diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,199 +1,5606 @@ --- -library_name: transformers -tags: [] +model-index: +- name: stella_en_400M_v5 + results: + - dataset: + config: en + name: MTEB AmazonCounterfactualClassification (en) + revision: e8379541af4e31359cca9fbcf4b00f2671dba205 + split: test + type: mteb/amazon_counterfactual + metrics: + - type: accuracy + value: 92.35820895522387 + - type: ap + value: 70.81322736988783 + - type: ap_weighted + value: 70.81322736988783 + - type: f1 + value: 88.9505466159595 + - type: f1_weighted + value: 92.68630932872613 + - type: main_score + value: 92.35820895522387 + task: + type: Classification + - dataset: + config: default + name: MTEB AmazonPolarityClassification + revision: e2d317d38cd51312af73b3d32a06d1a08b442046 + split: test + type: mteb/amazon_polarity + metrics: + - type: accuracy + value: 97.1945 + - type: ap + value: 96.08192192244094 + - type: ap_weighted + value: 96.08192192244094 + - type: f1 + value: 97.1936887167346 + - 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type: f1 + value: 71.5490757306975 + - type: f1_weighted + value: 89.14632533489856 + - type: main_score + value: 86.9384765625 + task: + type: Classification + - dataset: + config: default + name: MTEB TweetSentimentExtractionClassification + revision: d604517c81ca91fe16a244d1248fc021f9ecee7a + split: test + type: mteb/tweet_sentiment_extraction + metrics: + - type: accuracy + value: 73.57668364459535 + - type: f1 + value: 73.90467103648074 + - type: f1_weighted + value: 73.42158415034704 + - type: main_score + value: 73.57668364459535 + task: + type: Classification + - dataset: + config: default + name: MTEB TwentyNewsgroupsClustering + revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 + split: test + type: mteb/twentynewsgroups-clustering + metrics: + - type: main_score + value: 58.574148097494685 + - type: v_measure + value: 58.574148097494685 + - type: v_measure_std + value: 0.9443161637490822 + task: + type: Clustering + - dataset: + config: default + name: MTEB TwitterSemEval2015 + revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 + split: test + type: mteb/twittersemeval2015-pairclassification + metrics: + - 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type: similarity_recall + value: 76.56992084432717 + task: + type: PairClassification + - dataset: + config: default + name: MTEB TwitterURLCorpus + revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf + split: test + type: mteb/twitterurlcorpus-pairclassification + metrics: + - type: cosine_accuracy + value: 89.7892653393876 + - type: cosine_accuracy_threshold + value: 79.69566583633423 + - type: cosine_ap + value: 87.4579867302024 + - type: cosine_f1 + value: 79.91620843152658 + - type: cosine_f1_threshold + value: 78.53609323501587 + - type: cosine_precision + value: 77.7155329210622 + - type: cosine_recall + value: 82.24514936864799 + - type: dot_accuracy + value: 89.78732487289945 + - type: dot_accuracy_threshold + value: 80.05315661430359 + - type: dot_ap + value: 87.44916182456272 + - type: dot_f1 + value: 79.90419878751591 + - type: dot_f1_threshold + value: 78.57890725135803 + - type: dot_precision + value: 77.73409057812728 + - type: dot_recall + value: 82.19895287958116 + - type: euclidean_accuracy + value: 89.78538440641131 + - type: euclidean_accuracy_threshold + value: 62.29925751686096 + - type: euclidean_ap + value: 87.45904868911386 + - type: euclidean_f1 + value: 79.93127404474657 + - type: euclidean_f1_threshold + value: 65.61101078987122 + - type: euclidean_precision + value: 77.62060210373595 + - type: euclidean_recall + value: 82.38373883584848 + - type: main_score + value: 87.46554314325058 + - type: manhattan_accuracy + value: 89.76597974152986 + - type: manhattan_accuracy_threshold + value: 3988.5299682617188 + - type: manhattan_ap + value: 87.46554314325058 + - type: manhattan_f1 + value: 79.97181740645973 + - type: manhattan_f1_threshold + value: 4235.905838012695 + - type: manhattan_precision + value: 77.13713427283783 + - type: manhattan_recall + value: 83.02279026793964 + - type: max_ap + value: 87.46554314325058 + - type: max_f1 + value: 79.97181740645973 + - type: max_precision + value: 77.73409057812728 + - type: max_recall + value: 83.02279026793964 + - type: similarity_accuracy + value: 89.7892653393876 + - type: similarity_accuracy_threshold + value: 79.69566583633423 + - type: similarity_ap + value: 87.4579867302024 + - type: similarity_f1 + value: 79.91620843152658 + - type: similarity_f1_threshold + value: 78.53609323501587 + - type: similarity_precision + value: 77.7155329210622 + - type: similarity_recall + value: 82.24514936864799 + task: + type: PairClassification +tags: +- mteb +- sentence-transformers +- transformers +- sentence-similarity +license: mit --- -# Model Card for Model ID - +# Updates +New open-source models and ToDoList will be listed on https://github.com/DunZhang/Stella/blob/main/news_and_todo.md. -## Model Details +You can also find these models on my [homepage](https://huggingface.co/infgrad). -### Model Description +# 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! -This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. +**We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** -- **Developed by:** [More Information Needed] -- **Funded by [optional]:** [More Information Needed] -- **Shared by [optional]:** [More Information Needed] -- **Model type:** [More Information Needed] -- **Language(s) (NLP):** [More Information Needed] -- **License:** [More Information Needed] -- **Finetuned from model [optional]:** [More Information Needed] +Prompt of s2p task(e.g. retrieve task): -### Model Sources [optional] +```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): -- **Repository:** [More Information Needed] -- **Paper [optional]:** [More Information Needed] -- **Demo [optional]:** [More Information Needed] +```text +Instruct: Retrieve semantically similar text.\nQuery: {query} +``` -## Uses +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. -### Direct Use +# 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. -[More Information Needed] +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. -### Downstream Use [optional] +# Usage - +You can use `SentenceTransformers` or `transformers` library to encode text. -[More Information Needed] +## Sentence Transformers -### Out-of-Scope Use +```python +from sentence_transformers import SentenceTransformer - +# This model supports two prompts: "s2p_query" and "s2s_query" for sentence-to-passage and sentence-to-sentence tasks, respectively. +# They are defined in `config_sentence_transformers.json` +query_prompt_name = "s2p_query" +queries = [ + "What are some ways to reduce stress?", + "What are the benefits of drinking green tea?", +] +# docs do not need any prompts +docs = [ + "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.", + "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.", +] -[More Information Needed] +# ！The default dimension is 1024, if you need other dimensions, please clone the model and modify `modules.json` to replace `2_Dense_1024` with another dimension, e.g. `2_Dense_256` or `2_Dense_8192` ! +# on gpu +model = SentenceTransformer("dunzhang/stella_en_400M_v5", trust_remote_code=True).cuda() +# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU. +# model = SentenceTransformer( +# "dunzhang/stella_en_400M_v5", +# trust_remote_code=True, +# device="cpu", +# config_kwargs={"use_memory_efficient_attention": False, "unpad_inputs": False} +# ) +query_embeddings = model.encode(queries, prompt_name=query_prompt_name) +doc_embeddings = model.encode(docs) +print(query_embeddings.shape, doc_embeddings.shape) +# (2, 1024) (2, 1024) -## Bias, Risks, and Limitations +similarities = model.similarity(query_embeddings, doc_embeddings) +print(similarities) +# tensor([[0.8398, 0.2990], +# [0.3282, 0.8095]]) +``` - +## Transformers -[More Information Needed] +```python +import os +import torch +from transformers import AutoModel, AutoTokenizer +from sklearn.preprocessing import normalize -### Recommendations +query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: " +queries = [ + "What are some ways to reduce stress?", + "What are the benefits of drinking green tea?", +] +queries = [query_prompt + query for query in queries] +# docs do not need any prompts +docs = [ + "There are many effective ways to reduce stress. Some common techniques include deep breathing, meditation, and physical activity. Engaging in hobbies, spending time in nature, and connecting with loved ones can also help alleviate stress. Additionally, setting boundaries, practicing self-care, and learning to say no can prevent stress from building up.", + "Green tea has been consumed for centuries and is known for its potential health benefits. It contains antioxidants that may help protect the body against damage caused by free radicals. Regular consumption of green tea has been associated with improved heart health, enhanced cognitive function, and a reduced risk of certain types of cancer. The polyphenols in green tea may also have anti-inflammatory and weight loss properties.", +] - +# The path of your model after cloning it +model_dir = "{Your MODEL_PATH}" -Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. +vector_dim = 1024 +vector_linear_directory = f"2_Dense_{vector_dim}" +model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).cuda().eval() +# you can also use this model without the features of `use_memory_efficient_attention` and `unpad_inputs`. It can be worked in CPU. +# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True,use_memory_efficient_attention=False,unpad_inputs=False).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() -## How to Get Started with the Model +# Embed the queries +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) + query_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] + query_vectors = normalize(vector_linear(query_vectors).cpu().numpy()) -Use the code below to get started with the model. +# Embed the documents +with torch.no_grad(): + input_data = tokenizer(docs, 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) + docs_vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] + docs_vectors = normalize(vector_linear(docs_vectors).cpu().numpy()) -[More Information Needed] +print(query_vectors.shape, docs_vectors.shape) +# (2, 1024) (2, 1024) -## Training Details +similarities = query_vectors @ docs_vectors.T +print(similarities) +# [[0.8397531 0.29900077] +# [0.32818374 0.80954516]] +``` -### Training Data +# FAQ - +Q: The details of training? -[More Information Needed] +A: The training method and datasets will be released in the future. (specific time unknown, may be provided in a paper) -### Training Procedure +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. -#### Preprocessing [optional] +Q: How to reproduce MTEB results? -[More Information Needed] +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? -#### Training Hyperparameters +A: MRL has multiple training methods, we choose this method which has the best performance. -- **Training regime:** [More Information Needed] +Q: What is the sequence length of models? -#### Speeds, Sizes, Times [optional] +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. - - -[More Information Needed] - -## Evaluation - - - -### Testing Data, Factors & Metrics - -#### Testing Data - - - -[More Information Needed] - -#### Factors - - - -[More Information Needed] - -#### Metrics - - - -[More Information Needed] - -### Results - -[More Information Needed] - -#### Summary - - - -## Model Examination [optional] - - - -[More Information Needed] - -## Environmental Impact - - - -Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - -- **Hardware Type:** [More Information Needed] -- **Hours used:** [More Information Needed] -- **Cloud Provider:** [More Information Needed] -- **Compute Region:** [More Information Needed] -- **Carbon Emitted:** [More Information Needed] - -## Technical Specifications [optional] - -### Model Architecture and Objective - -[More Information Needed] - -### Compute Infrastructure - -[More Information Needed] - -#### Hardware - -[More Information Needed] - -#### Software - -[More Information Needed] - -## Citation [optional] - - - -**BibTeX:** - -[More Information Needed] - -**APA:** - -[More Information Needed] - -## Glossary [optional] - - - -[More Information Needed] - -## More Information [optional] - -[More Information Needed] - -## Model Card Authors [optional] - -[More Information Needed] - -## Model Card Contact - -[More Information Needed] \ No newline at end of file +If you have any questions, please start a discussion on community. \ No newline at end of file