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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
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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
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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: 0.6808
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- Rouge2 Precision: 0.6855
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- Rouge2 Recall: 0.486
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- Rouge2 Fmeasure: 0.5507
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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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| 0.9303 | 1.0 | 15827 | 0.8262 | 0.6603 | 0.4698 | 0.5318 |
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| 0.8677 | 2.0 | 31654 | 0.7679 | 0.6695 | 0.4762 | 0.539 |
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| 0.8315 | 3.0 | 47481 | 0.7393 | 0.6741 | 0.4783 | 0.5418 |
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| 0.7999 | 4.0 | 63308 | 0.7194 | 0.6774 | 0.4811 | 0.5448 |
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| 0.7746 | 5.0 | 79135 | 0.7059 | 0.6804 | 0.4815 | 0.5459 |
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| 0.7785 | 6.0 | 94962 | 0.6958 | 0.6827 | 0.4841 | 0.5485 |
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| 0.7592 | 7.0 | 110789 | 0.6893 | 0.6841 | 0.4849 | 0.5494 |
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| 0.745 | 8.0 | 126616 | 0.6849 | 0.6846 | 0.4852 | 0.5498 |
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| 0.7443 | 9.0 | 142443 | 0.6818 | 0.6854 | 0.4865 | 0.551 |
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| 0.7417 | 10.0 | 158270 | 0.6808 | 0.6855 | 0.486 | 0.5507 |
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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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