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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- Andyrasika/TweetSumm-tuned |
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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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model-index: |
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- name: t5-small-Full-TweetSumm-1724699443 |
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results: |
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- task: |
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name: Summarization |
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type: 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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- name: Rouge1 |
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type: rouge |
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value: 0.4576 |
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- name: F1 |
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type: f1 |
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value: 0.8917 |
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- name: Precision |
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type: precision |
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value: 0.8901 |
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- name: Recall |
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type: recall |
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value: 0.8936 |
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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-Full-TweetSumm-1724699443 |
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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: 1.9954 |
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- Rouge1: 0.4576 |
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- Rouge2: 0.2129 |
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- Rougel: 0.3814 |
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- Rougelsum: 0.4246 |
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- Gen Len: 49.4636 |
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- F1: 0.8917 |
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- Precision: 0.8901 |
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- Recall: 0.8936 |
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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: 0.0005 |
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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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| 2.3321 | 1.0 | 110 | 2.0722 | 0.462 | 0.2119 | 0.3832 | 0.429 | 49.4818 | 0.8916 | 0.8905 | 0.893 | |
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| 2.0488 | 2.0 | 220 | 2.0052 | 0.453 | 0.2025 | 0.3721 | 0.4167 | 49.5727 | 0.8912 | 0.8889 | 0.8938 | |
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| 1.7205 | 3.0 | 330 | 1.9954 | 0.4576 | 0.2129 | 0.3814 | 0.4246 | 49.4636 | 0.8917 | 0.8901 | 0.8936 | |
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### Framework versions |
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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 |
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