Instructions to use MBuechel/TTP_SOK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBuechel/TTP_SOK with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MBuechel/TTP_SOK")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MBuechel/TTP_SOK", device_map="auto") - Notebooks
- Google Colab
- Kaggle
TTP_SOK / Classification /data_augmentation /400_tram_artificial_FacebookAI-roberta-large /model_params.json
Download Classification/data_augmentation/400_tram_artificial_FacebookAI-roberta-large/model_params.json from MBuechel/TTP_SOK: direct link, hf CLI and curl.
- Browser
- Download file 139 Bytes
-
https://huggingface.co/MBuechel/TTP_SOK/resolve/main/Classification/data_augmentation/400_tram_artificial_FacebookAI-roberta-large/model_params.json
- Command line
-
hf download hf://MBuechel/TTP_SOK/Classification/data_augmentation/400_tram_artificial_FacebookAI-roberta-large/model_params.json
-
curl -L -o model_params.json https://huggingface.co/MBuechel/TTP_SOK/resolve/main/Classification/data_augmentation/400_tram_artificial_FacebookAI-roberta-large/model_params.json
139 Bytes
| { | |
| "epochs": 100, | |
| "batch_size": 16, | |
| "freeze_layers": 0, | |
| "learning_rate": 2e-05, | |
| "pos_weight": 0, | |
| "end_factor": 0.7 | |
| } |