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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/flan-t5-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- eli5_category |
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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-base-finetuned-t5 |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: eli5_category |
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type: eli5_category |
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config: default |
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split: validation1 |
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args: default |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 10.1877 |
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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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# flan-t5-base-finetuned-t5 |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the eli5_category dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: nan |
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- Rouge1: 10.1877 |
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- Rouge2: 0.0 |
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- Rougel: 10.1808 |
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- Rougelsum: 10.1824 |
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- Gen Len: 9.366 |
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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: 16 |
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- eval_batch_size: 16 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
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| 0.0 | 1.0 | 5736 | nan | 10.1877 | 0.0 | 10.1808 | 10.1824 | 9.366 | |
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| 0.0 | 2.0 | 11472 | nan | 10.1877 | 0.0 | 10.1808 | 10.1824 | 9.366 | |
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| 0.0 | 3.0 | 17208 | nan | 10.1877 | 0.0 | 10.1808 | 10.1824 | 9.366 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.19.1 |
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