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| Property | Value |
|---|---|
| Parameters | 1.7B |
| Context length | 32,768 tokens |
| Base model | Qwen/Qwen3-4B |
| Tensor type | BF16 |
| License | Apache-2.0 |
1from sentence_transformers import CrossEncoder
2
3model = CrossEncoder("devrev/zerank-1-small-seq")
4
5query = "Which planet is known as the Red Planet?"
6passages = [
7 "Venus is often called Earth's twin...",
8 "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
9]
10
11scores = model.predict([(query, passage) for passage in passages])
12print(scores)| Domain | Score |
|---|---|
| Finance | 0.861 |
| Legal | 0.817 |
| Medical | 0.773 |
| Code | 0.730 |
| STEM | 0.680 |
| Conversational | 0.556 |