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distilbert_km_10_v1_cola
Hartunka/distilbert_km_10_v1_cola
distilbert_km_10_v1_cola is a text classification model from Hartunka. 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
- distilbert
- Access
- Public
- Created
- Apr 14, 2025
- Updated
- Apr 14, 2025
- SHA
- a2ec29f7
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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. -->
distilbert_km_10_v1_cola
This model is a fine-tuned version of Hartunka/distilbert_km_10_v1 on the GLUE COLA dataset. It achieves the following results on the evaluation set:
- Loss: 0.6198
- Matthews Correlation: 0.0656
- Accuracy: 0.6932
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|---|---|---|---|---|---|
| 0.6167 | 1.0 | 34 | 0.6198 | 0.0656 | 0.6932 |
| 0.5902 | 2.0 | 68 | 0.6214 | 0.0584 | 0.6874 |
| 0.5443 | 3.0 | 102 | 0.6365 | 0.0209 | 0.6654 |
| 0.4812 | 4.0 | 136 | 0.6850 | 0.0702 | 0.6711 |
| 0.4113 | 5.0 | 170 | 0.7599 | 0.0616 | 0.6318 |
| 0.3473 | 6.0 | 204 | 0.8575 | 0.0842 | 0.6510 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- en
- Created
- Apr 14, 2025
- Updated
- Apr 14, 2025