---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: bart_samsum_v2
  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. -->

# bart_samsum_v2

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0236

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 8
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.4233        | 0.17  | 1    | 9.1990          |
| 9.5213        | 0.34  | 2    | 8.5394          |
| 8.7467        | 0.52  | 3    | 8.1115          |
| 8.4697        | 0.69  | 4    | 7.5747          |
| 7.752         | 0.86  | 5    | 6.8712          |
| 7.0515        | 1.03  | 6    | 5.8670          |
| 6.0874        | 1.2   | 7    | 4.6814          |
| 5.0408        | 1.38  | 8    | 3.8055          |
| 4.14          | 1.55  | 9    | 2.6678          |
| 2.9893        | 1.72  | 10   | 1.9701          |
| 2.4337        | 1.89  | 11   | 1.5191          |
| 1.9451        | 2.06  | 12   | 1.2105          |
| 1.53          | 2.24  | 13   | 0.9714          |
| 1.2369        | 2.41  | 14   | 0.7905          |
| 1.0014        | 2.58  | 15   | 0.6478          |
| 0.8419        | 2.75  | 16   | 0.5493          |
| 0.7338        | 2.92  | 17   | 0.4770          |
| 0.6393        | 3.1   | 18   | 0.4151          |
| 0.5747        | 3.27  | 19   | 0.3691          |
| 0.4962        | 3.44  | 20   | 0.3293          |
| 0.4516        | 3.61  | 21   | 0.2935          |
| 0.3995        | 3.78  | 22   | 0.2614          |
| 0.3618        | 3.96  | 23   | 0.2346          |
| 0.3246        | 4.13  | 24   | 0.2129          |
| 0.2929        | 4.3   | 25   | 0.1938          |
| 0.278         | 4.47  | 26   | 0.1770          |
| 0.2493        | 4.65  | 27   | 0.1627          |
| 0.2273        | 4.82  | 28   | 0.1500          |
| 0.2067        | 4.99  | 29   | 0.1381          |
| 0.1917        | 5.16  | 30   | 0.1274          |
| 0.1805        | 5.33  | 31   | 0.1174          |
| 0.1557        | 5.51  | 32   | 0.1081          |
| 0.1495        | 5.68  | 33   | 0.1002          |
| 0.1394        | 5.85  | 34   | 0.0933          |
| 0.1261        | 6.02  | 35   | 0.0868          |
| 0.1155        | 6.19  | 36   | 0.0809          |
| 0.1114        | 6.37  | 37   | 0.0755          |
| 0.1041        | 6.54  | 38   | 0.0705          |
| 0.0952        | 6.71  | 39   | 0.0657          |
| 0.0881        | 6.88  | 40   | 0.0615          |
| 0.0823        | 7.05  | 41   | 0.0577          |
| 0.0778        | 7.23  | 42   | 0.0545          |
| 0.071         | 7.4   | 43   | 0.0515          |
| 0.07          | 7.57  | 44   | 0.0487          |
| 0.0625        | 7.74  | 45   | 0.0463          |
| 0.0589        | 7.91  | 46   | 0.0440          |
| 0.0567        | 8.09  | 47   | 0.0422          |
| 0.0537        | 8.26  | 48   | 0.0411          |
| 0.05          | 8.43  | 49   | 0.0398          |
| 0.0472        | 8.6   | 50   | 0.0384          |
| 0.0458        | 8.77  | 51   | 0.0363          |
| 0.0455        | 8.95  | 52   | 0.0347          |
| 0.0412        | 9.12  | 53   | 0.0340          |
| 0.0414        | 9.29  | 54   | 0.0326          |
| 0.0403        | 9.46  | 55   | 0.0333          |
| 0.0384        | 9.63  | 56   | 0.0303          |
| 0.0353        | 9.81  | 57   | 0.0298          |
| 0.0348        | 9.98  | 58   | 0.0293          |
| 0.0342        | 10.15 | 59   | 0.0275          |
| 0.0311        | 10.32 | 60   | 0.0272          |
| 0.0317        | 10.49 | 61   | 0.0270          |
| 0.0315        | 10.67 | 62   | 0.0261          |
| 0.0289        | 10.84 | 63   | 0.0253          |
| 0.0285        | 11.01 | 64   | 0.0247          |
| 0.0273        | 11.18 | 65   | 0.0244          |
| 0.0277        | 11.35 | 66   | 0.0240          |
| 0.0267        | 11.53 | 67   | 0.0237          |
| 0.0263        | 11.7  | 68   | 0.0237          |
| 0.0258        | 11.87 | 69   | 0.0237          |
| 0.0254        | 12.04 | 70   | 0.0238          |
| 0.0248        | 12.22 | 71   | 0.0239          |
| 0.0246        | 12.39 | 72   | 0.0239          |
| 0.0249        | 12.56 | 73   | 0.0237          |
| 0.0239        | 12.73 | 74   | 0.0236          |
| 0.0247        | 12.9  | 75   | 0.0236          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2