diff --git a/moderation_classifier/main.py b/moderation_classifier/main.py
index 281b89a2e332e0348c322ad90d549b91e5b2ee94..0bb8d16c671c345bc76319657aed8fe54cca548d 100644
--- a/moderation_classifier/main.py
+++ b/moderation_classifier/main.py
@@ -13,15 +13,23 @@ import os
@click.command()
@click.option('-p', '--prepare_data', is_flag=True)
+@click.option('-tp', '--text_preprocessing', is_flag=True)
@click.option('-tm', '--train_mnb', is_flag=True)
@click.option('-tb', '--train_bert', is_flag=True)
@click.option('-tbto', '--train_bert_torch', is_flag=True)
@click.argument('input_data')
-def main(prepare_data: bool, train_mnb: bool, train_bert: bool, train_bert_torch: bool, input_data: Union[str, os.PathLike]):
+def main(prepare_data: bool,
+ text_preprocessing: bool,
+ train_mnb: bool,
+ train_bert: bool,
+ train_bert_torch: bool,
+ input_data: Union[str, os.PathLike]):
"""
Run moderation classifier.
:param prepare_data: Binary flag to specify if data should be prepared.
+ :param text_preprocessing: Binary flag to set text preprocessing.
:param train_mnb: Binary flag to specify whether MNB should be trained.
+ :param train_mnb: Binary flag to specify whether BERT should be trained.
:param input_data: Path to input dataframe.
"""
@@ -35,7 +43,7 @@ def main(prepare_data: bool, train_mnb: bool, train_bert: bool, train_bert_torch
train_MNB.main(input_data)
if train_bert:
- train_BERT.main(input_data)
+ train_BERT.main(input_data, text_preprocessing)
if train_bert_torch:
train_BERT_torch.main(input_data)
diff --git a/moderation_classifier/train_BERT.py b/moderation_classifier/train_BERT.py
index dcd9c528b2563cd0988429dc4b17e911cc834450..089782ff865b92790ab61842050969f2f1340c8b 100644
--- a/moderation_classifier/train_BERT.py
+++ b/moderation_classifier/train_BERT.py
@@ -1,111 +1,81 @@
-from datasets import Dataset, DatasetDict
-
-import evaluate
from transformers import AutoTokenizer
from transformers import DataCollatorWithPadding
-
-from transformers import create_optimizer
from transformers import TFAutoModelForSequenceClassification
-
-import numpy as np
-
from transformers.keras_callbacks import KerasMetricCallback
-from transformers.keras_callbacks import PushToHubCallback
import tensorflow as tf
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import TensorBoard
-import pandas as pd
+import click
import datetime
-from typing import Union
import os
+from pathlib import Path
+import spacy
+from typing import Union
-import click
-
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import f1_score, precision_recall_fscore_support
-
-from src.preprocessing_text import TextLoader
-
-def load_text(path: Union[str, os.PathLike], newspaper: str = 'tagesanzeiger', lang: str = 'de') -> pd.DataFrame:
- """
- Loads daraframe and extracts text depending on newspaper and langugae
- """
- df = pd.read_csv(path)
- df = df.loc[(df.originTenantId==newspaper) & (df.language==lang)]
- df = df[['text','rejected']]
- df = df.rename(columns={"rejected": "label"})
-
- return df
+from src.preprocessing_text import TextLoader, TextProcessor
+from src.prepare_bert_tf import df2dict, compute_metrics, prepare_training
-def df2dict(df: pd.DataFrame):
+def preprocess(text: str, nlp) -> str:
"""
- Converts Dataframe into Huggingface Dataset
+ Applies preprocessing to text
+ :param text: Input text
+ :param nlp: Loaded nlp model
"""
- df = df.sample(100)
- train, test = train_test_split(df, test_size=0.2)
-
-
- ds_train = Dataset.from_pandas(train)
- ds_test = Dataset.from_pandas(test)
-
- ds = DatasetDict()
- ds['train'] = ds_train
- ds['test'] = ds_test
+ tp = TextProcessor(nlp)
- return ds
+ text_proc = tp.remove_spaces(text)
+ text_proc = tp.remove_punctuation(text_proc)
+ text_proc = tp.remove_mentions(text_proc)
+ text_proc = tp.fold_case(text_proc)
-def compute_metrics(eval_pred):
- accuracy = evaluate.load("accuracy")
- predictions, labels = eval_pred
- predictions = np.argmax(predictions, axis=1)
- return accuracy.compute(predictions=predictions, references=labels)
+ return text_proc
-def prepare_training(dataset, batch_size: int = 16, num_epochs: int = 5):
+@click.argument('input_data')
+@click.argument('text_preprocessing', required=False)
+def main(input_data: Union[str, os.PathLike], text_preprocessing: bool):
"""
- Prepares training and sets params
+ Prepares data and trains BERT model with TF
+ :param input_data: path to input data
"""
- batches_per_epoch = len(dataset["train"]) // batch_size
- total_train_steps = int(batches_per_epoch * num_epochs)
- optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
-
- return optimizer, schedule
-
+ def preprocess_function(examples):
+ """
+ Prepares tokenizer for mapping
+ """
+ return tokenizer(examples["text"], truncation=True)
+
+ # Extract path
+ p = Path(input_data)
+ p_repo = p.parent.parent
-@click.argument('input_data')
-def main(input_data: Union[str, os.PathLike]):
- # load data and extract only german text from tagesanzeiger
- print('Load text')
+ # Load data and extract only german text from tagesanzeiger
+ print('Load and preprocess text')
tl = TextLoader(input_data)
df_de = tl.load_text_csv(newspaper = 'tagesanzeiger', load_subset=True)
+
+ if text_preprocessing:
+ nlp = spacy.load('de_core_news_sm')
+ text_proc = df_de.text.apply(preprocess, nlp=nlp)
+ df_de.text = text_proc
- ## Dataframe to dict/Train-test split
- ds = df2dict(df_de)
- ## Preprocessing/Tokenization
+ ## Prepare data for modeling
+ ds = df2dict(df_de)
tokenizer = AutoTokenizer.from_pretrained("bert-base-german-cased")
-
- def preprocess_function(examples):
- return tokenizer(examples["text"], truncation=True)
-
- #truncate sequences to be no longer than the models maximum input length
tokenized_text = ds.map(preprocess_function)
-
- #dynamically padding of sentences to the longest length in a batch
data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
- ## Evaluation
- accuracy = evaluate.load("accuracy")
# Training
+ print('Train model')
id2label = {0: "NEGATIVE", 1: "POSITIVE"}
label2id = {"NEGATIVE": 0, "POSITIVE": 1}
- optimizer, schedule = prepare_training(tokenized_text)
+ optimizer, _ = prepare_training(tokenized_text)
model = TFAutoModelForSequenceClassification.from_pretrained(
"bert-base-german-cased", num_labels=2, id2label=id2label, label2id=label2id
)
@@ -126,7 +96,8 @@ def main(input_data: Union[str, os.PathLike]):
model.compile(optimizer=optimizer)
- checkpoint_filepath = '/Users/franziskaoschmann/Documents/public_policy/tmp/checkpoint/'
+ path_checkpoint = (p_repo).joinpath('tmp/checkpoint/')
+ checkpoint_filepath = path_checkpoint
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
checkpoint_callback = ModelCheckpoint(checkpoint_filepath, monitor = 'val_loss',
save_best_only = True,
@@ -140,8 +111,12 @@ def main(input_data: Union[str, os.PathLike]):
callbacks = [metric_callback, checkpoint_callback, tensorboard_callback]
model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=5, callbacks=callbacks)
- model.save_pretrained('/Users/franziskaoschmann/Documents/public_policy/saved_models/')
- tokenizer.save_pretrained('/Users/franziskaoschmann/Documents/public_policy/saved_models/')
+
+
+ # Save model
+ path_model = (p_repo).joinpath('saved_models/')
+ model.save_pretrained(path_model)
+ tokenizer.save_pretrained(path_model)
diff --git a/moderation_classifier/train_BERT_tmp.py b/moderation_classifier/train_BERT_tmp.py
deleted file mode 100644
index 662bc789d1e434d91544d06b62e015a6026dfe8f..0000000000000000000000000000000000000000
--- a/moderation_classifier/train_BERT_tmp.py
+++ /dev/null
@@ -1,148 +0,0 @@
-from datasets import Dataset, DatasetDict
-
-import evaluate
-from transformers import AutoTokenizer
-from transformers import DataCollatorWithPadding
-
-from transformers import create_optimizer
-from transformers import TFAutoModelForSequenceClassification
-
-import numpy as np
-
-from transformers.keras_callbacks import KerasMetricCallback
-from transformers.keras_callbacks import PushToHubCallback
-
-import tensorflow as tf
-from tf.keras.callbacks import ModelCheckpoint
-
-import pandas as pd
-
-from typing import Union
-import os
-
-import click
-
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import f1_score, precision_recall_fscore_support
-
-from src.preprocessing_text import TextLoader
-
-def load_text(path: Union[str, os.PathLike], newspaper: str = 'tagesanzeiger', lang: str = 'de') -> pd.DataFrame:
- """
- Loads daraframe and extracts text depending on newspaper and langugae
- """
- df = pd.read_csv(path)
- df = df.loc[(df.originTenantId==newspaper) & (df.language==lang)]
- df = df[['text','rejected']]
- df = df.rename(columns={"rejected": "label"})
-
- return df
-
-def df2dict(df: pd.DataFrame):
- """
- Converts Dataframe into Huggingface Dataset
- """
-
- df = df.sample(100)
- train, test = train_test_split(df, test_size=0.2)
-
-
- ds_train = Dataset.from_pandas(train)
- ds_test = Dataset.from_pandas(test)
-
- ds = DatasetDict()
- ds['train'] = ds_train
- ds['test'] = ds_test
-
- return ds
-
-def compute_metrics(eval_pred):
- accuracy = evaluate.load("accuracy")
- predictions, labels = eval_pred
- predictions = np.argmax(predictions, axis=1)
- return accuracy.compute(predictions=predictions, references=labels)
-
-def prepare_training(dataset, batch_size: int = 16, num_epochs: int = 5):
- """
- Prepares training and sets params
- """
-
- batches_per_epoch = len(dataset["train"]) // batch_size
- total_train_steps = int(batches_per_epoch * num_epochs)
- optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
-
- return optimizer, schedule
-
-
-@click.argument('input_data')
-def main(input_data: Union[str, os.PathLike]):
-
- # load data and extract only german text from tagesanzeiger
- print('Load text')
- tl = TextLoader(input_data)
- df_de = tl.load_text_csv(newspaper = 'tagesanzeiger', load_subset=True)
-
- ## Dataframe to dict/Train-test split
- ds = df2dict(df_de)
-
- ## Preprocessing/Tokenization
- tokenizer = AutoTokenizer.from_pretrained("bert-base-german-cased")
-
- def preprocess_function(examples):
- return tokenizer(examples["text"], truncation=True)
-
- #truncate sequences to be no longer than the models maximum input length
- tokenized_text = ds.map(preprocess_function)
-
- #dynamically padding of sentences to the longest length in a batch
- data_collator = DataCollatorWithPadding(tokenizer=tokenizer, return_tensors="tf")
-
- ## Evaluation
- accuracy = evaluate.load("accuracy")
-
- # Training
- id2label = {0: "NEGATIVE", 1: "POSITIVE"}
- label2id = {"NEGATIVE": 0, "POSITIVE": 1}
-
- optimizer, schedule = prepare_training(tokenized_text)
- model = TFAutoModelForSequenceClassification.from_pretrained(
- "bert-base-german-cased", num_labels=2, id2label=id2label, label2id=label2id
- )
-
- tf_train_set = model.prepare_tf_dataset(
- tokenized_text["train"],
- shuffle=True,
- batch_size=16,
- collate_fn=data_collator,
- )
-
- tf_validation_set = model.prepare_tf_dataset(
- tokenized_text["test"],
- shuffle=False,
- batch_size=16,
- collate_fn=data_collator,
- )
-
- model.compile(optimizer=optimizer)
-
- checkpoint_filepath = '/cluster/work/sis/cdss/oschmanf/ppg-moderation-classifier/tmp/checkpoint/'
- metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
- checkpoint_callback = ModelCheckpoint(checkpoint_filepath,
- monitor: str = 'val_loss',
- save_best_only: bool = True,
- save_weights_only: bool = False,
- mode: str = 'min',
- save_freq='epoch',
- initial_value_threshold=None)
-
- callbacks = [metric_callback, checkpoint_callback]
-
- model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=5, callbacks=callbacks)
- model.save_pretrained('/cluster/work/sis/cdss/oschmanf/ppg-moderation-classifier/tmp/saved_models/')
- tokenizer.save_pretrained('/cluster/work/sis/cdss/oschmanf/ppg-moderation-classifier/tmp/saved_models/')
-
-
-
-
-if __name__ == "__main__":
- main()
diff --git a/src/prepare_bert_tf.py b/src/prepare_bert_tf.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d902b80dca34636137ef6fd17e7bfb189d2dfb7
--- /dev/null
+++ b/src/prepare_bert_tf.py
@@ -0,0 +1,44 @@
+import pandas as pd
+from datasets import Dataset, DatasetDict
+from sklearn.model_selection import train_test_split
+import evaluate
+import numpy as np
+from transformers import create_optimizer
+
+def df2dict(df: pd.DataFrame, test_size: float = 0.2):
+ """
+ Converts Dataframe into Huggingface Dataset
+ """
+ df = df.sample(100)
+ train, test = train_test_split(df, test_size=test_size)
+
+ ds_train = Dataset.from_pandas(train)
+ ds_test = Dataset.from_pandas(test)
+
+ ds = DatasetDict()
+ ds['train'] = ds_train
+ ds['test'] = ds_test
+
+ return ds
+
+
+def compute_metrics(eval_pred):
+ """
+ Computes metrics during training
+ """
+ accuracy = evaluate.load("accuracy")
+ predictions, labels = eval_pred
+ predictions = np.argmax(predictions, axis=1)
+
+ return accuracy.compute(predictions=predictions, references=labels)
+
+
+def prepare_training(dataset, batch_size: int = 16, num_epochs: int = 5):
+ """
+ Prepares training and sets params
+ """
+ batches_per_epoch = len(dataset["train"]) // batch_size
+ total_train_steps = int(batches_per_epoch * num_epochs)
+ optimizer, schedule = create_optimizer(init_lr=2e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
+
+ return optimizer, schedule
\ No newline at end of file