Project description
pybert
安装
>pip install pybert
预训练模型
下载地址:
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
THUCNews
- 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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file pybert-0.0.2.tar.gz
.
File metadata
-
Download URL:
pybert-0.0.2.tar.gz
- Upload date:
- Size: 28.6 kB
- Tags: Source
-
Uploaded using Trusted Publishing? 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
File hashes
Hashes for pybert-0.0.2.tar.gz
Algorithm |
Hash digest |
|
SHA256 |
88a558d14930ed546ff827e189955c8448dfdb90a06d3350e260fa34a32a1f93 |
|
MD5 |
c6b56aec2b40f9ef7572735b84d8c73e |
|
BLAKE2b-256 |
49f1372bd98b5ae7ebce3906ee284cb5fd507dcf9e897f020be7f1733f73e1e3 |
|
See more details on using hashes here.
File details
Details for the file pybert-0.0.2-py3-none-any.whl
.
File metadata
-
Download URL:
pybert-0.0.2-py3-none-any.whl
- Upload date:
- Size: 29.9 kB
- Tags: Python 3
-
Uploaded using Trusted Publishing? 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
File hashes
Hashes for pybert-0.0.2-py3-none-any.whl
Algorithm |
Hash digest |
|
SHA256 |
c8ffa6ccfd44232ada27082d285690c8cbda9b363c4c20f284365796151116d4 |
|
MD5 |
f1624b10f56f44e26084708998f68846 |
|
BLAKE2b-256 |
baee4873531be4c3c307749b15bd894ae38c805a16902c5acbaf0787a6bf1ddf |
|
See more details on using hashes here.