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-20-epochs-1500-input-256-output
  results: []
---

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# fine-tuned-bart-20-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: 0.9200
- Rouge1: 0.1515
- Rouge2: 0.0334
- Rougel: 0.115
- Rougelsum: 0.1156
- Gen Len: 37.06

## 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 151  | 6.0528          | 0.0    | 0.0    | 0.0    | 0.0       | 10.2    |
| No log        | 2.0   | 302  | 1.1375          | 0.0882 | 0.0191 | 0.0773 | 0.0785    | 9.24    |
| No log        | 3.0   | 453  | 0.9715          | 0.0982 | 0.0262 | 0.0779 | 0.0781    | 23.4    |
| 4.0047        | 4.0   | 604  | 0.9133          | 0.1155 | 0.0244 | 0.0896 | 0.0896    | 32.75   |
| 4.0047        | 5.0   | 755  | 0.8848          | 0.1762 | 0.0333 | 0.139  | 0.1399    | 36.38   |
| 4.0047        | 6.0   | 906  | 0.8709          | 0.1521 | 0.028  | 0.1225 | 0.1229    | 35.85   |
| 0.756         | 7.0   | 1057 | 0.8611          | 0.1522 | 0.0355 | 0.1131 | 0.1139    | 52.75   |
| 0.756         | 8.0   | 1208 | 0.8555          | 0.1677 | 0.0396 | 0.126  | 0.1268    | 41.22   |
| 0.756         | 9.0   | 1359 | 0.8640          | 0.1411 | 0.0251 | 0.109  | 0.1093    | 24.65   |
| 0.5214        | 10.0  | 1510 | 0.8645          | 0.1772 | 0.0382 | 0.1351 | 0.1348    | 43.11   |
| 0.5214        | 11.0  | 1661 | 0.8681          | 0.1828 | 0.0386 | 0.1399 | 0.1407    | 38.1    |
| 0.5214        | 12.0  | 1812 | 0.8741          | 0.2031 | 0.0436 | 0.1584 | 0.1592    | 46.33   |
| 0.5214        | 13.0  | 1963 | 0.8861          | 0.1752 | 0.0422 | 0.1315 | 0.1315    | 39.91   |
| 0.3632        | 14.0  | 2114 | 0.8922          | 0.132  | 0.0251 | 0.0999 | 0.1013    | 37.31   |
| 0.3632        | 15.0  | 2265 | 0.9004          | 0.165  | 0.0368 | 0.1302 | 0.1299    | 41.1    |
| 0.3632        | 16.0  | 2416 | 0.9072          | 0.1483 | 0.0347 | 0.1139 | 0.115     | 37.99   |
| 0.2595        | 17.0  | 2567 | 0.9121          | 0.1558 | 0.0304 | 0.1149 | 0.1151    | 39.95   |
| 0.2595        | 18.0  | 2718 | 0.9156          | 0.1519 | 0.0316 | 0.1168 | 0.1183    | 36.4    |
| 0.2595        | 19.0  | 2869 | 0.9178          | 0.1437 | 0.0309 | 0.1101 | 0.1115    | 36.49   |
| 0.2098        | 20.0  | 3020 | 0.9200          | 0.1515 | 0.0334 | 0.115  | 0.1156    | 37.06   |


### Framework versions

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