Instructions to use Tahira/Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tahira/Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tahira/Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tahira/Model") model = AutoModelForSequenceClassification.from_pretrained("Tahira/Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1b9b6ad28801afdac7248851eb0b9f89fec814286c45fae4595fc1b6cb3f048
- Size of remote file:
- 1.34 GB
- SHA256:
- 69e8469f3408bdd07accdd6617898d8118d1ad8b24f4ce392f85627a249778d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.