transformers safetensors bart text2text-generation generated_from_trainer base_model:gayanin/bart-noised-with-kaggle-dist license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: gayanin/bart-noised-with-kaggle-dist
tags:
- generated_from_trainer
model-index:
- name: bart-noised-with-kaggle-gcd-dist
  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-noised-with-kaggle-gcd-dist

This model is a fine-tuned version of [gayanin/bart-noised-with-kaggle-dist](https://huggingface.co/gayanin/bart-noised-with-kaggle-dist) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4538

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.6061        | 0.11  | 500   | 0.5365          |
| 0.5537        | 0.21  | 1000  | 0.5251          |
| 0.5591        | 0.32  | 1500  | 0.5202          |
| 0.5669        | 0.43  | 2000  | 0.5069          |
| 0.4669        | 0.54  | 2500  | 0.5038          |
| 0.5457        | 0.64  | 3000  | 0.4923          |
| 0.5237        | 0.75  | 3500  | 0.4922          |
| 0.5186        | 0.86  | 4000  | 0.4802          |
| 0.5148        | 0.96  | 4500  | 0.4777          |
| 0.4127        | 1.07  | 5000  | 0.4822          |
| 0.4207        | 1.18  | 5500  | 0.4807          |
| 0.4362        | 1.28  | 6000  | 0.4770          |
| 0.4072        | 1.39  | 6500  | 0.4763          |
| 0.4503        | 1.5   | 7000  | 0.4701          |
| 0.3683        | 1.61  | 7500  | 0.4693          |
| 0.3897        | 1.71  | 8000  | 0.4636          |
| 0.4421        | 1.82  | 8500  | 0.4561          |
| 0.3836        | 1.93  | 9000  | 0.4588          |
| 0.3405        | 2.03  | 9500  | 0.4634          |
| 0.3147        | 2.14  | 10000 | 0.4682          |
| 0.3115        | 2.25  | 10500 | 0.4622          |
| 0.3153        | 2.35  | 11000 | 0.4625          |
| 0.3295        | 2.46  | 11500 | 0.4597          |
| 0.3529        | 2.57  | 12000 | 0.4564          |
| 0.3191        | 2.68  | 12500 | 0.4555          |
| 0.2974        | 2.78  | 13000 | 0.4547          |
| 0.3253        | 2.89  | 13500 | 0.4534          |
| 0.3627        | 3.0   | 14000 | 0.4538          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
