diff --git "a/README.md" "b/README.md" --- "a/README.md" +++ "b/README.md" @@ -1,5596 +1,5643 @@ ---- -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 - - type: f1_weighted - 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- 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 ---- - - - -# 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 `SentenceTransformers` or `transformers` library to encode text. - -## Sentence Transformers - -```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` -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.", -] - -# ！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` ! -model = SentenceTransformer("infgrad/stella_en_400M_v5", trust_remote_code=True).cuda() -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) - -similarities = model.similarity(query_embeddings, doc_embeddings) -print(similarities) -# tensor([[0.8398, 0.2990], -# [0.3282, 0.8095]]) -``` - -## Transformers - -```python -import os -import torch -from transformers import AutoModel, AutoTokenizer -from sklearn.preprocessing import normalize - -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}" - -vector_dim = 1024 -vector_linear_directory = f"2_Dense_{vector_dim}" -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() - -# 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()) - -# 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()) - -print(query_vectors.shape, docs_vectors.shape) -# (2, 1024) (2, 1024) - -similarities = query_vectors @ docs_vectors.T -print(similarities) -# [[0.8397531 0.29900077] -# [0.32818374 0.80954516]] -``` - -# 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. - +--- +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 + - type: f1_weighted + value: 97.1936887167346 + - type: main_score + value: 97.1945 + task: + type: Classification + - 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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 +- transformers.js +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 `SentenceTransformers` or `transformers` library to encode text. + +## Sentence Transformers + +```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` +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.", +] + +# ！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` ! +model = SentenceTransformer("infgrad/stella_en_400M_v5", trust_remote_code=True).cuda() +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) + +similarities = model.similarity(query_embeddings, doc_embeddings) +print(similarities) +# tensor([[0.8398, 0.2990], +# [0.3282, 0.8095]]) +``` + +## Transformers + +```python +import os +import torch +from transformers import AutoModel, AutoTokenizer +from sklearn.preprocessing import normalize + +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}" + +vector_dim = 1024 +vector_linear_directory = f"2_Dense_{vector_dim}" +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() + +# 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()) + +# 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()) + +print(query_vectors.shape, docs_vectors.shape) +# (2, 1024) (2, 1024) + +similarities = query_vectors @ docs_vectors.T +print(similarities) +# [[0.8397531 0.29900077] +# [0.32818374 0.80954516]] +``` + +## Transformers.js + +You can also use the `transformers.js` library to run the model directly in JavaScript (in-browser, Node.js, Deno, etc.)! + +If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library (v3) using: +```bash +npm i xenova/transformers.js#v3 +``` + +Then, you can use the model as follows: +```js +import { AutoModel, AutoTokenizer, matmul } from '@xenova/transformers'; + +// Load model and tokenizer +const model_id = 'dunzhang/stella_en_400M_v5'; +const model = await AutoModel.from_pretrained(model_id); +const tokenizer = await AutoTokenizer.from_pretrained(model_id); + +// Embed the queries +const query_prompt = "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: "; +const queries = [ + "What are some ways to reduce stress?", + "What are the benefits of drinking green tea?", +].map(x => query_prompt + x); +const query_inputs = tokenizer(queries, { padding: true, truncation: true }); +const query_outputs = await model(query_inputs); +const normalized_query_vectors = query_outputs.sentence_embedding.normalize(); + +// Embed the documents +const 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.", +]; +const docs_inputs = tokenizer(docs, { padding: true, truncation: true }); +const docs_outputs = await model(docs_inputs); +const normalized_docs_vectors = docs_outputs.sentence_embedding.normalize(); + +// Compute similarity scores +const similarities = await matmul(normalized_query_vectors, normalized_docs_vectors.transpose(1, 0)); +console.log(similarities.tolist()); +// [ +// [ 0.768531322479248, 0.3589509129524231 ], +// [ 0.37918326258659363, 0.8047795295715332 ] +// ] +``` + +# 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. \ No newline at end of file