Text Classification
Transformers
PyTorch
Japanese
bert
zero-shot-classification
nli
Eval Results (legacy)
Instructions to use Formzu/bert-base-japanese-jsnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Formzu/bert-base-japanese-jsnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Formzu/bert-base-japanese-jsnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Formzu/bert-base-japanese-jsnli") model = AutoModelForSequenceClassification.from_pretrained("Formzu/bert-base-japanese-jsnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8eacb1bad0f10a7772bfb84b244be3d1b65b2ddbaff8f2de0715c202526e4b1b
- Size of remote file:
- 445 MB
- SHA256:
- e82604c4b2847e5a709153fd231dfc89be1a4ec5fa5cfe18f104731ddbd4b582
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.