Instructions to use svassileva/multiclin_robbert_ner_nl_symptom_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svassileva/multiclin_robbert_ner_nl_symptom_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="svassileva/multiclin_robbert_ner_nl_symptom_final")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("svassileva/multiclin_robbert_ner_nl_symptom_final") model = AutoModelForTokenClassification.from_pretrained("svassileva/multiclin_robbert_ner_nl_symptom_final", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 551 Bytes
8950ec9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"do_lower_case": false,
"eos_token": "</s>",
"errors": "replace",
"is_local": true,
"local_files_only": false,
"mask_token": "<mask>",
"max_length": 512,
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<pad>",
"sep_token": "</s>",
"stride": 128,
"tokenizer_class": "RobertaTokenizer",
"trim_offsets": true,
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "<unk>"
}
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