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hBERTv2_new_no_pretrain_rte
gokuls/hBERTv2_new_no_pretrain_rte
hBERTv2_new_no_pretrain_rte 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
- 3a43be38
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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_rte
This model is a fine-tuned version of on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.6981
- Accuracy: 0.5307
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 |
|---|---|---|---|---|
| 0.7697 | 1.0 | 20 | 0.7526 | 0.5271 |
| 0.7285 | 2.0 | 40 | 0.7208 | 0.5271 |
| 0.7201 | 3.0 | 60 | 0.7112 | 0.5343 |
| 0.7043 | 4.0 | 80 | 0.6981 | 0.5307 |
| 0.6569 | 5.0 | 100 | 0.7251 | 0.5235 |
| 0.5762 | 6.0 | 120 | 0.8571 | 0.4765 |
| 0.4336 | 7.0 | 140 | 0.9540 | 0.4765 |
| 0.3299 | 8.0 | 160 | 1.2464 | 0.4838 |
| 0.2561 | 9.0 | 180 | 1.4299 | 0.5018 |
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