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README.md
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---
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license: apache-2.0
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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-small-finetuned-glue-rte
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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
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type: glue
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config: rte
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split: train
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args: rte
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.631768953068592
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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-small-finetuned-glue-rte
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This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the glue dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.8715
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- Accuracy: 0.6318
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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: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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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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| No log | 2.62 | 50 | 1.8285 | 0.6318 |
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| No log | 5.26 | 100 | 2.0806 | 0.6462 |
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| No log | 7.87 | 150 | 2.1598 | 0.6282 |
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| No log | 10.51 | 200 | 2.2774 | 0.6318 |
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| No log | 13.15 | 250 | 2.3676 | 0.6245 |
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| No log | 15.77 | 300 | 2.4581 | 0.6462 |
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| No log | 18.41 | 350 | 2.6175 | 0.6354 |
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| No log | 21.05 | 400 | 2.6697 | 0.6354 |
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| No log | 23.67 | 450 | 2.7717 | 0.6354 |
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| 0.0101 | 26.31 | 500 | 2.7975 | 0.6462 |
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| 0.0101 | 28.92 | 550 | 2.8532 | 0.6390 |
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| 0.0101 | 31.56 | 600 | 2.9054 | 0.6209 |
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| 0.0101 | 34.21 | 650 | 2.8715 | 0.6318 |
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### Framework versions
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- Transformers 4.21.2
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- Pytorch 1.12.1+cu113
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- Datasets 2.4.0
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- Tokenizers 0.12.1
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