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Rakesh44
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Model Details
Model Provider
Rakesh44
Model Tree
Rakesh44/odyssey
Input Modalities
Output Modalities
Supported Functionality
SEQ_CLS)| Setting | Value |
|---|---|
| PEFT type | LoRA |
| Rank (r) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Bias | none |
| Target modules | q_proj, k_proj, v_proj, o_proj |
| Modules saved (full) | score / classifier head |
| RSLoRA / DoRA / QALoRA | disabled |
| PEFT version | 0.19.1 |
The adapter targets the attention projections only; the classification head (score/classifier) is trained in full and saved with the adapter.
Classifying the sentiment of financial or market text — headlines, reports, commentary — into fixed categories.
Not intended for text generation, question answering, or high-stakes automated decisions. Predictions on financial or market text should be independently validated before any real-world use.
python
from transformers import AutoTokenizer, AutoModelForSequenceClassificationfrom peft import PeftModelimport torch base_id = "Rakesh44/odyssey"adapter_id = "Rakesh44/odyssey-fin-sentiment" tokenizer = AutoTokenizer.from_pretrained(base_id)base = AutoModelForSequenceClassification.from_pretrained( base_id, num_labels=[NUM_LABELS])model = PeftModel.from_pretrained(base, adapter_id)model.eval() text = "The company beat earnings expectations this quarter."inputs = tokenizer(text, return_tensors="pt", truncation=True)with torch.no_grad(): logits = model(**inputs).logitspred = logits.argmax(-1).item()print(model.config.id2label[pred])from transformers import AutoTokenizer, AutoModelForSequenceClassificationfrom peft import PeftModelimport torch base_id = "Rakesh44/odyssey"adapter_id = "Rakesh44/odyssey-fin-sentiment" tokenizer = AutoTokenizer.from_pretrained(base_id)base = AutoModelForSequenceClassification.from_pretrained( base_id, num_labels=[NUM_LABELS])model = PeftModel.from_pretrained(base, adapter_id)model.eval() text = "The company beat earnings expectations this quarter."inputs = tokenizer(text, return_tensors="pt", truncation=True)with torch.no_grad(): logits = model(**inputs).logitspred = logits.argmax(-1).item()print(model.config.id2label[pred])