Sn Xlm Roberta Base Snli Mnli Anli Xnli

🚀 孿生網絡文本分類模型

這是一個經過訓練的孿生網絡模型，可用於零樣本和少樣本的文本分類任務。該模型以 [xlm - roberta - base](https://huggingface.co/xlm - roberta - base) 為基礎模型，在 SNLI、MNLI、ANLI 和 XNLI 數據集上進行訓練。此模型屬於 sentence - transformers 模型，它能夠將句子和段落映射到一個 768 維的密集向量空間。

🚀 快速開始

📦 安裝指南

若要使用該模型，你需要安裝 sentence - transformers：

pip install -U sentence-transformers

💻 使用示例

基礎用法（Sentence - Transformers）

安裝好 sentence - transformers 後，使用該模型就變得非常簡單：

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)

高級用法（HuggingFace Transformers）

若未安裝 sentence - transformers，你可以按以下方式使用該模型：首先，將輸入數據傳入 Transformer 模型，然後對上下文詞嵌入應用正確的池化操作。

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)

📚 詳細文檔