Instructions to use SU-FMI-AI/multiclinner-enigma-es-symptom-RigoBERTa-Clinical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SU-FMI-AI/multiclinner-enigma-es-symptom-RigoBERTa-Clinical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SU-FMI-AI/multiclinner-enigma-es-symptom-RigoBERTa-Clinical")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SU-FMI-AI/multiclinner-enigma-es-symptom-RigoBERTa-Clinical") model = AutoModelForTokenClassification.from_pretrained("SU-FMI-AI/multiclinner-enigma-es-symptom-RigoBERTa-Clinical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 428 Bytes
54713f5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"do_lower_case": false,
"eos_token": "</s>",
"is_local": false,
"keep_accents": true,
"mask_token": "<mask>",
"model_max_len": 512,
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"tokenizer_class": "XLMRobertaTokenizer",
"unk_token": "<unk>"
}
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