bert-large-cased-snli-model1
This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2434
- Accuracy: 0.9188
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: 512
- eval_batch_size: 512
- seed: 43
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3253 | 1.0 | 1073 | 0.2511 | 0.9069 |
| 0.2555 | 2.0 | 2146 | 0.2410 | 0.9159 |
| 0.2094 | 3.0 | 3219 | 0.2434 | 0.9188 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for varun-v-rao/bert-large-cased-snli-model1
Base model
google-bert/bert-large-cased