---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: all_2490_bart-base
  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. -->

# all_2490_bart-base

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: 1.0206
- Rouge1: 0.2426
- Rouge2: 0.1208
- Rougel: 0.2025
- Rougelsum: 0.2266
- Gen Len: 19.9945

## 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: 32
- eval_batch_size: 20
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.7151        | 0.8   | 500  | 1.1257          | 0.2361 | 0.1122 | 0.1955 | 0.2196    | 19.9978 |
| 1.0837        | 1.61  | 1000 | 1.0810          | 0.2401 | 0.1176 | 0.1997 | 0.2237    | 19.9953 |
| 1.0348        | 2.41  | 1500 | 1.0651          | 0.2401 | 0.1179 | 0.1999 | 0.2239    | 19.9957 |
| 1.0059        | 3.21  | 2000 | 1.0522          | 0.2402 | 0.1183 | 0.2001 | 0.2242    | 19.996  |
| 0.9855        | 4.02  | 2500 | 1.0439          | 0.2416 | 0.1197 | 0.2014 | 0.2257    | 19.9948 |
| 0.9642        | 4.82  | 3000 | 1.0361          | 0.2421 | 0.12   | 0.2019 | 0.2263    | 19.9936 |
| 0.9519        | 5.63  | 3500 | 1.0329          | 0.2415 | 0.1199 | 0.2016 | 0.2258    | 19.9948 |
| 0.9389        | 6.43  | 4000 | 1.0278          | 0.2424 | 0.1204 | 0.2022 | 0.2265    | 19.9942 |
| 0.9302        | 7.23  | 4500 | 1.0273          | 0.2422 | 0.1204 | 0.2022 | 0.2264    | 19.9943 |
| 0.9225        | 8.04  | 5000 | 1.0219          | 0.2421 | 0.1208 | 0.2023 | 0.2263    | 19.9946 |
| 0.9152        | 8.84  | 5500 | 1.0219          | 0.2429 | 0.1208 | 0.2027 | 0.227     | 19.9948 |
| 0.911         | 9.64  | 6000 | 1.0206          | 0.2426 | 0.1208 | 0.2025 | 0.2266    | 19.9945 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2