| --- |
| base_model: EleutherAI/pile-t5-base |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: pile-t5-base-inst |
| results: [] |
| language: |
| - en |
| metrics: |
| - rouge |
| library_name: transformers |
| --- |
| |
| <!-- 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. --> |
|
|
| # pile-t5-base-inst |
|
|
| This model is a fine-tuned version of [EleutherAI/pile-t5-base](https://huggingface.co/EleutherAI/pile-t5-base) on [taskydata/Pile-T5-Instruction](https://huggingface.co/datasets/taskydata/Pile-T5-Instruction) dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.5082 |
| - Rouge2 Precision: 0.2496 |
| - Rouge2 Recall: 0.1633 |
| - Rouge2 Fmeasure: 0.1786 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 50 |
| - num_epochs: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | 5.8554 | 0.58 | 1500 | 3.2533 | 0.1 | 0.0744 | 0.0721 | |
| | 4.2403 | 1.16 | 3000 | 2.7020 | 0.1704 | 0.1174 | 0.1248 | |
| | 3.8091 | 1.74 | 4500 | 2.5844 | 0.2476 | 0.1617 | 0.1767 | |
| | 3.6589 | 2.32 | 6000 | 2.5289 | 0.2467 | 0.1621 | 0.1769 | |
| | 3.5802 | 2.9 | 7500 | 2.5082 | 0.2496 | 0.1633 | 0.1786 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.39.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
