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bert_base_km_50_v1_qnli
Hartunka/bert_base_km_50_v1_qnli
bert_base_km_50_v1_qnli 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 19, 2025
- Updated
- Apr 19, 2025
- SHA
- 2df19b1b
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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_km_50_v1_qnli
This model is a fine-tuned version of Hartunka/bert_base_km_50_v1 on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6344
- Accuracy: 0.6414
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 | Accuracy |
|---|---|---|---|---|
| 0.6671 | 1.0 | 410 | 0.6413 | 0.6209 |
| 0.628 | 2.0 | 820 | 0.6344 | 0.6414 |
| 0.5603 | 3.0 | 1230 | 0.6817 | 0.6354 |
| 0.4472 | 4.0 | 1640 | 0.7238 | 0.6390 |
| 0.3245 | 5.0 | 2050 | 0.8553 | 0.6315 |
| 0.2263 | 6.0 | 2460 | 0.9603 | 0.6407 |
| 0.1643 | 7.0 | 2870 | 1.2036 | 0.6280 |
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 19, 2025
- Updated
- Apr 19, 2025