Instructions to use not-lain/test-dynamic-pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use not-lain/test-dynamic-pipeline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="not-lain/test-dynamic-pipeline")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("not-lain/test-dynamic-pipeline") model = AutoModelForSequenceClassification.from_pretrained("not-lain/test-dynamic-pipeline") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25f50bd816be065e9a80ee5596c69017a086dc96c5bd2291692bf5378a7a16e2
- Size of remote file:
- 384 kB
- SHA256:
- 74628f86b7b75be82ff0cdc678f187c363c864954a3c1480162b2ba63d240811
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.