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README.md
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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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- emotion
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metrics:
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- accuracy
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model-index:
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- name: jq_emo_gpt
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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: 0.941
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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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# jq_emo_gpt
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This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the emotion dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3379
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- Accuracy: 0.941
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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: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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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: 16000
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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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| 0.7328 | 1.0 | 16000 | 0.6227 | 0.899 |
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| 0.3989 | 2.0 | 32000 | 0.4351 | 0.927 |
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| 0.2888 | 3.0 | 48000 | 0.3162 | 0.9385 |
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| 0.2325 | 4.0 | 64000 | 0.2936 | 0.9445 |
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| 0.2774 | 5.0 | 80000 | 0.2903 | 0.94 |
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| 0.1423 | 6.0 | 96000 | 0.3410 | 0.9405 |
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| 0.1681 | 7.0 | 112000 | 0.3259 | 0.9385 |
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| 0.1743 | 8.0 | 128000 | 0.3225 | 0.9415 |
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| 0.1011 | 9.0 | 144000 | 0.3356 | 0.942 |
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| 0.1138 | 10.0 | 160000 | 0.3379 | 0.941 |
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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 2.12.0
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- Tokenizers 0.13.3
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