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---
license: apache-2.0
tags:
- summarisation
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
args: default
metrics:
- name: Rouge1
type: rouge
value: 38.9616
---
<!-- 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. -->
# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi-news
This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0185
- Rouge1: 38.9616
- Rouge2: 14.1539
- Rougel: 21.1788
- Rougelsum: 35.314
## 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: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 3.3679 | 1.0 | 11243 | 3.1314 | 38.4459 | 13.7777 | 20.8772 | 34.8321 |
| 3.1115 | 2.0 | 22486 | 3.0589 | 38.7419 | 13.9355 | 20.9911 | 35.0988 |
| 2.9826 | 3.0 | 33729 | 3.0311 | 38.7345 | 14.0365 | 21.0571 | 35.1604 |
| 2.8986 | 4.0 | 44972 | 3.0185 | 38.9616 | 14.1539 | 21.1788 | 35.314 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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