| | --- |
| | license: apache-2.0 |
| | tags: |
| | - bert |
| | - deberta |
| | - text-classification |
| | - fine-tuned |
| | - databricks-dolly |
| | - prompt-category |
| | language: en |
| | datasets: |
| | - databricks/databricks-dolly-15k |
| | base_model: |
| | - microsoft/deberta-v3-base |
| | --- |
| | |
| | # 🧠 DeBERTa-v3 Base - Prompt Category Classifier (Fine-tuned) |
| |
|
| | This model is a fine-tuned version of [`microsoft/deberta-v3-base`](https://huggingface.co/microsoft/deberta-v3-base) on the [databricks-dolly-15k](https://huggingface.co/datasets/databricks/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 |
| |
|
| | ```python |
| | 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. |
