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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: PTS-Bart-Large-CNN
results:
- task:
type: summarization
name: Summarization
dataset:
name: PTS Dataset
type: PTS-Dataset
metrics:
- name: Rouge1
type: rouge
value: 0.6551
- name: Rouge2
type: rouge
value: 0.4332
- name: Rougel
type: rouge
value: 0.5543
- name: Rougelsum
type: rouge
value: 0.5541
datasets:
- ahmedmbutt/PTS-Dataset
language:
- en
library_name: transformers
pipeline_tag: summarization
widget:
- text: >-
I have to say that I do miss talking to a good psychiatrist- however. I
could sit and argue for ages with a psychiatrist who is intelligent and kind
(quite hard to find- but they do exist). Especially now that I have a PhD in
philosophy and have read everything that can be found on madness- including
the notes they wrote about me when I was in the hospital. Nowadays-
psychiatrists have a tendency to sign me off pretty quickly when I come onto
their radar. They don’t wish to deal with me- I tire them out.
---
<!-- 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. -->
# PTS-Bart-Large-CNN
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the PTS dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1760
- Rouge1: 0.6551
- Rouge2: 0.4332
- Rougel: 0.5543
- Rougelsum: 0.5541
- Gen Len: 80.0886
<!-- ## 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: 2e-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: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 220 | 0.8239 | 0.6263 | 0.3973 | 0.5238 | 0.5237 | 84.2023 |
| No log | 2.0 | 440 | 0.8201 | 0.6461 | 0.4184 | 0.5417 | 0.5416 | 81.1659 |
| 0.7121 | 3.0 | 660 | 0.8661 | 0.6479 | 0.4226 | 0.5448 | 0.5454 | 80.5409 |
| 0.7121 | 4.0 | 880 | 0.9784 | 0.6474 | 0.4242 | 0.5424 | 0.5425 | 82.2932 |
| 0.2619 | 5.0 | 1100 | 1.0645 | 0.655 | 0.4327 | 0.5517 | 0.5517 | 80.8386 |
| 0.2619 | 6.0 | 1320 | 1.1098 | 0.6548 | 0.4339 | 0.5542 | 0.5543 | 81.3545 |
| 0.1124 | 7.0 | 1540 | 1.1528 | 0.6528 | 0.4298 | 0.5511 | 0.551 | 80.5705 |
| 0.1124 | 8.0 | 1760 | 1.1760 | 0.6551 | 0.4332 | 0.5543 | 0.5541 | 80.0886 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |