---
license: apache-2.0
tags:
- summarization
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-ftn-multi_news
  results:
  - task:
      type: summarization
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: multi_news
      type: multi_news
      args: default
    metrics:
    - type: rouge
      value: 41.6136
      name: Rouge1
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: test
    metrics:
    - type: rouge
      value: 39.6512
      name: ROUGE-1
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWZmOGE4MDFmODc2ZjYzOGQzZTRhNDdkYWJmYjYwZTBjMDQ3NjhkZmQwNTNjOThhY2ZkOWEzN2ExYmVhZWY1NyIsInZlcnNpb24iOjF9.s_JR_jcznilN8Z-In5H1njVEbAXndRnUq7ChjEFKscIuDwPRzsikWhJCbxgGpjUlqcb_oqDLFRVX3wzFkKevBA
    - type: rouge
      value: 14.333
      name: ROUGE-2
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZWU5YWI5ZmE4YjgxMDZmN2IxYjZjNTA3Y2ZjYzM4OGJjMjNkM2NjNzYxM2Y4ZmFkMGJlZGZmYWZiMjUzZTAwYSIsInZlcnNpb24iOjF9.ohu859pGplHtNDT7a-ap7PCb92OQDn1Je1rTuZgNQZxZiBNT4Us8DYrKOiEmWTLy6eQ84eDBZYmou2jymk3TDw
    - type: rouge
      value: 21.5797
      name: ROUGE-L
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjhmYjMwNGU1NmY4YWFkYjQ1ZjlmOWMxYzQ5MDdhOGE2YjdkZmQ5ZmViOGJkZWY0NDVlNjNhNzUyNmI3ZmMyMCIsInZlcnNpb24iOjF9.m2eT6JZZVTMcY0H7_mvCqRUv_esZ8VaL8lDmRu7Qv9GU3hoPgb2m6AxgYs_WiYMel3XotRNJwbKZoOLJraqrCw
    - type: rouge
      value: 35.5793
      name: ROUGE-LSUM
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiM2EzNWE4NWQzODQ1MGM3YTY2ZDFmZjFiMmEyMTlkZjQ5OTBkYzI3MzdmODc0ZWFlYWE4ZTY0ZDYxNDIxMDQ3ZSIsInZlcnNpb24iOjF9.hVjZtIcJ9SM9Minc83IkZ94-wVvt2s3vrk-250IINh6NKf3uDjQk5pPlS0HauTXl_c8rRlfH0UVJffFHRuNRAw
    - type: loss
      value: 5.507579803466797
      name: loss
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzU2NmI5ZmUyYWM0NzI3YTkxZjliOGUzZGI5OGFmNmIyODY0ZDEwZDlkZWY5MjhiOTE1MjAyOTc5YmY3YTJjMCIsInZlcnNpb24iOjF9.LOhbR1lcd8sDA_ZP1oHphVBjBSNGYDBIuMnOLZRS3P6GlmtkyPELxQPvqKnMq1BmZBW3PMRk5xBiQ2ThBIvfCg
    - type: gen_len
      value: 132.1745
      name: gen_len
      verified: true
      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTJkOGIxOTZkMmIzM2FjNjQ1MjBlYWJhYmY0YTM0ZTlkNmE0N2VmMjA5N2I5ZmJhODA3MjhkZjA1NmVmYTA0OSIsInZlcnNpb24iOjF9.0eiumS0wmfr-DSyV7KocpeFWziDwYUpSn4XVjpeeQxskAOgg5w28VOasXj0oY5KBPVaC5MiSfvY77rZXpvIIAg
---

<!-- 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. -->

# distilbart-cnn-12-6-ftn-multi_news

This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co./sshleifer/distilbart-cnn-12-6) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8143
- Rouge1: 41.6136
- Rouge2: 14.7454
- Rougel: 23.3597
- Rougelsum: 36.1973
- Gen Len: 130.874

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 3.8821        | 0.89  | 2000 | 3.8143          | 41.6136 | 14.7454 | 23.3597 | 36.1973   | 130.874 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1