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---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-xsum-finetuned-sst2
results: []
datasets:
- samsum
pipeline_tag: summarization
---
<!-- 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 |