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roberta-large-1-second
Adammz/roberta-large-1-second
roberta-large-1-second is a machine learning model from Adammz. 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 mit.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Dec 26, 2023
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At a glance
- License
- mit
- Access
- Public
- Created
- Dec 26, 2023
- Updated
- Dec 26, 2023
- SHA
- 5045c48a
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. -->
roberta-large-1-second
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9356
- Accuracy: 0.7715
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7871 | 1.0 | 769 | 1.6188 | 0.6694 |
| 1.5364 | 2.0 | 1538 | 1.4230 | 0.6828 |
| 1.4249 | 3.0 | 2307 | 1.3059 | 0.7067 |
| 1.336 | 4.0 | 3076 | 1.1884 | 0.7290 |
| 1.2366 | 5.0 | 3845 | 1.1214 | 0.74 |
| 1.1394 | 6.0 | 4614 | 1.0214 | 0.7601 |
| 1.0744 | 7.0 | 5383 | 0.9801 | 0.7664 |
| 1.0196 | 8.0 | 6152 | 0.9696 | 0.7646 |
| 0.9896 | 9.0 | 6921 | 0.9356 | 0.7715 |
| 0.9754 | 10.0 | 7690 | 0.9357 | 0.7704 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
- License
- mit
- Created
- Dec 26, 2023
- Updated
- Dec 26, 2023