Mirrors
/
the-algorithm
mirror of https://github.com/twitter/the-algorithm.git
2
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

python to jupyter NSFW text

This commit is contained in:
Guillaume Baquiast 2023-04-01 16:54:42 +01:00 committed by GitHub
parent ec83d01dca
commit 49f9f7fd82
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 348 additions and 152 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

348
trust_and_safety_models/nsfw/NSFW_text.ipynb Normal file
View File

@ -0,0 +1,348 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "faab08b8",
"metadata": {},
"source": [
"# NSFW text"
]
},
{
"cell_type": "markdown",
"id": "b919917f",
"metadata": {},
"source": [
"Builds a TensorFlow model that predicts the probability of a text to be NSFW."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7ee52623",
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"from functools import reduce\n",
"import os\n",
"import pandas as pd\n",
"import re\n",
"from sklearn.metrics import (\n",
" average_precision_score,\n",
" classification_report, \n",
" precision_recall_curve,\n",
" PrecisionRecallDisplay,\n",
")\n",
"from sklearn.model_selection import train_test_split\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"\n",
"from twitter.cuad.representation.models.optimization import create_optimizer #TODO: provide instruction for install\n",
"from twitter.cuad.representation.models.text_encoder import TextEncoder #TODO: provide instruction for install\n",
"\n",
"pd.set_option('display.max_colwidth', None)\n",
"pd.set_option('display.expand_frame_repr', False)\n",
"\n",
"print(tf.__version__)\n",
"print(tf.config.list_physical_devices())"
]
},
{
"cell_type": "markdown",
"id": "64a45fd4",
"metadata": {},
"source": [
"# 1. Parameters and helper function"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce628e8d",
"metadata": {},
"outputs": [],
"source": [
"log_path = os.path.join('pnsfwtweettext_model_runs', datetime.now().strftime('%Y-%m-%d_%H.%M.%S'))\n",
"\n",
"tweet_text_feature = 'text'\n",
"\n",
"params = {\n",
" 'batch_size': 32,\n",
" 'max_seq_lengths': 256,\n",
" 'model_type': 'twitter_bert_base_en_uncased_augmented_mlm',\n",
" 'trainable_text_encoder': True,\n",
" 'lr': 5e-5,\n",
" 'epochs': 10,\n",
"}\n",
"\n",
"REGEX_PATTERNS = [\n",
" r'^RT @[A-Za-z0-9_]+: ', \n",
" r\"@[A-Za-z0-9_]+\",\n",
" r'https:\\/\\/t\\.co\\/[A-Za-z0-9]{10}',\n",
" r'@\\?\\?\\?\\?\\?',\n",
"]\n",
"\n",
"EMOJI_PATTERN = re.compile(\n",
" \"([\"\n",
" \"\\U0001F1E0-\\U0001F1FF\"\n",
" \"\\U0001F300-\\U0001F5FF\"\n",
" \"\\U0001F600-\\U0001F64F\"\n",
" \"\\U0001F680-\\U0001F6FF\"\n",
" \"\\U0001F700-\\U0001F77F\"\n",
" \"\\U0001F780-\\U0001F7FF\"\n",
" \"\\U0001F800-\\U0001F8FF\"\n",
" \"\\U0001F900-\\U0001F9FF\"\n",
" \"\\U0001FA00-\\U0001FA6F\"\n",
" \"\\U0001FA70-\\U0001FAFF\"\n",
" \"\\U00002702-\\U000027B0\"\n",
" \"])\"\n",
" )\n",
"\n",
"def clean_tweet(text):\n",
" for pattern in REGEX_PATTERNS:\n",
" text = re.sub(pattern, '', text)\n",
"\n",
" text = re.sub(EMOJI_PATTERN, r' \\1 ', text)\n",
" \n",
" text = re.sub(r'\\n', ' ', text)\n",
" \n",
" return text.strip().lower()"
]
},
{
"cell_type": "markdown",
"id": "b55ad56e",
"metadata": {},
"source": [
"# 2. Create data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c491ecf7",
"metadata": {},
"outputs": [],
"source": [
"# Sample data just for demonstration\n",
"\n",
"df = pd.DataFrame(\n",
" {\n",
" \"text\": [\n",
" \"Vanilla Tweet\",\n",
" \"Love is Blind season 4 is 🔥\",\n",
" \"I like Apples\",\n",
" \"Big dick for you\",\n",
" \"Wanna see my puss*?\",\n",
" \"Elon Musk is the best\",\n",
" ],\n",
" \"is_nsfw\": [\n",
" 0, 0, 0, 1, 1, 1,\n",
" ]\n",
" }\n",
")\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "e2369e2f",
"metadata": {},
"source": [
"# 3. Train model"
]
},
{
"cell_type": "markdown",
"id": "83a2d177",
"metadata": {},
"source": [
"## 3.1. Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7843012c",
"metadata": {},
"outputs": [],
"source": [
"df['processed_text'] = df['text'].astype(str).map(clean_tweet)\n",
"df.sample(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e3e5ceb5",
"metadata": {},
"outputs": [],
"source": [
"X_train, X_val, y_train, y_val = train_test_split(df[['processed_text']], df['is_nsfw'], test_size=0.1, random_state=1)\n",
"\n",
"def df_to_ds(X, y, shuffle=False):\n",
" ds = tf.data.Dataset.from_tensor_slices((\n",
" X.values,\n",
" tf.one_hot(tf.cast(y.values, tf.int32), depth=2, axis=-1)\n",
" ))\n",
" \n",
" if shuffle:\n",
" ds = ds.shuffle(1000, seed=1, reshuffle_each_iteration=True)\n",
" \n",
" return ds.map(lambda text, label: ({ tweet_text_feature: text }, label)).batch(params['batch_size'])\n",
"\n",
"ds_train = df_to_ds(X_train, y_train, shuffle=True)\n",
"ds_val = df_to_ds(X_val, y_val)\n",
"X_train.values\n"
]
},
{
"cell_type": "markdown",
"id": "974c1248",
"metadata": {},
"source": [
"## 3.2. Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfd6fc8f",
"metadata": {},
"outputs": [],
"source": [
"inputs = tf.keras.layers.Input(shape=(), dtype=tf.string, name=tweet_text_feature)\n",
"encoder = TextEncoder(\n",
" max_seq_lengths=params['max_seq_lengths'],\n",
" model_type=params['model_type'],\n",
" trainable=params['trainable_text_encoder'],\n",
" local_preprocessor_path='demo-preprocessor'\n",
")\n",
"embedding = encoder([inputs])[\"pooled_output\"]\n",
"predictions = tf.keras.layers.Dense(2, activation='softmax')(embedding)\n",
"model = tf.keras.models.Model(inputs=inputs, outputs=predictions)\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f490f43e",
"metadata": {},
"outputs": [],
"source": [
"optimizer = create_optimizer(\n",
" params['lr'],\n",
" params['epochs'] * len(ds_train),\n",
" 0,\n",
" weight_decay_rate=0.01,\n",
" optimizer_type='adamw'\n",
")\n",
"bce = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
"pr_auc = tf.keras.metrics.AUC(curve='PR', num_thresholds=1000, from_logits=False)\n",
"model.compile(optimizer=optimizer, loss=bce, metrics=[pr_auc])\n",
"\n",
"callbacks = [\n",
" tf.keras.callbacks.EarlyStopping(\n",
" monitor='val_loss',\n",
" mode='min',\n",
" patience=1,\n",
" restore_best_weights=True\n",
" ),\n",
" tf.keras.callbacks.ModelCheckpoint(\n",
" filepath=os.path.join(log_path, 'checkpoints', '{epoch:02d}'),\n",
" save_freq='epoch'\n",
" ),\n",
" tf.keras.callbacks.TensorBoard(\n",
" log_dir=os.path.join(log_path, 'scalars'),\n",
" update_freq='batch',\n",
" write_graph=False\n",
" )\n",
"]\n",
"history = model.fit(\n",
" ds_train,\n",
" epochs=params['epochs'],\n",
" callbacks=callbacks,\n",
" validation_data=ds_val,\n",
" steps_per_epoch=len(ds_train)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "2779f1e1",
"metadata": {},
"source": [
"## 3.3. Check output and performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8a6383db",
"metadata": {},
"outputs": [],
"source": [
"# Expect prediction to be 1\n",
"model.predict([\"xxx 🍑\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0245eb0d",
"metadata": {},
"outputs": [],
"source": [
"preds = X_val.processed_text.apply(apply_model)\n",
"print(classification_report(y_val, preds >= 0.90, digits=4))\n",
"\n",
"precision, recall, thresholds = precision_recall_curve(y_val, preds)\n",
"\n",
"fig = plt.figure(figsize=(15, 10))\n",
"plt.plot(precision, recall, lw=2)\n",
"plt.grid()\n",
"plt.xlim(0.2, 1)\n",
"plt.ylim(0.3, 1)\n",
"plt.xlabel(\"Recall\", size=20)\n",
"plt.ylabel(\"Precision\", size=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ffb1ba3",
"metadata": {},
"outputs": [],
"source": [
"average_precision_score(y_val, preds)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

152
trust_and_safety_models/nsfw/nsfw_text.py
View File

@ -1,152 +0,0 @@
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)