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--- |
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license: apache-2.0 |
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base_model: t5-large |
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tags: |
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- generated_from_trainer |
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datasets: |
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- super_glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: t5-large_boolq_dense_epochs-5 |
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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: super_glue |
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type: super_glue |
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config: boolq |
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split: validation |
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args: boolq |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.846177370030581 |
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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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# t5-large_boolq_dense_epochs-5 |
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This model is a fine-tuned version of [t5-large](https://huggingface.co./t5-large) on the super_glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3715 |
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- Accuracy: 0.8462 |
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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: 8 |
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- eval_batch_size: 16 |
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- seed: 0 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 20 |
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- num_epochs: 5 |
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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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| 0.6792 | 0.17 | 50 | 0.6652 | 0.6217 | |
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| 0.66 | 0.34 | 100 | 0.6595 | 0.6220 | |
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| 0.6614 | 0.51 | 150 | 0.6548 | 0.6232 | |
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| 0.636 | 0.68 | 200 | 0.6122 | 0.6985 | |
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| 0.4882 | 0.85 | 250 | 0.4702 | 0.7847 | |
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| 0.5068 | 1.02 | 300 | 0.4639 | 0.7862 | |
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| 0.3332 | 1.19 | 350 | 0.5297 | 0.7908 | |
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| 0.4296 | 1.36 | 400 | 0.3955 | 0.8373 | |
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| 0.356 | 1.53 | 450 | 0.4013 | 0.8410 | |
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| 0.3227 | 1.7 | 500 | 0.3715 | 0.8462 | |
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| 0.3516 | 1.87 | 550 | 0.3724 | 0.8428 | |
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| 0.2169 | 2.04 | 600 | 0.3906 | 0.8477 | |
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| 0.2199 | 2.21 | 650 | 0.4061 | 0.8572 | |
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| 0.1969 | 2.37 | 700 | 0.4351 | 0.8550 | |
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| 0.2713 | 2.54 | 750 | 0.5411 | 0.8584 | |
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| 0.2458 | 2.71 | 800 | 0.3924 | 0.8627 | |
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| 0.2134 | 2.88 | 850 | 0.3973 | 0.8630 | |
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| 0.1636 | 3.05 | 900 | 0.4933 | 0.8590 | |
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| 0.1108 | 3.22 | 950 | 0.9926 | 0.8621 | |
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| 0.1433 | 3.39 | 1000 | 0.6679 | 0.8602 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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