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scam-alert-mobile-bert
JeswinMS4/scam-alert-mobile-bert
scam-alert-mobile-bert is a text classification model from JeswinMS4. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- mobilebert
- Access
- Public
- Created
- Jun 4, 2024
- Updated
- Jun 4, 2024
- SHA
- 3481195f
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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. -->
scam-alert-mobile-bert
This model is a fine-tuned version of google/mobilebert-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7097
- Accuracy: 0.9880
- F1: 0.9880
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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 0.1577 | 100 | 0.4729 | 0.9223 | 0.9145 |
| No log | 0.3155 | 200 | 2.1621 | 0.9801 | 0.9803 |
| No log | 0.4732 | 300 | 0.8327 | 0.9900 | 0.9900 |
| No log | 0.6309 | 400 | 3.3648 | 0.9900 | 0.9900 |
| No log | 0.7886 | 500 | 0.8376 | 0.9861 | 0.9861 |
| No log | 0.9464 | 600 | 0.7630 | 0.9861 | 0.9861 |
| No log | 1.1041 | 700 | 0.6559 | 0.9861 | 0.9861 |
| No log | 1.2618 | 800 | 2.2440 | 0.9880 | 0.9880 |
| No log | 1.4196 | 900 | 2.4358 | 0.9900 | 0.9900 |
| No log | 1.5773 | 1000 | 1.9655 | 0.9861 | 0.9859 |
| No log | 1.7350 | 1100 | 1.8927 | 0.9880 | 0.9880 |
| No log | 1.8927 | 1200 | 1.3919 | 0.9880 | 0.9880 |
| No log | 2.0505 | 1300 | 0.9143 | 0.9861 | 0.9860 |
| No log | 2.2082 | 1400 | 0.1891 | 0.9861 | 0.9859 |
| No log | 2.3659 | 1500 | 0.0815 | 0.9861 | 0.9861 |
| No log | 2.5237 | 1600 | 0.0853 | 0.9880 | 0.9880 |
| No log | 2.6814 | 1700 | 0.2719 | 0.9861 | 0.9860 |
| No log | 2.8391 | 1800 | 0.2175 | 0.9900 | 0.9900 |
| No log | 2.9968 | 1900 | 0.5407 | 0.9880 | 0.9880 |
| No log | 3.1546 | 2000 | 0.8695 | 0.9880 | 0.9880 |
| No log | 3.3123 | 2100 | 0.1031 | 0.9880 | 0.9880 |
| No log | 3.4700 | 2200 | 1.1922 | 0.9900 | 0.9900 |
| No log | 3.6278 | 2300 | 0.4830 | 0.9880 | 0.9880 |
| No log | 3.7855 | 2400 | 1.4562 | 0.9880 | 0.9880 |
| No log | 3.9432 | 2500 | 1.8929 | 0.9900 | 0.9900 |
| 2789.4062 | 4.1009 | 2600 | 0.6560 | 0.9880 | 0.9880 |
| 2789.4062 | 4.2587 | 2700 | 0.1473 | 0.9841 | 0.9842 |
| 2789.4062 | 4.4164 | 2800 | 0.3488 | 0.9880 | 0.9880 |
| 2789.4062 | 4.5741 | 2900 | 0.2347 | 0.9880 | 0.9880 |
| 2789.4062 | 4.7319 | 3000 | 0.7488 | 0.9900 | 0.9900 |
| 2789.4062 | 4.8896 | 3100 | 0.5055 | 0.9880 | 0.9880 |
| 2789.4062 | 5.0473 | 3200 | 0.8339 | 0.9900 | 0.9900 |
| 2789.4062 | 5.2050 | 3300 | 0.5382 | 0.9880 | 0.9880 |
| 2789.4062 | 5.3628 | 3400 | 0.6095 | 0.9880 | 0.9880 |
| 2789.4062 | 5.5205 | 3500 | 0.7142 | 0.9880 | 0.9880 |
| 2789.4062 | 5.6782 | 3600 | 0.6855 | 0.9880 | 0.9880 |
| 2789.4062 | 5.8360 | 3700 | 0.7152 | 0.9880 | 0.9880 |
| 2789.4062 | 5.9937 | 3800 | 0.7097 | 0.9880 | 0.9880 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- mobilebert
- License
- apache-2.0
- Created
- Jun 4, 2024
- Updated
- Jun 4, 2024