Instructions to use CAMeL-Lab/bert-base-arabic-camelbert-msa-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/bert-base-arabic-camelbert-msa-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CAMeL-Lab/bert-base-arabic-camelbert-msa-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa-ner") model = AutoModelForTokenClassification.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa-ner", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CAMeL-Lab/bert-base-arabic-camelbert-msa-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.35 kB
-
https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://CAMeL-Lab/bert-base-arabic-camelbert-msa-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-ner/resolve/main/training_args.bin
1.35 kB
- Xet hash:
- 34977f79c065e279e163355ee2cc5b9b400e695004a458e24f44c1474fd19dd7
- Size of remote file:
- 1.35 kB
- SHA256:
- 8158999f32dfb82f6232a397d72bdd08ab41a9f50bcd58de0f79faa41de57b33
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