bert-base-uncased-mnli
This model is a fine-tuned version of bert-base-uncased on the GLUE MNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.4056
- 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4526 | 1.0 | 12272 | 0.4244 | 0.8388 |
| 0.3344 | 2.0 | 24544 | 0.4252 | 0.8469 |
| 0.2307 | 3.0 | 36816 | 0.4974 | 0.8445 |
Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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Model tree for JeremiahZ/bert-base-uncased-mnli
Base model
google-bert/bert-base-uncasedDataset used to train JeremiahZ/bert-base-uncased-mnli
Evaluation results
- Accuracy on GLUE MNLIself-reported0.850
- Accuracy on gluevalidation set verified0.847
- Precision Macro on gluevalidation set verified0.846
- Precision Micro on gluevalidation set verified0.847
- Precision Weighted on gluevalidation set verified0.848
- Recall Macro on gluevalidation set verified0.846
- Recall Micro on gluevalidation set verified0.847
- Recall Weighted on gluevalidation set verified0.847
- F1 Macro on gluevalidation set verified0.846
- F1 Micro on gluevalidation set verified0.847