from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("KingTechnician/bert-osmosis-coverage-v2")
model = AutoModelForSequenceClassification.from_pretrained("KingTechnician/bert-osmosis-coverage-v2")

# Example usage
objective = "Your learning objective here"
response = "The response text to classify"

inputs = tokenizer(objective, response, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()
    
# Map prediction to label
id2label = {0: "low", 1: "fair", 2: "satisfactory"}
predicted_label = id2label[predicted_class]
print(f"Predicted coverage: {predicted_label}")

Training Details

Learning Rate: 2e-5
Batch Size: 64
Epochs: 3
Weight Decay: 0.01