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import pandas as pd
from datasets import Dataset
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
TrainingArguments,
Trainer
)
import torch
import numpy as np
from sklearn.metrics import accuracy_score, f1_score
# Load CSV
df = pd.read_csv("./../data/text.csv")
dataset = Dataset.from_pandas(df)
#Tokenizer & model (DistilBERT architecture)
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
def tokenize(batch):
return tokenizer(batch["text"], padding=True, truncation=True)
dataset = dataset.map(tokenize, batched=True)
dataset = dataset.rename_column("label", "labels")
dataset.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
train_test = dataset.train_test_split(test_size=0.2)
#Model
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
#Metrics
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = np.argmax(logits, axis=-1)
return {
"accuracy": accuracy_score(labels, preds),
"f1": f1_score(labels, preds),
}
#TrainingArguments
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
evaluation_strategy="epoch",
logging_dir="./logs",
save_strategy="epoch",
)
#Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_test["train"],
eval_dataset=train_test["test"],
compute_metrics=compute_metrics,
tokenizer=tokenizer,
)
#Train
trainer.train()
#Save
model.save_pretrained("PigeonAIModel1")
tokenizer.save_pretrained("PigeonAIModel1") |