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 task-head.json from TwilightTechie/vela-commitment-classifier-307m: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/task-head.json
- Command line
-
hf download hf://TwilightTechie/vela-commitment-classifier-307m/task-head.json
-
curl -L -o task-head.json https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/task-head.json
1.22 kB
| { | |
| "architecture": "ModernBertForSequenceClassification", | |
| "modules_to_save": null, | |
| "trainable_task_head_parameters": 592130, | |
| "tensors": { | |
| "head.dense.weight": { | |
| "shape": [ | |
| 768, | |
| 768 | |
| ], | |
| "dtype": "torch.float32", | |
| "numel": 589824, | |
| "trainable": true, | |
| "adapter_owned": false, | |
| "sha256": "01a8bfc3e8af91a9901f5088ec0fb1cdb1f1b8c47526a5d5e2fe5a94d7383003" | |
| }, | |
| "head.norm.weight": { | |
| "shape": [ | |
| 768 | |
| ], | |
| "dtype": "torch.float32", | |
| "numel": 768, | |
| "trainable": true, | |
| "adapter_owned": false, | |
| "sha256": "fd989126d90b0d85ba4e8863c791567d526c8d6bed155fc05672489c906a91b5" | |
| }, | |
| "classifier.weight": { | |
| "shape": [ | |
| 2, | |
| 768 | |
| ], | |
| "dtype": "torch.float32", | |
| "numel": 1536, | |
| "trainable": true, | |
| "adapter_owned": false, | |
| "sha256": "84ca652ca12370d26a59fc4039312c112dd5079c834a04f43cc48c568b0873ee" | |
| }, | |
| "classifier.bias": { | |
| "shape": [ | |
| 2 | |
| ], | |
| "dtype": "torch.float32", | |
| "numel": 2, | |
| "trainable": true, | |
| "adapter_owned": false, | |
| "sha256": "eace58362ea99f963002fb0ba0a36ab6d2ddfc1a504668108853dd5dbf1cba92" | |
| } | |
| } | |
| } | |