Text Classification
Transformers
PyTorch
English
wrag2
weight-retrieval
domain-adaptation
medical
legal
code
Instructions to use Gyeti123/wrag2-text-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gyeti123/wrag2-text-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gyeti123/wrag2-text-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gyeti123/wrag2-text-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "wrag2", | |
| "architecture": "WRAG2TextModel", | |
| "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", | |
| "num_shards": 10, | |
| "k": 3, | |
| "num_wr_layers": 3, | |
| "hidden_size": 2048, | |
| "num_classes": 2, | |
| "task": "text-classification", | |
| "domains": [ | |
| "medical", | |
| "legal", | |
| "code" | |
| ], | |
| "version": "1.0.0", | |
| "results": { | |
| "medical_accuracy": 0.7, | |
| "legal_accuracy": 0.84, | |
| "code_accuracy": 0.95, | |
| "average_accuracy": 0.83 | |
| } | |
| } |