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sf_20240220_v1
rparasa/sf_20240220_v1
sf_20240220_v1 is a machine learning model from rparasa. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- License
- other
- Model type
- segformer
- Access
- Public
- Created
- Feb 20, 2024
- Updated
- Feb 20, 2024
- SHA
- 55f4c9f8
Base models
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. -->
sf_20240220_v1
This model is a fine-tuned version of nvidia/mit-b0 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0768
- Validation Loss: 0.1343
- Epoch: 8
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': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 0.0006, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.1840 | 0.1748 | 0 |
| 0.1043 | 0.1361 | 1 |
| 0.0939 | 0.1137 | 2 |
| 0.0865 | 0.1056 | 3 |
| 0.0839 | 0.0860 | 4 |
| 0.0806 | 0.1238 | 5 |
| 0.0786 | 0.0906 | 6 |
| 0.0760 | 0.0895 | 7 |
| 0.0768 | 0.1343 | 8 |
Framework versions
- Transformers 4.37.0
- TensorFlow 2.15.0
- Datasets 2.1.0
- Tokenizers 0.15.1
- Library
- transformers
- Type
- segformer
- License
- other
- Created
- Feb 20, 2024
- Updated
- Feb 20, 2024