--- license: apache-2.0 tags: - generated_from_trainer datasets: - banking77 metrics: - accuracy - f1 widget: - text: Could you assist me in finding my lost card? example_title: Example 1 - text: I found my lost card. Am I still able to use it? example_title: Example 2 - text: "Hey, I thought my topup was all done but now the money is gone again \u2013\ \ what\u2019s up with that?" example_title: Example 3 - text: "Tell me why my topup wouldn\u2019t go through?" example_title: Example 4 model-index: - name: distilbert-base-uncased-finetuned-banking77 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 args: default metrics: - name: Accuracy type: accuracy value: 0.925 - name: F1 type: f1 value: 0.925018570680639 - task: type: text-classification name: Text Classification dataset: name: banking77 type: banking77 config: default split: test metrics: - name: Accuracy type: accuracy value: 0.925 verified: true - name: Precision Macro type: precision value: 0.9282769473964405 verified: true - name: Precision Micro type: precision value: 0.925 verified: true - name: Precision Weighted type: precision value: 0.9282769473964405 verified: true - name: Recall Macro type: recall value: 0.9250000000000002 verified: true - name: Recall Micro type: recall value: 0.925 verified: true - name: Recall Weighted type: recall value: 0.925 verified: true - name: F1 Macro type: f1 value: 0.9250185706806391 verified: true - name: F1 Micro type: f1 value: 0.925 verified: true - name: F1 Weighted type: f1 value: 0.925018570680639 verified: true - name: loss type: loss value: 0.2934279143810272 verified: true --- # distilbert-base-uncased-finetuned-banking77 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the banking77 dataset. It achieves the following results on the evaluation set: - Loss: 0.2935 - Accuracy: 0.925 - F1: 0.9250 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9.686210354742596e-05 - train_batch_size: 64 - eval_batch_size: 32 - seed: 40 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | 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 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.11.0 - Datasets 2.0.0 - Tokenizers 0.11.6