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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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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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base_model: google/electra-base-discriminator |
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model-index: |
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- name: electra-base-discriminator-finetuned-3d-sentiment |
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results: [] |
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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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# electra-base-discriminator-finetuned-3d-sentiment |
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This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5887 |
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- Accuracy: 0.7873 |
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- Precision: 0.7897 |
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- Recall: 0.7873 |
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- F1: 0.7864 |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 6381 |
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- num_epochs: 7 |
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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 | Precision | Recall | F1 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.797 | 1.0 | 1595 | 0.7075 | 0.7353 | 0.7434 | 0.7353 | 0.7357 | |
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| 0.5329 | 2.0 | 3190 | 0.6508 | 0.7550 | 0.7646 | 0.7550 | 0.7554 | |
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| 0.4597 | 3.0 | 4785 | 0.5889 | 0.7702 | 0.7803 | 0.7702 | 0.7695 | |
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| 0.3918 | 4.0 | 6380 | 0.5887 | 0.7873 | 0.7897 | 0.7873 | 0.7864 | |
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| 0.3093 | 5.0 | 7975 | 0.6412 | 0.7833 | 0.7877 | 0.7833 | 0.7836 | |
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| 0.2144 | 6.0 | 9570 | 0.7786 | 0.7844 | 0.7900 | 0.7844 | 0.7851 | |
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| 0.1507 | 7.0 | 11165 | 0.8455 | 0.7853 | 0.7903 | 0.7853 | 0.7862 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.3 |
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