Text Classification
Transformers
Safetensors
English
distilbert
finance
transaction-classification
text-embeddings-inference
Instructions to use killykilly/nigerian-transaction-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use killykilly/nigerian-transaction-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="killykilly/nigerian-transaction-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("killykilly/nigerian-transaction-classifier") model = AutoModelForSequenceClassification.from_pretrained("killykilly/nigerian-transaction-classifier") - Notebooks
- Google Colab
- Kaggle
| { | |
| "test_loss": 1.1728788614273071, | |
| "test_accuracy": 0.696969696969697, | |
| "test_macro_f1": 0.6597394126071277, | |
| "test_runtime": 2.8092, | |
| "test_samples_per_second": 46.988, | |
| "test_steps_per_second": 1.78, | |
| "classification_report": { | |
| "Bills & Utilities": { | |
| "precision": 0.4, | |
| "recall": 0.36363636363636365, | |
| "f1-score": 0.38095238095238093, | |
| "support": 11.0 | |
| }, | |
| "Entertainment": { | |
| "precision": 0.6666666666666666, | |
| "recall": 0.5, | |
| "f1-score": 0.5714285714285714, | |
| "support": 4.0 | |
| }, | |
| "Food & Dining": { | |
| "precision": 0.7333333333333333, | |
| "recall": 0.8461538461538461, | |
| "f1-score": 0.7857142857142857, | |
| "support": 13.0 | |
| }, | |
| "Health": { | |
| "precision": 0.4, | |
| "recall": 1.0, | |
| "f1-score": 0.5714285714285714, | |
| "support": 4.0 | |
| }, | |
| "Own Transfer": { | |
| "precision": 0.6363636363636364, | |
| "recall": 0.7, | |
| "f1-score": 0.6666666666666666, | |
| "support": 10.0 | |
| }, | |
| "Salary": { | |
| "precision": 0.625, | |
| "recall": 1.0, | |
| "f1-score": 0.7692307692307693, | |
| "support": 5.0 | |
| }, | |
| "Shopping": { | |
| "precision": 0.625, | |
| "recall": 0.35714285714285715, | |
| "f1-score": 0.45454545454545453, | |
| "support": 14.0 | |
| }, | |
| "Transfer In": { | |
| "precision": 1.0, | |
| "recall": 0.7692307692307693, | |
| "f1-score": 0.8695652173913043, | |
| "support": 13.0 | |
| }, | |
| "Transfer Out": { | |
| "precision": 0.8, | |
| "recall": 0.7346938775510204, | |
| "f1-score": 0.7659574468085106, | |
| "support": 49.0 | |
| }, | |
| "Transport": { | |
| "precision": 0.6666666666666666, | |
| "recall": 0.8888888888888888, | |
| "f1-score": 0.7619047619047619, | |
| "support": 9.0 | |
| }, | |
| "accuracy": 0.696969696969697, | |
| "macro avg": { | |
| "precision": 0.6553030303030303, | |
| "recall": 0.7159746602603745, | |
| "f1-score": 0.6597394126071277, | |
| "support": 132.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.7169593663911846, | |
| "recall": 0.696969696969697, | |
| "f1-score": 0.6935307040113196, | |
| "support": 132.0 | |
| } | |
| }, | |
| "confidence_gates": { | |
| "0.8": { | |
| "coverage": 0.7651515151515151, | |
| "accuracy": 0.7425742574257426, | |
| "accepted_examples": 101 | |
| }, | |
| "0.9": { | |
| "coverage": 0.5227272727272727, | |
| "accuracy": 0.8405797101449275, | |
| "accepted_examples": 69 | |
| }, | |
| "0.95": { | |
| "coverage": 0.3409090909090909, | |
| "accuracy": 0.9333333333333333, | |
| "accepted_examples": 45 | |
| } | |
| } | |
| } |