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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hashk1/EsperBERTo-malgranda") model = AutoModelForMaskedLM.from_pretrained("hashk1/EsperBERTo-malgranda") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bf17e96afd45613fe2141c554094fdb3419bec7546fc3b758be88ccb64c4cb3b
- Size of remote file:
- 334 MB
- SHA256:
- a5f3a0614d0a1ab0527e42a44f4ec096900d13bbfb925ca8bdbcdb2821ae2f66
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