bert-base-cased-finetuned-mrpc
This model is a fine-tuned version of bert-base-cased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.7132
- Accuracy: 0.8603
- F1: 0.9026
- Combined Score: 0.8814
The model was fine-tuned to compare google/fnet-base as introduced in this paper against bert-base-cased.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
This model is trained using the run_glue script. The following command was used:
#!/usr/bin/bash
python ../run_glue.py \\n --model_name_or_path bert-base-cased \\n --task_name mrpc \\n --do_train \\n --do_eval \\n --max_seq_length 512 \\n --per_device_train_batch_size 16 \\n --learning_rate 2e-5 \\n --num_train_epochs 5 \\n --output_dir bert-base-cased-finetuned-mrpc \\n --push_to_hub \\n --hub_strategy all_checkpoints \\n --logging_strategy epoch \\n --save_strategy epoch \\n --evaluation_strategy epoch \\n```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.5981 | 1.0 | 230 | 0.4580 | 0.7892 | 0.8562 | 0.8227 |
| 0.3739 | 2.0 | 460 | 0.3806 | 0.8480 | 0.8942 | 0.8711 |
| 0.1991 | 3.0 | 690 | 0.4879 | 0.8529 | 0.8958 | 0.8744 |
| 0.1286 | 4.0 | 920 | 0.6342 | 0.8529 | 0.8986 | 0.8758 |
| 0.0812 | 5.0 | 1150 | 0.7132 | 0.8603 | 0.9026 | 0.8814 |
### Framework versions
- Transformers 4.11.0.dev0
- Pytorch 1.9.0
- Datasets 1.12.1
- Tokenizers 0.10.3