Instructions to use Sunbird/t5_small_language_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunbird/t5_small_language_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sunbird/t5_small_language_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sunbird/t5_small_language_Classification") model = AutoModelForSequenceClassification.from_pretrained("Sunbird/t5_small_language_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8b181ae2f01040863c3247764dda2addc71a25e01e9f31716e1962054b1d0c3
- Size of remote file:
- 5.91 kB
- SHA256:
- 99176a66c23f6bce7df9c0b117e6fdfbdb6da0706b7751db0b85d8110678e434
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.