xiaolongliang / FenNLP

An out-of-the-box NLP toolkit can easily help you solve tasks such as entity recognition, relationship extraction, text categorization and so on.

FenNLP

An out-of-the-box NLP toolkit can easily help you solve tasks such as entity recognition, relationship extraction, text categorization and so on.

This is the early version of FenNLP. Update 2019/7/21

Status

• Still on the development （not complete）

Usage (example for bert model)

import tensorflow as tf
from FenNLP.models.bert import BERTModel
from FenNLP.trainers.trainer import Trainer
from FenNLP.optim import Optimizer
from FenNLP.losses import loss
from FenNLP.data import DataLoader
from FenNLP.config import BertConfig,ModelConfig
tf.enable_eager_execution()

# model config
config = ModelConfig(epoch=1,maxlen=128,lr=5e-5,
                     bert_config_file="cased_L-12_H-768_A-12/bert_config.json",
                     use_one_hot_embeddings=True,do_valid=True)

# Data
loader = DataLoader("imdb",use_mask=True, use_segment=True, shuffle=True, use_bert_tag=True)

# Model
class Model(tf.keras.Model):
    def __init__(self, config, use_one_hot_embeddings, scope=None):
        self.bert_config_file = config
        self.use_one_hot_embedding = use_one_hot_embeddings
        self.scope = scope
        self.bert_config = BertConfig.from_json_file(json_file=self.bert_config_file)
        super(Model, self).__init__()

    def call(self, inputs, masks=None,
             segments=None, is_training=True):
        model = BERTModel(config=self.bert_config,
                          is_training=is_training,
                          use_one_hot_embeddings=self.use_one_hot_embeddings,
                          scope=None)
        m = model(inputs, masks, segments)
        # bert_output = m.get_sequence_output()#返回序列
        bert_output = m.get_pooled_output()  # 返回分类向量
        return bert_output


model = Model(config.bert_config_file, use_one_hot_embeddings=config.use_one_hot_embeddings)

# Optimizer
optimizer = Optimizer('adam', config.lr)

# losser
loss = loss('CE', from_logits=True, label_smoothing=True)

# Train and valid
Trainer.train(
    loader=loader,
    model=model,  # 模型
    optimizer=optimizer,  # 优化器
    loss=loss,  # 损失函数
    config =config)  # 是否使用预训练权重