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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("skimai/spanberta-base-cased") model = AutoModel.from_pretrained("skimai/spanberta-base-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from skimai/spanberta-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/skimai/spanberta-base-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://skimai/spanberta-base-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/skimai/spanberta-base-cased/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- cfa59383647bcc6d1f68db217fc69bc3474edfed687bb290e64adc78d2447641
- Size of remote file:
- 499 MB
- SHA256:
- 6e56fd3b70ae5b4b2e7c552606037f7a745c6efa8506f3a4f3b83f2a71739dd3
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