Bert Large Portuguese Cased

🚀 BERTimbau Large（又名 “bert-large-portuguese-cased”）

BERTimbau Large 是一个针对巴西葡萄牙语的预训练 BERT 模型，在命名实体识别、句子文本相似度和识别文本蕴含这三个下游自然语言处理任务中达到了最先进的性能。它有基础版和大版本两种规格可供选择。

🚀 快速开始

BERTimbau Large 是为巴西葡萄牙语设计的预训练 BERT 模型，在三个下游自然语言处理任务中表现出色。若需更多信息或有相关需求，请访问 BERTimbau 仓库。

✨ 主要特性

• BERTimbau Large 在命名实体识别、句子文本相似度和识别文本蕴含这三个下游自然语言处理任务中达到了最先进的性能。
• 提供基础版和大版本两种规格。

📦 安装指南

文档未提及具体安装步骤，可参考 BERTimbau 仓库 中的说明。

💻 使用示例

基础用法

from transformers import AutoTokenizer  # Or BertTokenizer
from transformers import AutoModelForPreTraining  # Or BertForPreTraining for loading pretraining heads
from transformers import AutoModel  # or BertModel, for BERT without pretraining heads

model = AutoModelForPreTraining.from_pretrained('neuralmind/bert-large-portuguese-cased')
tokenizer = AutoTokenizer.from_pretrained('neuralmind/bert-large-portuguese-cased', do_lower_case=False)

高级用法

掩码语言模型预测示例

from transformers import pipeline

pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer)

pipe('Tinha uma [MASK] no meio do caminho.')
# [{'score': 0.5054386258125305,
#   'sequence': '[CLS] Tinha uma pedra no meio do caminho. [SEP]',
#   'token': 5028,
#   'token_str': 'pedra'},
#  {'score': 0.05616172030568123,
#   'sequence': '[CLS] Tinha uma curva no meio do caminho. [SEP]',
#   'token': 9562,
#   'token_str': 'curva'},
#  {'score': 0.02348282001912594,
#   'sequence': '[CLS] Tinha uma parada no meio do caminho. [SEP]',
#   'token': 6655,
#   'token_str': 'parada'},
#  {'score': 0.01795753836631775,
#   'sequence': '[CLS] Tinha uma mulher no meio do caminho. [SEP]',
#   'token': 2606,
#   'token_str': 'mulher'},
#  {'score': 0.015246033668518066,
#   'sequence': '[CLS] Tinha uma luz no meio do caminho. [SEP]',
#   'token': 3377,
#   'token_str': 'luz'}]

获取 BERT 嵌入示例

import torch

model = AutoModel.from_pretrained('neuralmind/bert-large-portuguese-cased')
input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt')

with torch.no_grad():
    outs = model(input_ids)
    encoded = outs[0][0, 1:-1]  # Ignore [CLS] and [SEP] special tokens

# encoded.shape: (8, 1024)
# tensor([[ 1.1872,  0.5606, -0.2264,  ...,  0.0117, -0.1618, -0.2286],
#         [ 1.3562,  0.1026,  0.1732,  ..., -0.3855, -0.0832, -0.1052],
#         [ 0.2988,  0.2528,  0.4431,  ...,  0.2684, -0.5584,  0.6524],
#         ...,
#         [ 0.3405, -0.0140, -0.0748,  ...,  0.6649, -0.8983,  0.5802],
#         [ 0.1011,  0.8782,  0.1545,  ..., -0.1768, -0.8880, -0.1095],
#         [ 0.7912,  0.9637, -0.3859,  ...,  0.2050, -0.1350,  0.0432]])

📚 详细文档

可用模型

📄 许可证

本项目采用 MIT 许可证。

📖 引用

如果您使用了我们的工作，请引用以下文献：

@inproceedings{souza2020bertimbau,
  author    = {F{\'a}bio Souza and
               Rodrigo Nogueira and
               Roberto Lotufo},
  title     = {{BERT}imbau: pretrained {BERT} models for {B}razilian {P}ortuguese},
  booktitle = {9th Brazilian Conference on Intelligent Systems, {BRACIS}, Rio Grande do Sul, Brazil, October 20-23 (to appear)},
  year      = {2020}
}