Add project files for IMDb sentiment analysis

README.md

@@ -11,3 +11,15 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+# IMDb Sentiment Analysis
+
+This project demonstrates a sentiment analysis model trained on the IMDb dataset using the Hugging Face Transformers library.
+
+## Installation
+
+To install the necessary dependencies, run:
+
+```bash
+pip install -r requirements.txt

main.py

@@ -0,0 +1,44 @@
+import torch
+print(torch.cuda.is_available())
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Load the IMDb dataset
+dataset = load_dataset('imdb')
+
+# Initialize the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
+model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
+
+# Tokenize the dataset
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding="max_length", truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Set up training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=1,  # Start with fewer epochs for quicker runs
+    weight_decay=0.01,
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"].shuffle(seed=42).select(range(1000)),  # Use a subset for quicker runs
+    eval_dataset=tokenized_datasets["test"].shuffle(seed=42).select(range(1000)),
+)
+
+# Train the model
+trainer.train()
+
+# Evaluate the model
+results = trainer.evaluate()
+print(results)

requirements.txt

@@ -0,0 +1,3 @@
+datasets
+transformers
+torch