Instructions to use hfunakura/bert-feedback-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfunakura/bert-feedback-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hfunakura/bert-feedback-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hfunakura/bert-feedback-classifier") model = AutoModelForSequenceClassification.from_pretrained("hfunakura/bert-feedback-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d893129bd90ccb76ec7d30a26625a8be2efa1cf72056437323a6df39ce05c6cc
- Size of remote file:
- 442 MB
- SHA256:
- 41f802f31b46cfb1ab543207c9b8ef30085f2ae40bc9019abd5d07e7de14ddaf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.