Text Classification
Transformers
PyTorch
English
distilbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use philschmid/DistilBERT-Banking77 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philschmid/DistilBERT-Banking77 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="philschmid/DistilBERT-Banking77", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("philschmid/DistilBERT-Banking77") model = AutoModelForSequenceClassification.from_pretrained("philschmid/DistilBERT-Banking77", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c22fff1dad73b6e02c8d6c6ce24d1fb0948ecc1a5f448c7efd6a820027abcdf
- Size of remote file:
- 2.55 kB
- SHA256:
- b02dd516038842d8f695dc4d86a90e77caf61415acbea43056e453f44014b820
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