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sa_BERT_no_pretrain_wnli
gokuls/sa_BERT_no_pretrain_wnli
sa_BERT_no_pretrain_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. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 29, 2023
- Updated
- Jun 15, 2023
- SHA
- 806fe607
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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. -->
sa_BERT_no_pretrain_wnli
This model is a fine-tuned version of bert-base-uncased on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6866
- 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: 96
- eval_batch_size: 96
- 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 |
|---|---|---|---|---|
| 1.0074 | 1.0 | 7 | 0.6958 | 0.4366 |
| 0.6986 | 2.0 | 14 | 0.7035 | 0.4366 |
| 0.7007 | 3.0 | 21 | 0.6866 | 0.5634 |
| 0.7052 | 4.0 | 28 | 0.7037 | 0.4366 |
| 0.7008 | 5.0 | 35 | 0.6951 | 0.4366 |
| 0.7107 | 6.0 | 42 | 0.6908 | 0.5634 |
| 0.6963 | 7.0 | 49 | 0.6945 | 0.4366 |
| 0.7012 | 8.0 | 56 | 0.6894 | 0.5634 |
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
- bert
- License
- apache-2.0
- Languages
- en
- Created
- May 29, 2023
- Updated
- Jun 15, 2023