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")# pip install -U transformers accelerate # 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 tokenizer.json from medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr/resolve/6d612ed4c1df7669885d06ff89abdc4e14346ba6/tokenizer.json
- Command line
-
hf download hf://medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr@6d612ed4c1df7669885d06ff89abdc4e14346ba6/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr/resolve/6d612ed4c1df7669885d06ff89abdc4e14346ba6/tokenizer.json
17.1 MB
- Xet hash:
- 31cfad7e457e392bdebe2bd63796205ff3f6ab825e13da0a03d83dfbf932c919
- Size of remote file:
- 17.1 MB
- SHA256:
- 62c24cdc13d4c9952d63718d6c9fa4c287974249e16b7ade6d5a85e7bbb75626
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.