How_to_fine-tune_a_model_for_common_downstream_tasks_V3
This model is a fine-tuned version of bert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.0517
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: 24
- eval_batch_size: 32
- seed: 42
- 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 |
|---|---|---|---|
| 1.1053 | 1.0 | 3650 | 1.0316 |
| 0.8401 | 2.0 | 7300 | 0.9943 |
| 0.6316 | 3.0 | 10950 | 1.0517 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for Tural/How_to_fine-tune_a_model_for_common_downstream_tasks_V3
Base model
google-bert/bert-base-uncased