---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
model-index:
- name: bart-finetuned-lyrlen-512
  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-finetuned-lyrlen-512

This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7206

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.221         | 0.04  | 500   | 1.9667          |
| 2.0336        | 0.08  | 1000  | 1.8762          |
| 1.9563        | 0.12  | 1500  | 1.8565          |
| 1.9555        | 0.17  | 2000  | 1.8392          |
| 1.9072        | 0.21  | 2500  | 1.8214          |
| 1.8796        | 0.25  | 3000  | 1.8246          |
| 1.8955        | 0.29  | 3500  | 1.8050          |
| 1.8254        | 0.33  | 4000  | 1.8069          |
| 1.8518        | 0.38  | 4500  | 1.7873          |
| 1.8471        | 0.42  | 5000  | 1.7880          |
| 1.8536        | 0.46  | 5500  | 1.7736          |
| 1.8075        | 0.5   | 6000  | 1.7772          |
| 1.8143        | 0.54  | 6500  | 1.7724          |
| 1.8383        | 0.58  | 7000  | 1.7670          |
| 1.746         | 0.62  | 7500  | 1.7741          |
| 1.7844        | 0.67  | 8000  | 1.7608          |
| 1.7761        | 0.71  | 8500  | 1.7680          |
| 1.7367        | 0.75  | 9000  | 1.7555          |
| 1.7656        | 0.79  | 9500  | 1.7508          |
| 1.7467        | 0.83  | 10000 | 1.7558          |
| 1.7744        | 0.88  | 10500 | 1.7449          |
| 1.7513        | 0.92  | 11000 | 1.7462          |
| 1.7482        | 0.96  | 11500 | 1.7576          |
| 1.724         | 1.0   | 12000 | 1.7525          |
| 1.7043        | 1.04  | 12500 | 1.7746          |
| 1.6869        | 1.08  | 13000 | 1.7531          |
| 1.7405        | 1.12  | 13500 | 1.7473          |
| 1.7343        | 1.17  | 14000 | 1.7396          |
| 1.649         | 1.21  | 14500 | 1.7384          |
| 1.7208        | 1.25  | 15000 | 1.7368          |
| 1.6931        | 1.29  | 15500 | 1.7404          |
| 1.5941        | 1.33  | 16000 | 1.8223          |
| 1.6651        | 1.38  | 16500 | 1.7287          |
| 1.6649        | 1.42  | 17000 | 1.7413          |
| 1.7108        | 1.46  | 17500 | 1.7304          |
| 1.713         | 1.5   | 18000 | 1.7263          |
| 1.6866        | 1.54  | 18500 | 1.7139          |
| 1.6461        | 1.58  | 19000 | 1.7221          |
| 1.6886        | 1.62  | 19500 | 1.7159          |
| 1.6511        | 1.67  | 20000 | 1.7302          |
| 1.6626        | 1.71  | 20500 | 1.7182          |
| 1.7052        | 1.75  | 21000 | 1.7163          |
| 1.6831        | 1.79  | 21500 | 1.7168          |
| 1.6057        | 1.83  | 22000 | 1.7151          |
| 1.6761        | 1.88  | 22500 | 1.7117          |
| 1.6668        | 1.92  | 23000 | 1.7164          |
| 1.612         | 1.96  | 23500 | 1.7122          |
| 1.6617        | 2.0   | 24000 | 1.7131          |
| 1.641         | 2.04  | 24500 | 1.7277          |
| 1.6595        | 2.08  | 25000 | 1.7289          |
| 1.6723        | 2.12  | 25500 | 1.7192          |
| 1.6347        | 2.17  | 26000 | 1.7259          |
| 1.6684        | 2.21  | 26500 | 1.7211          |
| 1.6098        | 2.25  | 27000 | 1.7316          |
| 1.6025        | 2.29  | 27500 | 1.7213          |
| 1.5567        | 2.33  | 28000 | 1.7238          |
| 1.6564        | 2.38  | 28500 | 1.7185          |
| 1.7078        | 2.42  | 29000 | 1.7393          |
| 1.6308        | 2.46  | 29500 | 1.7234          |
| 1.6402        | 2.5   | 30000 | 1.7319          |
| 1.6333        | 2.54  | 30500 | 1.7197          |
| 1.6249        | 2.58  | 31000 | 1.7298          |
| 1.6366        | 2.62  | 31500 | 1.7235          |
| 1.6245        | 2.67  | 32000 | 1.7289          |
| 1.6044        | 2.71  | 32500 | 1.7160          |
| 1.6095        | 2.75  | 33000 | 1.7172          |
| 1.6621        | 2.79  | 33500 | 1.7210          |
| 1.6883        | 2.83  | 34000 | 1.7169          |
| 1.6449        | 2.88  | 34500 | 1.7155          |
| 1.6439        | 2.92  | 35000 | 1.7201          |
| 1.6358        | 2.96  | 35500 | 1.7188          |
| 1.6033        | 3.0   | 36000 | 1.7206          |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.0.dev20230621+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2