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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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- amazon_reviews_multi |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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base_model: distilbert-base-uncased |
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model-index: |
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- name: distilbert-base-uncased-finetuned-amazon-review |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: amazon_reviews_multi |
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type: amazon_reviews_multi |
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args: es |
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metrics: |
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- type: accuracy |
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value: 0.693 |
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name: Accuracy |
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- type: f1 |
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value: 0.7002653469272611 |
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name: F1 |
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- type: precision |
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value: 0.709541681233075 |
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name: Precision |
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- type: recall |
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value: 0.693 |
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name: Recall |
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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-finetuned-amazon-review |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_reviews_multi dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3494 |
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- Accuracy: 0.693 |
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- F1: 0.7003 |
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- Precision: 0.7095 |
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- Recall: 0.693 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 0.5 | 500 | 0.8287 | 0.7104 | 0.7120 | 0.7152 | 0.7104 | |
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| 0.4238 | 1.0 | 1000 | 0.8917 | 0.7094 | 0.6989 | 0.6917 | 0.7094 | |
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| 0.4238 | 1.5 | 1500 | 0.9367 | 0.6884 | 0.6983 | 0.7151 | 0.6884 | |
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| 0.3152 | 2.0 | 2000 | 0.9845 | 0.7116 | 0.7144 | 0.7176 | 0.7116 | |
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| 0.3152 | 2.5 | 2500 | 1.0752 | 0.6814 | 0.6968 | 0.7232 | 0.6814 | |
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| 0.2454 | 3.0 | 3000 | 1.1215 | 0.6918 | 0.6954 | 0.7068 | 0.6918 | |
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| 0.2454 | 3.5 | 3500 | 1.2905 | 0.6976 | 0.7048 | 0.7138 | 0.6976 | |
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| 0.1989 | 4.0 | 4000 | 1.2938 | 0.694 | 0.7016 | 0.7113 | 0.694 | |
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| 0.1989 | 4.5 | 4500 | 1.3623 | 0.6972 | 0.7014 | 0.7062 | 0.6972 | |
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| 0.1746 | 5.0 | 5000 | 1.3494 | 0.693 | 0.7003 | 0.7095 | 0.693 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.17.0 |
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- Tokenizers 0.10.3 |
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