Token Classification
Transformers
PyTorch
Safetensors
Spanish
xlm-roberta
text-classification
biomedical
clinical
spanish
xlm-roberta-large
Eval Results (legacy)
Instructions to use IIC/xlm-roberta-large-ehealth_kd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/xlm-roberta-large-ehealth_kd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="IIC/xlm-roberta-large-ehealth_kd")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IIC/xlm-roberta-large-ehealth_kd") model = AutoModelForSequenceClassification.from_pretrained("IIC/xlm-roberta-large-ehealth_kd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 624926e6fbda44f5e7dea9b78c2e3bafa863d25b832fa31ddc6708b00e5124aa
- Size of remote file:
- 2.24 GB
- SHA256:
- 8655c1093ba28edea91ad4af5269cc98b31cacafee4a151bae5b73aa44f1d373
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