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--- |
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license: apache-2.0 |
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tags: |
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- bert |
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- deberta |
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- text-classification |
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- fine-tuned |
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- databricks-dolly |
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- prompt-category |
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language: en |
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datasets: |
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- databricks/databricks-dolly-15k |
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base_model: |
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- microsoft/deberta-v3-base |
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--- |
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# 🧠 DeBERTa-v3 Base - Prompt Category Classifier (Fine-tuned) |
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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. |
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It has been trained to classify the **prompt category** based solely on the **response** text. |
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## 🗂️ Task |
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**Text Classification** |
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**Input**: Response text |
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**Output**: One of the predefined categories such as: |
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- `brainstorming` |
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- `classification` |
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- `closed_qa` |
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- `creative_writing` |
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- `general_qa` |
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- `information_extraction` |
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- `open_qa` |
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- `summarization` |
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## 📊 Evaluation |
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The model was evaluated on a balanced version of the dataset. Here are the results: |
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- **Validation Accuracy**: ~85.5% |
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- **F1 Score**: ~85.0% |
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- Best performance on: `creative_writing`, `classification`, `summarization` |
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- Room for improvement on: `open_qa` |
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## 🧪 How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model = AutoModelForSequenceClassification.from_pretrained("mariadg/deberta-v3-prompt-recognition") |
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tokenizer = AutoTokenizer.from_pretrained("mariadg/deberta-v3-prompt-recognition") |
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text = "The mitochondria is known as the powerhouse of the cell." |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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outputs = model(**inputs) |
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pred = torch.argmax(outputs.logits, dim=1).item() |
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print(pred) # Map this index back to label if needed |
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``` |
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## 📦 Label Mapping |
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The model outputs a numerical label corresponding to a prompt category. Below is the mapping between label IDs and their respective categories: |
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- 0: `brainstorming` |
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- 1: `classification` |
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- 2: `closed_qa` |
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- 3: `creative_writing` |
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- 4: `general_qa` |
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- 5: `information_extraction` |
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- 6: `open_qa` |
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- 7: `summarization` |
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## 🛠️ Training Details |
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- **Base model**: `microsoft/deberta-v3-base` |
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- **Framework**: PyTorch |
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- **Max length**: 256 |
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- **Batch size**: 16 |
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- **Epochs**: 4 |
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- **Loss function**: `CrossEntropyLoss` |
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## 🔐 License |
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Apache 2.0 |
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--- |
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📝 Fine-tuned for research purposes. |
