Instructions to use roadrage10/RADAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roadrage10/RADAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="roadrage10/RADAR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("roadrage10/RADAR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from roadrage10/RADAR: direct link, hf CLI and curl.
- Browser
- Download file 458 Bytes
-
https://huggingface.co/roadrage10/RADAR/resolve/main/config.json
- Command line
-
hf download hf://roadrage10/RADAR/config.json
-
curl -L -o config.json https://huggingface.co/roadrage10/RADAR/resolve/main/config.json
458 Bytes
| { | |
| "architectures": [ | |
| "RADARModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_radar.RADARConfig", | |
| "AutoModelForSequenceClassification": "modeling_radar.RADARModel" | |
| }, | |
| "base_model_name": "answerdotai/ModernBERT-large", | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "fusion_dim": 512, | |
| "model_type": "radar", | |
| "num_attention_heads": 8, | |
| "style_dim": 38, | |
| "style_proj_dim": 128, | |
| "transformers_version": "5.0.0", | |
| "use_cache": false | |
| } | |