metadata
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: etsummerizer_v2
results: []
datasets:
- EasyTerms/Manuel_dataset
language:
- en
library_name: transformers
pipeline_tag: summarization
etsummerizer_v2
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on EasyTerms/Manuel_dataset. It achieves the following results on the evaluation set:
- Loss: 0.3484
- Rouge1: 0.5448
- Rouge2: 0.3092
- Rougel: 0.4363
- Rougelsum: 0.4370
Model description
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 in a simplified version lacking in legal jargon.
Intended uses & limitations
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.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 3.5 | 1.0 | 30 | 0.5565 | 0.5111 | 0.2863 | 0.4092 | 0.4093 |
| 0.3056 | 2.0 | 60 | 0.3612 | 0.5267 | 0.3021 | 0.4277 | 0.4286 |
| 0.1716 | 3.0 | 90 | 0.3484 | 0.5448 | 0.3092 | 0.4363 | 0.4370 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0+cpu
- Datasets 2.1.0
- Tokenizers 0.13.3