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")# pip install -U transformers accelerate # 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
Download sample_input.pkl from philschmid/DistilBERT-Banking77: direct link, hf CLI and curl.
- Browser
- Download file 2.55 kB
-
https://huggingface.co/philschmid/DistilBERT-Banking77/resolve/refs%2Fpr%2F2/sample_input.pkl
- Command line
-
hf download hf://philschmid/DistilBERT-Banking77@refs/pr/2/sample_input.pkl
-
curl -L -o sample_input.pkl https://huggingface.co/philschmid/DistilBERT-Banking77/resolve/refs%2Fpr%2F2/sample_input.pkl
2.55 kB
- Xet hash:
- 3c22fff1dad73b6e02c8d6c6ce24d1fb0948ecc1a5f448c7efd6a820027abcdf
- Size of remote file:
- 2.55 kB
- SHA256:
- b02dd516038842d8f695dc4d86a90e77caf61415acbea43056e453f44014b820
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