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mobilebert_sa_GLUE_Experiment_data_aug_sst2
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_sst2
mobilebert_sa_GLUE_Experiment_data_aug_sst2 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
- 6c904667
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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_sst2
This model is a fine-tuned version of google/mobilebert-uncased on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5408
- Accuracy: 0.7901
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 |
|---|---|---|---|---|
| 0.3509 | 1.0 | 8748 | 0.5408 | 0.7901 |
| 0.2706 | 2.0 | 17496 | 0.5891 | 0.7718 |
| 0.2265 | 3.0 | 26244 | 0.6881 | 0.7764 |
| 0.1947 | 4.0 | 34992 | 0.7266 | 0.7833 |
| 0.1708 | 5.0 | 43740 | 0.8411 | 0.7764 |
| 0.1526 | 6.0 | 52488 | 0.9321 | 0.7844 |
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