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 pytorch_model.bin from hashk1/EsperBERTo-malgranda: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/hashk1/EsperBERTo-malgranda/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hashk1/EsperBERTo-malgranda/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hashk1/EsperBERTo-malgranda/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- 9fc69a889d4cde7a8960c9cb6b2641b408b89289f2d80a3a75b58aa6f656034a
- Size of remote file:
- 334 MB
- SHA256:
- ca3bd1f036e457b3f7cc6f87183a68e4e7686c2e3bb8ac35f8097d00aafb8156
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