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 pytorch_model.bin from medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr/resolve/23ea5b90d71a3155cc4ddc7a31a68f40eb621d22/pytorch_model.bin
- Command line
-
hf download hf://medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr@23ea5b90d71a3155cc4ddc7a31a68f40eb621d22/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/medspaner/xlm-roberta-large-spanish-trials-cases-medic-attr/resolve/23ea5b90d71a3155cc4ddc7a31a68f40eb621d22/pytorch_model.bin
2.24 GB
- Xet hash:
- cef685812a1b1091c2facc83621b898ed3c5e481ab42937edc4bb99c82b7bda5
- Size of remote file:
- 2.24 GB
- SHA256:
- 538ea40c736c234d31efd42f8f7112840069a5587d5c294907427f807c9608d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.