Text Classification
Transformers
PyTorch
TensorBoard
English
fnet
Generated from Trainer
Eval Results (legacy)
Instructions to use gchhablani/fnet-large-finetuned-wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gchhablani/fnet-large-finetuned-wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gchhablani/fnet-large-finetuned-wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gchhablani/fnet-large-finetuned-wnli") model = AutoModelForSequenceClassification.from_pretrained("gchhablani/fnet-large-finetuned-wnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cc12f1fef75c30028b4bb7b0e6992eee36579bb9ee2e6b75f0704bef92f0dcc
- Size of remote file:
- 948 MB
- SHA256:
- 394144643c97b33b5b4ade657a741e8cd7f1aa0da201ed7803847233fecf4e02
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