flan-t5-large-stacked-samsum-1024
This model is a fine-tuned version of google/flan-t5-large on the stacked-summaries/stacked-samsum-1024
dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1846
- Rouge1: 57.9637
- Rouge2: 28.7446
- Rougel: 44.3826
- Rougelsum: 54.0399
- Gen Len: 122.77
Model description
This model card presents a model trained on a stacked dataset that aims to improve summarization by testing the benefits of "task-oriented pretraining". The model is designed to learn how to effectively condense and distill information from text by stacking summaries and separating them into independent concepts. In this way, the model can learn to identify essential information without simply mimicking the style of the dataset summaries.
The token used to identify a new concept in the summary is [NEXT_CONCEPT]
. You can split an output summary based on this token to see how it split the input text information: summary_text.split("[NEXT_CONCEPT]")
etc.
Intended uses & limitations
- max input/output is 1024 tokens
- this is mostly a test because
samsum
is not exactly the best dataset for general-purpose summarization
Training and evaluation data
See the dataset card linked on this page for info
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 24915
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1.0
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
---|---|---|---|---|---|---|---|---|
0.1195 | 0.17 | 20 | 2.0635 | 57.8829 | 28.7887 | 44.4256 | 54.1299 | 121.8 |
0.1084 | 0.35 | 40 | 2.1178 | 58.0416 | 28.6487 | 44.3905 | 54.1557 | 122.893 |
0.1019 | 0.52 | 60 | 2.1576 | 57.816 | 28.7069 | 44.4242 | 53.9598 | 120.524 |
0.0975 | 0.7 | 80 | 2.1821 | 57.9597 | 28.8178 | 44.4854 | 54.068 | 121.793 |
0.0947 | 0.87 | 100 | 2.1846 | 57.9637 | 28.7446 | 44.3826 | 54.0399 | 122.77 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1
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Model tree for stacked-summaries/flan-t5-large-stacked-samsum-1024
Base model
google/flan-t5-largeDataset used to train stacked-summaries/flan-t5-large-stacked-samsum-1024
Spaces using stacked-summaries/flan-t5-large-stacked-samsum-1024 2
Evaluation results
- ROUGE-1 on samsumtest set self-reported47.668
- ROUGE-2 on samsumtest set self-reported23.305
- ROUGE-L on samsumtest set self-reported39.768
- ROUGE-LSUM on samsumtest set self-reported43.259
- loss on samsumtest set self-reported2.373
- gen_len on samsumtest set self-reported17.424