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Deehan/
wic-gemini25flash-rule6prompt-xlm-roberta-large-no_rationale

Name: wic-gemini25flash-rule6prompt-xlm-roberta-large-no_rationale
Availability: InStock

safetensors

xlm-roberta

word-sense-disambiguation

wic

cross-encoder

Deehan1866/WiC_actual

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FacebookAI/xlm-roberta-large Fine-tuned on WiC (Angle 3 — no_rationale)

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

Base model

FacebookAI/xlm-roberta-large

Input format

[CLS] sentence1_marked [SEP] sentence2_marked [SEP] rationale [SEP]

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	0.7194
Test	0.6871

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("Deehan/wic-gemini25flash-rule6prompt-xlm-roberta-large-no_rationale")
model = AutoModelForSequenceClassification.from_pretrained("Deehan/wic-gemini25flash-rule6prompt-xlm-roberta-large-no_rationale")

s1 = "The <TGT>bank</TGT> raised its interest rates."
s2 = "She visited her local <TGT>bank</TGT> to deposit a cheque."
rationale = "In the first sentence 'bank' refers to a financial institution; in the second it also refers to a financial institution."

sep = tokenizer.sep_token
enc = tokenizer(s1, s2 + " " + sep + " " + rationale,
                return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    logits = model(**enc).logits
pred = torch.argmax(logits).item()
print("Same sense" if pred == 1 else "Different sense")