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--- |
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library_name: transformers |
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language: |
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- en |
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base_model: gokulsrinivasagan/bert_base_lda_100_v1 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert_base_lda_100_v1_qnli |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE QNLI |
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type: glue |
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args: qnli |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8063335163829398 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert_base_lda_100_v1_qnli |
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This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_100_v1](https://huggingface.co/gokulsrinivasagan/bert_base_lda_100_v1) on the GLUE QNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4155 |
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- Accuracy: 0.8063 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 256 |
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- eval_batch_size: 256 |
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- seed: 10 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.636 | 1.0 | 410 | 0.5723 | 0.7079 | |
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| 0.471 | 2.0 | 820 | 0.4177 | 0.8091 | |
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| 0.3689 | 3.0 | 1230 | 0.4155 | 0.8063 | |
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| 0.2829 | 4.0 | 1640 | 0.4850 | 0.7990 | |
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| 0.2021 | 5.0 | 2050 | 0.5746 | 0.7889 | |
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| 0.1431 | 6.0 | 2460 | 0.6877 | 0.7921 | |
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| 0.1066 | 7.0 | 2870 | 0.7942 | 0.7842 | |
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| 0.0827 | 8.0 | 3280 | 0.7340 | 0.7917 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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