Create app.py

app.py

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+from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification, Trainer, TrainingArguments
+import datasets
+
+# Load the pre-trained XLM-Roberta-Large model and tokenizer
+model_name = 'xlm-roberta-large'
+tokenizer = XLMRobertaTokenizer.from_pretrained(model_name)
+model = XLMRobertaForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+# Load the sentiment analysis dataset
+dataset = datasets.load_dataset('imdb')
+
+# Tokenize the dataset
+def tokenize(batch):
+    return tokenizer(batch['text'], padding=True, truncation=True)
+
+dataset = dataset.map(tokenize, batched=True)
+
+# Fine-tune the model on the dataset
+training_args = TrainingArguments(
+    output_dir='./results',
+    evaluation_strategy='epoch',
+    learning_rate=2e-5,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    push_to_hub=False,
+    logging_dir='./logs',
+    logging_steps=10,
+    load_best_model_at_end=True,
+    metric_for_best_model='accuracy'
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset['train'],
+    eval_dataset=dataset['test']
+)
+
+trainer.train()
+
+import torch
+
+# Load the fine-tuned XLM-Roberta-Large model
+model_path = './results/checkpoint-1000'
+model = XLMRobertaForSequenceClassification.from_pretrained(model_path)
+
+# Create a function that takes a text input and returns the predicted sentiment label
+def predict_sentiment(text):
+    inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1)
+    return 'positive' if predicted_class == 1 else 'negative'
+
+import gradio as gr
+
+# Create a Gradio interface for the predict_sentiment function
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.inputs.Textbox(placeholder='Enter text here...'),
+    outputs=gr.outputs.Textbox(placeholder='Sentiment prediction...')
+)
+
+# Launch the interface
+iface.launch()