Text Classification
Transformers
Safetensors
English
cev
multi-label-classification
intent-detection
hr
calibration
deberta
Instructions to use Nehaa/cev-141m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nehaa/cev-141m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nehaa/cev-141m")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nehaa/cev-141m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Nehaa/cev-141m: direct link, hf CLI and curl.
- Browser
- Download file 343 Bytes
-
https://huggingface.co/Nehaa/cev-141m/resolve/main/config.json
- Command line
-
hf download hf://Nehaa/cev-141m/config.json
-
curl -L -o config.json https://huggingface.co/Nehaa/cev-141m/resolve/main/config.json
343 Bytes
| { | |
| "architectures": [ | |
| "CevModel" | |
| ], | |
| "backbone": "microsoft/deberta-v3-small", | |
| "capabilities": [ | |
| "person_lookup", | |
| "skill_assessment", | |
| "learning_discovery", | |
| "assignment" | |
| ], | |
| "dropout": 0.1, | |
| "dtype": "float16", | |
| "hidden_size": 768, | |
| "mlp_hidden": 384, | |
| "model_type": "cev", | |
| "transformers_version": "5.17.0" | |
| } | |