IDA-SERICS/deberta-v3-prompt-recognition

🧠 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

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📝 Fine-tuned for research purposes.