Delete train_sentiment_model.py

train_sentiment_model.py

@@ -1,59 +0,0 @@
-from datasets import load_dataset
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSequenceClassification,
-    Trainer,
-    TrainingArguments,
-)
-import torch
-
-# STEP 1: Load IMDb Dataset
-dataset = load_dataset("imdb")
-
-# STEP 2: Tokenize the Data
-checkpoint = "distilbert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-
-def preprocess(example):
-    return tokenizer(example["text"], truncation=True, padding="max_length", max_length=256)
-
-tokenized = dataset.map(preprocess, batched=True)
-tokenized = tokenized.remove_columns(["text"])
-tokenized = tokenized.rename_column("label", "labels")
-tokenized.set_format("torch")
-
-# Use a smaller subset for quick training
-train_dataset = tokenized["train"].shuffle(seed=42).select(range(2000))
-val_dataset = tokenized["test"].shuffle(seed=42).select(range(500))
-
-# STEP 3: Load Model
-model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
-
-# STEP 4: Define Training Arguments
-training_args = TrainingArguments(
-    output_dir="./results",
-    evaluation_strategy="epoch",
-    save_strategy="epoch",
-    num_train_epochs=3,
-    per_device_train_batch_size=8,
-    per_device_eval_batch_size=8,
-    logging_dir="./logs",
-    logging_steps=50,
-    report_to="none"
-)
-
-# STEP 5: Train
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=train_dataset,
-    eval_dataset=val_dataset,
-    tokenizer=tokenizer,
-)
-
-trainer.train()
-
-# STEP 6: Save Locally to Repo Folder
-model.save_pretrained("./")
-tokenizer.save_pretrained("./")
-print("✅ Model and tokenizer saved locally!")