Downloads · 30 days
4
10% of all-time downloads
Qt15Classic_Unbalance
YakovElm/Qt15Classic_Unbalance
Qt15Classic_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. --
All-time downloads
42
Public
Repo size
438 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.h5438 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 25, 2023
- Updated
- May 25, 2023
- SHA
- 057329de
Try a prompt
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. -->
Qt15Classic_Unbalance
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0937
- Train Accuracy: 0.9686
- Validation Loss: 0.2791
- Validation Accuracy: 0.9424
- Epoch: 4
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': 0.001, '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 |
|---|---|---|---|---|
| 0.2526 | 0.9286 | 0.1931 | 0.9505 | 0 |
| 0.2277 | 0.9367 | 0.1823 | 0.9505 | 1 |
| 0.2120 | 0.9367 | 0.2099 | 0.9505 | 2 |
| 0.1642 | 0.9432 | 0.2405 | 0.9497 | 3 |
| 0.0937 | 0.9686 | 0.2791 | 0.9424 | 4 |
Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 25, 2023
- Updated
- May 25, 2023