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bert-base-uncased-mean-pooling-finetuned4-cola
ilkekas/bert-base-uncased-mean-pooling-finetuned4-cola
bert-base-uncased-mean-pooling-finetuned4-cola is a text classification model from ilkekas. 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 7, 2023
- Updated
- May 7, 2023
- SHA
- b7be0ee4
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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. -->
bert-base-uncased-mean-pooling-finetuned4-cola
This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5150
- Matthews Correlation: 0.5624
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: 3.9012058362716625e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.5521 | 1.0 | 535 | 0.4933 | 0.4312 |
| 0.4184 | 2.0 | 1070 | 0.4352 | 0.5291 |
| 0.3537 | 3.0 | 1605 | 0.5243 | 0.5055 |
| 0.3048 | 4.0 | 2140 | 0.5048 | 0.5573 |
| 0.2815 | 5.0 | 2675 | 0.5150 | 0.5624 |
| 0.2498 | 6.0 | 3210 | 0.5527 | 0.5495 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 7, 2023
- Updated
- May 7, 2023