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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- summarization |
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- stacked summaries |
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- prompt engineering |
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metrics: |
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- rouge |
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datasets: |
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- stacked-summaries/stacked-samsum-1024 |
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model-index: |
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- name: flan-t5-large-stacked-samsum1024-WIP3 |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: samsum |
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type: samsum |
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config: samsum |
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split: test |
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metrics: |
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- name: ROUGE-1 |
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type: rouge |
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value: 47.6682 |
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verified: true |
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- name: ROUGE-2 |
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type: rouge |
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value: 23.3053 |
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verified: true |
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- name: ROUGE-L |
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type: rouge |
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value: 39.7678 |
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verified: true |
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- name: ROUGE-LSUM |
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type: rouge |
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value: 43.259 |
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verified: true |
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- name: loss |
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type: loss |
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value: 2.372586965560913 |
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verified: true |
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- name: gen_len |
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type: gen_len |
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value: 17.4237 |
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verified: true |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: summarization |
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--- |
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# flan-t5-large-stacked-samsum-1024 |
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<a href="https://colab.research.google.com/gist/pszemraj/a4bf61f593ebda9a8db6dc58839d9de4/brief-demo-flan-t5-stacked-samsum.ipynb"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the `stacked-summaries/stacked-samsum-1024` dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1846 |
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- Rouge1: 57.9637 |
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- Rouge2: 28.7446 |
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- Rougel: 44.3826 |
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- Rougelsum: 54.0399 |
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- Gen Len: 122.77 |
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## Model description |
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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. |
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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. |
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## Intended uses & limitations |
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- max input/output is 1024 tokens |
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- this is mostly a test because `samsum` is not exactly the best dataset for general-purpose summarization |
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## Training and evaluation data |
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See the dataset card linked on this page for info |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 4 |
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- seed: 24915 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 32 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.02 |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 0.1195 | 0.17 | 20 | 2.0635 | 57.8829 | 28.7887 | 44.4256 | 54.1299 | 121.8 | |
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| 0.1084 | 0.35 | 40 | 2.1178 | 58.0416 | 28.6487 | 44.3905 | 54.1557 | 122.893 | |
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| 0.1019 | 0.52 | 60 | 2.1576 | 57.816 | 28.7069 | 44.4242 | 53.9598 | 120.524 | |
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| 0.0975 | 0.7 | 80 | 2.1821 | 57.9597 | 28.8178 | 44.4854 | 54.068 | 121.793 | |
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| 0.0947 | 0.87 | 100 | 2.1846 | 57.9637 | 28.7446 | 44.3826 | 54.0399 | 122.77 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
