Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use rjac/bert-20news-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjac/bert-20news-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rjac/bert-20news-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rjac/bert-20news-classification") model = AutoModelForSequenceClassification.from_pretrained("rjac/bert-20news-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14bd25d1cf472248bac546f2b9ebd81a4105a961818b06d29c7cf10dabcdacf1
- Size of remote file:
- 268 MB
- SHA256:
- 36a760c20d64dfe355ae63d9020ca0b3dfadcd611d79346a278fb05682b2cf34
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.