Instructions to use hashk1/EsperBERTo-malgranda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hashk1/EsperBERTo-malgranda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hashk1/EsperBERTo-malgranda")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hashk1/EsperBERTo-malgranda") model = AutoModelForMaskedLM.from_pretrained("hashk1/EsperBERTo-malgranda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hashk1/EsperBERTo-malgranda: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/hashk1/EsperBERTo-malgranda/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hashk1/EsperBERTo-malgranda/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hashk1/EsperBERTo-malgranda/resolve/main/flax_model.msgpack
334 MB
- Xet hash:
- bf17e96afd45613fe2141c554094fdb3419bec7546fc3b758be88ccb64c4cb3b
- Size of remote file:
- 334 MB
- SHA256:
- a5f3a0614d0a1ab0527e42a44f4ec096900d13bbfb925ca8bdbcdb2821ae2f66
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