Model save

README.md

@@ -0,0 +1,152 @@
+---
+library_name: transformers
+base_model: aubmindlab/bert-base-arabert
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: arabic-hs-2class-prediction
+  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. -->
+
+# arabic-hs-2class-prediction
+
+This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6540
+- Accuracy: 0.8442
+- Macro F1: 0.8331
+
+## 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-06
+- train_batch_size: 16
+- eval_batch_size: 20
+- seed: 42
+- optimizer: Use OptimizerNames.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: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Macro F1 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
+| 0.6218        | 0.1083 | 100  | 0.5720          | 0.6917   | 0.5951   |
+| 0.5615        | 0.2167 | 200  | 0.5156          | 0.7297   | 0.6866   |
+| 0.5132        | 0.3250 | 300  | 0.4785          | 0.7554   | 0.7400   |
+| 0.4878        | 0.4334 | 400  | 0.4444          | 0.7879   | 0.7657   |
+| 0.4623        | 0.5417 | 500  | 0.4162          | 0.8049   | 0.7858   |
+| 0.4643        | 0.6501 | 600  | 0.4030          | 0.8042   | 0.7923   |
+| 0.4455        | 0.7584 | 700  | 0.4118          | 0.8178   | 0.7969   |
+| 0.4406        | 0.8667 | 800  | 0.3787          | 0.8320   | 0.8176   |
+| 0.4145        | 0.9751 | 900  | 0.3786          | 0.8252   | 0.8136   |
+| 0.4014        | 1.0834 | 1000 | 0.3790          | 0.8340   | 0.8174   |
+| 0.3784        | 1.1918 | 1100 | 0.3703          | 0.8401   | 0.8285   |
+| 0.3858        | 1.3001 | 1200 | 0.3651          | 0.8388   | 0.8290   |
+| 0.3721        | 1.4085 | 1300 | 0.3605          | 0.8435   | 0.8323   |
+| 0.3836        | 1.5168 | 1400 | 0.3604          | 0.8394   | 0.8312   |
+| 0.3645        | 1.6251 | 1500 | 0.3658          | 0.8489   | 0.8343   |
+| 0.375         | 1.7335 | 1600 | 0.3790          | 0.8347   | 0.8144   |
+| 0.3909        | 1.8418 | 1700 | 0.3526          | 0.8469   | 0.8374   |
+| 0.3566        | 1.9502 | 1800 | 0.3701          | 0.8482   | 0.8300   |
+| 0.3243        | 2.0585 | 1900 | 0.3598          | 0.8388   | 0.8310   |
+| 0.3699        | 2.1668 | 2000 | 0.3753          | 0.8340   | 0.8108   |
+| 0.32          | 2.2752 | 2100 | 0.3541          | 0.8523   | 0.8408   |
+| 0.2987        | 2.3835 | 2200 | 0.3588          | 0.8469   | 0.8370   |
+| 0.3037        | 2.4919 | 2300 | 0.3687          | 0.8523   | 0.8398   |
+| 0.3156        | 2.6002 | 2400 | 0.3522          | 0.8469   | 0.8378   |
+| 0.3352        | 2.7086 | 2500 | 0.3440          | 0.8503   | 0.8415   |
+| 0.3225        | 2.8169 | 2600 | 0.3490          | 0.8489   | 0.8394   |
+| 0.3           | 2.9252 | 2700 | 0.3638          | 0.8476   | 0.8328   |
+| 0.2795        | 3.0336 | 2800 | 0.3904          | 0.8482   | 0.8334   |
+| 0.2787        | 3.1419 | 2900 | 0.4356          | 0.8415   | 0.8194   |
+| 0.2733        | 3.2503 | 3000 | 0.3792          | 0.8530   | 0.8435   |
+| 0.2765        | 3.3586 | 3100 | 0.3700          | 0.8496   | 0.8412   |
+| 0.2746        | 3.4670 | 3200 | 0.3816          | 0.8476   | 0.8343   |
+| 0.2823        | 3.5753 | 3300 | 0.3704          | 0.8503   | 0.8374   |
+| 0.2726        | 3.6836 | 3400 | 0.3795          | 0.8516   | 0.8406   |
+| 0.2661        | 3.7920 | 3500 | 0.4218          | 0.8469   | 0.8283   |
+| 0.265         | 3.9003 | 3600 | 0.3852          | 0.8523   | 0.8425   |
+| 0.2686        | 4.0087 | 3700 | 0.3782          | 0.8516   | 0.8428   |
+| 0.2173        | 4.1170 | 3800 | 0.4008          | 0.8496   | 0.8401   |
+| 0.2404        | 4.2254 | 3900 | 0.4072          | 0.8577   | 0.8453   |
+| 0.2635        | 4.3337 | 4000 | 0.3811          | 0.8564   | 0.8464   |
+| 0.1983        | 4.4420 | 4100 | 0.4038          | 0.8598   | 0.8506   |
+| 0.2512        | 4.5504 | 4200 | 0.4228          | 0.8482   | 0.8343   |
+| 0.2375        | 4.6587 | 4300 | 0.4070          | 0.8476   | 0.8359   |
+| 0.2405        | 4.7671 | 4400 | 0.4433          | 0.8415   | 0.8242   |
+| 0.2319        | 4.8754 | 4500 | 0.4176          | 0.8469   | 0.8370   |
+| 0.2249        | 4.9837 | 4600 | 0.4342          | 0.8462   | 0.8361   |
+| 0.1937        | 5.0921 | 4700 | 0.4543          | 0.8476   | 0.8369   |
+| 0.2207        | 5.2004 | 4800 | 0.4553          | 0.8489   | 0.8357   |
+| 0.1963        | 5.3088 | 4900 | 0.4779          | 0.8489   | 0.8331   |
+| 0.178         | 5.4171 | 5000 | 0.4590          | 0.8455   | 0.8344   |
+| 0.175         | 5.5255 | 5100 | 0.4663          | 0.8523   | 0.8421   |
+| 0.1999        | 5.6338 | 5200 | 0.4733          | 0.8523   | 0.8413   |
+| 0.2018        | 5.7421 | 5300 | 0.4649          | 0.8523   | 0.8411   |
+| 0.208         | 5.8505 | 5400 | 0.4617          | 0.8482   | 0.8368   |
+| 0.2096        | 5.9588 | 5500 | 0.4561          | 0.8482   | 0.8379   |
+| 0.1972        | 6.0672 | 5600 | 0.5073          | 0.8476   | 0.8331   |
+| 0.1815        | 6.1755 | 5700 | 0.4908          | 0.8469   | 0.8358   |
+| 0.1625        | 6.2839 | 5800 | 0.5171          | 0.8435   | 0.8298   |
+| 0.1869        | 6.3922 | 5900 | 0.5083          | 0.8462   | 0.8346   |
+| 0.1672        | 6.5005 | 6000 | 0.4982          | 0.8509   | 0.8405   |
+| 0.1757        | 6.6089 | 6100 | 0.5455          | 0.8462   | 0.8307   |
+| 0.1505        | 6.7172 | 6200 | 0.5220          | 0.8428   | 0.8313   |
+| 0.1455        | 6.8256 | 6300 | 0.5378          | 0.8428   | 0.8318   |
+| 0.2074        | 6.9339 | 6400 | 0.5219          | 0.8442   | 0.8336   |
+| 0.1551        | 7.0423 | 6500 | 0.5336          | 0.8394   | 0.8287   |
+| 0.1501        | 7.1506 | 6600 | 0.5581          | 0.8435   | 0.8323   |
+| 0.167         | 7.2589 | 6700 | 0.5594          | 0.8408   | 0.8290   |
+| 0.1559        | 7.3673 | 6800 | 0.5704          | 0.8449   | 0.8313   |
+| 0.1676        | 7.4756 | 6900 | 0.5758          | 0.8455   | 0.8319   |
+| 0.134         | 7.5840 | 7000 | 0.5662          | 0.8482   | 0.8371   |
+| 0.1773        | 7.6923 | 7100 | 0.5677          | 0.8435   | 0.8323   |
+| 0.1328        | 7.8007 | 7200 | 0.5843          | 0.8482   | 0.8363   |
+| 0.1241        | 7.9090 | 7300 | 0.5901          | 0.8435   | 0.8332   |
+| 0.1657        | 8.0173 | 7400 | 0.6129          | 0.8374   | 0.8281   |
+| 0.1348        | 8.1257 | 7500 | 0.6221          | 0.8442   | 0.8340   |
+| 0.1414        | 8.2340 | 7600 | 0.6272          | 0.8421   | 0.8319   |
+| 0.1166        | 8.3424 | 7700 | 0.6430          | 0.8428   | 0.8324   |
+| 0.1648        | 8.4507 | 7800 | 0.6359          | 0.8489   | 0.8376   |
+| 0.1359        | 8.5590 | 7900 | 0.6338          | 0.8476   | 0.8367   |
+| 0.16          | 8.6674 | 8000 | 0.6433          | 0.8421   | 0.8293   |
+| 0.1256        | 8.7757 | 8100 | 0.6438          | 0.8455   | 0.8339   |
+| 0.1673        | 8.8841 | 8200 | 0.6452          | 0.8428   | 0.8304   |
+| 0.151         | 8.9924 | 8300 | 0.6377          | 0.8394   | 0.8281   |
+| 0.1381        | 9.1008 | 8400 | 0.6409          | 0.8442   | 0.8318   |
+| 0.1199        | 9.2091 | 8500 | 0.6336          | 0.8408   | 0.8303   |
+| 0.1052        | 9.3174 | 8600 | 0.6434          | 0.8401   | 0.8286   |
+| 0.14          | 9.4258 | 8700 | 0.6460          | 0.8421   | 0.8310   |
+| 0.1357        | 9.5341 | 8800 | 0.6503          | 0.8428   | 0.8310   |
+| 0.1149        | 9.6425 | 8900 | 0.6580          | 0.8428   | 0.8301   |
+| 0.1486        | 9.7508 | 9000 | 0.6552          | 0.8442   | 0.8322   |
+| 0.1315        | 9.8592 | 9100 | 0.6518          | 0.8442   | 0.8335   |
+| 0.1125        | 9.9675 | 9200 | 0.6540          | 0.8442   | 0.8331   |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.5.1+cu124
+- Datasets 3.3.2
+- Tokenizers 0.21.0

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