Instructions to use connectivity/feather_berts_59 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use connectivity/feather_berts_59 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="connectivity/feather_berts_59")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("connectivity/feather_berts_59") model = AutoModelForSequenceClassification.from_pretrained("connectivity/feather_berts_59") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ab906a87bb9116b7f6d5faed418f46cef234e325444ce9e89d1b9e59d39c351
- Size of remote file:
- 12.1 MB
- SHA256:
- 40008420c3648b57ea85ab19b2a8905958e7b923c360ea1c318ae5a3f1effb98
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.