Instructions to use arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner") model = AutoModelForTokenClassification.from_pretrained("arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner/resolve/a4e68acc69e96123368fdc3eafa307b7d3ab8faa/training_args.bin
- Command line
-
hf download hf://arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner@a4e68acc69e96123368fdc3eafa307b7d3ab8faa/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/arpit-tiwari/xlm-roberta-base-finetuned-hindi-ner/resolve/a4e68acc69e96123368fdc3eafa307b7d3ab8faa/training_args.bin
5.37 kB
- Xet hash:
- 769ada0dc7a6cd2679534f3a6a84bfb910e16770e89ebd2868afa5df5a5ca5c5
- Size of remote file:
- 5.37 kB
- SHA256:
- ef290f018cc171a61b58b583ab248c9b06d8a23fe70d80905c6def8130661a13
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