name: stella_en_1.5B_v5 results:
- dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics:
  - type: accuracy value: 92.86567164179104
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  - type: ap_weighted value: 72.13503907102613
  - type: f1 value: 89.5586886376355
  - type: f1_weighted value: 93.13621183004571
  - type: main_score value: 92.86567164179104 task: type: Classification
- dataset: config: default name: MTEB AmazonPolarityClassification revision: e2d317d38cd51312af73b3d32a06d1a08b442046 split: test type: mteb/amazon_polarity metrics:
  - type: accuracy value: 97.16485
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  - type: ap_weighted value: 96.05546315415225
  - type: f1 value: 97.16351087403213
  - type: f1_weighted value: 97.16351087403213
  - type: main_score value: 97.16485 task: type: Classification
- dataset: config: en name: MTEB AmazonReviewsClassification (en) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics:
  - type: accuracy value: 59.358
  - type: f1 value: 59.0264615883114
  - type: f1_weighted value: 59.0264615883114
  - type: main_score value: 59.358 task: type: Classification
- dataset: config: default name: MTEB ArguAna revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics:
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  - type: precision_at_1 value: 41.607
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  - type: recall_at_1 value: 41.607
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- dataset: config: default name: MTEB ArxivClusteringP2P revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d split: test type: mteb/arxiv-clustering-p2p metrics:
  - type: main_score value: 55.437138353189994
  - type: v_measure value: 55.437138353189994
  - type: v_measure_std value: 14.718556601335491 task: type: Clustering
- dataset: config: default name: MTEB ArxivClusteringS2S revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 split: test type: mteb/arxiv-clustering-s2s metrics:
  - type: main_score value: 50.65858459544658
  - type: v_measure value: 50.65858459544658
  - type: v_measure_std value: 14.887033747525146 task: type: Clustering
- dataset: config: default name: MTEB AskUbuntuDupQuestions revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics:
  - type: main_score value: 67.32597152838535
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  - type: nAUC_map_diff1 value: 16.8624639710487
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- dataset: config: default name: MTEB BIOSSES revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics:
  - type: cosine_pearson value: 85.790820496042
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  - type: euclidean_spearman value: 83.17297949010973
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- dataset: config: default name: MTEB Banking77Classification revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics:
  - type: accuracy value: 89.78896103896105
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  - type: f1_weighted value: 89.76107366333488
  - type: main_score value: 89.78896103896105 task: type: Classification
- dataset: config: default name: MTEB BiorxivClusteringP2P revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 split: test type: mteb/biorxiv-clustering-p2p metrics:
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- dataset: config: default name: MTEB BiorxivClusteringS2S revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 split: test type: mteb/biorxiv-clustering-s2s metrics:
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- dataset: config: default name: MTEB CQADupstackRetrieval revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 split: test type: mteb/cqadupstack metrics:
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- dataset: config: default name: MTEB ToxicConversationsClassification revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de split: test type: mteb/toxic_conversations_50k metrics:
  - type: accuracy value: 88.759765625
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  - type: ap_weighted value: 36.49152357863017
  - type: f1 value: 74.4692714448641
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- dataset: config: default name: MTEB TweetSentimentExtractionClassification revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics:
  - type: accuracy value: 74.8443689869836
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  - type: f1_weighted value: 74.7369003946243
  - type: main_score value: 74.8443689869836 task: type: Classification
- dataset: config: default name: MTEB TwentyNewsgroupsClustering revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics:
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- dataset: config: default name: MTEB TwitterSemEval2015 revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics:
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- dataset: config: default name: MTEB TwitterURLCorpus revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics:
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  - type: similarity_recall value: 83.50015398829689 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.

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):

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):

Instruct: Retrieve semantically similar text.\nQuery: {query}

The models are finally trained by MRL, 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`
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.",
]

# ！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("dunzhang/stella_en_1.5B_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([[0.8179, 0.2958],
#         [0.3194, 0.7854]])

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)
# [[0.8178789  0.2958377 ]
#  [0.31938642 0.7853526 ]]

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.

RichardErkhov/linhphanff_-_stella_en_1.5B_v5_clone-4bits

Original model description: