Instructions to use abidlabs/test_push_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abidlabs/test_push_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abidlabs/test_push_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abidlabs/test_push_output") model = AutoModelForSequenceClassification.from_pretrained("abidlabs/test_push_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6caf41695e3dc65facb41baf821ad97c71ea55a2e22dc38c9b3079bdececcc0b
- Size of remote file:
- 5.14 kB
- SHA256:
- ed1230b130014ae81b0a65f519f23d7752e563f8c6f701d037fb6b33845c07eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.