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: 0.6376
          - name: Rouge2
            type: rouge
            value: 0.4143
          - name: Rougel
            type: rouge
            value: 0.538
          - name: Rougelsum
            type: rouge
            value: 0.5387
pipeline_tag: summarization
datasets:
  - ahmedmbutt/PTS-Dataset
language:
  - en
library_name: transformers
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: 1.2638
Rouge1: 0.6376
Rouge2: 0.4143
Rougel: 0.538
Rougelsum: 0.5387
Gen Len: 76.8417

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	180	0.8748	0.6166	0.3827	0.5058	0.5055	77.6583
No log	2.0	360	0.8774	0.6307	0.4064	0.5302	0.531	77.5111
0.6761	3.0	540	0.9064	0.635	0.4052	0.5309	0.5311	76.2833
0.6761	4.0	720	1.0386	0.6329	0.4038	0.5261	0.5262	78.4889
0.6761	5.0	900	1.0993	0.6285	0.4016	0.5239	0.5246	77.0083
0.2016	6.0	1080	1.2025	0.6351	0.4126	0.5351	0.5356	76.0722
0.2016	7.0	1260	1.2399	0.6356	0.4108	0.5362	0.5368	78.5361
0.2016	8.0	1440	1.2638	0.6376	0.4143	0.538	0.5387	76.8417

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1