metadata
library_name: transformers
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8480392156862745
- name: F1
type: f1
value: 0.8923611111111112
bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of bert-base-uncased on the GLUE MRPC dataset. It achieves the following results on the evaluation set:
- Loss: 0.4198
- Accuracy: 0.8480
- F1: 0.8924
- Combined Score: 0.8702
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: 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: 3.0
Training results
| Training Loss | Epoch | Step | Accuracy | Combined Score | F1 | Validation Loss |
|---|---|---|---|---|---|---|
| 0.5401 | 1.0 | 230 | 0.8358 | 0.8584 | 0.8810 | 0.3958 |
| 0.3312 | 2.0 | 460 | 0.8431 | 0.8662 | 0.8893 | 0.3634 |
| 0.1913 | 3.0 | 690 | 0.8529 | 0.8744 | 0.8958 | 0.4322 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1