Instructions to use medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr") model = AutoModelForTokenClassification.from_pretrained("medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -42,12 +42,12 @@ The model is fine-tuned on the [CT-EBM-ES corpus (Campillos-Llanos et al. 2021)]
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If you use this model, please, cite as follows:
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```
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@article{
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title = {Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish},
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author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},
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journal = {
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year={
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publisher={
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}
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```
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If you use this model, please, cite as follows:
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```
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@article{campillosetal2024,
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title = {{Hybrid tool for semantic annotation and concept extraction of medical texts in Spanish}},
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author = {Campillos-Llanos, Leonardo and Valverde-Mateos, Ana and Capllonch-Carri{\'o}n, Adri{\'a}n},
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journal = {BMC Bioinformatics},
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year={2024},
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publisher={Springer}
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}
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```
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