Instructions to use arodriguezf/xlmr-multi-borrowings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arodriguezf/xlmr-multi-borrowings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="arodriguezf/xlmr-multi-borrowings")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("arodriguezf/xlmr-multi-borrowings") model = AutoModelForTokenClassification.from_pretrained("arodriguezf/xlmr-multi-borrowings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 908f413428917dd428f1e81c8dd01e21d74e4f4811646e41a91a1d6d02068022
- Size of remote file:
- 17.1 MB
- SHA256:
- d9939f4e42f9ff8f27aa7a6b818bfd897c05e3c26ba25595b7dcf36c7b449d34
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