usingF1

README.md

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+---
+library_name: transformers
+base_model: aubmindlab/bert-base-arabertv02-twitter
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: Model4_arabertv2_base_T1_WS_A100_2nd_F1
+  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. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/so/Model4-with-add-clasess-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/so/Model4-with-add-clasess-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/so/Model4-with-add-clasess-T1-ArabertTv2-Bas-WS-A100/runs/tr0iviop)
+# Model4_arabertv2_base_T1_WS_A100_2nd_F1
+
+This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2587
+- F1 Micro: 0.8495
+- F1 Macro: 0.7753
+- Roc Auc: 0.9091
+- Accuracy: 0.8254
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|
+| 0.0226        | 1.0   | 507  | 0.2380          | 0.8341   | 0.7767   | 0.9012  | 0.8094   |
+| 0.0303        | 2.0   | 1014 | 0.2433          | 0.8341   | 0.7606   | 0.8980  | 0.8094   |
+| 0.0179        | 3.0   | 1521 | 0.2635          | 0.8308   | 0.7540   | 0.8987  | 0.8024   |
+| 0.0141        | 4.0   | 2028 | 0.2587          | 0.8495   | 0.7753   | 0.9091  | 0.8254   |
+| 0.0083        | 5.0   | 2535 | 0.2919          | 0.8353   | 0.7731   | 0.9017  | 0.8059   |
+| 0.0066        | 6.0   | 3042 | 0.2922          | 0.8329   | 0.7611   | 0.9006  | 0.8045   |
+| 0.0054        | 7.0   | 3549 | 0.3193          | 0.8358   | 0.7752   | 0.9016  | 0.8128   |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.20.3

model.safetensors

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