---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: summarization_fine_tuning
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: validation
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 53.215
---

<!-- 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. -->

# summarization_fine_tuning

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5474
- Rouge1: 53.215
- Rouge2: 28.4755
- Rougel: 43.9337
- Rougelsum: 48.5873
- Gen Len: 27.2592

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3867        | 1.0   | 14732 | 1.6283          | 52.82   | 28.3657 | 43.6768 | 48.5632   | 27.1137 |
| 0.9705        | 2.0   | 29464 | 1.5474          | 53.215  | 28.4755 | 43.9337 | 48.5873   | 27.2592 |
| 0.5877        | 3.0   | 44196 | 1.7343          | 53.8648 | 28.8011 | 44.1837 | 49.2032   | 29.2225 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1