bert-large-cased-finetuned-rte
This model is a fine-tuned version of bert-large-cased on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 1.5187
- Accuracy: 0.6643
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: 4
- 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 |
|---|---|---|---|---|
| 0.6969 | 1.0 | 623 | 0.7039 | 0.5343 |
| 0.5903 | 2.0 | 1246 | 0.6461 | 0.7184 |
| 0.4557 | 3.0 | 1869 | 1.5187 | 0.6643 |
Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3
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Dataset used to train gchhablani/bert-large-cased-finetuned-rte
Evaluation results
- Accuracy on GLUE RTEself-reported0.664