the-algorithm/trust_and_safety_models/nsfw/nsfw_text.py
twitter-team ef4c5eb65e Twitter Recommendation Algorithm
Please note we have force-pushed a new initial commit in order to remove some publicly-available Twitter user information. Note that this process may be required in the future.
2023-03-31 17:36:31 -05:00

153 lines
4.1 KiB
Python

from datetime import datetime
from functools import reduce
import os
import pandas as pd
import re
from sklearn.metrics import average_precision_score, classification_report, precision_recall_curve, PrecisionRecallDisplay
from sklearn.model_selection import train_test_split
import tensorflow as tf
import matplotlib.pyplot as plt
import re
from twitter.cuad.representation.models.optimization import create_optimizer
from twitter.cuad.representation.models.text_encoder import TextEncoder
pd.set_option('display.max_colwidth', None)
pd.set_option('display.expand_frame_repr', False)
print(tf.__version__)
print(tf.config.list_physical_devices())
log_path = os.path.join('pnsfwtweettext_model_runs', datetime.now().strftime('%Y-%m-%d_%H.%M.%S'))
tweet_text_feature = 'text'
params = {
'batch_size': 32,
'max_seq_lengths': 256,
'model_type': 'twitter_bert_base_en_uncased_augmented_mlm',
'trainable_text_encoder': True,
'lr': 5e-5,
'epochs': 10,
}
REGEX_PATTERNS = [
r'^RT @[A-Za-z0-9_]+: ',
r"@[A-Za-z0-9_]+",
r'https:\/\/t\.co\/[A-Za-z0-9]{10}',
r'@\?\?\?\?\?',
]
EMOJI_PATTERN = re.compile(
"(["
"\U0001F1E0-\U0001F1FF"
"\U0001F300-\U0001F5FF"
"\U0001F600-\U0001F64F"
"\U0001F680-\U0001F6FF"
"\U0001F700-\U0001F77F"
"\U0001F780-\U0001F7FF"
"\U0001F800-\U0001F8FF"
"\U0001F900-\U0001F9FF"
"\U0001FA00-\U0001FA6F"
"\U0001FA70-\U0001FAFF"
"\U00002702-\U000027B0"
"])"
)
def clean_tweet(text):
for pattern in REGEX_PATTERNS:
text = re.sub(pattern, '', text)
text = re.sub(EMOJI_PATTERN, r' \1 ', text)
text = re.sub(r'\n', ' ', text)
return text.strip().lower()
df['processed_text'] = df['text'].astype(str).map(clean_tweet)
df.sample(10)
X_train, X_val, y_train, y_val = train_test_split(df[['processed_text']], df['is_nsfw'], test_size=0.1, random_state=1)
def df_to_ds(X, y, shuffle=False):
ds = tf.data.Dataset.from_tensor_slices((
X.values,
tf.one_hot(tf.cast(y.values, tf.int32), depth=2, axis=-1)
))
if shuffle:
ds = ds.shuffle(1000, seed=1, reshuffle_each_iteration=True)
return ds.map(lambda text, label: ({ tweet_text_feature: text }, label)).batch(params['batch_size'])
ds_train = df_to_ds(X_train, y_train, shuffle=True)
ds_val = df_to_ds(X_val, y_val)
X_train.values
inputs = tf.keras.layers.Input(shape=(), dtype=tf.string, name=tweet_text_feature)
encoder = TextEncoder(
max_seq_lengths=params['max_seq_lengths'],
model_type=params['model_type'],
trainable=params['trainable_text_encoder'],
local_preprocessor_path='demo-preprocessor'
)
embedding = encoder([inputs])["pooled_output"]
predictions = tf.keras.layers.Dense(2, activation='softmax')(embedding)
model = tf.keras.models.Model(inputs=inputs, outputs=predictions)
model.summary()
optimizer = create_optimizer(
params['lr'],
params['epochs'] * len(ds_train),
0,
weight_decay_rate=0.01,
optimizer_type='adamw'
)
bce = tf.keras.losses.BinaryCrossentropy(from_logits=False)
pr_auc = tf.keras.metrics.AUC(curve='PR', num_thresholds=1000, from_logits=False)
model.compile(optimizer=optimizer, loss=bce, metrics=[pr_auc])
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor='val_loss',
mode='min',
patience=1,
restore_best_weights=True
),
tf.keras.callbacks.ModelCheckpoint(
filepath=os.path.join(log_path, 'checkpoints', '{epoch:02d}'),
save_freq='epoch'
),
tf.keras.callbacks.TensorBoard(
log_dir=os.path.join(log_path, 'scalars'),
update_freq='batch',
write_graph=False
)
]
history = model.fit(
ds_train,
epochs=params['epochs'],
callbacks=callbacks,
validation_data=ds_val,
steps_per_epoch=len(ds_train)
)
model.predict(["xxx 🍑"])
preds = X_val.processed_text.apply(apply_model)
print(classification_report(y_val, preds >= 0.90, digits=4))
precision, recall, thresholds = precision_recall_curve(y_val, preds)
fig = plt.figure(figsize=(15, 10))
plt.plot(precision, recall, lw=2)
plt.grid()
plt.xlim(0.2, 1)
plt.ylim(0.3, 1)
plt.xlabel("Recall", size=20)
plt.ylabel("Precision", size=20)
average_precision_score(y_val, preds)