如何解决如何在BERT模型上应用修剪?
我已经使用ktrain(张量流包装器)训练了BERT模型来识别文本上的情绪,它虽然有效,但是却受制于缓慢的推理。这使我的模型不适用于生产环境。我已经做过一些研究,看来修剪可能会有所帮助。
Tensorflow提供了一些修剪选项,例如tf.contrib.model_pruning。问题在于它不是一种广泛使用的技术,我找不到足够简单的示例来帮助我理解如何使用它。有人可以帮忙吗?
我在下面提供我的工作代码以供参考。
import pandas as pd
import numpy as np
import preprocessor as p
import emoji
import re
import ktrain
from ktrain import text
from unidecode import unidecode
import nltk
#text preprocessing class
class TextPreprocessing:
def __init__(self):
p.set_options(p.OPT.MENTION,p.OPT.URL)
def _punctuation(self,val):
val = re.sub(r'[^\w\s]',' ',val)
val = re.sub('_',val)
return val
def _whitespace(self,val):
return " ".join(val.split())
def _removenumbers(self,val):
val = re.sub('[0-9]+','',val)
return val
def _remove_unicode(self,text):
text = unidecode(text).encode("ascii")
text = str(text,"ascii")
return text
def _split_to_sentences(self,body_text):
sentences = re.split(r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s",body_text)
return sentences
def _clean_text(self,val):
val = val.lower()
val = self._removenumbers(val)
val = p.clean(val)
val = ' '.join(self._punctuation(emoji.demojize(val)).split())
val = self._remove_unicode(val)
val = self._whitespace(val)
return val
def text_preprocessor(self,body_text):
body_text_df = pd.DataFrame({"body_text": body_text},index=[1])
sentence_split_df = body_text_df.copy()
sentence_split_df["body_text"] = sentence_split_df["body_text"].apply(
self._split_to_sentences)
lst_col = "body_text"
sentence_split_df = pd.DataFrame(
{
col: np.repeat(
sentence_split_df[col].values,sentence_split_df[lst_col].str.len(
)
)
for col in sentence_split_df.columns.drop(lst_col)
}
).assign(**{lst_col: np.concatenate(sentence_split_df[lst_col].values)})[
sentence_split_df.columns
]
body_text_df["body_text"] = body_text_df["body_text"].apply(self._clean_text)
final_df = (
pd.concat([sentence_split_df,body_text_df])
.reset_index()
.drop(columns=["index"])
)
return final_df["body_text"]
#instantiate data preprocessing object
text1 = TextPreprocessing()
#import data
data_train = pd.read_csv('data_train_v5.csv',encoding='utf8',engine='python')
data_test = pd.read_csv('data_test_v5.csv',engine='python')
#clean the data
data_train['Text'] = data_train['Text'].apply(text1._clean_text)
data_test['Text'] = data_test['Text'].apply(text1._clean_text)
X_train = data_train.Text.tolist()
X_test = data_test.Text.tolist()
y_train = data_train.Emotion.tolist()
y_test = data_test.Emotion.tolist()
data = data_train.append(data_test,ignore_index=True)
class_names = ['joy','sadness','fear','anger','neutral']
encoding = {
'joy': 0,'sadness': 1,'fear': 2,'anger': 3,'neutral': 4
}
# Integer values for each class
y_train = [encoding[x] for x in y_train]
y_test = [encoding[x] for x in y_test]
trn,val,preproc = text.texts_from_array(x_train=X_train,y_train=y_train,x_test=X_test,y_test=y_test,class_names=class_names,preprocess_mode='distilbert',maxlen=350)
model = text.text_classifier('distilbert',train_data=trn,preproc=preproc)
learner = ktrain.get_learner(model,val_data=val,batch_size=6)
predictor = ktrain.get_predictor(learner.model,preproc)
#save the model on a file for later use
predictor.save("models/bert_model")
message = "This is a happy message"
#cleaning - takes 5ms to run
clean = text1._clean_text(message)
#prediction - takes 325 ms to run
predictor.predict_proba(clean)
解决方法
火车中的distilbert
模型是使用Hugging Face 变形金刚创建的,这意味着您可以使用该库来修剪模型。有关更多信息,请参见this link和the example script。您可能需要在使用脚本之前将模型转换为PyTorch(除了对脚本本身进行一些修改之外)。该方法基于论文Are Sixteen Heads Really Better Than One?。
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