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--- |
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license: apache-2.0 |
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tags: |
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- summarization |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: etsummerizer_v2 |
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results: [] |
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datasets: |
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- EasyTerms/Manuel_dataset |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: summarization |
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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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# etsummerizer_v2 |
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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 [EasyTerms/Manuel_dataset](https://huggingface.co/datasets/EasyTerms/Manuel_dataset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3484 |
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- Rouge1: 0.5448 |
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- Rouge2: 0.3092 |
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- Rougel: 0.4363 |
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- Rougelsum: 0.4370 |
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## Model description |
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This model was finetuned on legal text extracted from different terms and conditions documents. Its objective is to efficiently summerize such text and present the generation |
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in a simplified version lacking in legal jargon. |
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## Intended uses & limitations |
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As it is the second version of this model it effectively summerize legal text however, further training will be required to improve the simplification task. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 3.5 | 1.0 | 30 | 0.5565 | 0.5111 | 0.2863 | 0.4092 | 0.4093 | |
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| 0.3056 | 2.0 | 60 | 0.3612 | 0.5267 | 0.3021 | 0.4277 | 0.4286 | |
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| 0.1716 | 3.0 | 90 | 0.3484 | 0.5448 | 0.3092 | 0.4363 | 0.4370 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.0+cpu |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
