The pipy version of FastBERT
Project description
FastBERT-pypi
The pypi version of FastBERT.
Install
Install fastbert
with pip
.
$ pip install fastbert
Single sentence classification
An example of single sentence classification are shown in single_sentence_classification.
from fastbert import FastBERT
# Loading your dataset
labels = ['T', 'F']
sents_train = [
'Do you like FastBERT?',
'Yes, it runs faster than BERT!',
...
]
labels_train = [
'T',
'F',
...
]
# Creating and training model
model = FastBERT(
kernel_name="google_bert_base_en", # "google_bert_base_zh" for Chinese
labels=labels,
device='cuda:0'
)
model.fit(
sents_train,
labels_train,
model_saving_path='./fastbert.bin',
)
# Loading model and making inference
model.load_model('./fastbert.bin')
label, exec_layers = model('I like FastBERT', speed=0.7)
Two sentences classification
from fastbert import FastBERT_S2
# Loading your dataset
labels = ['T', 'F']
questions_train = [
'FastBERT快吗?',
'你在业务里使用FastBERT了吗?',
...
]
answers_train = [
'快!而且速度还可调.',
'用了啊,帮我省了好几百台机器.',
...
]
labels_train = [
'T',
'T',
...
]
# Creating and training model
model = FastBERT_S2(
kernel_name="google_bert_base_zh", # "google_bert_base_en" for English
labels=labels,
device='cuda:0'
)
model.fit(
sents_a_train=questions_train,
sents_b_train=answers_train,
labels_train=labels_train,
model_saving_path='./fastbert.bin',
)
# Loading model and making inference
model.load_model('./fastbert.bin')
label, exec_layers = model(
sent_a='我也要用FastBERT!',
sent_b='来,吃老干妈!',
speed=0.7)
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