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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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model-index: |
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- name: my_awesome_model_3 |
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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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# my_awesome_model_3 |
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This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0954 |
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- Accuracy: 0.9680 |
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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: 2 |
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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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| No log | 0.09 | 200 | 0.2369 | 0.9040 | |
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| No log | 0.19 | 400 | 0.1859 | 0.9324 | |
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| 0.2931 | 0.28 | 600 | 0.1624 | 0.9442 | |
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| 0.2931 | 0.38 | 800 | 0.1194 | 0.9569 | |
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| 0.1456 | 0.47 | 1000 | 0.1245 | 0.9588 | |
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| 0.1456 | 0.57 | 1200 | 0.1044 | 0.9617 | |
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| 0.1456 | 0.66 | 1400 | 0.1063 | 0.9611 | |
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| 0.1194 | 0.75 | 1600 | 0.1021 | 0.9634 | |
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| 0.1194 | 0.85 | 1800 | 0.1618 | 0.9490 | |
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| 0.1107 | 0.94 | 2000 | 0.1113 | 0.9643 | |
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| 0.1107 | 1.04 | 2200 | 0.1163 | 0.9630 | |
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| 0.1107 | 1.13 | 2400 | 0.0954 | 0.9680 | |
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| 0.079 | 1.22 | 2600 | 0.1272 | 0.9635 | |
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| 0.079 | 1.32 | 2800 | 0.0976 | 0.9657 | |
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| 0.0715 | 1.41 | 3000 | 0.0995 | 0.9680 | |
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| 0.0715 | 1.51 | 3200 | 0.0996 | 0.9660 | |
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| 0.0715 | 1.6 | 3400 | 0.1001 | 0.9670 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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