| | --- |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: wiki-sparql-models |
| | 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. --> |
| |
|
| | # wiki-sparql-models |
| |
|
| | This model is a fine-tuned version of [htriedman/wiki-sparql-models](https://huggingface.co/htriedman/wiki-sparql-models) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0189 |
| | - Rouge2 Precision: 0.8846 |
| | - Rouge2 Recall: 0.1611 |
| | - Rouge2 Fmeasure: 0.2648 |
| |
|
| | ## 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: 5e-05 |
| | - train_batch_size: 4 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 5 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
| | |:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:| |
| | | 0.0303 | 1.0 | 55180 | 0.0258 | 0.8688 | 0.1586 | 0.2605 | |
| | | 0.0231 | 2.0 | 110360 | 0.0218 | 0.8776 | 0.1597 | 0.2625 | |
| | | 0.02 | 3.0 | 165540 | 0.0201 | 0.8821 | 0.1607 | 0.2641 | |
| | | 0.0164 | 4.0 | 220720 | 0.0192 | 0.8842 | 0.1611 | 0.2646 | |
| | | 0.0175 | 5.0 | 275900 | 0.0189 | 0.8846 | 0.1611 | 0.2648 | |
| |
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| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.28.1 |
| | - Pytorch 2.0.0+cu118 |
| | - Datasets 2.11.0 |
| | - Tokenizers 0.13.3 |
| |
|
