glue-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: 0.6566
- Accuracy: 0.8554
- F1: 0.8974
- Combined Score: 0.8764
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
- Downloads last month
- 47
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train sgugger/glue-mrpc
Evaluation results
- Accuracy on GLUE MRPCself-reported0.855
- F1 on GLUE MRPCself-reported0.897
- Accuracy on gluevalidation set self-reported0.855
- Precision on gluevalidation set self-reported0.872
- Recall on gluevalidation set self-reported0.925
- AUC on gluevalidation set self-reported0.905
- F1 on gluevalidation set self-reported0.897
- loss on gluevalidation set self-reported0.656