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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: finetuned_multi_news_bart_text_summarisation
results:
- task:
name: Sequence-to-sequence Language Modeling
type: textsummarization
dataset:
name: multi_news
type: multi_news
config: default
split: test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.4038
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. -->
# finetuned_multi_news_bart_text_summarisation
This model is a fine-tuned version of [slauw87/bart_summarisation](https://huggingface.co/slauw87/bart_summarisation) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8952
- Rouge1: 0.4038
- Rouge2: 0.1389
- Rougel: 0.2155
- Rougelsum: 0.2147
- Gen Len: 138.7667
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log | 1.0 | 15 | 2.9651 | 0.3903 | 0.134 | 0.21 | 0.2098 | 137.6 |
| No log | 2.0 | 30 | 2.8952 | 0.4038 | 0.1389 | 0.2155 | 0.2147 | 138.7667 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 |