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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-xl |
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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: cnn_dailymail |
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type: cnn_dailymail |
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config: 3.0.0 |
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split: validation |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 45.1318 |
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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-xl |
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This model is a fine-tuned version of [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2648 |
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- Rouge1: 45.1318 |
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- Rouge2: 22.2773 |
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- Rougel: 31.9084 |
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- Rougelsum: 42.0558 |
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- Gen Len: 94.2332 |
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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: 0.0001 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 96 |
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- total_eval_batch_size: 96 |
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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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### 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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| 1.4352 | 1.0 | 2991 | 1.2645 | 43.8582 | 21.2227 | 30.7038 | 40.761 | 101.9968 | |
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| 1.3198 | 2.0 | 5982 | 1.2525 | 44.4594 | 21.8174 | 31.4304 | 41.4563 | 94.0733 | |
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| 1.2151 | 3.0 | 8973 | 1.2648 | 45.1318 | 22.2773 | 31.9084 | 42.0558 | 94.2332 | |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 2.0.0 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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