metadata
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-text_summarization_data
results: []
language:
- en
pipeline_tag: summarization
led-base-16384-text_summarization_data
This model is a fine-tuned version of allenai/led-base-16384 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9531
- Rouge1: 43.3689
- Rouge2: 19.9885
- Rougel: 39.9887
- Rougelsum: 40.0679
- Gen Len: 14.0392
Model description
This is a text summarization model.
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
Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/cuitengfeui/textsummarization-data
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.329 | 1.0 | 1197 | 0.9704 | 42.4111 | 19.8995 | 39.4717 | 39.5449 | 14.254 |
| 0.8367 | 2.0 | 2394 | 0.9425 | 43.1141 | 19.6089 | 39.7533 | 39.8298 | 14.1058 |
| 0.735 | 3.0 | 3591 | 0.9421 | 42.8101 | 19.8281 | 39.617 | 39.6751 | 13.7101 |
| 0.6737 | 4.0 | 4788 | 0.9531 | 43.3689 | 19.9885 | 39.9887 | 40.0679 | 14.0392 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1