Instructions to use junaidali/multilingual_30_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junaidali/multilingual_30_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="junaidali/multilingual_30_ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("junaidali/multilingual_30_ner") model = AutoModelForTokenClassification.from_pretrained("junaidali/multilingual_30_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4007b65dae233eb4bdfe201b66f8747f37fd27a8dd2e6fdccb31db21247dc632
- Size of remote file:
- 941 MB
- SHA256:
- 68b0684479caf78de2745ad783599edd9388061349e2257bbd9fcb6a8731cb27
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