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hBERTv1_new_pretrain_w_init_48_ver2_wnli
gokuls/hBERTv1_new_pretrain_w_init_48_ver2_wnli
hBERTv1_new_pretrain_w_init_48_ver2_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
- Oct 18, 2023
- Updated
- Oct 18, 2023
- SHA
- 6ddca061
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Base models
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. -->
hBERTv1_new_pretrain_w_init_48_ver2_wnli
This model is a fine-tuned version of gokuls/bert_12_layer_model_v1_complete_training_new_wt_init_48 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6880
- 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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7455 | 1.0 | 10 | 0.7146 | 0.4366 |
| 0.7087 | 2.0 | 20 | 0.6880 | 0.5634 |
| 0.7086 | 3.0 | 30 | 0.7292 | 0.5634 |
| 0.7098 | 4.0 | 40 | 0.6897 | 0.5634 |
| 0.6995 | 5.0 | 50 | 0.7766 | 0.4366 |
| 0.7286 | 6.0 | 60 | 0.7309 | 0.4366 |
| 0.7537 | 7.0 | 70 | 0.7128 | 0.5634 |
Framework versions
- Transformers 4.34.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.14.5
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- Oct 18, 2023
- Updated
- Oct 18, 2023