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bert_base_for_whole_train_result_Spam-Ham_farshad_1_1
CatBarks/bert_base_for_whole_train_result_Spam-Ham_farshad_1_1
bert_base_for_whole_train_result_Spam-Ham_farshad_1_1 is a machine learning model from CatBarks. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Feb 17, 2025
- Updated
- Feb 17, 2025
- SHA
- c396cc34
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. -->
bert_base_for_whole_train_result_Spam-Ham_farshad_1_1
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0896
- Accuracy: 0.9835
- F1: 0.9838
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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6392 | 2.9250 | 50 | 0.4154 | 0.8901 | 0.8891 |
| 0.2368 | 5.8501 | 100 | 0.0955 | 0.9759 | 0.9765 |
| 0.0516 | 8.7751 | 150 | 0.0346 | 0.9901 | 0.9905 |
| 0.0207 | 11.7002 | 200 | 0.0306 | 0.9916 | 0.9919 |
| 0.0122 | 14.6252 | 250 | 0.0468 | 0.9875 | 0.9879 |
| 0.0075 | 17.5503 | 300 | 0.0442 | 0.9887 | 0.9890 |
| 0.0047 | 20.4753 | 350 | 0.0321 | 0.9927 | 0.9930 |
| 0.0043 | 23.4004 | 400 | 0.0382 | 0.9910 | 0.9913 |
| 0.0028 | 26.3254 | 450 | 0.0545 | 0.9884 | 0.9887 |
| 0.0022 | 29.2505 | 500 | 0.0480 | 0.9913 | 0.9916 |
| 0.0021 | 32.1755 | 550 | 0.0582 | 0.9875 | 0.9879 |
| 0.0031 | 35.1005 | 600 | 0.0454 | 0.9901 | 0.9904 |
| 0.0033 | 38.0256 | 650 | 0.0618 | 0.9872 | 0.9876 |
| 0.0014 | 40.9506 | 700 | 0.0396 | 0.9925 | 0.9927 |
| 0.0008 | 43.8757 | 750 | 0.0504 | 0.9896 | 0.9898 |
| 0.0009 | 46.8007 | 800 | 0.0563 | 0.9893 | 0.9896 |
| 0.0009 | 49.7258 | 850 | 0.0896 | 0.9835 | 0.9838 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
- Type
- bert
- License
- apache-2.0
- Created
- Feb 17, 2025
- Updated
- Feb 17, 2025