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--- |
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base_model: google-t5/t5-small |
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datasets: |
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- Andyrasika/TweetSumm-tuned |
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library_name: peft |
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license: apache-2.0 |
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metrics: |
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- rouge |
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- f1 |
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- precision |
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- recall |
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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-LoRA-TweetSumm-1724701402 |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: Andyrasika/TweetSumm-tuned |
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type: Andyrasika/TweetSumm-tuned |
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metrics: |
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- type: rouge |
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value: 0.4387 |
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name: Rouge1 |
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- type: f1 |
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value: 0.8896 |
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name: F1 |
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- type: precision |
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value: 0.8881 |
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name: Precision |
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- type: recall |
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value: 0.8913 |
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name: Recall |
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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-LoRA-TweetSumm-1724701402 |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the Andyrasika/TweetSumm-tuned dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0811 |
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- Rouge1: 0.4387 |
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- Rouge2: 0.196 |
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- Rougel: 0.3605 |
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- Rougelsum: 0.4055 |
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- Gen Len: 49.5909 |
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- F1: 0.8896 |
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- Precision: 0.8881 |
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- Recall: 0.8913 |
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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: 0.001 |
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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: 3 |
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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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:---------:|:------:| |
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| 2.3972 | 1.0 | 110 | 2.1384 | 0.4219 | 0.1801 | 0.3545 | 0.3925 | 49.9818 | 0.8833 | 0.8806 | 0.8861 | |
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| 2.2593 | 2.0 | 220 | 2.0982 | 0.4125 | 0.1843 | 0.3448 | 0.3837 | 49.9091 | 0.8853 | 0.8822 | 0.8886 | |
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| 1.9318 | 3.0 | 330 | 2.0811 | 0.4387 | 0.196 | 0.3605 | 0.4055 | 49.5909 | 0.8896 | 0.8881 | 0.8913 | |
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### Framework versions |
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- PEFT 0.12.1.dev0 |
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- Transformers 4.44.0 |
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- Pytorch 2.4.0 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
