Keras ERNIE
Project description
keras ERNIE
Pre-trained ERNIE models could be loaded for feature extraction and prediction.
Install
pip install keras-ernie
Usage
- Download pre-trained ERNIE models
- Load the pre-trained ERNIE models
- Convert pre-trained ERNIE model to Tensor model
Download Pre-trained ERNIE Models
Notes: Currently, only the following models are supported.
Model | Description |
---|---|
ERNIE 1.0 Base for Chinese | with params, config and vocabs |
ERNIE 1.0 Base for Chinese(max-len-512) | with params, config and vocabs |
ERNIE 2.0 Base for English | with params, config and vocabs |
Load Pre-trained ERNIE Models
import os from keras_ernie import load_from_checkpoint ernie_path = "/root/ERNIE_stable-1.0.1" init_checkpoint = os.path.join(ernie_path, 'params') ernie_config_path = os.path.join(ernie_path, 'ernie_config.json') ernie_vocab_path = os.path.join(ernie_path, 'vocab.txt') ernie_version = "stable-1.0.1" model = load_from_checkpoint(init_checkpoint, ernie_config_path, ernie_vocab_path, ernie_version, max_seq_len=128, num_labels=2, use_fp16=False, training=False, seq_len=None, name='ernie') model.summary()
Convert Pre-trained ERNIE Model To Tensor Model
import os from keras_ernie import ErnieArgs from keras_ernie import convert_paddle_to_tensor ernie_path = "/root/ERNIE_stable-1.0.1" init_checkpoint = os.path.join(ernie_path, 'params') ernie_config_path = os.path.join(ernie_path, 'ernie_config.json') ernie_vocab_path = os.path.join(ernie_path, 'vocab.txt') tensor_checkpoints_dir = "/root/checkpoints" args = ErnieArgs(init_checkpoint, ernie_config_path, ernie_vocab_path, max_seq_len=128, num_labels=2, use_fp16=False) convert_paddle_to_tensor(args, tensor_checkpoints_dir)
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Filename, size | File type | Python version | Upload date | Hashes |
---|---|---|---|---|
Filename, size keras-ernie-1.0.7.tar.gz (25.2 kB) | File type Source | Python version None | Upload date | Hashes View |