Instructions to use Shaer-AI/ARBERT-base-submeter-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaer-AI/ARBERT-base-submeter-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shaer-AI/ARBERT-base-submeter-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shaer-AI/ARBERT-base-submeter-classifier") model = AutoModelForSequenceClassification.from_pretrained("Shaer-AI/ARBERT-base-submeter-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Shaer-AI/ARBERT-base-submeter-classifier: direct link, hf CLI and curl.
- Browser
- Download file 5.97 kB
-
https://huggingface.co/Shaer-AI/ARBERT-base-submeter-classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://Shaer-AI/ARBERT-base-submeter-classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Shaer-AI/ARBERT-base-submeter-classifier/resolve/main/training_args.bin
5.97 kB
- Xet hash:
- 04b37654f4f3109db127cf335b3deb80d6055186a8a05ffea845afd30d446f9b
- Size of remote file:
- 5.97 kB
- SHA256:
- 7391b4561150a0fb5f32f255054cc3c25e11bac3c41461362aa1a635b289593a
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