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--- |
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license: apache-2.0 |
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language: |
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- vi |
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library_name: transformers |
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pipeline_tag: text-classification |
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tags: |
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- transformers |
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- cross-encoder |
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- rerank |
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datasets: |
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- unicamp-dl/mmarco |
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widget: |
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- text: tỉnh nào có diện tích lớn nhất việt nam. |
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output: |
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- label: >- |
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nghệ an có diện tích lớn nhất việt nam |
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score: 0.999989 |
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- label: >- |
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bắc ninh có diện tích nhỏ nhất việt nam |
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score: 0.372391 |
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--- |
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# Reranker |
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* [Usage](#usage) |
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* [Using FlagEmbedding](#using-flagembedding) |
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* [Using Huggingface transformers](#using-huggingface-transformers) |
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* [Fine tune](#fine-tune) |
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* [Data format](#data-format) |
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Different from embedding model, reranker uses question and document as input and directly output similarity instead of |
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embedding. |
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You can get a relevance score by inputting query and passage to the reranker. |
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And the score can be mapped to a float value in [0,1] by sigmoid function. |
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## Usage |
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### Using FlagEmbedding |
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``` |
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pip install -U FlagEmbedding |
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``` |
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Get relevance scores (higher scores indicate more relevance): |
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```python |
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from FlagEmbedding import FlagReranker |
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reranker = FlagReranker('namdp/bge-reranker-vietnamese', |
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use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
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score = reranker.compute_score(['query', 'passage']) |
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print(score) # -5.65234375 |
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# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score |
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score = reranker.compute_score(['query', 'passage'], normalize=True) |
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print(score) # 0.003497010252573502 |
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scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', |
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'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) |
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print(scores) # [-8.1875, 5.26171875] |
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# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score |
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scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', |
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'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], |
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normalize=True) |
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print(scores) # [0.00027803096387751553, 0.9948403768236574] |
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``` |
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### Using Huggingface transformers |
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``` |
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pip install -U transformers |
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``` |
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Get relevance scores (higher scores indicate more relevance): |
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```python |
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained('namdp/bge-reranker-vietnamese') |
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model = AutoModelForSequenceClassification.from_pretrained('namdp/bge-reranker-vietnamese') |
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model.eval() |
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pairs = [['what is panda?', 'hi'], ['what is panda?', |
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'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] |
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with torch.no_grad(): |
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inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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scores = model(**inputs, return_dict=True).logits.view(-1, ).float() |
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print(scores) |
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``` |
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## Fine-tune |
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### Data Format |
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Train data should be a json file, where each line is a dict like this: |
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``` |
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{"query": str, "pos": List[str], "neg": List[str]} |
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``` |
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`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the |
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relationship between query and texts. If you have no negative texts for a query, you can random sample some from the |
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entire corpus as the negatives. |
