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mobilebert_sa_GLUE_Experiment_data_aug_rte
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_rte
mobilebert_sa_GLUE_Experiment_data_aug_rte is a text classification model from gokuls. 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.
should 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
- mobilebert
- Access
- Public
- Created
- Feb 3, 2023
- Updated
- Feb 3, 2023
- SHA
- 6ee33c24
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Datasets
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. -->
mobilebert_sa_GLUE_Experiment_data_aug_rte
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 2.9188
- Accuracy: 0.4874
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: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2441 | 1.0 | 1136 | 2.9188 | 0.4874 |
| 0.0476 | 2.0 | 2272 | 4.3208 | 0.5054 |
| 0.0262 | 3.0 | 3408 | 5.1027 | 0.4801 |
| 0.0145 | 4.0 | 4544 | 7.7593 | 0.5199 |
| 0.0091 | 5.0 | 5680 | 7.3446 | 0.5018 |
| 0.0076 | 6.0 | 6816 | 9.3303 | 0.5054 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- mobilebert
- License
- apache-2.0
- Languages
- en
- Created
- Feb 3, 2023
- Updated
- Feb 3, 2023