IDA-SERICS/PromptClassifier

🧠 DeBERTa-v3 Base - Prompt Category Classifier (Fine-tuned)

This model is a fine-tuned version of `microsoft/deberta-v3-base` on the databricks-dolly-15k dataset. It has been trained to classify the prompt category based solely on the response text.

🗂️ Task

Text Classification Input: Response text Output: One of the predefined categories such as:

• —brainstorming
• —classification
• —closed_qa
• —creative_writing
• —general_qa
• —information_extraction
• —open_qa
• —summarization

📊 Evaluation

The model was evaluated on a balanced version of the dataset. Here are the results:

• —Validation Accuracy: ~85.5%
• —F1 Score: ~85.0%
• —Best performance on: creative_writing, classification, summarization
• —Room for improvement on: open_qa

🧪 How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("mariadg/deberta-v3-prompt-recognition")
tokenizer = AutoTokenizer.from_pretrained("mariadg/deberta-v3-prompt-recognition")

text = "The mitochondria is known as the powerhouse of the cell."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
outputs = model(**inputs)
pred = torch.argmax(outputs.logits, dim=1).item()

print(pred)  # Map this index back to label if needed

📦 Label Mapping

The model outputs a numerical label corresponding to a prompt category. Below is the mapping between label IDs and their respective categories:

• —0: brainstorming
• —1: classification
• —2: closed_qa
• —3: creative_writing
• —4: general_qa
• —5: information_extraction
• —6: open_qa
• —7: summarization

🛠️ Training Details

• —Base model: microsoft/deberta-v3-base
• —Framework: PyTorch
• —Max length: 256
• —Batch size: 16
• —Epochs: 4
• —Loss function: CrossEntropyLoss

🔐 License

Apache 2.0

📝 Fine-tuned for research purposes.