Token Classification
Transformers
PyTorch
Safetensors
Spanish
roberta
text-classification
biomedical
clinical
spanish
roberta-large-bne
Eval Results (legacy)
Instructions to use IIC/roberta-large-bne-nubes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/roberta-large-bne-nubes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/roberta-large-bne-nubes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/roberta-large-bne-nubes") model = AutoModelForSequenceClassification.from_pretrained("IIC/roberta-large-bne-nubes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 957 Bytes
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"bos_token": {
"content": "<s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"cls_token": {
"content": "<s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"eos_token": {
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"mask_token": {
"content": "<mask>",
"lstrip": true,
"normalized": true,
"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<pad>",
"lstrip": false,
"normalized": true,
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"single_word": false
},
"sep_token": {
"content": "</s>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
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}
}
|