| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: mt5-summarize-sum-test-internal |
| | 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. --> |
| |
|
| | # mt5-summarize-sum-test-internal |
| |
|
| | This model is a fine-tuned version of [raquelclemente/mt5-summarize-sum](https://huggingface.co/raquelclemente/mt5-summarize-sum) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.0371 |
| | - Rouge1: 0.4910 |
| | - Rouge2: 0.3100 |
| | - Rougel: 0.3734 |
| | - Rougelsum: 0.3734 |
| |
|
| | ## 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: 0.0005 |
| | - train_batch_size: 2 |
| | - eval_batch_size: 2 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 16 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 19 |
| | - num_epochs: 19 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | | No log | 1.9 | 5 | 1.0002 | 0.4672 | 0.3016 | 0.3936 | 0.3936 | |
| | | 0.9822 | 3.81 | 10 | 0.9942 | 0.3989 | 0.3127 | 0.3713 | 0.3713 | |
| | | 0.9822 | 5.71 | 15 | 1.0433 | 0.5093 | 0.3425 | 0.4298 | 0.4298 | |
| | | 0.8459 | 7.62 | 20 | 1.0712 | 0.5394 | 0.3463 | 0.4320 | 0.4320 | |
| | | 0.8459 | 9.52 | 25 | 1.0358 | 0.4281 | 0.2476 | 0.3160 | 0.3160 | |
| | | 0.642 | 11.43 | 30 | 1.0335 | 0.4439 | 0.2582 | 0.3224 | 0.3224 | |
| | | 0.642 | 13.33 | 35 | 1.0371 | 0.4910 | 0.3100 | 0.3734 | 0.3734 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.27.4 |
| | - Pytorch 1.13.0 |
| | - Datasets 2.1.0 |
| | - Tokenizers 0.13.2 |
| |
|
