Python LoadCheckpoint Examples

Example #1

File: bert_ner_test.py Project: youngjt/fennlp

import numpy as np
import tensorflow as tf

from fennlp.datas.checkpoint import LoadCheckpoint
from fennlp.datas.dataloader import TFWriter, TFLoader
from fennlp.metrics import Metric
from fennlp.models import bert

# 载入参数
load_check = LoadCheckpoint(language='en')
param, vocab_file, model_path = load_check.load_bert_param()

# 定制参数
param.batch_size = 8
param.maxlen = 100
param.label_size = 9

class BERT_NER(tf.keras.Model):
    def __init__(self, param, **kwargs):
        super(BERT_NER, self).__init__(**kwargs)
        self.batch_size = param.batch_size
        self.maxlen = param.maxlen
        self.label_size = param.label_size

        self.bert = bert.BERT(param)

        self.dense = tf.keras.layers.Dense(self.label_size, activation="relu")

    def call(self, inputs, is_training=True):
        bert = self.bert(inputs, is_training)
        sequence_output = bert.get_sequence_output()  # batch,sequence,768

Example #2

File: bert_classification_train.py Project: youngjt/fennlp

from fennlp.datas.checkpoint import LoadCheckpoint
from fennlp.datas.dataloader import TFWriter, TFLoader
from fennlp.metrics import Metric
from fennlp.models import bert
from fennlp.optimizers import optim
from fennlp.tools import bert_init_weights_from_checkpoint

# 载入参数
# LoadCheckpoint(language='zh', model="bert", parameters="base", cased=True, url=None)
# language: the language you used in your input data
# model: the model you choose,could be bert albert and gpt2
# parameters: can be base large xlarge xxlarge for albert, base medium large for gpt2, base large for BERT.
# cased: True or false, only for bert model.
# url: you can give a link of other checkpoint.
load_check = LoadCheckpoint()
param, vocab_file, model_path = load_check.load_bert_param()

# 定制参数
param.batch_size = 2
param.maxlen = 4
param.label_size = 15


# 构建模型
class BERT_NER(tf.keras.Model):
    def __init__(self, param, **kwargs):
        super(BERT_NER, self).__init__(**kwargs)
        self.batch_size = param.batch_size
        self.maxlen = param.maxlen
        self.label_size = param.label_size

Example #3

File: albert_cls_test.py Project: youngjt/fennlp

import numpy as np
import tensorflow as tf

from fennlp.datas.checkpoint import LoadCheckpoint
from fennlp.datas.dataloader import TFWriter, TFLoader
from fennlp.metrics import Metric
from fennlp.models import albert

# 载入参数
load_check = LoadCheckpoint(language='en', model="albert", paramaters="base")
param, vocab_file, model_path, spm_model_file = load_check.load_albert_param()
# 定制参数
param.batch_size = 8
param.maxlen = 100
param.label_size = 2


# 构建模型
class ALBERT_NER(tf.keras.Model):
    def __init__(self, param, **kwargs):
        super(ALBERT_NER, self).__init__(**kwargs)
        self.batch_size = param.batch_size
        self.maxlen = param.maxlen
        self.label_size = param.label_size
        self.albert = albert.ALBERT(param)
        self.dense = tf.keras.layers.Dense(self.label_size, activation="relu")

    def call(self, inputs, is_training=True):
        albert = self.albert(inputs, is_training)
        sequence_output = albert.get_pooled_output()  # batch,768
        pre = self.dense(sequence_output)

Example #4

import tensorflow as tf

from fennlp.datas.checkpoint import LoadCheckpoint
from fennlp.models import gpt2
from fennlp.sample import samples
from fennlp.tokenizers import gpt2_tokenization
from fennlp.tools import gpt2_init_weights_from_checkpoint

# 载入参数
# LoadCheckpoint(language='zh', model="bert", parameters="base", cased=True, url=None)
# language: the language you used in your input data
# model: the model you choose,could be bert albert and gpt2
# parameters: can be base large xlarge xxlarge for albert, base medium large for gpt2, base large for BERT.
# cased: True or false, only for bert model.
# url: you can give a link of other checkpoint.
load_check = LoadCheckpoint(language='en', model="gpt2", parameters="base")
param, vocab_file, model_path, encoder_file = load_check.load_gpt2_param()
print(param)

param["batch_size"] = 10

tokenizer = gpt2_tokenization.FullTokenizer(encoder_file, vocab_file)
start_token = tokenizer.convert_token_to_id("<|endoftext|>")


# 构建模型
class GenGPT2(tf.keras.Model):
    def __init__(self, param, **kwargs):
        super(GenGPT2, self).__init__(**kwargs)
        self.model = gpt2.GPT2(param)