transformers tensorboard safetensors bart text2text-generation generated_from_trainer summarization dataset:samsum base_model:facebook/bart-large-xsum base_model:finetune:facebook/bart-large-xsum license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/bart-large-xsum
datasets:
- samsum
license: mit
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: bart-large-xsum-finetuned-sst2
  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-large-xsum-finetuned-sst2

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4333
- Rouge1: 0.5389
- Rouge2: 0.2841
- Rougel: 0.4406
- Rougelsum: 0.4935

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.3028        | 1.0   | 920  | 0.3135          | 0.5331 | 0.2844 | 0.4417 | 0.4908    |
| 0.2301        | 2.0   | 1841 | 0.3304          | 0.5371 | 0.2878 | 0.4393 | 0.4936    |
| 0.1626        | 3.0   | 2762 | 0.3395          | 0.5415 | 0.2907 | 0.4503 | 0.4978    |
| 0.112         | 4.0   | 3683 | 0.3898          | 0.5415 | 0.2830 | 0.4406 | 0.4952    |
| 0.0747        | 5.0   | 4600 | 0.4333          | 0.5389 | 0.2841 | 0.4406 | 0.4935    |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2