Create offensive_osint_trainer.py

offensive_osint_trainer.py

@@ -0,0 +1,44 @@
+import torch
+from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
+
+# Load the data
+train_data = ... # load your training data here
+eval_data = ... # load your evaluation data here
+
+# Define the model architecture
+model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=8)
+
+# Set up the training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    num_train_epochs=3,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=64,
+    warmup_steps=500,
+    weight_decay=0.01,
+    logging_dir='./logs',
+    logging_first_step=True,
+    logging_steps=50,
+    save_total_limit=2,
+    save_steps=500,
+    eval_steps=500,
+    learning_rate=5e-5,
+    seed=42,
+)
+
+# Create the trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_data,
+    eval_dataset=eval_data,
+    compute_metrics=lambda pred: {'accuracy': torch.tensor(pred).argmax().item()},
+)
+
+# Train the model
+trainer.train()
+
+# Evaluate the model
+loss, metrics = trainer.evaluate()
+print(f'Loss: {loss}')
+print(f'Metrics: {metrics}')