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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-base-uncased-wandb-week-3-complaints-classifier-512 |
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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: 0.6745323887671373 |
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- name: F1 |
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type: f1 |
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value: 0.6355967633316707 |
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- name: Recall |
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type: recall |
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value: 0.6745323887671373 |
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- name: Precision |
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type: precision |
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value: 0.6122130681567332 |
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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-base-uncased-wandb-week-3-complaints-classifier-512 |
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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: 1.0839 |
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- Accuracy: 0.6745 |
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- F1: 0.6356 |
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- Recall: 0.6745 |
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- Precision: 0.6122 |
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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: 0.0007879237562376572 |
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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: 512 |
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- num_epochs: 2 |
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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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| 1.2707 | 0.61 | 1500 | 1.3009 | 0.6381 | 0.5848 | 0.6381 | 0.5503 | |
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| 1.1348 | 1.22 | 3000 | 1.1510 | 0.6610 | 0.6178 | 0.6610 | 0.5909 | |
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| 1.0649 | 1.83 | 4500 | 1.0839 | 0.6745 | 0.6356 | 0.6745 | 0.6122 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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