transformers pytorch tensorboard bart text2text-generation summarization generated_from_trainer dataset:cnn_dailymail license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- cnn_dailymail
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: bart-base-finetuned-cnn_dailymail
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: cnn_dailymail
      type: cnn_dailymail
      config: 3.0.0
      split: train
      args: 3.0.0
    metrics:
    - type: rouge
      value: 0.35105989316705805
      name: Rouge1
---

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# bart-base-finetuned-cnn_dailymail

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5396
- Rouge1: 0.3511
- Rouge2: 0.1925
- Rougel: 0.3086
- Rougelsum: 0.3292

## 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: 5.6e-05
- 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
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.9486        | 1.0   | 35890  | 1.5941          | 0.3498 | 0.1893 | 0.3063 | 0.3272    |
| 1.6706        | 2.0   | 71780  | 1.5601          | 0.3503 | 0.1916 | 0.3079 | 0.3279    |
| 1.4809        | 3.0   | 107670 | 1.5423          | 0.3520 | 0.1923 | 0.3086 | 0.3295    |
| 1.3293        | 4.0   | 143560 | 1.5396          | 0.3511 | 0.1925 | 0.3086 | 0.3292    |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
