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
File size: 459 Bytes
b6564d4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"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
}
} |