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--- |
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license: apache-2.0 |
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pipeline_tag: text-classification |
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tags: |
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- transformers |
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- sentence-transformers |
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language: |
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- multilingual |
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--- |
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# Reranker |
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**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).** |
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- [Model List](#model-list) |
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- [Usage](#usage) |
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- [Fine-tuning](#fine-tune) |
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- [Evaluation](#evaluation) |
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- [Citation](#citation) |
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Different from embedding model, reranker uses question and document as input and directly output similarity instead of 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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## Model List |
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| Model | Base model | Language | layerwise | feature | |
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|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:| |
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| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. | |
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| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) | Chinese and English | - | Lightweight reranker model, easy to deploy, with fast inference. | |
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| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | [bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | |
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| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | [gemma-2b](https://huggingface.co/google/gemma-2b) | Multilingual | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | |
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| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) | Multilingual | 8-40 | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | |
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You can select the model according your senario and resource. |
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- For **multilingual**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) |
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- For **Chinese or English**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). |
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- For **efficiency**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). |
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- For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) |
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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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#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) |
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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('BAAI/bge-reranker-v2-m3', 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?', '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?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True) |
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print(scores) # [0.00027803096387751553, 0.9948403768236574] |
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``` |
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#### For LLM-based reranker |
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```python |
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from FlagEmbedding import FlagLLMReranker |
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reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
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# reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation |
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score = reranker.compute_score(['query', 'passage']) |
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print(score) |
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scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', '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) |
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``` |
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#### For LLM-based layerwise reranker |
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```python |
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from FlagEmbedding import LayerWiseFlagLLMReranker |
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reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
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# reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation |
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score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score. |
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print(score) |
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scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28]) |
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print(scores) |
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``` |
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### Using Huggingface transformers |
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#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 ) |
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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('BAAI/bge-reranker-v2-m3') |
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model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3') |
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model.eval() |
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pairs = [['what is panda?', 'hi'], ['what is panda?', '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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#### For LLM-based reranker |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): |
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if prompt is None: |
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prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." |
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sep = "\n" |
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prompt_inputs = tokenizer(prompt, |
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return_tensors=None, |
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add_special_tokens=False)['input_ids'] |
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sep_inputs = tokenizer(sep, |
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return_tensors=None, |
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add_special_tokens=False)['input_ids'] |
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inputs = [] |
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for query, passage in pairs: |
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query_inputs = tokenizer(f'A: {query}', |
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return_tensors=None, |
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add_special_tokens=False, |
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max_length=max_length * 3 // 4, |
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truncation=True) |
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passage_inputs = tokenizer(f'B: {passage}', |
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return_tensors=None, |
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add_special_tokens=False, |
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max_length=max_length, |
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truncation=True) |
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item = tokenizer.prepare_for_model( |
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[tokenizer.bos_token_id] + query_inputs['input_ids'], |
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sep_inputs + passage_inputs['input_ids'], |
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truncation='only_second', |
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max_length=max_length, |
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padding=False, |
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return_attention_mask=False, |
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return_token_type_ids=False, |
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add_special_tokens=False |
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) |
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item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs |
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item['attention_mask'] = [1] * len(item['input_ids']) |
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inputs.append(item) |
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return tokenizer.pad( |
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inputs, |
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padding=True, |
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max_length=max_length + len(sep_inputs) + len(prompt_inputs), |
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pad_to_multiple_of=8, |
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return_tensors='pt', |
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) |
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma') |
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model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma') |
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yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0] |
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model.eval() |
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pairs = [['what is panda?', 'hi'], ['what is panda?', '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 = get_inputs(pairs, tokenizer) |
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scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float() |
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print(scores) |
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``` |
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#### For LLM-based layerwise reranker |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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def get_inputs(pairs, tokenizer, prompt=None, max_length=1024): |
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if prompt is None: |
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prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'." |
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sep = "\n" |
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prompt_inputs = tokenizer(prompt, |
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return_tensors=None, |
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add_special_tokens=False)['input_ids'] |
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sep_inputs = tokenizer(sep, |
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return_tensors=None, |
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add_special_tokens=False)['input_ids'] |
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inputs = [] |
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for query, passage in pairs: |
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query_inputs = tokenizer(f'A: {query}', |
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return_tensors=None, |
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add_special_tokens=False, |
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max_length=max_length * 3 // 4, |
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truncation=True) |
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passage_inputs = tokenizer(f'B: {passage}', |
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return_tensors=None, |
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add_special_tokens=False, |
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max_length=max_length, |
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truncation=True) |
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item = tokenizer.prepare_for_model( |
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[tokenizer.bos_token_id] + query_inputs['input_ids'], |
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sep_inputs + passage_inputs['input_ids'], |
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truncation='only_second', |
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max_length=max_length, |
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padding=False, |
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return_attention_mask=False, |
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return_token_type_ids=False, |
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add_special_tokens=False |
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) |
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item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs |
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item['attention_mask'] = [1] * len(item['input_ids']) |
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inputs.append(item) |
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return tokenizer.pad( |
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inputs, |
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padding=True, |
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max_length=max_length + len(sep_inputs) + len(prompt_inputs), |
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pad_to_multiple_of=8, |
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return_tensors='pt', |
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) |
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16) |
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model = model.to('cuda') |
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model.eval() |
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pairs = [['what is panda?', 'hi'], ['what is panda?', '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 = get_inputs(pairs, tokenizer).to(model.device) |
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all_scores = model(**inputs, return_dict=True, cutoff_layers=[28]) |
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all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]] |
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print(all_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], "prompt": 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 relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives. |
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See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file. |
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### Train |
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You can fine-tune the reranker with the following code: |
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**For llm-based reranker** |
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```shell |
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torchrun --nproc_per_node {number of gpus} \ |
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-m FlagEmbedding.llm_reranker.finetune_for_instruction.run \ |
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--output_dir {path to save model} \ |
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--model_name_or_path google/gemma-2b \ |
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--train_data ./toy_finetune_data.jsonl \ |
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--learning_rate 2e-4 \ |
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--num_train_epochs 1 \ |
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--per_device_train_batch_size 1 \ |
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--gradient_accumulation_steps 16 \ |
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--dataloader_drop_last True \ |
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--query_max_len 512 \ |
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--passage_max_len 512 \ |
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--train_group_size 16 \ |
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--logging_steps 1 \ |
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--save_steps 2000 \ |
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--save_total_limit 50 \ |
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--ddp_find_unused_parameters False \ |
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--gradient_checkpointing \ |
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--deepspeed stage1.json \ |
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--warmup_ratio 0.1 \ |
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--bf16 \ |
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--use_lora True \ |
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--lora_rank 32 \ |
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--lora_alpha 64 \ |
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--use_flash_attn True \ |
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--target_modules q_proj k_proj v_proj o_proj |
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``` |
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**For llm-based layerwise reranker** |
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```shell |
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torchrun --nproc_per_node {number of gpus} \ |
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-m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \ |
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--output_dir {path to save model} \ |
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--model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \ |
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--train_data ./toy_finetune_data.jsonl \ |
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--learning_rate 2e-4 \ |
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--num_train_epochs 1 \ |
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--per_device_train_batch_size 1 \ |
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--gradient_accumulation_steps 16 \ |
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--dataloader_drop_last True \ |
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--query_max_len 512 \ |
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--passage_max_len 512 \ |
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--train_group_size 16 \ |
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--logging_steps 1 \ |
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--save_steps 2000 \ |
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--save_total_limit 50 \ |
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--ddp_find_unused_parameters False \ |
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--gradient_checkpointing \ |
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--deepspeed stage1.json \ |
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--warmup_ratio 0.1 \ |
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--bf16 \ |
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--use_lora True \ |
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--lora_rank 32 \ |
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--lora_alpha 64 \ |
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--use_flash_attn True \ |
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--target_modules q_proj k_proj v_proj o_proj \ |
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--start_layer 8 \ |
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--head_multi True \ |
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--head_type simple \ |
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--lora_extra_parameters linear_head |
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``` |
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Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) (for llm-based layerwise reranker), and we train it on a mixture of multilingual datasets: |
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- [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) |
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- [quora train data](https://huggingface.co/datasets/quora) |
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- [fever train data](https://fever.ai/dataset/fever.html) |
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## Evaluation |
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- llama-index. |
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 |
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- BEIR. |
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rereank the top 100 results from bge-en-v1.5 large. |
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rereank the top 100 results from e5 mistral 7b instruct. |
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- CMTEB-retrieval. |
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It rereank the top 100 results from bge-zh-v1.5 large. |
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- miracl (multi-language). |
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It rereank the top 100 results from bge-m3. |
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## Citation |
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If you find this repository useful, please consider giving a star and citation |
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```bibtex |
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@misc{li2023making, |
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title={Making Large Language Models A Better Foundation For Dense Retrieval}, |
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author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao}, |
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year={2023}, |
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eprint={2312.15503}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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@misc{chen2024bge, |
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title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, |
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author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu}, |
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year={2024}, |
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eprint={2402.03216}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
