removing train.py

train.py

@@ -1,64 +0,0 @@
-from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
-import numpy as np
-import pandas as pd
-from datasets import Dataset
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support
-
-
-# Load dataset dynamically or from a config
-df = pd.read_csv("AI_Human.csv")
-train_df, eval_df = train_test_split(df, test_size=0.2)
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-
-def tokenize_function(examples):
-    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
-
-# Convert DataFrames to Datasets and apply tokenization
-train_dataset = Dataset.from_pandas(train_df)
-eval_dataset = Dataset.from_pandas(eval_df)
-
-train_dataset = train_dataset.map(tokenize_function, batched=True)
-train_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
-
-eval_dataset = eval_dataset.map(tokenize_function, batched=True)
-eval_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels'])
-
-model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
-
-
-training_args = TrainingArguments(
-    output_dir="./results",
-    num_train_epochs=3,
-    per_device_train_batch_size=8,
-    per_device_eval_batch_size=8,
-    warmup_steps=500,
-    weight_decay=0.01,
-    logging_dir='./logs',
-    evaluation_strategy="steps",
-    save_steps=500,
-    logging_steps=100,
-)
-def compute_metrics(pred):
-    labels = pred.label_ids
-    preds = np.argmax(pred.predictions, axis=-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
-    }
-
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=train_dataset,
-    eval_dataset=eval_dataset,
-    compute_metrics=compute_metrics
-)
-
-trainer.train()
-model.save_pretrained("./trained_model")
-tokenizer.save_pretrained("./trained_model")