I have trained a BERT model using ktrain (tensorflow wrapper) to recognize emotion on text, it works but it suffers from really slow inference. That makes my model not suitable for a production environment. I have done some research and it seems pruning could help.

Tensorflow provides some options for pruning e.g. tf.contrib.model_pruning .The problem is that it is not a widely used technique and I can not find a simple enough example that could help me to understand how to use it. Can someone help?

Only answers that include a coding solution will be considered for the bounty.

I provide my working code below for reference.

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(

        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)})[
        body_text_df["body_text"] = body_text_df["body_text"].apply(self._clean_text)

        final_df = (
            pd.concat([sentence_split_df, body_text_df])
        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', encoding='utf8', 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,

model = text.text_classifier('distilbert', train_data=trn, preproc=preproc)

learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=6)

predictor = ktrain.get_predictor(learner.model, preproc)

#save the model on a file for later use

message = "This is a happy message"

#cleaning - takes 5ms to run
clean = text1._clean_text(message)

#prediction - takes 325 ms to run


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