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Qt5ALBERT_2_Unbalance
YakovElm/Qt5ALBERT_2_Unbalance
Qt5ALBERT_2_Unbalance is a text classification model from YakovElm. 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.
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
- albert
- Access
- Public
- Created
- May 25, 2023
- Updated
- May 25, 2023
- SHA
- 65932b49
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Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
Qt5ALBERT_2_Unbalance
This model is a fine-tuned version of albert-base-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.3223
- Train Accuracy: 0.8943
- Validation Loss: 0.2558
- Validation Accuracy: 0.9294
- Epoch: 2
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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.3446 | 0.8894 | 0.2498 | 0.9294 | 0 |
| 0.3297 | 0.8943 | 0.2820 | 0.9294 | 1 |
| 0.3223 | 0.8943 | 0.2558 | 0.9294 | 2 |
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- albert
- License
- apache-2.0
- Created
- May 25, 2023
- Updated
- May 25, 2023