Instructions to use jaimin/arabic-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaimin/arabic-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jaimin/arabic-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jaimin/arabic-bert") model = AutoModelForSequenceClassification.from_pretrained("jaimin/arabic-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jaimin/arabic-bert: direct link, hf CLI and curl.
- Browser
- Download file 2.67 kB
-
https://huggingface.co/jaimin/arabic-bert/resolve/main/training_args.bin
- Command line
-
hf download hf://jaimin/arabic-bert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jaimin/arabic-bert/resolve/main/training_args.bin
2.67 kB
- Xet hash:
- 4ab5146c6a36ca45733216c46093d5e716ace913f2f7680926858dd8d0e81181
- Size of remote file:
- 2.67 kB
- SHA256:
- d7f5d7f5a49d1dcb6cdf44ccb2677022c5b57c59e765f50872daf6741c0ef725
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.