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
Download training_args.bin from medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr: direct link, hf CLI and curl.
- Browser
- Download file 3.7 kB
-
https://huggingface.co/medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr/resolve/23ea5b90d71a3155cc4ddc7a31a68f40eb621d22/training_args.bin
- Command line
-
hf download hf://medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr@23ea5b90d71a3155cc4ddc7a31a68f40eb621d22/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr/resolve/23ea5b90d71a3155cc4ddc7a31a68f40eb621d22/training_args.bin
3.7 kB
- Xet hash:
- 1d43ef4611c7598a785b018eeddfd9e0ffba78b1f1c5d5f645f20c8a52bf141c
- Size of remote file:
- 3.7 kB
- SHA256:
- fc053189d3faf5cd27b3961ef03dd97fd8112acab45d12d29e766206fd61e667
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