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# Convert Reddit data into a format that can be used by the BERT model
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_fscore_support, accuracy_score
import torch
from transformers import (
DistilBertTokenizerFast,
DistilBertForSequenceClassification,
Trainer,
TrainingArguments,
)
# train test split
def read_reddit_split(reddit_csv):
df = pd.read_csv(reddit_csv)
texts = df["body"].tolist()
labels = df["Class"].tolist()
# 80% train, 10% test, 10% valid
train_texts, test_texts, train_labels, test_labels = train_test_split(
texts, labels, test_size=0.2, stratify=labels
)
train_texts, val_texts, train_labels, val_labels = train_test_split(
train_texts, train_labels, test_size=1.0 / 8.0, stratify=train_labels
)
print(f"size of train data is {len(train_texts)}")
print(f"size of test data is {len(test_texts)}")
print(f"size of valid data is {len(val_texts)}")
return train_texts, test_texts, val_texts, train_labels, test_labels, val_labels
# tokenize data
def tokenize_data(train_texts, test_texts, val_texts, tokenizer):
train_enc = tokenizer(train_texts, truncation=True, padding=True)
test_enc = tokenizer(test_texts, truncation=True, padding=True)
valid_enc = tokenizer(val_texts, truncation=True, padding=True)
return train_enc, test_enc, valid_enc
# convert to Dataset object
class RedditDataset(torch.utils.data.Dataset):
def __init__(self, encodings, labels):
self.encodings = encodings
self.labels = labels
def __getitem__(self, idx):
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
item["labels"] = torch.tensor(self.labels[idx])
return item
def __len__(self):
return len(self.labels)
def compute_metrics(pred):
labels = pred.label_ids
preds = pred.predictions.argmax(-1)
precision, recall, f1, _ = precision_recall_fscore_support(
labels, preds, average="binary"
)
acc = accuracy_score(labels, preds)
return {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall}
# run on main
if __name__ == "__main__":
# tokenizer
bert_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
# model
model = DistilBertForSequenceClassification.from_pretrained(
"distilbert-base-uncased"
)
# read data
x_train, x_test, x_valid, y_train, y_test, y_valid = read_reddit_split(
"/workspaces/Michelle_Li_NLP_Project/reddit_data/reddit_annotated.csv"
)
# tokenize data
train_encodings, test_encodings, valid_encodings = tokenize_data(
x_train, x_test, x_valid, bert_tokenizer
)
train_dataset = RedditDataset(train_encodings, y_train)
test_dataset = RedditDataset(test_encodings, y_test)
val_dataset = RedditDataset(valid_encodings, y_valid)
# fine-tune BERT model
training_args = TrainingArguments(
output_dir="./results", # output directory
num_train_epochs=3, # total number of training epochs
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=64, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir="./logs", # directory for storing logs
logging_steps=10,
)
trainer = Trainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=val_dataset, # evaluation dataset
compute_metrics=compute_metrics, # compute metrics
)
trainer.train()
# test model
trainer.evaluate(test_dataset)
# save model
trainer.save_model("./models")
# save tokenizer
bert_tokenizer.save_pretrained("./models")
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