Text Classification
Transformers
PyTorch
English
bert
Trained with AutoTrain
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/BERT-Banking77 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/BERT-Banking77 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/BERT-Banking77")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/BERT-Banking77") model = AutoModelForSequenceClassification.from_pretrained("philschmid/BERT-Banking77") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c0e7007549bb1094ec00cfc62f10078f51c5e0149241c8fe7e37da2f4814dcef
- Size of remote file:
- 438 MB
- SHA256:
- f7b13360b32a24cb5de1fd24dced21184a9e64b9450d01e26cfe9c78bf335b26
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