🚀 孪生网络文本分类模型

这是一个经过训练的孪生网络模型，可用于零样本和少样本的文本分类任务。该模型以 [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) 为基础模型，在 SNLI、MNLI、ANLI 和 XNLI 数据集上进行训练。此模型属于 sentence - transformers 模型，它能够将句子和段落映射到一个 768 维的密集向量空间。

🚀 快速开始

📦 安装指南

若要使用该模型，你需要安装 sentence - transformers：

pip install -U sentence-transformers

💻 使用示例

基础用法（Sentence - Transformers）

安装好 sentence - transformers 后，使用该模型就变得非常简单：

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

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

高级用法（HuggingFace Transformers）

若未安装 sentence - transformers，你可以按以下方式使用该模型：首先，将输入数据传入 Transformer 模型，然后对上下文词嵌入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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')

# 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)

📚 详细文档