metadata

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.

PTS-Bart-Large-CNN

This model is a fine-tuned version of 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

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