Instructions to use Jeevesh8/multiberts_seed_7_ft_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/multiberts_seed_7_ft_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/multiberts_seed_7_ft_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/multiberts_seed_7_ft_1") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/multiberts_seed_7_ft_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ccbaa5c11c0e20e677a35aea5fcaa3d037e3e3f5b27c9bcdf7902b3038a2237e
- Size of remote file:
- 438 MB
- SHA256:
- 9eb5a6ef68a42401c23649ce171d367847a336d01a9ec7d5b41731de3e25afd4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.