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--- |
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license: apache-2.0 |
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base_model: google-t5/t5-base |
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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-base-Full-TweetSumm-1724683206 |
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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: 0.4709 |
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- name: F1 |
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type: f1 |
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value: 0.8952 |
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- name: Precision |
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type: precision |
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value: 0.8934 |
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- name: Recall |
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type: recall |
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value: 0.8971 |
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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-Full-TweetSumm-1724683206 |
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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: 1.8697 |
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- Rouge1: 0.4709 |
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- Rouge2: 0.2223 |
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- Rougel: 0.3999 |
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- Rougelsum: 0.4391 |
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- Gen Len: 41.8455 |
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- F1: 0.8952 |
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- Precision: 0.8934 |
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- Recall: 0.8971 |
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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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| 2.2928 | 1.0 | 220 | 1.8094 | 0.466 | 0.2146 | 0.3912 | 0.4301 | 41.9182 | 0.891 | 0.8891 | 0.8931 | |
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| 1.2939 | 2.0 | 440 | 1.7929 | 0.4605 | 0.2125 | 0.3897 | 0.4259 | 42.0 | 0.8928 | 0.8914 | 0.8944 | |
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| 0.7227 | 3.0 | 660 | 1.8697 | 0.4709 | 0.2223 | 0.3999 | 0.4391 | 41.8455 | 0.8952 | 0.8934 | 0.8971 | |
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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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