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hBERTv2_new_pretrain_48_KD_w_init_sst2
gokuls/hBERTv2_new_pretrain_48_KD_w_init_sst2
hBERTv2_new_pretrain_48_KD_w_init_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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- hybridbert
- Access
- Public
- Created
- Jun 15, 2023
- Updated
- Jun 15, 2023
- SHA
- b5222796
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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. -->
hBERTv2_new_pretrain_48_KD_w_init_sst2
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_48_KD_wt_init on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4188
- Accuracy: 0.8394
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: 4e-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 |
|---|---|---|---|---|
| 0.3594 | 1.0 | 527 | 0.4188 | 0.8394 |
| 0.2344 | 2.0 | 1054 | 0.5086 | 0.8337 |
| 0.2012 | 3.0 | 1581 | 0.5127 | 0.8177 |
| 0.1723 | 4.0 | 2108 | 0.4814 | 0.8200 |
| 0.1425 | 5.0 | 2635 | 0.4872 | 0.8314 |
| 0.12 | 6.0 | 3162 | 0.5835 | 0.8222 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.13.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- Jun 15, 2023
- Updated
- Jun 15, 2023