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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- consumer-finance-complaints |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: distilbert-complaints-wandb |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: consumer-finance-complaints |
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type: consumer-finance-complaints |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.868877906608376 |
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- name: F1 |
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type: f1 |
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value: 0.8630522401242867 |
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- name: Recall |
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type: recall |
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value: 0.868877906608376 |
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- name: Precision |
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type: precision |
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value: 0.8616053523512515 |
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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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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-complaints-wandb |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4448 |
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- Accuracy: 0.8689 |
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- F1: 0.8631 |
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- Recall: 0.8689 |
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- Precision: 0.8616 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.571 | 0.51 | 2000 | 0.5150 | 0.8469 | 0.8349 | 0.8469 | 0.8249 | |
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| 0.4765 | 1.01 | 4000 | 0.4676 | 0.8561 | 0.8451 | 0.8561 | 0.8376 | |
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| 0.3376 | 1.52 | 6000 | 0.4560 | 0.8609 | 0.8546 | 0.8609 | 0.8547 | |
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| 0.268 | 2.03 | 8000 | 0.4399 | 0.8684 | 0.8611 | 0.8684 | 0.8607 | |
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| 0.2654 | 2.53 | 10000 | 0.4448 | 0.8689 | 0.8631 | 0.8689 | 0.8616 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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