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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- cross-encoder |
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- sentence-similarity |
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- transformers |
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- legal |
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- reranker |
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library_name: generic |
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language: |
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- vi |
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--- |
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# NaverHustQA/viLegal_cross_Quang |
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This is an cross-encoder model for Vietnamese legal domain: It returns a relevance score of a query-context input and can be used for information retrieval. |
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We use [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) as the pre-trained backbone. |
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<!--- Describe your model here --> |
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## Usage (HuggingFace Transformers) |
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You can use the model like below (Remember to word-segment inputs first): |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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import torch |
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# Load cross-encoder |
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model_name = "NaverHustQA/viLegal_cross_Quang" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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# Define query and context |
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query = "'Uống rượu lái_xe bị phạt bao_nhiêu tiền ?'" |
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context = "Uống rượu lái_xe bị phạt 500,000 đồng ." |
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# Tokenize input (Cross-encoder format: query and context as a single input) |
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inputs = tokenizer(query, context, return_tensors="pt", padding=True, truncation=True) |
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# Run through model |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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score = outputs.logits.item() # Extract classification score |
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print(f"Relevance Score: {score}") |
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``` |
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## Training |
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You can find full information of our training methods and datasets in our reports. |
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## Authors |
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Le Thanh Huong, Nguyen Nhat Quang. |
