legacy-datasets/banking77
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How to use optimum/distilbert-base-uncased-finetuned-banking77 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="optimum/distilbert-base-uncased-finetuned-banking77") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("optimum/distilbert-base-uncased-finetuned-banking77")
model = AutoModelForSequenceClassification.from_pretrained("optimum/distilbert-base-uncased-finetuned-banking77")This model is a fine-tuned version of distilbert-base-uncased on the banking77 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 126 | 1.1457 | 0.7896 | 0.7685 |
| No log | 2.0 | 252 | 0.4673 | 0.8906 | 0.8889 |
| No log | 3.0 | 378 | 0.3488 | 0.9150 | 0.9151 |
| 0.9787 | 4.0 | 504 | 0.3238 | 0.9180 | 0.9179 |
| 0.9787 | 5.0 | 630 | 0.3126 | 0.9225 | 0.9226 |
Base model
distilbert/distilbert-base-uncased