E5-small

新闻（2023年5月）：请切换到 e5-small-v2 ，该模型性能更好，使用方法相同。

Text Embeddings by Weakly-Supervised Contrastive Pre-training 。Liang Wang，Nan Yang，Xiaolong Huang，Binxing Jiao，Linjun Yang，Daxin Jiang，Rangan Majumder，Furu Wei，arXiv 2022

该模型有12层，嵌入大小为384。

用法

下面是一个示例，用于对MS-MARCO文本排序数据集中的查询和段落进行编码。

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]


# Each input text should start with "query: " or "passage: ".
# For tasks other than retrieval, you can simply use the "query: " prefix.
input_texts = ['query: how much protein should a female eat',
               'query: summit define',
               "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
               "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."]

tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small')
model = AutoModel.from_pretrained('intfloat/e5-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'])

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

培训细节

请参考我们在 https://arxiv.org/pdf/2212.03533.pdf 的论文。

基准评估

请查看 unilm/e5 以重现在 BEIR 和 MTEB benchmark 上的评估结果。

对于Sentence Transformers的支持

以下是使用sentence_transformers的示例。

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/e5-small')
input_texts = [
    'query: how much protein should a female eat',
    'query: summit define',
    "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
embeddings = model.encode(input_texts, normalize_embeddings=True)

软件包要求

pip install sentence_transformers〜=2.2.2

贡献者： michaelfeil

常见问题

1. 我需要在输入文本中添加前缀“query：”和“passage：”吗？

是的，这是模型的训练方式，否则会导致性能下降。

以下是一些建议：

• 在非对称任务中，如在开放式QA中进行段落检索和即席信息检索，相应地使用“query：”和“passage：”。

• 对于对称任务，如语义相似性和释义检索，使用“query：”前缀。

• 如果要使用嵌入作为特征，例如线性探测分类、聚类等，请使用“query：”前缀。

2. 为什么我的重现结果与模型卡片中报告的结果略有不同？

transformers和pytorch的不同版本可能会导致微小但非零的性能差异。

引文

如果我们的论文或模型对您有所帮助，请考虑引用如下：

@article{wang2022text,
  title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2212.03533},
  year={2022}
}

限制

这个模型只适用于英语文本。长文本将被截断为最多512个标记。