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bart-base-multi-news
This model is a fine-tuned version of facebook/bart-base on the multi_news dataset. It achieves the following results on the evaluation set:
- Loss: 2.4147
- Rouge1: 26.31
- Rouge2: 9.6
- Rougel: 20.87
- Rougelsum: 21.54
Intended uses & limitations
The inteded use of this model is text summarization. The model requires additional training in order to perform better in the task of summarization.
Training and evaluation data
The training data were 10000 samples from the multi-news training dataset and the evaluation data were 500 samples from the multi-news evaluation dataset
Training procedure
For the training procedure the Seq2SeqTrainer class was used from the transformers library.
Training hyperparameters
The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library.
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.4041 | 1.0 | 1250 | 2.4147 | 26.31 | 9.6 | 20.87 | 21.54 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
