Deehan1866/finetuned-deberta-wic-actual-version1

DeBERTa-v3-large Fine-tuned on WiC

Cross-encoder model for the Word-in-Context (WiC) binary sense disambiguation task. Both sentences are fed together so the model can attend across them simultaneously.

Target Word Marking

The target word is wrapped with <tgt>word</tgt> using the exact token position from the dataset (start1/start2 columns), so marking is always precise regardless of lemma or morphological variation.

Performance

Split	Accuracy
Validation	0.7508
Test	0.7157

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Deehan1866/finetuned-deberta-wic-actual-version1")
model = AutoModelForSequenceClassification.from_pretrained("Deehan1866/finetuned-deberta-wic-actual-version1")

s1 = "The <tgt>bank</tgt> raised its interest rates."
s2 = "She visited her local <tgt>bank</tgt> to deposit a cheque."

enc = tokenizer(s1, s2, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    logits = model(**enc).logits
pred = torch.argmax(logits).item()
print("Same sense" if pred == 1 else "Different sense")