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 trainer_state.json from jaimin/arabic-bert: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/jaimin/arabic-bert/resolve/main/trainer_state.json
- Command line
-
hf download hf://jaimin/arabic-bert/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/jaimin/arabic-bert/resolve/main/trainer_state.json
1.07 kB
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| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.06682556300536832, | |
| "global_step": 3000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
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| "epoch": 0.01, | |
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| "max_steps": 134679, | |
| "num_train_epochs": 3, | |
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| "trial_name": null, | |
| "trial_params": null | |
| } | |