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/source-files.json from TwilightTechie/vela-commitment-classifier-307m: direct link, hf CLI and curl.
- Browser
- Download file 1.75 kB
-
https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/reproducibility/source-files.json
- Command line
-
hf download hf://TwilightTechie/vela-commitment-classifier-307m/reproducibility/source-files.json
-
curl -L -o source-files.json https://huggingface.co/TwilightTechie/vela-commitment-classifier-307m/resolve/main/reproducibility/source-files.json
1.75 kB
| { | |
| "runtime_mapping.py": "ec0c553ef625001c09c53a27659cc5162629e303bdef6a93c6091767f49b7fd9", | |
| "retention.py": "3c8bc8aa60388508bc400bd68a6de2ed6ac18b48a24d3acd66d8e591223736eb", | |
| "complete_adapter.py": "bc9a9365b1740b18d9cbe59d5250d8b5d214a0e133850c4921857c8e870ea13a", | |
| "hydrate_annotations.py": "ea0c44b51afddbeb7dabf084db006eed545022ed819fa40682f4be846b207d62", | |
| "export.py": "2f3ec1b038eae154ad32b28dc3cdc465e57e8d8b6fa842c2b67b0a66d3938fa9", | |
| "task_head.py": "9d2a19743980c35e224c752a35e1e91a7e82dee3430f83b9008a4d01270a5a42", | |
| "prepare_context.py": "9f9fe1ebd52e97d3e6901dc42bbf81f7e0703b7f2293703eabd89acd900a254a", | |
| "corpus.py": "ba3d4f91607db085e55ae1c76701b9b1319fea5ee7dece9851d4ee7375bca108", | |
| "prepare_retention.py": "56f48cc26b4889f93b43734e66ad8aaccd0046701519278b6b446d495f605ff4", | |
| "initialize.py": "ade17c61ba94231a83e124e368bda4f468574cbb77689b6f71694c6489bb94a6", | |
| "evaluate.py": "41617631838a1f72e33d402470a9bebd1ab0d7b42b80ac7e8c424a487068c562", | |
| "selection.py": "764150454d150ad8971956faa2198d84380c8ee28f23ea63a650b07d045fc735", | |
| "data.py": "84f922c20a2f3b029f7d363cf7f96f98cd239669f7777de099c63cfa1a1ef199", | |
| "model.py": "c85ca12237e8735e20289e6e630803c59a6919dd473021d42291956c4b32f434", | |
| "optimization.py": "71dde43dbe288cd8fab636a9266905f7898bac66ae155ded5158e37786492082", | |
| "__init__.py": "04cd2a4c037dd1cc2070d57bea979cacb76d4660313a190fdf612007cb33b88e", | |
| "context.py": "8e51031478aca4a825a77d057b2a66a9d09d80e42fb66ccbec3c527f3a891303", | |
| "fetch_sources.py": "1abd30d33f456357553facdbde11116134835a087009c63c433faeccf8214638", | |
| "train.py": "8b91020b3fcf93ae5925e9d15597d6f37ea3ca5d5c03745a2096b76be246e038", | |
| "training_order.py": "88492bbb97b93743d637474595cffbc82e79d18bd115245adf60ffadea3bef44" | |
| } | |