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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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metrics: |
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- rouge |
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model-index: |
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- name: flan-t5-base-text_summarization_data_6_epochs |
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results: [] |
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language: |
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- en |
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pipeline_tag: summarization |
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--- |
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# flan-t5-base-text_summarization_data_6_epochs |
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This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base). |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6783 |
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- Rouge1: 43.5994 |
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- Rouge2: 20.4446 |
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- Rougel: 40.132 |
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- Rougelsum: 40.1692 |
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- Gen Len: 14.5837 |
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## Model description |
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This is a text summarization model. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/Text-Summarized%20Data%20-%20Comparison/Flan-T5%20-%20Text%20Summarization%20-%206%20Epochs.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/cuitengfeui/textsummarization-data |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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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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| 2.0079 | 1.0 | 1174 | 1.7150 | 43.4218 | 19.8984 | 40.0059 | 40.0582 | 14.5011 | |
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| 1.9122 | 2.0 | 2348 | 1.7020 | 44.0374 | 20.5756 | 40.5915 | 40.5683 | 14.617 | |
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| 1.8588 | 3.0 | 3522 | 1.6881 | 43.9498 | 20.5633 | 40.4656 | 40.5116 | 14.4528 | |
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| 1.8243 | 4.0 | 4696 | 1.6812 | 43.6024 | 20.4845 | 40.1784 | 40.2211 | 14.5075 | |
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| 1.7996 | 5.0 | 5870 | 1.6780 | 43.6652 | 20.553 | 40.2209 | 40.2651 | 14.5236 | |
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| 1.7876 | 6.0 | 7044 | 1.6783 | 43.5994 | 20.4446 | 40.132 | 40.1692 | 14.5837 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
