---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v49
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# text_shortening_model_v49

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7760
- Rouge1: 0.5119
- Rouge2: 0.2768
- Rougel: 0.4448
- Rougelsum: 0.4444
- Bert precision: 0.8755
- Bert recall: 0.8801
- Average word count: 8.8492
- Max word count: 20
- Min word count: 5
- Average token count: 16.4709
- % shortened texts with length > 12: 8.7302

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 1.8542        | 1.0   | 83   | 1.6189          | 0.5121 | 0.2699 | 0.4302 | 0.4304    | 0.863          | 0.8909      | 11.3386            | 21             | 5              | 19.4312             | 31.746                             |
| 0.9651        | 2.0   | 166  | 1.4837          | 0.4957 | 0.2664 | 0.4347 | 0.4362    | 0.8687         | 0.8758      | 8.8598             | 19             | 4              | 16.9815             | 9.2593                             |
| 0.608         | 3.0   | 249  | 1.4074          | 0.5012 | 0.2693 | 0.4346 | 0.4342    | 0.8725         | 0.8781      | 8.836              | 20             | 4              | 15.5265             | 5.5556                             |
| 0.3788        | 4.0   | 332  | 1.5646          | 0.5202 | 0.2836 | 0.4535 | 0.4537    | 0.876          | 0.881       | 8.9312             | 18             | 5              | 16.4365             | 10.3175                            |
| 0.2296        | 5.0   | 415  | 1.7760          | 0.5119 | 0.2768 | 0.4448 | 0.4444    | 0.8755         | 0.8801      | 8.8492             | 20             | 5              | 16.4709             | 8.7302                             |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3