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")# pip install -U transformers accelerate # 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 pytorch_model.bin from CAMeL-Lab/bert-base-arabic-camelbert-msa-ner: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CAMeL-Lab/bert-base-arabic-camelbert-msa-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-ner/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 1a26159bfb1b92d8a32b49e1285d86c56a34f6b81a820e8c50c3c8474c7954e1
- Size of remote file:
- 436 MB
- SHA256:
- 6bb57947c055feedfb765d3b82f2ae5efbf7c01be6bc289b9e51968d1bb51920
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