples, 1 epoch |
| Max length | 256 |
| Learning rate | 1e-4 (reduced for stacked fine-tuning) |
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch tokenizer = AutoTokenizer.from_pretrained("poseidon1113/gpt2-lora-financial-sentiment-v2")model = PeftModel.from_pretrained( AutoModelForCausalLM.from_pretrained("gpt2", torch_dtype=torch.float16), "poseidon1113/gpt2-lora-financial-sentiment-v2").eval() def predict(sentence): inputs = tokenizer(f"### Sentence:\n{sentence}\n\n### Sentiment:\n", return_tensors="pt") with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=10, do_sample=False, pad_token_id=tokenizer.eos_token_id) result = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip().lower() return next((w for w in result.split() if w in ("positive", "negative", "neutral")), "neutral") predict("Operating profit rose to EUR 13.1 mn from EUR 21.1 mn.") # → positivepredict("The company reported a loss for the third consecutive quarter.") # → negative| Class | Correct | Total | Accuracy |
|---|---|---|---|
| Positive | 210 | 706 | 29.7% |
| Neutral | 82 | 115 | 71.3% |
| Negative | 213 | 373 | 57.1% |
| Overall | 505 | 1194 |
| v1 (PhraseBank only) | v2 (+ NOSIBLE) | |
|---|---|---|
| Training examples | 4,840 | +100,000 |
| Overall accuracy | — | 42.3% |
bibtex
@article{Malo2014GoodDO, title={Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts}, author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Virtanen}, journal={Journal of the Association for Information Science and Technology}, year={2014}, volume={65}}