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--- |
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license: apache-2.0 |
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base_model: sshleifer/distilbart-cnn-12-6 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- dialogstudio |
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metrics: |
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- rouge |
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model-index: |
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- name: my_awesome_billsum_model |
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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: dialogstudio |
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type: dialogstudio |
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config: TweetSumm |
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split: test |
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args: TweetSumm |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.4187 |
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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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# my_awesome_billsum_model |
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This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the dialogstudio dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9811 |
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- Rouge1: 0.4187 |
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- Rouge2: 0.1911 |
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- Rougel: 0.3373 |
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- Rougelsum: 0.338 |
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- Gen Len: 65.1636 |
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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: 4 |
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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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| No log | 1.0 | 55 | 2.0591 | 0.4232 | 0.1899 | 0.3412 | 0.342 | 64.8545 | |
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| No log | 2.0 | 110 | 1.9802 | 0.4125 | 0.19 | 0.3329 | 0.3334 | 66.7545 | |
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| No log | 3.0 | 165 | 1.9671 | 0.4172 | 0.1927 | 0.3348 | 0.3357 | 65.3545 | |
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| No log | 4.0 | 220 | 1.9811 | 0.4187 | 0.1911 | 0.3373 | 0.338 | 65.1636 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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