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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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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: t5-large_cola_dense_sp0_ar0 |
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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: glue |
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type: glue |
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config: cola |
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split: validation |
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args: cola |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.0 |
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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_cola_dense_sp0_ar0 |
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This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the glue dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.7611 |
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- Accuracy: 0.0 |
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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: 16 |
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- eval_batch_size: 32 |
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- seed: 1 |
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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: 6 |
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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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| 0.6121 | 0.05 | 25 | 0.6257 | 0.6913 | |
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| 0.4507 | 0.09 | 50 | 0.6018 | 0.6913 | |
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| 0.2862 | 0.14 | 75 | 0.5646 | 0.8006 | |
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| 0.5917 | 0.19 | 100 | 0.5203 | 0.7929 | |
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| 0.3317 | 0.23 | 125 | 0.4479 | 0.8236 | |
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| 0.3637 | 0.28 | 150 | 0.4355 | 0.8245 | |
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| 0.2844 | 0.33 | 175 | 0.5032 | 0.8245 | |
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| 0.3406 | 0.37 | 200 | 0.5102 | 0.8121 | |
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| 0.4321 | 0.42 | 225 | 0.4290 | 0.8150 | |
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| 0.5212 | 0.47 | 250 | 0.4134 | 0.8293 | |
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| 0.4152 | 0.51 | 275 | 0.5055 | 0.8207 | |
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| 0.453 | 0.56 | 300 | 0.3974 | 0.8265 | |
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| 0.3412 | 0.61 | 325 | 0.4409 | 0.8245 | |
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| 0.3251 | 0.65 | 350 | 0.4538 | 0.8255 | |
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| 0.3255 | 0.7 | 375 | 0.3817 | 0.8313 | |
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| 0.2671 | 0.75 | 400 | 0.4162 | 0.8255 | |
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| 0.3995 | 0.79 | 425 | 0.4150 | 0.8303 | |
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| 0.4005 | 0.84 | 450 | 0.4125 | 0.8303 | |
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| 0.2897 | 0.89 | 475 | 0.4895 | 0.8226 | |
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| 0.4079 | 0.93 | 500 | 0.4064 | 0.8351 | |
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| 0.2597 | 0.98 | 525 | 0.6631 | 0.8447 | |
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| 0.2189 | 1.03 | 550 | 0.5056 | 0.8236 | |
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| 0.329 | 1.07 | 575 | 6.1282 | 0.8284 | |
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| 0.44 | 1.12 | 600 | 0.5057 | 0.8380 | |
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| 0.164 | 1.17 | 625 | 0.5032 | 0.8313 | |
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| 0.2996 | 1.21 | 650 | 0.9884 | 0.8341 | |
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| 0.2425 | 1.26 | 675 | 0.5208 | 0.8418 | |
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| 0.1987 | 1.31 | 700 | 0.4573 | 0.8389 | |
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| 0.1581 | 1.36 | 725 | 1.1812 | 0.8150 | |
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| 0.4067 | 1.4 | 750 | 0.6437 | 0.8293 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.11.6 |
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