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bert-base-uncased-finetuned-squad
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: 1.4571
Model description
Most base model weights were frozen leaving only to finetune the last layer (qa outputs) and 3 last layers of the encoder.
Training and evaluation data
Achieved EM: 76.77388836329234, F1: 85.41893520501723
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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 |
|---|---|---|---|
| 1.2944 | 1.0 | 44262 | 1.3432 |
| 1.0152 | 2.0 | 88524 | 1.3450 |
| 1.0062 | 3.0 | 132786 | 1.4571 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
