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")# 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
Add evaluation results on the default config of banking77
#2
by autoevaluator HF Staff - opened
Beep boop, I am a bot from Hugging Face's automatic model evaluator π!
Your model has been evaluated on the default config of the banking77 dataset by @nickprock , using the predictions stored here.
Accept this pull request to see the results displayed on the Hub leaderboard.
Evaluate your model on more datasets here.
nickprock changed pull request status to merged