Instructions to use Classical/Yinka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Classical/Yinka with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Classical/Yinka")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Classical/Yinka") model = AutoModel.from_pretrained("Classical/Yinka", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Classical/Yinka: direct link, hf CLI and curl.
- Browser
- Download file 186 Bytes
-
https://huggingface.co/Classical/Yinka/resolve/refs%2Fpr%2F4/1_Pooling/config.json
- Command line
-
hf download hf://Classical/Yinka@refs/pr/4/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Classical/Yinka/resolve/refs%2Fpr%2F4/1_Pooling/config.json
186 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |