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distilbert_lda_5_v1_book_qnli
gokulsrinivasagan/distilbert_lda_5_v1_book_qnli
distilbert_lda_5_v1_book_qnli is a text classification model from gokulsrinivasagan. 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
- Dec 9, 2024
- Updated
- Dec 9, 2024
- SHA
- 2f1ec726
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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. -->
distilbert_lda_5_v1_book_qnli
This model is a fine-tuned version of gokulsrinivasagan/distilbert_lda_5_v1_book on the GLUE QNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.3496
- Accuracy: 0.8501
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 OptimizerNames.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 |
|---|---|---|---|---|
| 0.4713 | 1.0 | 410 | 0.3704 | 0.8393 |
| 0.3508 | 2.0 | 820 | 0.3496 | 0.8501 |
| 0.2628 | 3.0 | 1230 | 0.3793 | 0.8420 |
| 0.1866 | 4.0 | 1640 | 0.4583 | 0.8404 |
| 0.1292 | 5.0 | 2050 | 0.5074 | 0.8325 |
| 0.0935 | 6.0 | 2460 | 0.5796 | 0.8386 |
| 0.0715 | 7.0 | 2870 | 0.5766 | 0.8356 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.2.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- en
- Created
- Dec 9, 2024
- Updated
- Dec 9, 2024