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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/BERT-Banking77") model = AutoModelForSequenceClassification.from_pretrained("philschmid/BERT-Banking77", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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"tags" must be an array
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Add evaluation results on banking77 dataset (#1)
- Add evaluation results on banking77 dataset (889d9a18e2521be0bcc5a80d19e84a8cdbb8d654) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>e08d5e1
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Update README.md
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Commit From AutoTrain
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initial commit
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