---
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