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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-mrpc
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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
|