Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use nickprock/distilbert-base-uncased-banking77-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickprock/distilbert-base-uncased-banking77-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickprock/distilbert-base-uncased-banking77-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nickprock/distilbert-base-uncased-banking77-classification") model = AutoModelForSequenceClassification.from_pretrained("nickprock/distilbert-base-uncased-banking77-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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type: accuracy
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value: 0.924025974025974
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widget:
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- text: "
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example_title: "
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- text: "I can't pay by my credit card"
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example_title: "
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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type: accuracy
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value: 0.924025974025974
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widget:
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- text: "Can I track the card you sent to me? "
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example_title: "Card Arrival Example"
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- text: "Can you explain your exchange rate policy to me?"
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example_title: "Exchange Rate Example"
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- text: "I can't pay by my credit card"
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example_title: "Card Not Working Example"
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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