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transformers - sentence-similarity license: mit --- # Updates New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md. You can also find these models on my [homepage](https://huggingface.co/infgrad). # Introduction The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for their contributions! **We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** Prompt of s2p task(e.g. retrieve task): ```text Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query} ``` Prompt of s2s task(e.g. semantic textual similarity task): ```text Instruct: Retrieve semantically similar text.\nQuery: {query} ``` The models are finally trained by [MRL]((https://arxiv.org/abs/2205.13147)), so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192. The higher the dimension, the better the performance. **Generally speaking, 1024d is good enough.** The MTEB score of 1024d is only 0.001 lower than 8192d. # Model directory structure The model directory structure is very simple, it is a standard SentenceTransformer directory **with a series of `2_Dense_{dims}` folders**, where `dims` represents the final vector dimension. For example, the `2_Dense_256` folder stores Linear weights that convert vector dimensions to 256 dimensions. Please refer to the following chapters for specific instructions on how to use them. # Usage You can use `SentenceTransformer` or `transformer` library to encode text. ```python import os import torch from transformers import AutoModel, AutoTokenizer from sentence_transformers import SentenceTransformer from sklearn.preprocessing import normalize prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: {query}" queries = ["query1", "query2"] queries = [prompt.replace("{query}", query) for query in queries] # doc do not need any prompts docs = ["doc1", "doc2"] model_dir = "{Your MODEL_PATH}" #### method1: SentenceTransformer # ！！！！The default dimension is 8192，if you need other dimensions, please copy the files from the `2_Dense_{dims}` folder to overwrite them. For example, `copy -r ./2_Dense_1024/* ./2_Dense/` ！！！！ model = SentenceTransformer(model_dir, trust_remote_code=True).cuda() vectors = model.encode(queries, convert_to_numpy=True, normalize_embeddings=True) print(vectors.shape) print(vectors[:, :4]) #### method2：transformers vector_linear_directory = "2_Dense" vector_dim = 8192 model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval() tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) vector_linear = torch.nn.Linear(in_features=model.config.hidden_size, out_features=vector_dim) vector_linear_dict = { k.replace("linear.", ""): v for k, v in torch.load(os.path.join(model_dir, f"{vector_linear_directory}/pytorch_model.bin")).items() } vector_linear.load_state_dict(vector_linear_dict) vector_linear.cuda() with torch.no_grad(): input_data = tokenizer(queries, padding="longest", truncation=True, max_length=512, return_tensors="pt") input_data = {k: v.cuda() for k, v in input_data.items()} attention_mask = input_data["attention_mask"] last_hidden_state = model(**input_data)[0] last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] vectors = normalize(vector_linear(vectors).cpu().numpy()) print(vectors.shape) print(vectors[:, :4]) ``` # FAQ Q: The details of training? A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper) Q: How to choose a suitable prompt for my own task? A: In most cases, please use the s2p and s2s prompts. These two prompts account for the vast majority of the training data. Q: How to reproduce MTEB results? A: Please use evaluation scripts in `Alibaba-NLP/gte-Qwen2-1.5B-instruct` or `intfloat/e5-mistral-7b-instruct` Q: Why each dimension has a linear weight? A: MRL has multiple training methods, we choose this method which has the best performance. Q: What is the sequence length of models? A: 512 is recommended, in our experiments, almost all models perform poorly on specialized long text retrieval datasets. Besides, the model is trained on datasets of 512 length. This may be an optimization term. If you have any questions, please start a discussion on community.