| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - accuracy |
| model-index: |
| - name: Is_there_relation |
| 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. --> |
|
|
| # Is_there_relation |
|
|
| This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8855 |
| - Macro F1: 0.7979 |
| - Precision: 0.8002 |
| - Recall: 0.7995 |
| - Kappa: 0.5894 |
| - Accuracy: 0.7995 |
|
|
| ## 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: 16 |
| - eval_batch_size: 128 |
| - seed: 25 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall | Kappa | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 218 | 0.5160 | 0.7251 | 0.7659 | 0.7398 | 0.4511 | 0.7398 | |
| | No log | 2.0 | 437 | 0.4608 | 0.8014 | 0.8108 | 0.8049 | 0.5970 | 0.8049 | |
| | 0.4812 | 3.0 | 655 | 0.5087 | 0.7864 | 0.7900 | 0.7886 | 0.5661 | 0.7886 | |
| | 0.4812 | 4.0 | 874 | 0.5219 | 0.8107 | 0.8118 | 0.8103 | 0.6177 | 0.8103 | |
| | 0.2407 | 5.0 | 1092 | 0.5657 | 0.8319 | 0.8416 | 0.8347 | 0.6588 | 0.8347 | |
| | 0.2407 | 6.0 | 1311 | 0.6980 | 0.7988 | 0.8074 | 0.8022 | 0.5917 | 0.8022 | |
| | 0.1383 | 7.0 | 1529 | 0.7769 | 0.7933 | 0.8017 | 0.7967 | 0.5805 | 0.7967 | |
| | 0.1383 | 8.0 | 1748 | 0.7336 | 0.8059 | 0.8087 | 0.8076 | 0.6058 | 0.8076 | |
| | 0.1383 | 9.0 | 1966 | 0.7426 | 0.7988 | 0.8074 | 0.8022 | 0.5917 | 0.8022 | |
| | 0.0878 | 10.0 | 2185 | 0.8211 | 0.8017 | 0.8098 | 0.8049 | 0.5975 | 0.8049 | |
| | 0.0878 | 11.0 | 2403 | 0.8737 | 0.7955 | 0.7969 | 0.7967 | 0.5846 | 0.7967 | |
| | 0.0573 | 12.0 | 2622 | 0.9043 | 0.7900 | 0.7914 | 0.7913 | 0.5735 | 0.7913 | |
| | 0.0573 | 13.0 | 2840 | 0.8937 | 0.7906 | 0.7909 | 0.7913 | 0.5751 | 0.7913 | |
| | 0.0423 | 14.0 | 3059 | 0.9004 | 0.8013 | 0.8019 | 0.8022 | 0.5967 | 0.8022 | |
| | 0.0423 | 14.97 | 3270 | 0.8855 | 0.7979 | 0.8002 | 0.7995 | 0.5894 | 0.7995 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.29.2 |
| - Pytorch 2.0.1+cu118 |
| - Tokenizers 0.13.3 |
| |
