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--- |
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license: gemma |
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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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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. |
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Our model primarily demonstrates the following capabilities: |
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- Lightweight: The model can be made lightweight through token compression, layerwise reduction, or a combination of both. |
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- Outstanding performance: The model has achieved new state-of-the-art (SOTA) performance on both BEIR and MIRACL. |
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We will release a technical report about lightweight reranker soon with more details. |
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------ |
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You can use **bge-reranker-v2.5-gemma2-lightweight** with the following different prompts: |
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- Predict whether passage B contains an answer to query A. |
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- Predict whether passages A and B have the same meaning. |
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- Predict whether queries A and B are asking the same thing. |
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- Predict whether argument A and counterargument B express contradictory opinions. |
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## Model List |
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| Model | Base model | Language | layerwise | compress ratio | compress layers | 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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| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | [google/gemma-2-9b](https://huggingface.co/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. | |
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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), [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) and [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) |
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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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git clone https://github.com/FlagOpen/FlagEmbedding.git |
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cd FlagEmbedding |
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pip install -e . |
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``` |
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#### For LLM-based lightweight reranker |
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```python |
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from FlagEmbedding import LightWeightFlagLLMReranker |
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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 |
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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. |
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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], compress_ratio=2, compress_layer=[24, 40]) |
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print(scores) |
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``` |
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### Using Huggingface transformers |
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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 last_logit_pool(logits: torch.Tensor, |
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attention_mask: torch.Tensor) -> torch.Tensor: |
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left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) |
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if left_padding: |
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return logits[:, -1] |
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else: |
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sequence_lengths = attention_mask.sum(dim=1) - 1 |
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batch_size = logits.shape[0] |
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return torch.stack([logits[i, sequence_lengths[i]] for i in range(batch_size)], dim=0) |
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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 = "Predict whether passage B contains an answer to query A." |
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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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query_lengths = [] |
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prompt_lengths = [] |
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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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query_lengths.append(len([tokenizer.bos_token_id] + query_inputs['input_ids'] + sep_inputs)) |
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prompt_lengths.append(len(sep_inputs + prompt_inputs)) |
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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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), query_lengths, prompt_lengths |
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2.5-gemma2-lightweight', trust_remote_code=True) |
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tokenizer.padding_side = 'right' |
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model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2.5-gemma2-lightweight', trust_remote_code=True) |
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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, query_lengths, prompt_lengths = get_inputs(pairs, tokenizer) |
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inputs = inputs.to(model.device) |
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outputs = model(**inputs, |
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return_dict=True, |
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cutoff_layers=[28], |
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compress_ratio=2, |
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compress_layer=[24, 40], |
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query_lengths=query_lengths, |
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prompt_lengths=prompt_lengths) |
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scores = [] |
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for i in range(len(outputs.logits)): |
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logits = last_logit_pool(outputs.logits[i], outputs.attention_masks[i]) |
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scores.append(logits.cpu().float().tolist()) |
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print(scores) |
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``` |
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## Load model in local |
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1. make sure `gemma_config.py` and `gemma_model.py` from [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight/tree/main) in your local path. |
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2. modify the following part of config.json: |
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``` |
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"auto_map": { |
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"AutoConfig": "gemma_config.CostWiseGemmaConfig", |
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"AutoModel": "gemma_model.CostWiseGemmaModel", |
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"AutoModelForCausalLM": "gemma_model.CostWiseGemmaForCausalLM" |
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}, |
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``` |
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## Evaluation |
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The configuration of saving 60% Flops is: `compress_ratios=2`, `compress_layer=[8]`, `cutoff_layers=[25]`. |
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- **BEIR:** |
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| 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 | |
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| :----------------: | :---------------: | :-----------------: | :--------------------------------: | :-------------------: | :----------------------------------: | :----------------------------------: | |
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| **Save** **Flops** | - | - | - | - | 60% | 0 | |
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| **ArguAna** | 63.54 | 37.7 | 52.23 | 78.68 | 86.04 | 86.16 | |
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| **ClimateFEVER** | 36.49 | 37.99 | 34.65 | 39.07 | 48.41 | 48.48 | |
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| **CQA** | 42.23 | 38.24 | 40.21 | 45.85 | 49.18 | 48.9 | |
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| **DBPedia** | 44.16 | 48.15 | 49.31 | 49.92 | 51.98 | 52.11 | |
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| **FEVER** | 87.17 | 90.15 | 92.44 | 90.15 | 94.71 | 94.69 | |
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| **FiQA2018** | 44.97 | 49.32 | 45.88 | 49.32 | 60.48 | 60.95 | |
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| **HotpotQA** | 74.11 | 84.51 | 81.81 | 86.15 | 87.84 | 87.89 | |
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| **MSMARCO** | 42.48 | 47.79 | 47.83 | 48.07 | 47.23 | 47.26 | |
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| **NFCorpus** | 38.12 | 34.85 | 37.73 | 39.73 | 41.4 | 41.64 | |
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| **NQ** | 55.04 | 69.37 | 67.35 | 72.6 | 75.37 | 75.58 | |
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| **QuoraRetrieval** | 89.06 | 89.13 | 87.81 | 90.37 | 91.25 | 91.18 | |
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| **SCIDOCS** | 22.62 | 18.25 | 20.21 | 21.65 | 23.71 | 23.87 | |
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| **SciFact** | 74.64 | 73.08 | 76.93 | 77.22 | 80.5 | 80.38 | |
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| **Touche2020** | 25.08 | 35.68 | 32.45 | 35.68 | 30.64 | 31.09 | |
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| **TRECCOVID** | 74.89 | 83.39 | 80.89 | 85.51 | 84.26 | 84.85 | |
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| **Mean** | 54.31 | 55.36 | 56.52 | 60.71 | 63.1 | **63.67** | |
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| BEIR | e5-mistral-7b-instruct | bge-reranker-v2-gemma | bge-reranker-v2.5-gemma-lightweight | bge-reranker-v2.5-gemma-lightweight | |
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| :----------------: | :--------------------: | :-------------------: | :---------------------------------: | :---------------------------------: | |
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| **Save Flops** | - | - | 60% | 0 | |
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| **ArguAna** | 61.8 | 79.05 | 86.02 | 86.58 | |
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| **ClimateFEVER** | 38.37 | 37.66 | 47.27 | 47.13 | |
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| **CQA** | 42.97 | 46.16 | 49.06 | 49.53 | |
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| **DBPedia** | 48.84 | 50.77 | 52.45 | 52.87 | |
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| **FEVER** | 87.82 | 91.36 | 94.85 | 95.19 | |
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| **FiQA2018** | 56.58 | 50.96 | 58.81 | 61.19 | |
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| **HotpotQA** | 75.72 | 86.99 | 88.49 | 88.82 | |
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| **MSMARCO** | 43.06 | 48.35 | 47.65 | 47.4 | |
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| **NFCorpus** | 38.58 | 39.25 | 42.28 | 42.17 | |
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| **NQ** | 63.56 | 73.44 | 75 | 76.28 | |
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| **QuoraRetrieval** | 89.59 | 90.44 | 91.09 | 91.18 | |
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| **SCIDOCS** | 16.3 | 20.77 | 22.2 | 22.69 | |
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| **SciFact** | 76.26 | 77.78 | 79.94 | 80.98 | |
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| **Touche2020** | 26.24 | 35.79 | 28.69 | 31.17 | |
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| **TRECCOVID** | 87.07 | 88.13 | 86.61 | 87.36 | |
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| **Mean** | 56.85 | 61.13 | 63.36 | **64.04** | |
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- **MIRACL**: |
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| 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 | |
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| :--------------------------------------: | :----------: | :--------: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | |
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| **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 | |
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| **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 | |
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| **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 | |
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| **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 | |
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| **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 | |
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| **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 | |
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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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``` |
