BAAI
/

bge-reranker-v2-minicpm-layerwise

@@ -1,360 +1,38 @@
-# 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) | [google/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) | [openbmb/MiniCPM-2B-dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16/tree/main) |    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_bf16=True) # Setting use_bf16 to True speeds up computation with a slight performance degradation
-score = reranker.compute_score(['query', 'passage'])
-print(score) # 2.15625
-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) # [-0.84765625, 10.625]
-```
-#### For LLM-based layerwise reranker
-```python
-from FlagEmbedding import LayerWiseFlagLLMReranker
-reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # Setting use_bf16 to True speeds 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) # -7.03125
-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) # [-10.0, 1.8203125]
-```
-### 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, torch_dtype=torch.bfloat16)
-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
-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 BAAI/bge-reranker-v2-gemma \
---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 BAAI/bge-reranker-v2-minicpm-layerwise \
---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
-```
-Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16/tree/main) (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.
-![image-20240317193909373](./evaluation/llama-index.png)
-- BEIR.
-rereank the top 100 results from bge-en-v1.5 large.
-![image-20240317174633333](./evaluation/BEIR-bge-en-v1.5.png)
-rereank the top 100 results from e5 mistral 7b instruct.
-![image-20240317172949713](./evaluation/BEIR-e5-mistral.png)
-- CMTEB-retrieval.
-It rereank the top 100 results from bge-zh-v1.5 large.
-![image-20240317173026235](./evaluation/CMTEB-retrieval-bge-zh-v1.5.png)
-- miracl (multi-language).
-It rereank the top 100 results from bge-m3.
-![image-20240317173117639](./evaluation/miracl-bge-m3.png)
-## Citation
-If you find this repository useful, please consider giving a star :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}
-}

+{
+  "_name_or_path": "BAAI/bge-reranker-v2-minicpm-layerwise",
+  "architectures": [
+    "LayerWiseMiniCPMForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "BAAI/bge-reranker-v2-minicpm-layerwise--configuration_minicpm_reranker.LayerWiseMiniCPMConfig",
+    "AutoModel": "BAAI/bge-reranker-v2-minicpm-layerwise--modeling_minicpm_reranker.LayerWiseMiniCPMModel",
+    "AutoModelForCausalLM": "BAAI/bge-reranker-v2-minicpm-layerwise--modeling_minicpm_reranker.LayerWiseMiniCPMForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dim_model_base": 256,
+  "eos_token_id": 2,
+  "head_multi": true,
+  "head_type": "simple",
+  "hidden_act": "silu",
+  "hidden_size": 2304,
+  "initializer_range": 0.1,
+  "intermediate_size": 5760,
+  "max_position_embeddings": 2048,
+  "model_type": "minicpm",
+  "num_attention_heads": 36,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 36,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "scale_depth": 1.4,
+  "scale_emb": 12,
+  "start_layer": 8,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.1",
+  "use_cache": false,
+  "vocab_size": 122753
 }