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hBERTv2_new_pretrain_w_init__wnli
gokuls/hBERTv2_new_pretrain_w_init__wnli
hBERTv2_new_pretrain_w_init__wnli 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 6, 2023
- Updated
- Jun 6, 2023
- SHA
- 1526068f
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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_w_init__wnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v2_complete_training_new_wt_init on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6990
- Accuracy: 0.5634
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 |
|---|---|---|---|---|
| 0.9111 | 1.0 | 5 | 0.7288 | 0.5493 |
| 0.7278 | 2.0 | 10 | 0.7028 | 0.5634 |
| 0.707 | 3.0 | 15 | 0.6990 | 0.5634 |
| 0.7068 | 4.0 | 20 | 0.7351 | 0.4366 |
| 0.7424 | 5.0 | 25 | 0.7129 | 0.5634 |
| 0.7298 | 6.0 | 30 | 0.7102 | 0.4366 |
| 0.7043 | 7.0 | 35 | 0.7217 | 0.4366 |
| 0.7081 | 8.0 | 40 | 0.7003 | 0.5634 |
Framework versions
- Transformers 4.29.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- Jun 6, 2023
- Updated
- Jun 6, 2023