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 reproducibility/training.json from TwilightTechie/vela-commitment-classifier-307m: direct link, hf CLI and curl.
- Browser
- Download file 713 Bytes
-
https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/reproducibility/training.json
- Command line
-
hf download hf://TwilightTechie/vela-commitment-classifier-307m/reproducibility/training.json
-
curl -L -o training.json https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/reproducibility/training.json
713 Bytes
| { | |
| "steps": 100, | |
| "gpu": "NVIDIA L4", | |
| "torch": "2.8.0+cu128", | |
| "cuda": "12.8", | |
| "python": "3.12.10", | |
| "selection": { | |
| "step": 75, | |
| "score": 1.0, | |
| "metric": "macro-f1", | |
| "test_used": false | |
| }, | |
| "base_model": "vllm-sr/Vela-1.0-Encoder-307M", | |
| "base_revision": "720ab37904ce15054068d429381bfae7549f00f6", | |
| "method": "full; fresh head; final two encoder layers", | |
| "batch_size": 2, | |
| "accumulation": 2, | |
| "max_length": 256, | |
| "seed": 42, | |
| "learning_rate": 2e-05, | |
| "head_learning_rate": 0.0001, | |
| "dataset_files": { | |
| "train.jsonl": "69022836ee506922a74b3583088a242fcbedc5fd181c4dd7bcea4e7eca60f814", | |
| "dev.jsonl": "5909fe10961b0c6500a5e7d6ac9efd99b114c16f3545d45cd04036a070dbd1c3" | |
| } | |
| } | |