End of training
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README.md
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---
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license: apache-2.0
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base_model: google/bert_uncased_L-2_H-128_A-2
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tags:
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- generated_from_trainer
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datasets:
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- emotion
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metrics:
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- accuracy
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model-index:
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- name: bert_uncased_L-2_H-128_A-2_emotion
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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: emotion
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type: emotion
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config: split
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split: validation
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args: split
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.893
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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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# bert_uncased_L-2_H-128_A-2_emotion
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This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the emotion dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3583
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- Accuracy: 0.893
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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: 64
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- eval_batch_size: 64
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- seed: 33
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- distributed_type: multi-GPU
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.5403 | 1.0 | 250 | 1.3422 | 0.554 |
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| 1.1641 | 2.0 | 500 | 0.9492 | 0.6855 |
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| 0.8396 | 3.0 | 750 | 0.6949 | 0.796 |
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| 0.6356 | 4.0 | 1000 | 0.5556 | 0.8485 |
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| 0.517 | 5.0 | 1250 | 0.4748 | 0.868 |
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| 0.4351 | 6.0 | 1500 | 0.4231 | 0.8845 |
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| 0.393 | 7.0 | 1750 | 0.3877 | 0.8875 |
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| 0.3641 | 8.0 | 2000 | 0.3767 | 0.891 |
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| 0.3462 | 9.0 | 2250 | 0.3621 | 0.8925 |
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| 0.3352 | 10.0 | 2500 | 0.3583 | 0.893 |
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### Framework versions
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- Transformers 4.34.0
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- Pytorch 1.14.0a0+410ce96
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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