Instructions to use climba/my_awesome_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use climba/my_awesome_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="climba/my_awesome_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("climba/my_awesome_model") model = AutoModelForSequenceClassification.from_pretrained("climba/my_awesome_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1fea9ba654f73a5b819cfee4fbac34b1628ab34d99bb87a917358cdb88bf6ed
- Size of remote file:
- 4.6 kB
- SHA256:
- b5be9374b2b4f5b57748104adae5d210c72e1ece5dd69e8f1b7a047e1adfb01a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.