Instructions to use skimai/spanberta-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skimai/spanberta-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="skimai/spanberta-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("skimai/spanberta-base-cased") model = AutoModel.from_pretrained("skimai/spanberta-base-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfa59383647bcc6d1f68db217fc69bc3474edfed687bb290e64adc78d2447641
- Size of remote file:
- 499 MB
- SHA256:
- 6e56fd3b70ae5b4b2e7c552606037f7a745c6efa8506f3a4f3b83f2a71739dd3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.