Instructions to use mbruton/spa_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_XLM-R")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_XLM-R", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mbruton/spa_XLM-R: direct link, hf CLI and curl.
- Browser
- Download file 3.5 kB
-
https://huggingface.co/mbruton/spa_XLM-R/resolve/main/training_args.bin
- Command line
-
hf download hf://mbruton/spa_XLM-R/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mbruton/spa_XLM-R/resolve/main/training_args.bin
3.5 kB
- Xet hash:
- fffc894a37edeb793a16b9d79cff9f767929bdf42cb7306e8c8ddc04aaa5b748
- Size of remote file:
- 3.5 kB
- SHA256:
- 0ea9a039a2c97c351c3d1b0c9fc01c627f95bb8e4f82fced9a266ad3393875ed
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