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README.md
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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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model-index:
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- name: t5-small-med-term-conditional-masking-0
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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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# t5-small-med-term-conditional-masking-0
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6688
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- Rouge2 Precision: 0.694
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- Rouge2 Recall: 0.4781
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- Rouge2 Fmeasure: 0.5479
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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: 8
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- eval_batch_size: 8
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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: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
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|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
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| 0.9525 | 1.0 | 13915 | 0.8148 | 0.6657 | 0.4581 | 0.5252 |
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| 0.8541 | 2.0 | 27830 | 0.7562 | 0.6779 | 0.4694 | 0.5371 |
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| 0.8183 | 3.0 | 41745 | 0.7268 | 0.6827 | 0.4722 | 0.5405 |
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| 0.8033 | 4.0 | 55660 | 0.7074 | 0.6861 | 0.4729 | 0.5419 |
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| 0.7727 | 5.0 | 69575 | 0.6934 | 0.6872 | 0.4726 | 0.5419 |
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| 0.7704 | 6.0 | 83490 | 0.6832 | 0.6901 | 0.4742 | 0.544 |
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| 0.7485 | 7.0 | 97405 | 0.6771 | 0.6926 | 0.4772 | 0.5469 |
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| 0.7528 | 8.0 | 111320 | 0.6722 | 0.6934 | 0.4782 | 0.5478 |
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| 0.7535 | 9.0 | 125235 | 0.6696 | 0.6944 | 0.4782 | 0.5481 |
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| 0.7444 | 10.0 | 139150 | 0.6688 | 0.694 | 0.4781 | 0.5479 |
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### Framework versions
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- Transformers 4.17.0
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- Pytorch 1.10.0+cu111
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- Datasets 2.0.0
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- Tokenizers 0.11.6
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