Instructions to use Baktashans/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Baktashans/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Baktashans/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Baktashans/results") model = AutoModelForSequenceClassification.from_pretrained("Baktashans/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80b51b194bd1d6d2961e8a0d2ab29e7471dababbf48f6fae3e0f0045a6df8531
- Size of remote file:
- 4.54 kB
- SHA256:
- ec3f71c5c4db4193ad92eac8a5a8e834cca67cf36cd38e78a969b13079bdec65
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.