transformers safetensors bart text2text-generation generated_from_trainer base_model:facebook/bart-base license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-with-woz-noise-data-0.1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart-with-woz-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: 0.0710

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2068        | 0.04  | 500   | 0.1735          |
| 0.1785        | 0.09  | 1000  | 0.1508          |
| 0.2136        | 0.13  | 1500  | 0.1359          |
| 0.1249        | 0.18  | 2000  | 0.1281          |
| 0.1114        | 0.22  | 2500  | 0.1180          |
| 0.1327        | 0.26  | 3000  | 0.1153          |
| 0.1603        | 0.31  | 3500  | 0.1065          |
| 0.1422        | 0.35  | 4000  | 0.1032          |
| 0.1166        | 0.39  | 4500  | 0.1019          |
| 0.1266        | 0.44  | 5000  | 0.1001          |
| 0.1087        | 0.48  | 5500  | 0.0996          |
| 0.1284        | 0.53  | 6000  | 0.0967          |
| 0.0919        | 0.57  | 6500  | 0.0938          |
| 0.0924        | 0.61  | 7000  | 0.0927          |
| 0.1124        | 0.66  | 7500  | 0.0913          |
| 0.0843        | 0.7   | 8000  | 0.0920          |
| 0.1012        | 0.74  | 8500  | 0.0881          |
| 0.1058        | 0.79  | 9000  | 0.0867          |
| 0.0894        | 0.83  | 9500  | 0.0867          |
| 0.0858        | 0.88  | 10000 | 0.0828          |
| 0.0991        | 0.92  | 10500 | 0.0867          |
| 0.0471        | 0.96  | 11000 | 0.0867          |
| 0.0663        | 1.01  | 11500 | 0.0833          |
| 0.0743        | 1.05  | 12000 | 0.0843          |
| 0.0821        | 1.09  | 12500 | 0.0835          |
| 0.0826        | 1.14  | 13000 | 0.0812          |
| 0.0943        | 1.18  | 13500 | 0.0809          |
| 0.0708        | 1.23  | 14000 | 0.0813          |
| 0.0902        | 1.27  | 14500 | 0.0791          |
| 0.051         | 1.31  | 15000 | 0.0822          |
| 0.0782        | 1.36  | 15500 | 0.0800          |
| 0.0802        | 1.4   | 16000 | 0.0777          |
| 0.0671        | 1.44  | 16500 | 0.0787          |
| 0.0872        | 1.49  | 17000 | 0.0776          |
| 0.091         | 1.53  | 17500 | 0.0766          |
| 0.0722        | 1.58  | 18000 | 0.0775          |
| 0.0539        | 1.62  | 18500 | 0.0754          |
| 0.067         | 1.66  | 19000 | 0.0754          |
| 0.0372        | 1.71  | 19500 | 0.0758          |
| 0.0838        | 1.75  | 20000 | 0.0763          |
| 0.0496        | 1.79  | 20500 | 0.0736          |
| 0.0542        | 1.84  | 21000 | 0.0744          |
| 0.0435        | 1.88  | 21500 | 0.0746          |
| 0.0568        | 1.93  | 22000 | 0.0731          |
| 0.0521        | 1.97  | 22500 | 0.0713          |
| 0.0377        | 2.01  | 23000 | 0.0743          |
| 0.0277        | 2.06  | 23500 | 0.0747          |
| 0.0587        | 2.1   | 24000 | 0.0742          |
| 0.0345        | 2.14  | 24500 | 0.0748          |
| 0.0364        | 2.19  | 25000 | 0.0761          |
| 0.0524        | 2.23  | 25500 | 0.0737          |
| 0.0407        | 2.28  | 26000 | 0.0736          |
| 0.0425        | 2.32  | 26500 | 0.0730          |
| 0.044         | 2.36  | 27000 | 0.0734          |
| 0.0477        | 2.41  | 27500 | 0.0731          |
| 0.0382        | 2.45  | 28000 | 0.0732          |
| 0.0387        | 2.5   | 28500 | 0.0726          |
| 0.0459        | 2.54  | 29000 | 0.0731          |
| 0.0554        | 2.58  | 29500 | 0.0720          |
| 0.0348        | 2.63  | 30000 | 0.0727          |
| 0.0449        | 2.67  | 30500 | 0.0717          |
| 0.0386        | 2.71  | 31000 | 0.0720          |
| 0.0436        | 2.76  | 31500 | 0.0712          |
| 0.0345        | 2.8   | 32000 | 0.0720          |
| 0.0509        | 2.85  | 32500 | 0.0712          |
| 0.0402        | 2.89  | 33000 | 0.0710          |
| 0.055         | 2.93  | 33500 | 0.0711          |
| 0.0413        | 2.98  | 34000 | 0.0710          |


### Framework versions

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