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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: FLAN-T5_GLUE_finetuning_lr3e-4 |
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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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# FLAN-T5_GLUE_finetuning_lr3e-4 |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0936 |
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- Accuracy: 0.8849 |
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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: 0.0003 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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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: 5.0 |
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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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| 0.1314 | 0.17 | 2500 | 0.1069 | 0.8312 | |
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| 0.1087 | 0.34 | 5000 | 0.0960 | 0.8513 | |
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| 0.1029 | 0.51 | 7500 | 0.0913 | 0.8543 | |
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| 0.1007 | 0.68 | 10000 | 0.0890 | 0.8587 | |
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| 0.0961 | 0.85 | 12500 | 0.0870 | 0.865 | |
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| 0.0933 | 1.02 | 15000 | 0.0875 | 0.8644 | |
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| 0.0777 | 1.18 | 17500 | 0.0844 | 0.869 | |
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| 0.0784 | 1.35 | 20000 | 0.0843 | 0.8701 | |
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| 0.078 | 1.52 | 22500 | 0.0827 | 0.8712 | |
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| 0.0775 | 1.69 | 25000 | 0.0841 | 0.8711 | |
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| 0.0774 | 1.86 | 27500 | 0.0824 | 0.8737 | |
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| 0.0732 | 2.03 | 30000 | 0.0862 | 0.8727 | |
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| 0.0591 | 2.2 | 32500 | 0.0842 | 0.8766 | |
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| 0.06 | 2.37 | 35000 | 0.0858 | 0.8762 | |
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| 0.0595 | 2.54 | 37500 | 0.0843 | 0.8781 | |
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| 0.061 | 2.71 | 40000 | 0.0818 | 0.88 | |
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| 0.0598 | 2.88 | 42500 | 0.0813 | 0.8803 | |
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| 0.0558 | 3.05 | 45000 | 0.0896 | 0.8805 | |
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| 0.0448 | 3.22 | 47500 | 0.0885 | 0.881 | |
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| 0.0451 | 3.38 | 50000 | 0.0888 | 0.8805 | |
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| 0.0449 | 3.55 | 52500 | 0.0873 | 0.882 | |
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| 0.0457 | 3.72 | 55000 | 0.0853 | 0.8839 | |
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| 0.0456 | 3.89 | 57500 | 0.0856 | 0.8835 | |
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| 0.0413 | 4.06 | 60000 | 0.0931 | 0.8828 | |
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| 0.0342 | 4.23 | 62500 | 0.0961 | 0.8851 | |
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| 0.0348 | 4.4 | 65000 | 0.0945 | 0.8851 | |
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| 0.0351 | 4.57 | 67500 | 0.0936 | 0.8845 | |
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| 0.0346 | 4.74 | 70000 | 0.0940 | 0.8846 | |
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| 0.0347 | 4.91 | 72500 | 0.0936 | 0.8849 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.12.1 |
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