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--- |
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license: apache-2.0 |
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base_model: allenai/led-base-16384 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- wcep-10 |
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metrics: |
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- rouge |
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model-index: |
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- name: thesis-led-finetuned-on-wcep |
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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: wcep-10 |
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type: wcep-10 |
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config: roberta |
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split: validation |
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args: roberta |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 43.4358 |
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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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# thesis-led-finetuned-on-wcep |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the wcep-10 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6816 |
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- Rouge1: 43.4358 |
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- Rouge2: 21.8159 |
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- Rougel: 35.0411 |
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- Rougelsum: 36.1007 |
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- Gen Len: 27.2843 |
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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: 4 |
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- eval_batch_size: 4 |
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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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| 1.6843 | 1.0 | 2040 | 1.6753 | 42.8519 | 21.8933 | 35.0226 | 35.9911 | 25.6647 | |
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| 1.4083 | 2.0 | 4080 | 1.6672 | 43.5166 | 22.0845 | 35.283 | 36.4006 | 26.4098 | |
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| 1.1981 | 3.0 | 6120 | 1.6816 | 43.4358 | 21.8159 | 35.0411 | 36.1007 | 27.2843 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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