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miny-bert-aug-sst2-distilled
moshew/miny-bert-aug-sst2-distilled
miny-bert-aug-sst2-distilled is a text classification model from moshew. 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
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 17, 2022
- SHA
- 12ae20e9
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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. -->
miny-bert-aug-sst2-distilled
This model is a fine-tuned version of google/bert_uncased_L-4_H-256_A-4 on the augmented_glue_sst2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2643
- Accuracy: 0.9128
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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.602 | 1.0 | 6227 | 0.3389 | 0.9186 |
| 0.4195 | 2.0 | 12454 | 0.2989 | 0.9151 |
| 0.3644 | 3.0 | 18681 | 0.2794 | 0.9117 |
| 0.3304 | 4.0 | 24908 | 0.2793 | 0.9106 |
| 0.3066 | 5.0 | 31135 | 0.2659 | 0.9186 |
| 0.2881 | 6.0 | 37362 | 0.2668 | 0.9140 |
| 0.2754 | 7.0 | 43589 | 0.2643 | 0.9128 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Feb 17, 2022