Instructions to use enesmanan/multilingual-xlm-roberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use enesmanan/multilingual-xlm-roberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="enesmanan/multilingual-xlm-roberta-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("enesmanan/multilingual-xlm-roberta-ner") model = AutoModelForTokenClassification.from_pretrained("enesmanan/multilingual-xlm-roberta-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e36546a0cdf3f6722572c06d1dea10fe655967fa9bf3a216c7fc5cb419872fe9
- Size of remote file:
- 5.3 kB
- SHA256:
- ce429f6fd9e77bbb26315264a8518ed0433e57769d98983b1a7f9a56d912a02b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.