pybert
安装
>pip install pybert
预训练模型
下载地址:
- bert_Chinese 模型文件: https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz
- 词表 https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt
- 【备用】百度网盘:https://pan.baidu.com/s/1HPZBvkMAyu0nDUqHWsb0SA?pwd=abbn
所需文件:
- pytorch_model.bin
- bert_config.json
- vocab.txt
放到 bert_pretrain 文件夹中
训练数据下载
- 可以任意指定文件夹名称,训练数据的格式要和上面一致
训练和预测
训练
from pybert.models import bert
from pybert.train_eval import load_and_train
dataset = 'THUCNews' # 数据集
logfile = 'log.txt' # 日志文件
config = bert.Config(dataset, logfile=logfile)
load_and_train(config)
预测
# coding: UTF-8
import pybert.models.bert as bert
from pybert.train_eval import Prediction
config = bert.Config(dataset='THUCNews')
prediction = Prediction(config)
sentences = ['野兽用纪录打爆第二中锋 掘金版三巨头已巍然成型', '56所高校预估2009年湖北录取分数线出炉']
predict_label, score = prediction.predict(sentences)
print("predict label:")
print(predict_label)
对应论文
[1] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Metadata
Release files for pybert 0.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pybert-0.0.2.tar.gz | 28.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pybert-0.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 58.5 kB
Release files / pybert-0.0.2.tar.gz
| Download URL | pybert-0.0.2.tar.gz |
|---|---|
| Size | 28.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
Release files / pybert-0.0.2-py3-none-any.whl
| Download URL | pybert-0.0.2-py3-none-any.whl |
|---|---|
| Size | 29.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.6.1 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.8.5
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