sentence-transformers/paraphrase-mpnet-base-v2

这是 sentence-transformers 模型：它将句子和段落映射到 768 维的密集向量空间，可用于聚类或语义搜索等任务。

Usage (Sentence-Transformers)

当您安装了 sentence-transformers 后，使用此模型变得非常简单：

pip install -U sentence-transformers

然后您可以像这样使用该模型：

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('sentence-transformers/paraphrase-mpnet-base-v2')
embeddings = model.encode(sentences)
print(embeddings)

Usage (HuggingFace Transformers)

在没有 sentence-transformers 的情况下，您可以像这样使用该模型：首先，将输入通过 transformer 模型，然后必须在上下文词嵌入之上应用正确的池化操作。

from transformers import AutoTokenizer, AutoModel
import torch


# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] # First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2')
model = AutoModel.from_pretrained('sentence-transformers/paraphrase-mpnet-base-v2')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")
print(sentence_embeddings)

Usage (Text Embeddings Inference (TEI))

Text Embeddings Inference (TEI) 是一个用于文本嵌入模型的极速推理解决方案。

• CPU:

docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-latest \
  --model-id sentence-transformers/paraphrase-mpnet-base-v2 \
  --pooling mean \
  --dtype float16

• NVIDIA GPU:

docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cuda-latest \
  --model-id sentence-transformers/paraphrase-mpnet-base-v2 \
  --pooling mean \
  --dtype float16

通过 OpenAI Embeddings API 向 /v1/embeddings 发送请求以生成嵌入：

curl -s http://localhost:8080/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sentence-transformers/paraphrase-mpnet-base-v2",
    "input": "This is an example sentence"
  }'

或者查看 Text Embeddings Inference API 规范。

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)

Citing & Authors

该模型由 sentence-transformers 训练。

如果您发现此模型有帮助，欢迎引用我们的论文 Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks：

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "http://arxiv.org/abs/1908.10084",
}