Instructions to use BSC-LT/roberta-base-biomedical-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/roberta-base-biomedical-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BSC-LT/roberta-base-biomedical-es")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/roberta-base-biomedical-es") model = AutoModelForMaskedLM.from_pretrained("BSC-LT/roberta-base-biomedical-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download process.log from BSC-LT/roberta-base-biomedical-es: direct link, hf CLI and curl.
- Browser
- Download file 81 Bytes
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https://huggingface.co/BSC-LT/roberta-base-biomedical-es/resolve/main/process.log
- Command line
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hf download hf://BSC-LT/roberta-base-biomedical-es/process.log
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curl -L -o process.log https://huggingface.co/BSC-LT/roberta-base-biomedical-es/resolve/main/process.log
81 Bytes
| INFO:root:Function "train_tokenizer" took 306.3926444167737 seconds to complete. | |