Instructions to use SinaLab/ArabicWojood-FlatNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinaLab/ArabicWojood-FlatNER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SinaLab/ArabicWojood-FlatNER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SinaLab/ArabicWojood-FlatNER") model = AutoModelForMaskedLM.from_pretrained("SinaLab/ArabicWojood-FlatNER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SinaLab/ArabicWojood-FlatNER: direct link, hf CLI and curl.
- Browser
- Download file 1.62 GB
-
https://huggingface.co/SinaLab/ArabicWojood-FlatNER/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SinaLab/ArabicWojood-FlatNER/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SinaLab/ArabicWojood-FlatNER/resolve/main/pytorch_model.bin
1.62 GB
- Xet hash:
- 3311d7d9f241bd5db515dc53333254c184f565e45899c2374dbbe0565bce235b
- Size of remote file:
- 1.62 GB
- SHA256:
- 9e00f0183022f887321596425a7bbbfd3e26ad1b702cff871ec594bffebe9221
ยท
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