transformers tensorboard safetensors bart text2text-generation generated_from_trainer base_model:bart-base autotrain_compatible endpoints_compatible region:us
---
base_model: bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fine-tuned-bart-15-epochs-1500-input-256-output
  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. -->

# fine-tuned-bart-15-epochs-1500-input-256-output

This model is a fine-tuned version of [bart-base](https://huggingface.co/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8826
- Rouge1: 0.1734
- Rouge2: 0.0367
- Rougel: 0.1299
- Rougelsum: 0.1304
- Gen Len: 37.9

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 151  | 4.7528          | 0.0    | 0.0    | 0.0    | 0.0       | 13.65   |
| No log        | 2.0   | 302  | 1.0942          | 0.1251 | 0.0251 | 0.1187 | 0.1205    | 10.18   |
| No log        | 3.0   | 453  | 0.9616          | 0.0757 | 0.0193 | 0.0606 | 0.0612    | 18.24   |
| 3.6176        | 4.0   | 604  | 0.9080          | 0.119  | 0.0278 | 0.0913 | 0.0926    | 31.52   |
| 3.6176        | 5.0   | 755  | 0.8831          | 0.1595 | 0.0325 | 0.1307 | 0.1316    | 30.88   |
| 3.6176        | 6.0   | 906  | 0.8731          | 0.1295 | 0.0276 | 0.0917 | 0.0922    | 39.26   |
| 0.7542        | 7.0   | 1057 | 0.8610          | 0.1595 | 0.0379 | 0.1159 | 0.1169    | 48.91   |
| 0.7542        | 8.0   | 1208 | 0.8556          | 0.172  | 0.0407 | 0.1286 | 0.1286    | 51.53   |
| 0.7542        | 9.0   | 1359 | 0.8619          | 0.1576 | 0.0272 | 0.1262 | 0.1263    | 27.97   |
| 0.5404        | 10.0  | 1510 | 0.8606          | 0.1763 | 0.0473 | 0.138  | 0.139     | 36.5    |
| 0.5404        | 11.0  | 1661 | 0.8644          | 0.1924 | 0.0364 | 0.1538 | 0.1537    | 32.84   |
| 0.5404        | 12.0  | 1812 | 0.8701          | 0.1832 | 0.036  | 0.1386 | 0.139     | 38.88   |
| 0.5404        | 13.0  | 1963 | 0.8776          | 0.1817 | 0.0399 | 0.1383 | 0.1398    | 34.36   |
| 0.4139        | 14.0  | 2114 | 0.8821          | 0.1888 | 0.0399 | 0.1407 | 0.1418    | 37.35   |
| 0.4139        | 15.0  | 2265 | 0.8826          | 0.1734 | 0.0367 | 0.1299 | 0.1304    | 37.9    |


### Framework versions

- Transformers 4.36.2
- Pytorch 1.12.1+cu113
- Datasets 2.16.1
- Tokenizers 0.15.0
