metadata
license: gemma
pipeline_tag: text-classification
tags:
- transformers
- sentence-transformers
language:
- multilingual
Reranker
More details please refer to our Github: FlagEmbedding.
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
Here, we introduce a lightweight reranker bge-reranker-v2.5-gemma2-lightweight, which is a multilingual model trained based on gemma2-9b. By integrating token compression capabilities and layerwise reduction, the model can maintain outstanding performance while saving significant resources.
Our model primarily demonstrates the following capabilities:
- Lightweight: The model can be made lightweight through token compression, layerwise reduction, or a combination of both.
- Outstanding performance: The model has achieved new state-of-the-art (SOTA) performance on both BEIR and MIRACL.
We will release a technical report about lightweight reranker soon with more details.
Model List
| Model | Base model | Language | layerwise | compress ratio | compress layers | feature |
|---|---|---|---|---|---|---|
| BAAI/bge-reranker-base | xlm-roberta-base | Chinese and English | - | - | - | Lightweight reranker model, easy to deploy, with fast inference. |
| BAAI/bge-reranker-large | xlm-roberta-large | Chinese and English | - | - | - | Lightweight reranker model, easy to deploy, with fast inference. |
| BAAI/bge-reranker-v2-m3 | bge-m3 | Multilingual | - | - | - | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. |
| BAAI/bge-reranker-v2-gemma | gemma-2b | Multilingual | - | - | - | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. |
| BAAI/bge-reranker-v2-minicpm-layerwise | 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. |
| BAAI/bge-reranker-v2.5-gemma2-lightweight | google/gemma-2-9b | Multilingual | 8-42 | 1, 2, 4, 8 | [8, 16, 24, 32, 40] | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. |
You can select the model according your senario and resource.
For multilingual, utilize BAAI/bge-reranker-v2-m3, BAAI/bge-reranker-v2-gemma and BAAI/bge-reranker-v2.5-gemma2-lightweight
For Chinese or English, utilize BAAI/bge-reranker-v2-m3 and BAAI/bge-reranker-v2-minicpm-layerwise.
For efficiency, utilize BAAI/bge-reranker-v2-m3 and the low layer of BAAI/bge-reranker-v2-minicpm-layerwise.
For better performance, recommand BAAI/bge-reranker-v2-minicpm-layerwise and BAAI/bge-reranker-v2-gemma
Usage
Using FlagEmbedding
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
Get relevance scores (higher scores indicate more relevance):
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score) # -5.65234375
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
score = reranker.compute_score(['query', 'passage'], normalize=True)
print(score) # 0.003497010252573502
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.']])
print(scores) # [-8.1875, 5.26171875]
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
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)
print(scores) # [0.00027803096387751553, 0.9948403768236574]
For LLM-based reranker
from FlagEmbedding import FlagLLMReranker
reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
# 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
score = reranker.compute_score(['query', 'passage'])
print(score)
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.']])
print(scores)
For LLM-based layerwise reranker
from FlagEmbedding import LayerWiseFlagLLMReranker
reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
# 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
score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
print(score)
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])
print(scores)
For LLM-based lightweight reranker
from FlagEmbedding import LightWeightFlagLLMReranker
reranker = LightWeightFlagLLMReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
print(score)
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], compress_ratio=2, compress_layer=[24, 40])
print(scores)
Using Huggingface transformers
For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
Get relevance scores (higher scores indicate more relevance):
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3')
model.eval()
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.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
For LLM-based reranker
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
if prompt is None:
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'."
sep = "\n"
prompt_inputs = tokenizer(prompt,
return_tensors=None,
add_special_tokens=False)['input_ids']
sep_inputs = tokenizer(sep,
return_tensors=None,
add_special_tokens=False)['input_ids']
inputs = []
for query, passage in pairs:
query_inputs = tokenizer(f'A: {query}',
return_tensors=None,
add_special_tokens=False,
max_length=max_length * 3 // 4,
truncation=True)
passage_inputs = tokenizer(f'B: {passage}',
return_tensors=None,
add_special_tokens=False,
max_length=max_length,
truncation=True)
item = tokenizer.prepare_for_model(
[tokenizer.bos_token_id] + query_inputs['input_ids'],
sep_inputs + passage_inputs['input_ids'],
truncation='only_second',
max_length=max_length,
padding=False,
return_attention_mask=False,
return_token_type_ids=False,
add_special_tokens=False
)
item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
item['attention_mask'] = [1] * len(item['input_ids'])
inputs.append(item)
return tokenizer.pad(
inputs,
padding=True,
max_length=max_length + len(sep_inputs) + len(prompt_inputs),
pad_to_multiple_of=8,
return_tensors='pt',
)
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma')
model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma')
yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0]
model.eval()
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.']]
with torch.no_grad():
inputs = get_inputs(pairs, tokenizer)
scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float()
print(scores)
For LLM-based layerwise reranker
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
if prompt is None:
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'."
sep = "\n"
prompt_inputs = tokenizer(prompt,
return_tensors=None,
add_special_tokens=False)['input_ids']
sep_inputs = tokenizer(sep,
return_tensors=None,
add_special_tokens=False)['input_ids']
inputs = []
for query, passage in pairs:
query_inputs = tokenizer(f'A: {query}',
return_tensors=None,
add_special_tokens=False,
max_length=max_length * 3 // 4,
truncation=True)
passage_inputs = tokenizer(f'B: {passage}',
return_tensors=None,
add_special_tokens=False,
max_length=max_length,
truncation=True)
item = tokenizer.prepare_for_model(
[tokenizer.bos_token_id] + query_inputs['input_ids'],
sep_inputs + passage_inputs['input_ids'],
truncation='only_second',
max_length=max_length,
padding=False,
return_attention_mask=False,
return_token_type_ids=False,
add_special_tokens=False
)
item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
item['attention_mask'] = [1] * len(item['input_ids'])
inputs.append(item)
return tokenizer.pad(
inputs,
padding=True,
max_length=max_length + len(sep_inputs) + len(prompt_inputs),
pad_to_multiple_of=8,
return_tensors='pt',
)
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
model = model.to('cuda')
model.eval()
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.']]
with torch.no_grad():
inputs = get_inputs(pairs, tokenizer).to(model.device)
all_scores = model(**inputs, return_dict=True, cutoff_layers=[28])
all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]]
print(all_scores)
For LLM-based lightweight reranker
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def last_logit_pool(logits: torch.Tensor,
attention_mask: torch.Tensor) -> torch.Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return logits[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = logits.shape[0]
return torch.stack([logits[i, sequence_lengths[i]] for i in range(batch_size)], dim=0)
def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
if prompt is None:
prompt = "Predict whether passage B contains an answer to query A."
sep = "\n"
prompt_inputs = tokenizer(prompt,
return_tensors=None,
add_special_tokens=False)['input_ids']
sep_inputs = tokenizer(sep,
return_tensors=None,
add_special_tokens=False)['input_ids']
inputs = []
query_lengths = []
prompt_lengths = []
for query, passage in pairs:
query_inputs = tokenizer(f'A: {query}',
return_tensors=None,
add_special_tokens=False,
max_length=max_length * 3 // 4,
truncation=True)
passage_inputs = tokenizer(f'B: {passage}',
return_tensors=None,
add_special_tokens=False,
max_length=max_length,
truncation=True)
item = tokenizer.prepare_for_model(
[tokenizer.bos_token_id] + query_inputs['input_ids'],
sep_inputs + passage_inputs['input_ids'],
truncation='only_second',
max_length=max_length,
padding=False,
return_attention_mask=False,
return_token_type_ids=False,
add_special_tokens=False
)
item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
item['attention_mask'] = [1] * len(item['input_ids'])
inputs.append(item)
query_lengths.append(len([tokenizer.bos_token_id] + query_inputs['input_ids'] + sep_inputs))
prompt_lengths.append(len(sep_inputs + prompt_inputs))
return tokenizer.pad(
inputs,
padding=True,
max_length=max_length + len(sep_inputs) + len(prompt_inputs),
pad_to_multiple_of=8,
return_tensors='pt',
), query_lengths, prompt_lengths
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2.5-gemma2-lightweight', trust_remote_code=True)
tokenizer.padding_side = 'right'
model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2.5-gemma2-lightweight', trust_remote_code=True)
model = model.to('cuda')
model.eval()
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.']]
with torch.no_grad():
inputs, query_lengths, prompt_lengths = get_inputs(pairs, tokenizer)
inputs = inputs.to(model.device)
outputs = model(**inputs,
return_dict=True,
cutoff_layers=[28],
compress_ratio=2,
compress_layer=[24, 40],
query_lengths=query_lengths,
prompt_lengths=prompt_lengths)
scores = []
for i in range(len(outputs.logits)):
logits = last_logit_pool(outputs.logits[i], outputs.attention_masks[i])
scores.append(logits.cpu().float().tolist())
print(scores)
Evaluation
- BEIR:
| BEIR | bge-large-en-v1.5 | Bge-rearanker v2 m3 | jina-reranker-v2-base-multilingual | bge-reranker-v2-gemma | bge-reranker-v2.5-gemma2-lightweight | bge-reranker-v2.5-gemma2-lightweight |
|---|---|---|---|---|---|---|
| Save Flops | - | - | - | - | 60% | 0 |
| ArguAna | 63.54 | 37.7 | 52.23 | 78.68 | 86.04 | 86.16 |
| ClimateFEVER | 36.49 | 37.99 | 34.65 | 39.07 | 48.41 | 48.48 |
| CQA | 42.23 | 38.24 | 40.21 | 45.85 | 49.18 | 48.9 |
| DBPedia | 44.16 | 48.15 | 49.31 | 49.92 | 51.98 | 52.11 |
| FEVER | 87.17 | 90.15 | 92.44 | 90.15 | 94.71 | 94.69 |
| FiQA2018 | 44.97 | 49.32 | 45.88 | 49.32 | 60.48 | 60.95 |
| HotpotQA | 74.11 | 84.51 | 81.81 | 86.15 | 87.84 | 87.89 |
| MSMARCO | 42.48 | 47.79 | 47.83 | 48.07 | 47.23 | 47.26 |
| NFCorpus | 38.12 | 34.85 | 37.73 | 39.73 | 41.4 | 41.64 |
| NQ | 55.04 | 69.37 | 67.35 | 72.6 | 75.37 | 75.58 |
| QuoraRetrieval | 89.06 | 89.13 | 87.81 | 90.37 | 91.25 | 91.18 |
| SCIDOCS | 22.62 | 18.25 | 20.21 | 21.65 | 23.71 | 23.87 |
| SciFact | 74.64 | 73.08 | 76.93 | 77.22 | 80.5 | 80.38 |
| Touche2020 | 25.08 | 35.68 | 32.45 | 35.68 | 30.64 | 31.09 |
| TRECCOVID | 74.89 | 83.39 | 80.89 | 85.51 | 84.26 | 84.85 |
| Mean | 54.31 | 55.36 | 56.52 | 60.71 | 63.1 | 63.67 |
| BEIR | e5-mistral-7b-instruct | bge-reranker-v2-gemma | bge-reranker-v2.5-gemma-lightweight | bge-reranker-v2.5-gemma-lightweight |
|---|---|---|---|---|
| Save Flops | - | - | 60% | 0 |
| ArguAna | 61.8 | 79.05 | 86.02 | 86.58 |
| ClimateFEVER | 38.37 | 37.66 | 47.27 | 47.13 |
| CQA | 42.97 | 46.16 | 49.06 | 49.53 |
| DBPedia | 48.84 | 50.77 | 52.45 | 52.87 |
| FEVER | 87.82 | 91.36 | 94.85 | 95.19 |
| FiQA2018 | 56.58 | 50.96 | 58.81 | 61.19 |
| HotpotQA | 75.72 | 86.99 | 88.49 | 88.82 |
| MSMARCO | 43.06 | 48.35 | 47.65 | 47.4 |
| NFCorpus | 38.58 | 39.25 | 42.28 | 42.17 |
| NQ | 63.56 | 73.44 | 75 | 76.28 |
| QuoraRetrieval | 89.59 | 90.44 | 91.09 | 91.18 |
| SCIDOCS | 16.3 | 20.77 | 22.2 | 22.69 |
| SciFact | 76.26 | 77.78 | 79.94 | 80.98 |
| Touche2020 | 26.24 | 35.79 | 28.69 | 31.17 |
| TRECCOVID | 87.07 | 88.13 | 86.61 | 87.36 |
| Mean | 56.85 | 61.13 | 63.36 | 64.04 |
- MIRACL:
| MIRACL (dev, nDCG@10) | Average (18) | save flops | ar | bn | en | es | fa | fi | fr | hi | id | ja | ko | ru | sw | te | th | zh | de | yo |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bge-m3 (Dense) | 69.2 | - | 78.4 | 80.0 | 56.9 | 56.1 | 60.9 | 78.6 | 58.3 | 59.5 | 56.1 | 72.8 | 69.9 | 70.1 | 78.7 | 86.2 | 82.6 | 62.7 | 56.7 | 81.8 |
| jina-reranker-v2-base-multilingual | 69.6 | - | 73.4 | 81.9 | 58.9 | 58.6 | 60.5 | 77.2 | 56.1 | 62.7 | 59.6 | 72.7 | 74.0 | 67.1 | 78.1 | 85.8 | 81.2 | 63.0 | 58.2 | 84.2 |
| bge-reranker-v2-m3 | 74.4 | - | 81.7 | 84.6 | 63.5 | 64.4 | 65.7 | 82.4 | 63.7 | 68.5 | 62.7 | 80.0 | 73.8 | 76.9 | 82.3 | 89.4 | 85.3 | 65.2 | 62.7 | 87.4 |
| bge-reranker-v2-gemma | 75.0 | - | 82.3 | 85.0 | 66.6 | 65.3 | 65.5 | 82.6 | 65.4 | 69.4 | 61.2 | 79.7 | 75.1 | 78.3 | 81.8 | 89.6 | 86.1 | 66.8 | 64.0 | 85.9 |
| bge-reranker-v2.5-gemma2-lightweight | 77.1 | 60% | 82.5 | 87.8 | 68.6 | 67.6 | 67.5 | 82.8 | 68.5 | 71.4 | 63.8 | 82.8 | 75.9 | 79.8 | 84.8 | 90.8 | 88.1 | 69.9 | 65.8 | 89.6 |
| bge-reranker-v2.5-gemma-lightweight | 77.3 | 0 | 82.8 | 87.6 | 69.3 | 67.8 | 67.4 | 83.3 | 68.5 | 71.3 | 63.8 | 83.6 | 75.7 | 80.1 | 85.1 | 90.8 | 88.7 | 69.9 | 65.6 | 89.8 |
Citation
If you find this repository useful, please consider giving a star and citation
@misc{li2023making,
title={Making Large Language Models A Better Foundation For Dense Retrieval},
author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},
year={2023},
eprint={2312.15503},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{chen2024bge,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
year={2024},
eprint={2402.03216},
archivePrefix={arXiv},
primaryClass={cs.CL}
}