Text Classification
Transformers
PyTorch
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use pradhap2/expenses-policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pradhap2/expenses-policy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="pradhap2/expenses-policy")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("pradhap2/expenses-policy") model = AutoModelForSequenceClassification.from_pretrained("pradhap2/expenses-policy") - Notebooks
- Google Colab
- Kaggle
| { | |
| "label2id": { | |
| "Policy Over Limit": 0, | |
| "Within Policy": 1 | |
| }, | |
| "id2label": { | |
| "0": "Policy Over Limit", | |
| "1": "Within Policy" | |
| } | |
| } |