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README.md
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---
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license: apache-2.0
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base_model: t5-base
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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-base_rte_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: rte
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split: validation
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args: rte
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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-base_rte_dense_sp0_ar0
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This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9086
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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: 8
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- eval_batch_size: 16
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- seed: 1
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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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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| 0.6787 | 0.16 | 25 | 0.6850 | 0.5307 |
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| 0.7034 | 0.32 | 50 | 0.6689 | 0.5704 |
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| 0.6478 | 0.48 | 75 | 0.6356 | 0.6570 |
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| 0.6889 | 0.64 | 100 | 0.6188 | 0.6859 |
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| 0.588 | 0.8 | 125 | 0.5892 | 0.6859 |
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| 0.5989 | 0.96 | 150 | 0.6802 | 0.6606 |
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| 0.5392 | 1.12 | 175 | 0.5836 | 0.7329 |
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| 0.5497 | 1.28 | 200 | 0.6758 | 0.6715 |
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| 0.5567 | 1.44 | 225 | 0.7056 | 0.6643 |
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| 0.5063 | 1.6 | 250 | 0.5617 | 0.7401 |
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| 0.5644 | 1.76 | 275 | 0.5737 | 0.7256 |
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| 0.6018 | 1.92 | 300 | 0.6179 | 0.7112 |
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| 0.4554 | 2.08 | 325 | 0.5339 | 0.7509 |
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| 0.3778 | 2.24 | 350 | 0.5495 | 0.7726 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.9.0
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- Tokenizers 0.14.1
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