---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v36
  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_v36

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: 2.9367
- Rouge1: 0.4747
- Rouge2: 0.2555
- Rougel: 0.4212
- Rougelsum: 0.4204
- Bert precision: 0.8696
- Bert recall: 0.87
- Average word count: 8.6396
- Max word count: 17
- Min word count: 4
- Average token count: 16.6216
- % shortened texts with length > 12: 6.3063

## 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: 0.0003
- 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: 15

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------------:|:-----------:|:------------------:|:--------------:|:--------------:|:-------------------:|:----------------------------------:|
| 1.571         | 1.0   | 73   | 2.1904          | 0.4703 | 0.2572 | 0.4224 | 0.4216    | 0.8718         | 0.8704      | 8.2583             | 14             | 3              | 14.2973             | 1.5015                             |
| 0.8809        | 2.0   | 146  | 1.9224          | 0.4588 | 0.2414 | 0.4177 | 0.417     | 0.8734         | 0.8673      | 8.2492             | 22             | 3              | 15.8078             | 3.9039                             |
| 0.7135        | 3.0   | 219  | 2.7437          | 0.2535 | 0.082  | 0.2289 | 0.2294    | 0.8131         | 0.8149      | 8.4324             | 11             | 5              | 14.973              | 0.0                                |
| 0.5646        | 4.0   | 292  | 2.0495          | 0.4689 | 0.249  | 0.4155 | 0.4156    | 0.8653         | 0.8736      | 9.8438             | 20             | 4              | 18.0961             | 21.3213                            |
| 0.4158        | 5.0   | 365  | 2.0101          | 0.4707 | 0.2539 | 0.4241 | 0.4243    | 0.8688         | 0.8725      | 8.9009             | 14             | 4              | 14.7988             | 4.2042                             |
| 0.3445        | 6.0   | 438  | 2.0642          | 0.4606 | 0.255  | 0.4133 | 0.4132    | 0.866          | 0.8705      | 9.0991             | 15             | 4              | 14.955              | 6.3063                             |
| 0.2473        | 7.0   | 511  | 2.2675          | 0.4668 | 0.2441 | 0.4137 | 0.413     | 0.8683         | 0.8694      | 8.7177             | 19             | 3              | 16.5766             | 9.3093                             |
| 0.2084        | 8.0   | 584  | 2.4474          | 0.4793 | 0.2608 | 0.4256 | 0.4257    | 0.8701         | 0.8741      | 9.1021             | 17             | 4              | 17.048              | 9.9099                             |
| 0.1703        | 9.0   | 657  | 2.3961          | 0.4754 | 0.2609 | 0.4253 | 0.4253    | 0.8676         | 0.8749      | 9.2943             | 17             | 4              | 17.2402             | 11.4114                            |
| 0.1293        | 10.0  | 730  | 2.4721          | 0.4581 | 0.2463 | 0.409  | 0.4082    | 0.8657         | 0.8671      | 8.7057             | 18             | 4              | 16.3514             | 6.9069                             |
| 0.1312        | 11.0  | 803  | 2.4027          | 0.4667 | 0.2497 | 0.4117 | 0.4113    | 0.868          | 0.8683      | 8.4925             | 18             | 4              | 15.4294             | 5.1051                             |
| 0.1424        | 12.0  | 876  | 2.5041          | 0.476  | 0.2506 | 0.4214 | 0.4214    | 0.8715         | 0.8699      | 8.6186             | 18             | 4              | 16.1862             | 6.006                              |
| 0.0926        | 13.0  | 949  | 2.7011          | 0.4723 | 0.2582 | 0.4238 | 0.4227    | 0.8695         | 0.8701      | 8.6096             | 19             | 4              | 16.4505             | 5.7057                             |
| 0.0663        | 14.0  | 1022 | 2.8149          | 0.467  | 0.2504 | 0.4157 | 0.4153    | 0.8674         | 0.8675      | 8.6336             | 17             | 4              | 16.4985             | 5.1051                             |
| 0.0684        | 15.0  | 1095 | 2.9367          | 0.4747 | 0.2555 | 0.4212 | 0.4204    | 0.8696         | 0.87        | 8.6396             | 17             | 4              | 16.6216             | 6.3063                             |


### Framework versions

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