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bert-eval-256
AndrewDOrlov/bert-eval-256
bert-eval-256 is a text classification model from AndrewDOrlov. 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. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jul 5, 2023
- Updated
- Jul 7, 2023
- SHA
- 122c7ace
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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-eval-256
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2605
- F1: 0.7522
- Roc Auc: 0.8283
- Accuracy: 0.3007
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.4097 | 1.0 | 751 | 0.3153 | 0.6628 | 0.7598 | 0.1638 |
| 0.2603 | 2.0 | 1502 | 0.2751 | 0.7205 | 0.7998 | 0.2328 |
| 0.2103 | 3.0 | 2253 | 0.2594 | 0.7507 | 0.8239 | 0.2837 |
| 0.1581 | 4.0 | 3004 | 0.2605 | 0.7522 | 0.8283 | 0.3007 |
| 0.1342 | 5.0 | 3755 | 0.2591 | 0.7513 | 0.8279 | 0.2897 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Jul 5, 2023
- Updated
- Jul 7, 2023