In [10]:
Labels.value_counts().sort_values(ascending=False).plot(kind='bar')
Out[10]:
<AxesSubplot:>
Build a learning-based entity extraction model (e.g., a custom bert model) to extract the store_number from the transaction_descriptor. The dataset contains three sections: train, validate, and test. Write a document explaining your approach to this problem.
BERT NLP model is a group of Transformers encoders stacked on each other. – BERT is a precise, huge transformer masked language model in more technical terms.
Models are the output of an algorithm run on data, including the procedures used to make predictions on data.
#Import the bert functions
!wget https://raw.githubusercontent.com/google-research/bert/master/modeling.py
!wget https://raw.githubusercontent.com/google-research/bert/master/optimization.py
!wget https://raw.githubusercontent.com/google-research/bert/master/run_classifier.py
!wget https://raw.githubusercontent.com/google-research/bert/master/tokenization.py
import numpy as np # linear algebra
import re, os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.preprocessing import LabelEncoder
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
import tensorflow as tf
import nltk
nltk.download('stopwords')
from nltk.corpus import stopwords
import datetime
import matplotlib
from datetime import datetime
# BERT
import optimization
import run_classifier
import tokenization
import tensorflow_hub as hub
WARNING:tensorflow:From C:\Users\PC-8783213\Desktop\Data Science\NLP\BERTHomeWork1\optimization.py:87: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
[nltk_data] Downloading package stopwords to [nltk_data] C:\Users\PC-8783213\AppData\Roaming\nltk_data... [nltk_data] Package stopwords is already up-to-date!
df = pd.read_csv('C:/Users/PC-8783213/Desktop/Data Science/NLP/Use Bert NLP/BERTNLPHomeWork/Data/HomeworkExercise.csv')
df.head()
| transaction_descriptor | store_number | dataset | |
|---|---|---|---|
| 0 | DOLRTREE 2257 00022574 ROSWELL | 2257 | train |
| 1 | AUTOZONE #3547 | 3547 | train |
| 2 | TGI FRIDAYS 1485 0000 | 1485 | train |
| 3 | BUFFALO WILD WINGS 003 | 3 | train |
| 4 | J. CREW #568 0 | 568 | train |
df.shape
(300, 3)
The store number always follows the following conditions below, so it would be easy to set up an order to start counting the input
Starts with “#” and name hash Starts with “MCDONALD’S” named f_val Starts only with digits names ss_s Starts with “FOOTWARE”, “STORE”, “PETRO” it gets named st_n The function will return the parameters of hash ,f_val,st_n,ss_s Are the four conditions that I observed from the input in order to convert the string into Binary, either 0 or 1
def conv_data(S):
# 1. Starts with “#” and name hash assign either 1 if found and 0 if not found
if type(re.search('#',S))==re.Match:
hash=1
else:
hash=0
# 2. Starts with “MCDONALD’S” named f_val assign either 1 if found and 0 if not found
if type(re.search("MCDONALD'S",S))==re.Match:
f_val=1
else:
f_val=0
stp_s= S.split()
# 3. Starts only with digits names ss_s assign either 1 if found and 0 if not found
for i in range(len(stp_s)):
if stp_s[i].isdigit():
ss_s=1
break
else:
ss_s=0
# 4. Starts with “FOOTWARE”, “STORE”, “PETRO” it gets named st_n assign either 1 if found and 0 if not found
if (type(re.search(" FOOTWEAR",S))==re.Match) or (type(re.search(" STORE",S))==re.Match) or (type(
re.search(" PETRO",S))==re.Match):
st_n=1
else:
st_n=0
return hash ,f_val,st_n,ss_s
Labels=[]
dim = df.shape
for i in range(dim[0]):
hash_val,ff_val,sts_n,s_s_s=conv_data(df['transaction_descriptor'][i])
if hash_val==1:
Labels.append('Hash_String')
elif ff_val==1:
Labels.append('F_strings')
elif sts_n==1:
Labels.append('Store_string')
elif s_s_s==1:
Labels.append('PETRO_string')
else:
Labels.append('Unkown_string')
Labels=pd.Series(Labels)
Labels.shape
(300,)
for label in np.unique(Labels):
print(label)
F_strings Hash_String PETRO_string Store_string Unkown_string
Labels.value_counts().sort_values(ascending=False).plot(kind='bar')
<AxesSubplot:>
le = LabelEncoder()
Label = le.fit_transform(Labels)
print(Label)
[2 1 2 2 1 2 2 3 2 1 1 2 1 1 1 1 2 0 2 1 0 2 3 2 2 1 2 2 1 0 1 2 1 1 2 2 3 0 1 2 0 1 1 4 1 2 1 2 2 2 1 2 1 1 2 1 1 1 1 1 1 1 1 1 2 2 1 1 2 3 3 2 1 1 0 2 3 2 2 1 1 2 2 1 2 1 1 1 3 1 0 3 2 2 4 1 1 1 1 2 2 3 1 1 0 1 2 2 1 2 1 1 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 2 1 1 4 3 1 2 3 2 2 0 2 2 3 2 2 1 2 1 2 1 2 1 1 1 4 2 1 2 1 2 2 2 2 2 1 2 2 1 2 2 2 1 1 1 1 2 1 1 1 2 1 1 2 2 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 2 1 1 3 1 2 1 1 2 1 2 1 1 1 1 2 1 4 2 1 1 1 1 1 2 2 2 2 2 1 3 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 2 0 1 1 0 1 2 0 1 1 2 1 3 1 1 2 1 1 1 1 2 2 2 1 2 1 1 1 1 1 3 3 2 1 3 2 1 1 4 1 2 2 1 0 1 1 1 2]
fit_transform() is used on the training data so that we can scale the training data and also learn the scaling parameters of that data. Here, the model built by us will learn the mean and variance of the features of the training set. These learned parameters are then used to scale our test data.
REPLACE_BY_SPACE_RE = re.compile('[/(){}\[\]\|@,;]')
BAD_SYMBOLS_RE = re.compile('[^0-9a-z +_]')
STOPWORDS = set(stopwords.words('english'))
def clean_text(text):
"""
text: a string
return: modified initial string
"""
text = text.lower() # lowercase text
text = REPLACE_BY_SPACE_RE.sub(' ', text) # replace REPLACE_BY_SPACE_RE symbols by space in text. substitute the matched string in REPLACE_BY_SPACE_RE with space.
# text = re.sub(r'\W+', '', text)
text = ' '.join(word for word in text.split() if word not in STOPWORDS) # remove stopwors from text
return text
df['transaction_descriptor'] = df['transaction_descriptor'].apply(clean_text)
df.head()
| transaction_descriptor | store_number | dataset | |
|---|---|---|---|
| 0 | dolrtree 2257 00022574 roswell | 2257 | train |
| 1 | autozone #3547 | 3547 | train |
| 2 | tgi fridays 1485 0000 | 1485 | train |
| 3 | buffalo wild wings 003 | 3 | train |
| 4 | j. crew #568 0 | 568 | train |
def get_split(text1):
l_total = []
l_parcial = []
if len(text1.split())//150 >0:
n = len(text1.split())//150
else:
n = 1
for w in range(n):
if w == 0:
l_parcial = text1.split()[:200]
l_total.append(" ".join(l_parcial))
else:
l_parcial = text1.split()[w*150:w*150 + 200]
l_total.append(" ".join(l_parcial))
return l_total
df['text_split'] = df['transaction_descriptor'].apply(get_split)
df.head()
| transaction_descriptor | store_number | dataset | text_split | |
|---|---|---|---|---|
| 0 | dolrtree 2257 00022574 roswell | 2257 | train | [dolrtree 2257 00022574 roswell] |
| 1 | autozone #3547 | 3547 | train | [autozone #3547] |
| 2 | tgi fridays 1485 0000 | 1485 | train | [tgi fridays 1485 0000] |
| 3 | buffalo wild wings 003 | 3 | train | [buffalo wild wings 003] |
| 4 | j. crew #568 0 | 568 | train | [j. crew #568 0] |
This class is just a wrapper around an internal HubModuleSplitter. It offers the same functionality, but with 'token'-based method names: e.g., one can use tokenize() instead of the more general and less informatively named split().
BERT_MODEL_HUB = "https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"
def create_tokenizer_from_hub_module():
"""Get the vocab file and casing info from the Hub module."""
with tf.Graph().as_default():
bert_module = hub.Module(BERT_MODEL_HUB)
tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
with tf.Session() as sess:
# with tf.compat.v1.Session() as sess:
vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
tokenization_info["do_lower_case"]])
return tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
tokenizer = create_tokenizer_from_hub_module()
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From C:\Users\PC-8783213\AppData\Local\Temp\ipykernel_3064\2607540136.py:8: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
WARNING:tensorflow:From C:\Users\PC-8783213\AppData\Local\Temp\ipykernel_3064\2607540136.py:8: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
WARNING:tensorflow:From C:\Users\PC-8783213\Desktop\Data Science\NLP\BERTHomeWork1\tokenization.py:125: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.
WARNING:tensorflow:From C:\Users\PC-8783213\Desktop\Data Science\NLP\BERTHomeWork1\tokenization.py:125: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.
len(tokenizer.vocab.keys())
30522
df['label']=Label
X_train=df['transaction_descriptor']
type(X_train.iloc[0])
str
#Here is what the tokenised sample of the first training set observation looks like
print(tokenizer.tokenize(X_train.iloc[0]))
['do', '##lr', '##tree', '225', '##7', '000', '##22', '##57', '##4', 'ro', '##swell']
X_train=df[['transaction_descriptor','label','text_split']]
X_train.shape
(300, 3)
train_l = []
label_l = []
index_l =[]
for idx,row in X_train.iterrows():
for l in row['text_split']:
train_l.append(l)
label_l.append(row['label'])
index_l.append(idx)
len(train_l), len(label_l), len(index_l)
(300, 300, 300)
train_df = pd.DataFrame({'DATA_COLUMN':train_l, 'LABEL_COLUMN':label_l})
train_df.head()
| DATA_COLUMN | LABEL_COLUMN | |
|---|---|---|
| 0 | dolrtree 2257 00022574 roswell | 2 |
| 1 | autozone #3547 | 1 |
| 2 | tgi fridays 1485 0000 | 2 |
| 3 | buffalo wild wings 003 | 2 |
| 4 | j. crew #568 0 | 1 |
# Use the InputExample class from BERT's run_classifier code to create examples from the data
train_InputExamples = train_df.apply(lambda x: run_classifier.InputExample(guid=None, # Globally unique ID for bookkeeping, unused in this example
text_a = x['DATA_COLUMN'],
text_b = None,
label = x['LABEL_COLUMN']), axis = 1)
train_InputExamples
0 <run_classifier.InputExample object at 0x00000...
1 <run_classifier.InputExample object at 0x00000...
2 <run_classifier.InputExample object at 0x00000...
3 <run_classifier.InputExample object at 0x00000...
4 <run_classifier.InputExample object at 0x00000...
...
295 <run_classifier.InputExample object at 0x00000...
296 <run_classifier.InputExample object at 0x00000...
297 <run_classifier.InputExample object at 0x00000...
298 <run_classifier.InputExample object at 0x00000...
299 <run_classifier.InputExample object at 0x00000...
Length: 300, dtype: object
print("Row 0 - guid of training set : ", train_InputExamples.iloc[0].guid)
print("\n__________\nRow 0 - text_a of training set : ", train_InputExamples.iloc[0].text_a)
print("\n__________\nRow 0 - text_b of training set : ", train_InputExamples.iloc[0].text_b)
print("\n__________\nRow 0 - label of training set : ", train_InputExamples.iloc[0].label)
Row 0 - guid of training set : None __________ Row 0 - text_a of training set : dolrtree 2257 00022574 roswell __________ Row 0 - text_b of training set : None __________ Row 0 - label of training set : 2
BERT_MODEL_HUB = "https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"
def create_tokenizer_from_hub_module():
"""Get the vocab file and casing info from the Hub module."""
with tf.Graph().as_default():
bert_module = hub.Module(BERT_MODEL_HUB)
tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
with tf.Session() as sess:
# with tf.compat.v1.Session() as sess:
vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
tokenization_info["do_lower_case"]])
return tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
tokenizer = create_tokenizer_from_hub_module()
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
len(tokenizer.vocab.keys())
30522
print(tokenizer.tokenize(train_InputExamples.iloc[0].text_a))
['do', '##lr', '##tree', '225', '##7', '000', '##22', '##57', '##4', 'ro', '##swell']
label_list = [x for x in np.unique(df.label)]
label_list
[0, 1, 2, 3, 4]
MAX_SEQ_LENGTH = 200
train_features = run_classifier.convert_examples_to_features(train_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)
WARNING:tensorflow:From C:\Users\PC-8783213\Desktop\Data Science\NLP\BERTHomeWork1\run_classifier.py:774: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.
WARNING:tensorflow:From C:\Users\PC-8783213\Desktop\Data Science\NLP\BERTHomeWork1\run_classifier.py:774: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.
INFO:tensorflow:Writing example 0 of 300
INFO:tensorflow:Writing example 0 of 300
INFO:tensorflow:*** Example ***
INFO:tensorflow:*** Example ***
INFO:tensorflow:guid: None
INFO:tensorflow:guid: None
INFO:tensorflow:tokens: [CLS] do ##lr ##tree 225 ##7 000 ##22 ##57 ##4 ro ##swell [SEP]
INFO:tensorflow:tokens: [CLS] do ##lr ##tree 225 ##7 000 ##22 ##57 ##4 ro ##swell [SEP]
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INFO:tensorflow:label: 2 (id = 2)
INFO:tensorflow:*** Example ***
INFO:tensorflow:*** Example ***
INFO:tensorflow:guid: None
INFO:tensorflow:guid: None
INFO:tensorflow:tokens: [CLS] auto ##zone # 354 ##7 [SEP]
INFO:tensorflow:tokens: [CLS] auto ##zone # 354 ##7 [SEP]
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INFO:tensorflow:label: 1 (id = 1)
INFO:tensorflow:label: 1 (id = 1)
INFO:tensorflow:*** Example ***
INFO:tensorflow:*** Example ***
INFO:tensorflow:guid: None
INFO:tensorflow:guid: None
INFO:tensorflow:tokens: [CLS] t ##gi fridays 148 ##5 000 ##0 [SEP]
INFO:tensorflow:tokens: [CLS] t ##gi fridays 148 ##5 000 ##0 [SEP]
INFO:tensorflow:input_ids: 101 1056 5856 26587 16459 2629 2199 2692 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_ids: 101 1056 5856 26587 16459 2629 2199 2692 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:label: 2 (id = 2)
INFO:tensorflow:label: 2 (id = 2)
INFO:tensorflow:*** Example ***
INFO:tensorflow:*** Example ***
INFO:tensorflow:guid: None
INFO:tensorflow:guid: None
INFO:tensorflow:tokens: [CLS] buffalo wild wings 00 ##3 [SEP]
INFO:tensorflow:tokens: [CLS] buffalo wild wings 00 ##3 [SEP]
INFO:tensorflow:input_ids: 101 6901 3748 4777 4002 2509 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_ids: 101 6901 3748 4777 4002 2509 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:label: 2 (id = 2)
INFO:tensorflow:label: 2 (id = 2)
INFO:tensorflow:*** Example ***
INFO:tensorflow:*** Example ***
INFO:tensorflow:guid: None
INFO:tensorflow:guid: None
INFO:tensorflow:tokens: [CLS] j . crew # 56 ##8 0 [SEP]
INFO:tensorflow:tokens: [CLS] j . crew # 56 ##8 0 [SEP]
INFO:tensorflow:input_ids: 101 1046 1012 3626 1001 5179 2620 1014 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_ids: 101 1046 1012 3626 1001 5179 2620 1014 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:input_mask: 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
INFO:tensorflow:label: 1 (id = 1)
INFO:tensorflow:label: 1 (id = 1)
#Example on first observation in the training set
print("Sentence : ", train_InputExamples.iloc[0].text_a)
print("-"*30)
print("Tokens : ", tokenizer.tokenize(train_InputExamples.iloc[0].text_a))
print("-"*30)
print("Input IDs : ", train_features[0].input_ids)
print("-"*30)
print("Input Masks : ", train_features[0].input_mask)
print("-"*30)
print("Segment IDs : ", train_features[0].segment_ids)
Sentence : dolrtree 2257 00022574 roswell ------------------------------ Tokens : ['do', '##lr', '##tree', '225', '##7', '000', '##22', '##57', '##4', 'ro', '##swell'] ------------------------------ Input IDs : [101, 2079, 20974, 13334, 14993, 2581, 2199, 19317, 28311, 2549, 20996, 19228, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ------------------------------ Input Masks : [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ------------------------------ Segment IDs : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
def create_model(is_predicting, input_ids, input_mask, segment_ids, labels,
num_labels):
"""
Create models for prediction using BERT.
"""
bert_module = hub.Module(
BERT_MODEL_HUB,
trainable=True)
bert_inputs = dict(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids)
bert_outputs = bert_module(
inputs=bert_inputs,
signature="tokens",
as_dict=True)
# Use "pooled_output" for classification tasks on an entire sentence.
# Use "sequence_outputs" for token-level output.
output_layer = bert_outputs["pooled_output"]
# with tf.Session() as sess:
output_layer1 = bert_outputs["pooled_output"]
# output_layer1 = 999
hidden_size = output_layer.shape[-1].value
# Create our own layer to tune for politeness data.
output_weights = tf.get_variable(
"output_weights", [num_labels, hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable(
"output_bias", [num_labels], initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
# Dropout helps prevent overfitting
output_layer = tf.nn.dropout(output_layer, keep_prob=0.8)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
# Convert labels into one-hot encoding
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
predicted_labels = tf.squeeze(tf.argmax(log_probs, axis=-1, output_type=tf.int32))
# If we're predicting, we want predicted labels and the probabiltiies.
if is_predicting:
return (predicted_labels, log_probs, output_layer1)
# If we're train/eval, compute loss between predicted and actual label
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, predicted_labels, log_probs)
def model_fn_builder(num_labels, learning_rate, num_train_steps,
num_warmup_steps):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)
# TRAIN and EVAL
if not is_predicting:
(loss, predicted_labels, log_probs) = create_model(
is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)
train_op = optimization.create_optimizer(
loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)
# Calculate evaluation metrics.
def metric_fn(label_ids, predicted_labels):
accuracy = tf.metrics.accuracy(label_ids, predicted_labels)
true_pos = tf.metrics.true_positives(
label_ids,
predicted_labels)
true_neg = tf.metrics.true_negatives(
label_ids,
predicted_labels)
false_pos = tf.metrics.false_positives(
label_ids,
predicted_labels)
false_neg = tf.metrics.false_negatives(
label_ids,
predicted_labels)
return {
"eval_accuracy": accuracy,
"true_positives": true_pos,
"true_negatives": true_neg,
"false_positives": false_pos,
"false_negatives": false_neg,
}
eval_metrics = metric_fn(label_ids, predicted_labels)
if mode == tf.estimator.ModeKeys.TRAIN:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op)
else:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops=eval_metrics)
else:
(predicted_labels, log_probs, output_layer) = create_model(
is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)
predictions = {
'probabilities': log_probs,
'labels': predicted_labels,
'pooled_output': output_layer
}
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
# Return the actual model function in the closure
return model_fn
# Set the output directory for saving model file
OUTPUT_DIR = '/bert_news_category'
#@markdown Whether or not to clear/delete the directory and create a new one
DO_DELETE = True #@param {type:"boolean"}
if DO_DELETE:
try:
tf.gfile.DeleteRecursively(OUTPUT_DIR)
# tf.compat.v1.gfile.DeleteRecursively(OUTPUT_DIR)
except:
pass
tf.gfile.MakeDirs(OUTPUT_DIR)
print('***** Model output directory: {} *****'.format(OUTPUT_DIR))
WARNING:tensorflow:From C:\Users\PC-8783213\AppData\Local\Temp\ipykernel_3064\2725379644.py:9: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead.
WARNING:tensorflow:From C:\Users\PC-8783213\AppData\Local\Temp\ipykernel_3064\2725379644.py:9: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead.
WARNING:tensorflow:From C:\Users\PC-8783213\AppData\Local\Temp\ipykernel_3064\2725379644.py:14: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.
WARNING:tensorflow:From C:\Users\PC-8783213\AppData\Local\Temp\ipykernel_3064\2725379644.py:14: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.
***** Model output directory: /bert_news_category *****
BATCH_SIZE = 16
LEARNING_RATE = 2e-5
NUM_TRAIN_EPOCHS = 1.0
# Warmup is a period of time where the learning rate is small and gradually increases--usually helps training.
WARMUP_PROPORTION = 0.1
# Model configs
SAVE_CHECKPOINTS_STEPS = 300
SAVE_SUMMARY_STEPS = 100
# Compute train and warmup steps from batch size
num_train_steps = int(len(train_features) / BATCH_SIZE * NUM_TRAIN_EPOCHS)
num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)
# Specify output directory and number of checkpoint steps to save
run_config = tf.estimator.RunConfig(
model_dir=OUTPUT_DIR,
save_summary_steps=SAVE_SUMMARY_STEPS,
save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)
# Specify output directory and number of checkpoint steps to save
run_config = tf.estimator.RunConfig(
model_dir=OUTPUT_DIR,
save_summary_steps=SAVE_SUMMARY_STEPS,
save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)
num_train_steps, len(label_list)
(18, 5)
model_fn = model_fn_builder(
num_labels=len(label_list),
learning_rate=LEARNING_RATE,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps)
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config,
params={"batch_size": BATCH_SIZE})
INFO:tensorflow:Using config: {'_model_dir': '/bert_news_category', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 300, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7ff9b9280e10>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
INFO:tensorflow:Using config: {'_model_dir': '/bert_news_category', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 300, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7ff9b9280e10>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
train_input_fn = run_classifier.input_fn_builder(
features=train_features,
seq_length=MAX_SEQ_LENGTH,
is_training=True,
drop_remainder=False)
# Training
print(f'Beginning Training!')
current_time = datetime.now()
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
print("Training took time ", datetime.now() - current_time)
Beginning Training! WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
WARNING:tensorflow:From <ipython-input-47-71ef87d40b03>:36: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From <ipython-input-47-71ef87d40b03>:36: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version. Instructions for updating: Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From /content/optimization.py:27: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.
WARNING:tensorflow:From /content/optimization.py:27: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.
WARNING:tensorflow:From /content/optimization.py:32: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.
WARNING:tensorflow:From /content/optimization.py:32: The name tf.train.polynomial_decay is deprecated. Please use tf.compat.v1.train.polynomial_decay instead.
WARNING:tensorflow:From /content/optimization.py:70: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.
WARNING:tensorflow:From /content/optimization.py:70: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into /bert_news_category/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /bert_news_category/model.ckpt.
INFO:tensorflow:loss = 1.6556565, step = 1
INFO:tensorflow:loss = 1.6556565, step = 1
INFO:tensorflow:Saving checkpoints for 18 into /bert_news_category/model.ckpt.
INFO:tensorflow:Saving checkpoints for 18 into /bert_news_category/model.ckpt.
INFO:tensorflow:Loss for final step: 0.9200936.
INFO:tensorflow:Loss for final step: 0.9200936.
Training took time 0:10:37.991200
estimator.evaluate(input_fn=train_input_fn, steps=1)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Saver not created because there are no variables in the graph to restore
INFO:tensorflow:Saver not created because there are no variables in the graph to restore /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/indexed_slices.py:424: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory. "Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-05-23T05:18:49Z
INFO:tensorflow:Starting evaluation at 2022-05-23T05:18:49Z
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /bert_news_category/model.ckpt-18
INFO:tensorflow:Restoring parameters from /bert_news_category/model.ckpt-18
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/1]
INFO:tensorflow:Evaluation [1/1]
INFO:tensorflow:Finished evaluation at 2022-05-23-05:19:11
INFO:tensorflow:Finished evaluation at 2022-05-23-05:19:11
INFO:tensorflow:Saving dict for global step 18: eval_accuracy = 0.8125, false_negatives = 0.0, false_positives = 1.0, global_step = 18, loss = 0.7765753, true_negatives = 0.0, true_positives = 15.0
INFO:tensorflow:Saving dict for global step 18: eval_accuracy = 0.8125, false_negatives = 0.0, false_positives = 1.0, global_step = 18, loss = 0.7765753, true_negatives = 0.0, true_positives = 15.0
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 18: /bert_news_category/model.ckpt-18
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 18: /bert_news_category/model.ckpt-18
{'eval_accuracy': 0.8125,
'false_negatives': 0.0,
'false_positives': 1.0,
'global_step': 18,
'loss': 0.7765753,
'true_negatives': 0.0,
'true_positives': 15.0}
This means that the set accuracy was 81.25%. Small sets like the one we are using we have a high variance in the set accuracy, even when starting from the same pre-training checkpoint. If you re-run multiple times (making sure to point to different output_dir), you should see results between 84% and 88%.
A few other pre-trained models are implemented off-the-shelf in run_classifier.py, so it should be straightforward to follow those examples to use BERT for any single-sentence or sentence-pair classification task.