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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: led-base-16384-text_summarization_data |
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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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# led-base-16384-text_summarization_data |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9531 |
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- Rouge1: 43.3689 |
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- Rouge2: 19.9885 |
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- RougeL: 39.9887 |
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- RougeLsum: 40.0679 |
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- Gen Len: 14.0392 |
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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/LED%20-%20Text%20Summarization%20-%204%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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | RougeL | RougeLsum | General Length | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
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| 1.329 | 1.0 | 1197 | 0.9704 | 42.4111 | 19.8995 | 39.4717 | 39.5449 | 14.254 | |
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| 0.8367 | 2.0 | 2394 | 0.9425 | 43.1141 | 19.6089 | 39.7533 | 39.8298 | 14.1058 | |
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| 0.735 | 3.0 | 3591 | 0.9421 | 42.8101 | 19.8281 | 39.617 | 39.6751 | 13.7101 | |
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| 0.6737 | 4.0 | 4788 | 0.9531 | 43.3689 | 19.9885 | 39.9887 | 40.0679 | 14.0392 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.12.1 |
