Instructions to use chriskhanhtran/spanberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chriskhanhtran/spanberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="chriskhanhtran/spanberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("chriskhanhtran/spanberta") model = AutoModelForMaskedLM.from_pretrained("chriskhanhtran/spanberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from chriskhanhtran/spanberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/chriskhanhtran/spanberta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://chriskhanhtran/spanberta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/chriskhanhtran/spanberta/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 7c4fc164983766a6ab10cc5fe6ffca8b7363a110bd0f308d9be74ddce73e9a6e
- Size of remote file:
- 499 MB
- SHA256:
- bf48223a375a21d2c6eb74fed6eeae46fa810161475b5ee78d4cc517a11ed972
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