272 lines
16 KiB
Markdown
272 lines
16 KiB
Markdown
# Embedding Model
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## Frequently asked questions
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**The very poor results caused by incorrect usage**
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Different from other embedding models using mean pooling, BGE uses the last hidden state of `[cls]` as the sentence embedding: `sentence_embeddings = model_output[0][:, 0]`.
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If you use mean pooling, there will be a significant decrease in performance.
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Therefore, make sure to use the correct method to obtain sentence vectors. You can refer to the usage method we provide.
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**1. How to fine-tune bge embedding model?**
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Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder) to prepare data and fine-tune your model.
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Some suggestions:
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- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder#hard-negatives), which can improve the retrieval performance.
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- In general, larger hyper-parameter `per_device_train_batch_size` brings better performance. You can expand it by enabling `--fp16`, `--deepspeed df_config.json` (df_config.json can refer to [ds_config.json](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/ds_stage0.json), `--gradient_checkpointing`, etc.
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- If you want to maintain the performance on other tasks when fine-tuning on your data, you can use [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LM_Cocktail) to merge the fine-tuned model and the original bge model. Besides, if you want to fine-tune on multiple tasks, you also can approximate the multi-task learning via model merging as [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LM_Cocktail).
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- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
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- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
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Here is the way we used to fine-tune `bge-large-zh-v1.5`:
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The fine-tuning datasets consist of t2ranking, dulreader, mmarco, cmedqav2, mulit-cpr, nli-zh, ocmnli, and cmnli.
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For t2ranking, dulreader, and mmarco, we mine hard negatives;
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For nli-zh, ocmnli, and cmnli, we use the pairs whose label equal to 0 as negatives;
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For cmedqav2 and mulit-cpr, we randomly sample negatives.
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The settings of fine-tuning are: train_group_size=2, learning_rate=1e-5, max_epoch=5.
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We train two models: one fine-tune with `--query_instruction_for_retrieval "为这个句子生成表示以用于检索相关文章："`,
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and the other model is fine-tuned with `--query_instruction_for_retrieval ""`,
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and then we merge two variants into one model to make the final model can be used both with and without instruction.
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<details>
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<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
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<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
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**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
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Since we finetune the models by contrastive learning with a temperature of 0.01,
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the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
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So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
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For downstream tasks, such as passage retrieval or semantic similarity,
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**what matters is the relative order of the scores, not the absolute value.**
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If you need to filter similar sentences based on a similarity threshold,
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please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
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</details>
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<details>
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<summary>3. When does the query instruction need to be used</summary>
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<!-- ### When does the query instruction need to be used -->
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For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
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No instruction only has a slight degradation in retrieval performance compared with using instruction.
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So you can generate embedding without instruction in all cases for convenience.
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For a retrieval task that uses short queries to find long related documents,
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it is recommended to add instructions for these short queries.
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**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
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In all cases, the documents/passages do not need to add the instruction.
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</details>
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## Usage
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### Using FlagEmbedding
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Install:
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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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or:
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```
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pip install -U FlagEmbedding
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```
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```python
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from FlagEmbedding import FlagModel
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sentences_1 = ["样例数据-1", "样例数据-2"]
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sentences_2 = ["样例数据-3", "样例数据-4"]
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model = FlagModel('BAAI/bge-large-zh-v1.5',
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query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
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use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
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embeddings_1 = model.encode(sentences_1)
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embeddings_2 = model.encode(sentences_2)
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similarity = embeddings_1 @ embeddings_2.T
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print(similarity)
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# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
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# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
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queries = ['query_1', 'query_2']
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passages = ["样例文档-1", "样例文档-2"]
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q_embeddings = model.encode_queries(queries)
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p_embeddings = model.encode(passages)
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scores = q_embeddings @ p_embeddings.T
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```
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For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
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By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
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You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
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### Using Sentence-Transformers
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You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
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```
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pip install -U sentence-transformers
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```
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```python
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from sentence_transformers import SentenceTransformer
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sentences_1 = ["样例数据-1", "样例数据-2"]
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sentences_2 = ["样例数据-3", "样例数据-4"]
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model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
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embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
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embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
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similarity = embeddings_1 @ embeddings_2.T
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print(similarity)
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```
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For s2p(short query to long passage) retrieval task,
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each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
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But the instruction is not needed for passages.
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```python
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from sentence_transformers import SentenceTransformer
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queries = ['query_1', 'query_2']
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passages = ["样例文档-1", "样例文档-2"]
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instruction = "为这个句子生成表示以用于检索相关文章："
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model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
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q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
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p_embeddings = model.encode(passages, normalize_embeddings=True)
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scores = q_embeddings @ p_embeddings.T
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```
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### Using Langchain
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You can use `bge` in langchain like this:
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```python
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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model_name = "BAAI/bge-large-en-v1.5"
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model_kwargs = {'device': 'cuda'}
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encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
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model = HuggingFaceBgeEmbeddings(
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model_name=model_name,
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model_kwargs=model_kwargs,
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encode_kwargs=encode_kwargs,
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query_instruction="为这个句子生成表示以用于检索相关文章："
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)
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model.query_instruction = "为这个句子生成表示以用于检索相关文章："
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```
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### Using HuggingFace Transformers
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With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
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```python
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from transformers import AutoTokenizer, AutoModel
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import torch
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# Sentences we want sentence embeddings for
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sentences = ["样例数据-1", "样例数据-2"]
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# Load model from HuggingFace Hub
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
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model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
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model.eval()
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# Tokenize sentences
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
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# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
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# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
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# Compute token embeddings
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with torch.no_grad():
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model_output = model(**encoded_input)
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# Perform pooling. In this case, cls pooling.
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sentence_embeddings = model_output[0][:, 0]
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# normalize embeddings
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sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
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print("Sentence embeddings:", sentence_embeddings)
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```
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## Evaluation
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`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
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For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/C_MTEB)
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If you want to evaluate the model(or your model) on **your data**, you can refer to this [tool](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation#8-custom-dataset).
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- **MTEB**:
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| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
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|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
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| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
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| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
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| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
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| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
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| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
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| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
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| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
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| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
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| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
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| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
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| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
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| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
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| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
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| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
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| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
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| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
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| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
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- **C-MTEB**:
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We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
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Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/research/C_MTEB/README.md) for a detailed introduction.
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| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
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|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
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| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
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| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
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| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
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| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
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| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
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| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
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| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
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| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
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| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
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| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
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| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
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| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
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| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
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| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
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| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
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## Acknowledgement
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Part of the code is developed based on [Dense](https://github.com/luyug/Dense).
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## Citation
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If you find this repository useful, please consider giving a star :star: and citation
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```
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@misc{bge_embedding,
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title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
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author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
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year={2023},
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eprint={2309.07597},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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