语言: 法语
许可证: MIT
数据集:

• oscar

CamemBERT：一款美味的法语语言模型

此模型是此模型仓库在特定提交版本482393b6198924f9da270b1aaf37d238aafca99b的副本。

简介

CamemBERT 是基于RoBERTa模型开发的法语先进语言模型。

现已在Hugging Face上提供6种不同版本，参数数量、预训练数据量及数据来源领域各不相同。

更多信息或请求，请访问Camembert官网

预训练模型

如何在HuggingFace中使用CamemBERT

加载CamemBERT及其子词分词器：

from transformers import CamembertModel, CamembertTokenizer  

# 可将"camembert-base"替换为表中其他模型，如"camembert/camembert-large"。  
tokenizer = CamembertTokenizer.from_pretrained("camembert-base")  
camembert = CamembertModel.from_pretrained("camembert-base")  

camembert.eval()  # 关闭dropout（或保持训练模式以微调）  

使用pipeline进行掩码填充

from transformers import pipeline   

camembert_fill_mask = pipeline("fill-mask", model="camembert-base", tokenizer="camembert-base")  
results = camembert_fill_mask("Le camembert est <mask> :)")  
# 结果  
#[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200},  
# {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183},   
# {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202},   
# {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528},   
# {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}]  

从Camembert输出中提取上下文嵌入特征

import torch  
# 使用SentencePiece进行子词分词  
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")  
# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']   

# 进行1-hot编码并添加特殊起始和结束标记  
encoded_sentence = tokenizer.encode(tokenized_sentence)  
# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]   
# 注：可一步完成：tokenize.encode("J'aime le camembert !")  

# 将标记作为torch张量输入Camembert（批次维度为1）  
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)  
embeddings, _ = camembert(encoded_sentence)  
# embeddings.detach()  
# embeddings.size torch.Size([1, 10, 768])  
# tensor([[[-0.0254,  0.0235,  0.1027,  ..., -0.1459, -0.0205, -0.0116],  
#         [ 0.0606, -0.1811, -0.0418,  ..., -0.1815,  0.0880, -0.0766],  
#         [-0.1561, -0.1127,  0.2687,  ..., -0.0648,  0.0249,  0.0446],  
#         ...,  

从所有Camembert层提取上下文嵌入特征

from transformers import CamembertConfig  
# （需用新配置重新加载模型）  
config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True)  
camembert = CamembertModel.from_pretrained("camembert-base", config=config)  

embeddings, _, all_layer_embeddings = camembert(encoded_sentence)  
# all_layer_embeddings列表长度len(all_layer_embeddings) == 13（输入嵌入层 + 12个自注意力层）  
all_layer_embeddings[5]  
# 第5层上下文嵌入：尺寸torch.Size([1, 10, 768])  
#tensor([[[-0.0032,  0.0075,  0.0040,  ..., -0.0025, -0.0178, -0.0210],  
#         [-0.0996, -0.1474,  0.1057,  ..., -0.0278,  0.1690, -0.2982],  
#         [ 0.0557, -0.0588,  0.0547,  ..., -0.0726, -0.0867,  0.0699],  
#         ...,  

作者

CamemBERT由Louis Martin*、Benjamin Muller*、Pedro Javier Ortiz Suárez*、Yoann Dupont、Laurent Romary、Éric Villemonte de la Clergerie、Djamé Seddah和Benoît Sagot训练和评估。

引用

若使用我们的工作，请引用：

@inproceedings{martin2020camembert,  
  title={CamemBERT: a Tasty French Language Model},  
  author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},  
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},  
  year={2020}  
}