End of training
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README.md
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---
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base_model: gokuls/HBERTv1_48_L4_H64_A2
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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: HBERTv1_48_L4_H64_A2_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: 0.7725
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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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# HBERTv1_48_L4_H64_A2_emotion
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This model is a fine-tuned version of [gokuls/HBERTv1_48_L4_H64_A2](https://huggingface.co/gokuls/HBERTv1_48_L4_H64_A2) on the emotion dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7101
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- Accuracy: 0.7725
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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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| 1.6141 | 1.0 | 250 | 1.5167 | 0.4625 |
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| 1.4469 | 2.0 | 500 | 1.3688 | 0.492 |
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| 1.2843 | 3.0 | 750 | 1.1952 | 0.5785 |
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| 1.1128 | 4.0 | 1000 | 1.0349 | 0.6375 |
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| 0.9601 | 5.0 | 1250 | 0.9099 | 0.6835 |
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| 0.8528 | 6.0 | 1500 | 0.8403 | 0.726 |
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| 0.7793 | 7.0 | 1750 | 0.7835 | 0.74 |
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| 0.7291 | 8.0 | 2000 | 0.7433 | 0.7635 |
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| 0.6851 | 9.0 | 2250 | 0.7165 | 0.77 |
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| 0.6717 | 10.0 | 2500 | 0.7101 | 0.7725 |
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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 0.14.0
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