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--- |
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base_model: google-t5/t5-base |
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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-base-ia3-finetune-tweetsumm-1724827331 |
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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: 0.4407 |
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name: Rouge1 |
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- type: f1 |
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value: 0.8906 |
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name: F1 |
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- type: precision |
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value: 0.8894 |
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name: Precision |
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- type: recall |
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value: 0.8921 |
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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-base-ia3-finetune-tweetsumm-1724827331 |
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This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co./google-t5/t5-base) on the Andyrasika/TweetSumm-tuned dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8276 |
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- Rouge1: 0.4407 |
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- Rouge2: 0.1997 |
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- Rougel: 0.3672 |
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- Rougelsum: 0.4075 |
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- Gen Len: 49.5727 |
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- F1: 0.8906 |
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- Precision: 0.8894 |
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- Recall: 0.8921 |
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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: 5 |
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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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| 2.2511 | 1.0 | 879 | 1.9364 | 0.4398 | 0.1855 | 0.3668 | 0.411 | 49.5182 | 0.8883 | 0.8875 | 0.8892 | |
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| 1.4557 | 2.0 | 1758 | 1.8611 | 0.4491 | 0.2031 | 0.3721 | 0.4148 | 49.6091 | 0.8901 | 0.8889 | 0.8915 | |
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| 1.8149 | 3.0 | 2637 | 1.8386 | 0.4436 | 0.2001 | 0.3707 | 0.4092 | 49.5636 | 0.8905 | 0.889 | 0.8923 | |
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| 2.7192 | 4.0 | 3516 | 1.8271 | 0.4366 | 0.1966 | 0.3643 | 0.4041 | 49.6091 | 0.8897 | 0.8878 | 0.8917 | |
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| 1.7838 | 5.0 | 4395 | 1.8276 | 0.4407 | 0.1997 | 0.3672 | 0.4075 | 49.5727 | 0.8906 | 0.8894 | 0.8921 | |
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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 |
