Downloads · 30 days
23
39% of all-time downloads
Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold0
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold0
Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold0 is a text classification model from MayBashendy. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
59
Public
Parameters
135M
541 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors541 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Nov 25, 2024
- Updated
- Nov 25, 2024
- SHA
- f531dc8d
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- 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_FineTuningAraBERT_AugV5_k1_task3_organization_fold0
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: 1.2734
- Qwk: -0.2754
- Mse: 1.2734
- Rmse: 1.1285
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 | 0.1818 | 2 | 5.4156 | -0.0292 | 5.4156 | 2.3271 |
| No log | 0.3636 | 4 | 3.6617 | -0.2094 | 3.6617 | 1.9136 |
| No log | 0.5455 | 6 | 1.7012 | 0.0 | 1.7012 | 1.3043 |
| No log | 0.7273 | 8 | 1.0970 | -0.1786 | 1.0970 | 1.0474 |
| No log | 0.9091 | 10 | 1.0868 | -0.2754 | 1.0868 | 1.0425 |
| No log | 1.0909 | 12 | 1.0924 | -0.2754 | 1.0924 | 1.0452 |
| No log | 1.2727 | 14 | 1.1038 | -0.2754 | 1.1038 | 1.0506 |
| No log | 1.4545 | 16 | 0.9153 | 0.0 | 0.9153 | 0.9567 |
| No log | 1.6364 | 18 | 0.9010 | 0.0 | 0.9010 | 0.9492 |
| No log | 1.8182 | 20 | 1.0129 | 0.0 | 1.0129 | 1.0064 |
| No log | 2.0 | 22 | 1.3185 | -0.0185 | 1.3185 | 1.1483 |
| No log | 2.1818 | 24 | 1.6122 | 0.0 | 1.6122 | 1.2697 |
| No log | 2.3636 | 26 | 1.6877 | 0.0 | 1.6877 | 1.2991 |
| No log | 2.5455 | 28 | 1.4446 | 0.0 | 1.4446 | 1.2019 |
| No log | 2.7273 | 30 | 1.1684 | -0.2384 | 1.1684 | 1.0809 |
| No log | 2.9091 | 32 | 1.0148 | 0.0 | 1.0148 | 1.0074 |
| No log | 3.0909 | 34 | 1.0280 | -0.375 | 1.0280 | 1.0139 |
| No log | 3.2727 | 36 | 1.0826 | -0.2754 | 1.0826 | 1.0405 |
| No log | 3.4545 | 38 | 1.0144 | -0.375 | 1.0144 | 1.0072 |
| No log | 3.6364 | 40 | 0.9965 | -0.375 | 0.9965 | 0.9983 |
| No log | 3.8182 | 42 | 1.0448 | -0.375 | 1.0448 | 1.0221 |
| No log | 4.0 | 44 | 1.1405 | -0.0927 | 1.1405 | 1.0680 |
| No log | 4.1818 | 46 | 1.3178 | 0.1921 | 1.3178 | 1.1480 |
| No log | 4.3636 | 48 | 1.3605 | 0.1921 | 1.3605 | 1.1664 |
| No log | 4.5455 | 50 | 1.2817 | 0.0610 | 1.2817 | 1.1321 |
| No log | 4.7273 | 52 | 1.1605 | -0.1159 | 1.1605 | 1.0773 |
| No log | 4.9091 | 54 | 1.0889 | -0.1440 | 1.0889 | 1.0435 |
| No log | 5.0909 | 56 | 1.0355 | -0.1440 | 1.0355 | 1.0176 |
| No log | 5.2727 | 58 | 0.9977 | 0.2143 | 0.9977 | 0.9988 |
| No log | 5.4545 | 60 | 1.0169 | -0.375 | 1.0169 | 1.0084 |
| No log | 5.6364 | 62 | 1.0958 | -0.375 | 1.0958 | 1.0468 |
| No log | 5.8182 | 64 | 1.2334 | -0.0927 | 1.2334 | 1.1106 |
| No log | 6.0 | 66 | 1.4297 | 0.1921 | 1.4297 | 1.1957 |
| No log | 6.1818 | 68 | 1.5680 | 0.1921 | 1.5680 | 1.2522 |
| No log | 6.3636 | 70 | 1.4288 | -0.0927 | 1.4288 | 1.1953 |
| No log | 6.5455 | 72 | 1.2091 | -0.2754 | 1.2091 | 1.0996 |
| No log | 6.7273 | 74 | 1.0771 | -0.375 | 1.0771 | 1.0379 |
| No log | 6.9091 | 76 | 1.0034 | -0.375 | 1.0034 | 1.0017 |
| No log | 7.0909 | 78 | 0.9514 | 0.2143 | 0.9514 | 0.9754 |
| No log | 7.2727 | 80 | 0.9583 | 0.2143 | 0.9583 | 0.9789 |
| No log | 7.4545 | 82 | 0.9820 | -0.1440 | 0.9820 | 0.9909 |
| No log | 7.6364 | 84 | 1.0389 | -0.1440 | 1.0389 | 1.0193 |
| No log | 7.8182 | 86 | 1.1352 | -0.1159 | 1.1352 | 1.0655 |
| No log | 8.0 | 88 | 1.2511 | -0.0927 | 1.2511 | 1.1185 |
| No log | 8.1818 | 90 | 1.3158 | -0.2754 | 1.3158 | 1.1471 |
| No log | 8.3636 | 92 | 1.3591 | -0.2754 | 1.3591 | 1.1658 |
| No log | 8.5455 | 94 | 1.3669 | -0.2754 | 1.3669 | 1.1692 |
| No log | 8.7273 | 96 | 1.3660 | -0.2754 | 1.3660 | 1.1688 |
| No log | 8.9091 | 98 | 1.3430 | -0.2754 | 1.3430 | 1.1589 |
| No log | 9.0909 | 100 | 1.3263 | -0.2754 | 1.3263 | 1.1516 |
| No log | 9.2727 | 102 | 1.3146 | -0.2754 | 1.3146 | 1.1466 |
| No log | 9.4545 | 104 | 1.2942 | -0.2754 | 1.2942 | 1.1377 |
| No log | 9.6364 | 106 | 1.2856 | -0.2754 | 1.2856 | 1.1338 |
| No log | 9.8182 | 108 | 1.2768 | -0.2754 | 1.2768 | 1.1300 |
| No log | 10.0 | 110 | 1.2734 | -0.2754 | 1.2734 | 1.1285 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Nov 25, 2024
- Updated
- Nov 25, 2024