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bert-tiny-sst2-KD-BERT_and_distilBERT
gokuls/bert-tiny-sst2-KD-BERT_and_distilBERT
bert-tiny-sst2-KD-BERT_and_distilBERT 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
- bert
- Access
- Public
- Created
- Sep 24, 2022
- Updated
- Sep 24, 2022
- SHA
- 2c90abad
Try a prompt
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. -->
bert-tiny-sst2-KD-BERT_and_distilBERT
This model is a fine-tuned version of google/bert_uncased_L-2_H-128_A-2 on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 1.5530
- Accuracy: 0.8326
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: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.7317 | 1.0 | 4210 | 1.5887 | 0.8222 |
| 1.0068 | 2.0 | 8420 | 1.5530 | 0.8326 |
| 0.7961 | 3.0 | 12630 | 1.7072 | 0.8245 |
| 0.6852 | 4.0 | 16840 | 1.8794 | 0.8177 |
| 0.6039 | 5.0 | 21050 | 1.8691 | 0.8142 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Sep 24, 2022
- Updated
- Sep 24, 2022