Instructions to use Mahmoud3899/ARATECT_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud3899/ARATECT_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud3899/ARATECT_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud3899/ARATECT_model") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud3899/ARATECT_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 973f015c375634fd19fc22316035ed8c31f5b0dff79051358d5605a0dbd45670
- Size of remote file:
- 17.1 MB
- SHA256:
- 93189c5d9a15db043017cfd920e00cf72fe9a4220bd74b460b635f6aa85a61a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.