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hBERTv2_new_no_pretrain_qqp
gokuls/hBERTv2_new_no_pretrain_qqp
hBERTv2_new_no_pretrain_qqp 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
- May 29, 2023
- Updated
- Jun 15, 2023
- SHA
- d81aaa62
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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_no_pretrain_qqp
This model is a fine-tuned version of on the GLUE QQP dataset. It achieves the following results on the evaluation set:
- Loss: 0.4537
- Accuracy: 0.7856
- F1: 0.6931
- Combined Score: 0.7393
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 | F1 | Combined Score |
|---|---|---|---|---|---|---|
| 0.5037 | 1.0 | 2843 | 0.4537 | 0.7856 | 0.6931 | 0.7393 |
| 0.4066 | 2.0 | 5686 | 0.4549 | 0.7946 | 0.6758 | 0.7352 |
| 0.3367 | 3.0 | 8529 | 0.4630 | 0.7950 | 0.6650 | 0.7300 |
| 0.2876 | 4.0 | 11372 | 0.5279 | 0.8180 | 0.7598 | 0.7889 |
| 0.2498 | 5.0 | 14215 | 0.4857 | 0.8217 | 0.7650 | 0.7933 |
| 0.2371 | 6.0 | 17058 | 0.5113 | 0.8216 | 0.7376 | 0.7796 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- May 29, 2023
- Updated
- Jun 15, 2023