Instructions to use MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization") model = AutoModelForSequenceClassification.from_pretrained("MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6825
- Qwk: 0.6894
- Mse: 0.6825
- Rmse: 0.8261
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|---|---|---|---|---|---|---|
| No log | 0.25 | 2 | 4.8181 | -0.0214 | 4.8181 | 2.1950 |
| No log | 0.5 | 4 | 3.0472 | 0.0775 | 3.0472 | 1.7456 |
| No log | 0.75 | 6 | 1.7749 | 0.0950 | 1.7749 | 1.3322 |
| No log | 1.0 | 8 | 1.2721 | 0.2081 | 1.2721 | 1.1279 |
| No log | 1.25 | 10 | 1.0288 | 0.2586 | 1.0288 | 1.0143 |
| No log | 1.5 | 12 | 1.0643 | 0.2689 | 1.0643 | 1.0317 |
| No log | 1.75 | 14 | 1.1435 | 0.2587 | 1.1435 | 1.0693 |
| No log | 2.0 | 16 | 1.0403 | 0.2535 | 1.0403 | 1.0200 |
| No log | 2.25 | 18 | 0.9411 | 0.4160 | 0.9411 | 0.9701 |
| No log | 2.5 | 20 | 0.9341 | 0.4316 | 0.9341 | 0.9665 |
| No log | 2.75 | 22 | 0.8895 | 0.4966 | 0.8895 | 0.9431 |
| No log | 3.0 | 24 | 0.7714 | 0.5662 | 0.7714 | 0.8783 |
| No log | 3.25 | 26 | 0.8362 | 0.5832 | 0.8362 | 0.9144 |
| No log | 3.5 | 28 | 0.8891 | 0.5890 | 0.8891 | 0.9429 |
| No log | 3.75 | 30 | 0.7211 | 0.6294 | 0.7211 | 0.8492 |
| No log | 4.0 | 32 | 0.6850 | 0.6875 | 0.6850 | 0.8276 |
| No log | 4.25 | 34 | 0.7811 | 0.6358 | 0.7811 | 0.8838 |
| No log | 4.5 | 36 | 0.7747 | 0.6509 | 0.7747 | 0.8802 |
| No log | 4.75 | 38 | 0.7769 | 0.6630 | 0.7769 | 0.8814 |
| No log | 5.0 | 40 | 0.7258 | 0.6832 | 0.7258 | 0.8519 |
| No log | 5.25 | 42 | 0.7599 | 0.6653 | 0.7599 | 0.8717 |
| No log | 5.5 | 44 | 0.7971 | 0.6426 | 0.7971 | 0.8928 |
| No log | 5.75 | 46 | 0.7474 | 0.6758 | 0.7474 | 0.8645 |
| No log | 6.0 | 48 | 0.6884 | 0.7054 | 0.6884 | 0.8297 |
| No log | 6.25 | 50 | 0.7031 | 0.7066 | 0.7031 | 0.8385 |
| No log | 6.5 | 52 | 0.6788 | 0.7117 | 0.6788 | 0.8239 |
| No log | 6.75 | 54 | 0.6686 | 0.7221 | 0.6686 | 0.8177 |
| No log | 7.0 | 56 | 0.6695 | 0.7261 | 0.6695 | 0.8182 |
| No log | 7.25 | 58 | 0.6645 | 0.6965 | 0.6645 | 0.8152 |
| No log | 7.5 | 60 | 0.7008 | 0.6547 | 0.7008 | 0.8371 |
| No log | 7.75 | 62 | 0.6921 | 0.6547 | 0.6921 | 0.8319 |
| No log | 8.0 | 64 | 0.6828 | 0.6547 | 0.6828 | 0.8263 |
| No log | 8.25 | 66 | 0.6972 | 0.6547 | 0.6972 | 0.8350 |
| No log | 8.5 | 68 | 0.7154 | 0.6585 | 0.7154 | 0.8458 |
| No log | 8.75 | 70 | 0.7211 | 0.6768 | 0.7211 | 0.8492 |
| No log | 9.0 | 72 | 0.7272 | 0.6689 | 0.7272 | 0.8528 |
| No log | 9.25 | 74 | 0.7081 | 0.6797 | 0.7081 | 0.8415 |
| No log | 9.5 | 76 | 0.6927 | 0.6882 | 0.6927 | 0.8323 |
| No log | 9.75 | 78 | 0.6847 | 0.6894 | 0.6847 | 0.8274 |
| No log | 10.0 | 80 | 0.6825 | 0.6894 | 0.6825 | 0.8261 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
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Model tree for MayBashendy/ArabicNewSplits6_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k1_task1_organization
Base model
aubmindlab/bert-base-arabertv02