Text Classification
Transformers
Safetensors
English
bert
intent-classification
customer-support
banking
banking77
Eval Results (legacy)
text-embeddings-inference
Instructions to use functionX86/banking77-intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use functionX86/banking77-intent-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="functionX86/banking77-intent-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("functionX86/banking77-intent-classifier") model = AutoModelForSequenceClassification.from_pretrained("functionX86/banking77-intent-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from functionX86/banking77-intent-classifier: direct link, hf CLI and curl.
- Browser
- Download file 134 MB
-
https://huggingface.co/functionX86/banking77-intent-classifier/resolve/main/model.safetensors
- Command line
-
hf download hf://functionX86/banking77-intent-classifier/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/functionX86/banking77-intent-classifier/resolve/main/model.safetensors
134 MB
- Xet hash:
- b47603483610943349de236bfe081fb59bfb94a576897b3de1bf3932f7556f04
- Size of remote file:
- 134 MB
- SHA256:
- 8fa858510c522804c299d925f821d4425447e8aaa7f59d68ac5bad03af3d0e91
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