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")# 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
| { | |
| "output_root": "/gpfs/projects/bsc88/corpus-utils-lm/23-12-2020-72f8c7e/output/model-ready_output/2020-12-23-1900-daf4-ab38", | |
| "files": "/gpfs/projects/bsc88/corpus-utils-lm/23-12-2020-72f8c7e/output/model-ready_output/2020-12-23-1900-daf4-ab38/train_valid_test_split_output/2020-12-23-1905-daf4-a0e0/train.txt", | |
| "vocab_name": "roberta-ca", | |
| "clean_text": true, | |
| "handle_chinese_chars": true, | |
| "strip_accents": false, | |
| "lowercase": false, | |
| "vocab_size": 52000, | |
| "limit_alphabet": 1000, | |
| "show_progress": true, | |
| "min_frequency": 2, | |
| "extra_tokens": [], | |
| "reserve_tokens": 0, | |
| "tokenizer": "bbpe", | |
| "commit_hash": "daf4d660ec8a4b28d2bc29b3063779100ab85796\n" | |
| } |