| --- |
| license: apache-2.0 |
| base_model: allenai/led-base-16384 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: LED-Base-NSPCC |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # LED-Base-NSPCC |
|
|
| This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.8734 |
| - Rouge1: 0.4910 |
| - Rouge2: 0.2207 |
| - Rougel: 0.2847 |
| - Rougelsum: 0.2840 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0003 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 4 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | 2.4662 | 0.9947 | 47 | 1.9451 | 0.4528 | 0.1809 | 0.2560 | 0.2558 | |
| | 1.6508 | 1.9894 | 94 | 1.8497 | 0.4889 | 0.2146 | 0.2720 | 0.2716 | |
| | 1.2549 | 2.9841 | 141 | 1.8268 | 0.4812 | 0.2092 | 0.2756 | 0.2753 | |
| | 0.9955 | 3.9788 | 188 | 1.8734 | 0.4910 | 0.2207 | 0.2847 | 0.2840 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.40.1 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
|
|
