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
File size: 713 Bytes
f0e7192 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"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"
}
}
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