---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-with-pubmed-noise-data-0.1
  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-with-pubmed-noise-data-0.1

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co./facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1956

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.3905        | 0.04  | 500   | 0.3471          |
| 0.3136        | 0.07  | 1000  | 0.3261          |
| 0.2971        | 0.11  | 1500  | 0.2971          |
| 0.3361        | 0.14  | 2000  | 0.2788          |
| 0.2502        | 0.18  | 2500  | 0.2780          |
| 0.2613        | 0.21  | 3000  | 0.2690          |
| 0.2683        | 0.25  | 3500  | 0.2591          |
| 0.2995        | 0.29  | 4000  | 0.2539          |
| 0.2317        | 0.32  | 4500  | 0.2481          |
| 0.2361        | 0.36  | 5000  | 0.2453          |
| 0.2523        | 0.39  | 5500  | 0.2440          |
| 0.236         | 0.43  | 6000  | 0.2391          |
| 0.2301        | 0.46  | 6500  | 0.2372          |
| 0.2259        | 0.5   | 7000  | 0.2328          |
| 0.2231        | 0.54  | 7500  | 0.2344          |
| 0.2098        | 0.57  | 8000  | 0.2285          |
| 0.2663        | 0.61  | 8500  | 0.2220          |
| 0.2139        | 0.64  | 9000  | 0.2265          |
| 0.2372        | 0.68  | 9500  | 0.2204          |
| 0.1946        | 0.71  | 10000 | 0.2213          |
| 0.1843        | 0.75  | 10500 | 0.2214          |
| 0.1872        | 0.79  | 11000 | 0.2178          |
| 0.2182        | 0.82  | 11500 | 0.2127          |
| 0.2123        | 0.86  | 12000 | 0.2118          |
| 0.1865        | 0.89  | 12500 | 0.2113          |
| 0.1782        | 0.93  | 13000 | 0.2080          |
| 0.1894        | 0.96  | 13500 | 0.2053          |
| 0.1989        | 1.0   | 14000 | 0.2097          |
| 0.1721        | 1.03  | 14500 | 0.2083          |
| 0.1353        | 1.07  | 15000 | 0.2102          |
| 0.164         | 1.11  | 15500 | 0.2140          |
| 0.1541        | 1.14  | 16000 | 0.2086          |
| 0.1421        | 1.18  | 16500 | 0.2112          |
| 0.1752        | 1.21  | 17000 | 0.2085          |
| 0.1452        | 1.25  | 17500 | 0.2105          |
| 0.1836        | 1.28  | 18000 | 0.2066          |
| 0.1444        | 1.32  | 18500 | 0.2083          |
| 0.1473        | 1.36  | 19000 | 0.2090          |
| 0.1723        | 1.39  | 19500 | 0.2084          |
| 0.1328        | 1.43  | 20000 | 0.2042          |
| 0.1842        | 1.46  | 20500 | 0.2032          |
| 0.1934        | 1.5   | 21000 | 0.2031          |
| 0.1412        | 1.53  | 21500 | 0.2008          |
| 0.1302        | 1.57  | 22000 | 0.2003          |
| 0.142         | 1.61  | 22500 | 0.2008          |
| 0.1479        | 1.64  | 23000 | 0.2025          |
| 0.1628        | 1.68  | 23500 | 0.2005          |
| 0.1126        | 1.71  | 24000 | 0.2016          |
| 0.1515        | 1.75  | 24500 | 0.1985          |
| 0.1605        | 1.78  | 25000 | 0.1984          |
| 0.1659        | 1.82  | 25500 | 0.1970          |
| 0.1404        | 1.86  | 26000 | 0.1980          |
| 0.1386        | 1.89  | 26500 | 0.1972          |
| 0.1119        | 1.93  | 27000 | 0.1976          |
| 0.168         | 1.96  | 27500 | 0.1940          |
| 0.1318        | 2.0   | 28000 | 0.1958          |
| 0.1307        | 2.03  | 28500 | 0.1987          |
| 0.1312        | 2.07  | 29000 | 0.2012          |
| 0.1237        | 2.11  | 29500 | 0.2002          |
| 0.1339        | 2.14  | 30000 | 0.2010          |
| 0.1471        | 2.18  | 30500 | 0.1999          |
| 0.1195        | 2.21  | 31000 | 0.1998          |
| 0.1002        | 2.25  | 31500 | 0.2000          |
| 0.1009        | 2.28  | 32000 | 0.2012          |
| 0.1608        | 2.32  | 32500 | 0.1995          |
| 0.1198        | 2.36  | 33000 | 0.2009          |
| 0.1053        | 2.39  | 33500 | 0.1990          |
| 0.1399        | 2.43  | 34000 | 0.2001          |
| 0.1043        | 2.46  | 34500 | 0.1994          |
| 0.1254        | 2.5   | 35000 | 0.1996          |
| 0.0987        | 2.53  | 35500 | 0.1966          |
| 0.119         | 2.57  | 36000 | 0.1974          |
| 0.1167        | 2.61  | 36500 | 0.1983          |
| 0.1119        | 2.64  | 37000 | 0.1974          |
| 0.1391        | 2.68  | 37500 | 0.1973          |
| 0.1036        | 2.71  | 38000 | 0.1971          |
| 0.1203        | 2.75  | 38500 | 0.1976          |
| 0.1498        | 2.78  | 39000 | 0.1976          |
| 0.1037        | 2.82  | 39500 | 0.1975          |
| 0.1141        | 2.85  | 40000 | 0.1961          |
| 0.0935        | 2.89  | 40500 | 0.1960          |
| 0.0985        | 2.93  | 41000 | 0.1963          |
| 0.108         | 2.96  | 41500 | 0.1955          |
| 0.1054        | 3.0   | 42000 | 0.1956          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1