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gte-small

General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning

The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including information retrieval, semantic textual similarity, text reranking, etc.

Metrics

We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.

Model Name Model Size (GB) Dimension Sequence Length Average (56) Clustering (11) Pair Classification (3) Reranking (4) Retrieval (15) STS (10) Summarization (1) Classification (12)
gte-large 0.67 1024 512 63.13 46.84 85.00 59.13 52.22 83.35 31.66 73.33
gte-base 0.22 768 512 62.39 46.2 84.57 58.61 51.14 82.3 31.17 73.01
e5-large-v2 1.34 1024 512 62.25 44.49 86.03 56.61 50.56 82.05 30.19 75.24
e5-base-v2 0.44 768 512 61.5 43.80 85.73 55.91 50.29 81.05 30.28 73.84
gte-small 0.07 384 512 61.36 44.89 83.54 57.7 49.46 82.07 30.42 72.31
text-embedding-ada-002 - 1536 8192 60.99 45.9 84.89 56.32 49.25 80.97 30.8 70.93
e5-small-v2 0.13 384 512 59.93 39.92 84.67 54.32 49.04 80.39 31.16 72.94
sentence-t5-xxl 9.73 768 512 59.51 43.72 85.06 56.42 42.24 82.63 30.08 73.42
all-mpnet-base-v2 0.44 768 514 57.78 43.69 83.04 59.36 43.81 80.28 27.49 65.07
sgpt-bloom-7b1-msmarco 28.27 4096 2048 57.59 38.93 81.9 55.65 48.22 77.74 33.6 66.19
all-MiniLM-L12-v2 0.13 384 512 56.53 41.81 82.41 58.44 42.69 79.8 27.9 63.21
all-MiniLM-L6-v2 0.09 384 512 56.26 42.35 82.37 58.04 41.95 78.9 30.81 63.05
contriever-base-msmarco 0.44 768 512 56.00 41.1 82.54 53.14 41.88 76.51 30.36 66.68
sentence-t5-base 0.22 768 512 55.27 40.21 85.18 53.09 33.63 81.14 31.39 69.81

Usage

Code example

import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def average_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]

tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
model = AutoModel.from_pretrained("thenlper/gte-small")

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

Use with sentence-transformers:

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['That is a happy person', 'That is a very happy person']

model = SentenceTransformer('thenlper/gte-large')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))

Limitation

This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.

Citation

If you find our paper or models helpful, please consider citing them as follows:

@misc{li2023general,
      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
      year={2023},
      eprint={2308.03281},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
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