Text Classification
Transformers
Safetensors
English
modernbert
commitment-detection
decision-detection
experimental
vela
text-embeddings-inference
Instructions to use TwilightTechie/vela-commitment-classifier-307m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TwilightTechie/vela-commitment-classifier-307m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TwilightTechie/vela-commitment-classifier-307m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TwilightTechie/vela-commitment-classifier-307m") model = AutoModelForSequenceClassification.from_pretrained("TwilightTechie/vela-commitment-classifier-307m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download label-contract.json from TwilightTechie/vela-commitment-classifier-307m: direct link, hf CLI and curl.
- Browser
- Download file 218 Bytes
-
https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/label-contract.json
- Command line
-
hf download hf://TwilightTechie/vela-commitment-classifier-307m/label-contract.json
-
curl -L -o label-contract.json https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/label-contract.json
218 Bytes
| { | |
| "problem_type": "single_label_classification", | |
| "label2id": { | |
| "NO_DECISION_EXPRESSED": 0, | |
| "DECISION_EXPRESSED": 1 | |
| }, | |
| "id2label": { | |
| "0": "NO_DECISION_EXPRESSED", | |
| "1": "DECISION_EXPRESSED" | |
| } | |
| } | |