389 lines
17 KiB
Markdown
389 lines
17 KiB
Markdown
---
|
|
license: apache-2.0
|
|
pipeline_tag: text-classification
|
|
tags:
|
|
- transformers
|
|
- sentence-transformers
|
|
- text-embeddings-inference
|
|
language:
|
|
- multilingual
|
|
---
|
|
|
|
# Reranker
|
|
|
|
**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).**
|
|
|
|
- [Model List](#model-list)
|
|
- [Usage](#usage)
|
|
- [Fine-tuning](#fine-tune)
|
|
- [Evaluation](#evaluation)
|
|
- [Citation](#citation)
|
|
|
|
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.
|
|
|
|
|
|
## Model List
|
|
|
|
| Model | Base model | Language | layerwise | feature |
|
|
|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
|
|
| [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. |
|
|
| [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. |
|
|
| [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. |
|
|
| [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. |
|
|
| [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. |
|
|
|
|
|
|
You can select the model according your senario and resource.
|
|
- 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)
|
|
|
|
- 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).
|
|
|
|
- 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).
|
|
|
|
- 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)
|
|
|
|
## Usage
|
|
### Using FlagEmbedding
|
|
|
|
```
|
|
pip install -U FlagEmbedding
|
|
```
|
|
|
|
#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
|
|
|
|
Get relevance scores (higher scores indicate more relevance):
|
|
|
|
```python
|
|
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
|
|
|
|
```python
|
|
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
|
|
|
|
```python
|
|
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)
|
|
```
|
|
|
|
### Using Huggingface transformers
|
|
|
|
#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
|
|
|
|
Get relevance scores (higher scores indicate more relevance):
|
|
|
|
```python
|
|
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
|
|
|
|
```python
|
|
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
|
|
|
|
```python
|
|
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)
|
|
```
|
|
|
|
## Fine-tune
|
|
|
|
### Data Format
|
|
|
|
Train data should be a json file, where each line is a dict like this:
|
|
|
|
```
|
|
{"query": str, "pos": List[str], "neg":List[str], "prompt": str}
|
|
```
|
|
|
|
`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.
|
|
|
|
See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file.
|
|
|
|
### Train
|
|
|
|
You can fine-tune the reranker with the following code:
|
|
|
|
**For llm-based reranker**
|
|
|
|
```shell
|
|
torchrun --nproc_per_node {number of gpus} \
|
|
-m FlagEmbedding.llm_reranker.finetune_for_instruction.run \
|
|
--output_dir {path to save model} \
|
|
--model_name_or_path google/gemma-2b \
|
|
--train_data ./toy_finetune_data.jsonl \
|
|
--learning_rate 2e-4 \
|
|
--num_train_epochs 1 \
|
|
--per_device_train_batch_size 1 \
|
|
--gradient_accumulation_steps 16 \
|
|
--dataloader_drop_last True \
|
|
--query_max_len 512 \
|
|
--passage_max_len 512 \
|
|
--train_group_size 16 \
|
|
--logging_steps 1 \
|
|
--save_steps 2000 \
|
|
--save_total_limit 50 \
|
|
--ddp_find_unused_parameters False \
|
|
--gradient_checkpointing \
|
|
--deepspeed stage1.json \
|
|
--warmup_ratio 0.1 \
|
|
--bf16 \
|
|
--use_lora True \
|
|
--lora_rank 32 \
|
|
--lora_alpha 64 \
|
|
--use_flash_attn True \
|
|
--target_modules q_proj k_proj v_proj o_proj
|
|
```
|
|
|
|
**For llm-based layerwise reranker**
|
|
|
|
```shell
|
|
torchrun --nproc_per_node {number of gpus} \
|
|
-m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \
|
|
--output_dir {path to save model} \
|
|
--model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \
|
|
--train_data ./toy_finetune_data.jsonl \
|
|
--learning_rate 2e-4 \
|
|
--num_train_epochs 1 \
|
|
--per_device_train_batch_size 1 \
|
|
--gradient_accumulation_steps 16 \
|
|
--dataloader_drop_last True \
|
|
--query_max_len 512 \
|
|
--passage_max_len 512 \
|
|
--train_group_size 16 \
|
|
--logging_steps 1 \
|
|
--save_steps 2000 \
|
|
--save_total_limit 50 \
|
|
--ddp_find_unused_parameters False \
|
|
--gradient_checkpointing \
|
|
--deepspeed stage1.json \
|
|
--warmup_ratio 0.1 \
|
|
--bf16 \
|
|
--use_lora True \
|
|
--lora_rank 32 \
|
|
--lora_alpha 64 \
|
|
--use_flash_attn True \
|
|
--target_modules q_proj k_proj v_proj o_proj \
|
|
--start_layer 8 \
|
|
--head_multi True \
|
|
--head_type simple \
|
|
--lora_extra_parameters linear_head
|
|
```
|
|
|
|
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:
|
|
|
|
- [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data)
|
|
- [quora train data](https://huggingface.co/datasets/quora)
|
|
- [fever train data](https://fever.ai/dataset/fever.html)
|
|
|
|
## Evaluation
|
|
|
|
- llama-index.
|
|
|
|
|
|
|
|
|
|
- BEIR.
|
|
|
|
rereank the top 100 results from bge-en-v1.5 large.
|
|
|
|
|
|
|
|
rereank the top 100 results from e5 mistral 7b instruct.
|
|
|
|
|
|
|
|
- CMTEB-retrieval.
|
|
It rereank the top 100 results from bge-zh-v1.5 large.
|
|
|
|
|
|
|
|
- miracl (multi-language).
|
|
It rereank the top 100 results from bge-m3.
|
|
|
|
|
|
|
|
|
|
|
|
## Citation
|
|
|
|
If you find this repository useful, please consider giving a star and citation
|
|
|
|
```bibtex
|
|
@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}
|
|
}
|
|
``` |