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---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: baseline-ft-mrpc-IRoberta-b-8bit
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8970588235294118
- name: F1
type: f1
value: 0.9257950530035336
---
<!-- 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. -->
# baseline-ft-mrpc-IRoberta-b-8bit
This model is a fine-tuned version of [vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized](https://huggingface.co/vuiseng9/baseline-ft-mrpc-IRoberta-b-unquantized) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3871
- Accuracy: 0.8971
- F1: 0.9258
- Combined Score: 0.9114
## 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-07
- 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: 12.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.0021 | 1.0 | 230 | 0.4017 | 0.8848 | 0.9147 | 0.8998 |
| 0.0026 | 2.0 | 460 | 0.4105 | 0.8873 | 0.9173 | 0.9023 |
| 0.0026 | 3.0 | 690 | 0.3707 | 0.8946 | 0.9236 | 0.9091 |
| 0.0037 | 4.0 | 920 | 0.3893 | 0.8946 | 0.9228 | 0.9087 |
| 1.324 | 5.0 | 1150 | 0.3871 | 0.8897 | 0.9204 | 0.9050 |
| 0.0227 | 6.0 | 1380 | 0.3951 | 0.8897 | 0.9201 | 0.9049 |
| 0.0081 | 7.0 | 1610 | 0.3818 | 0.8824 | 0.9155 | 0.8989 |
| 0.0054 | 8.0 | 1840 | 0.3902 | 0.8873 | 0.9181 | 0.9027 |
| 0.0383 | 9.0 | 2070 | 0.3659 | 0.8922 | 0.9225 | 0.9073 |
| 0.3861 | 10.0 | 2300 | 0.4260 | 0.8652 | 0.9030 | 0.8841 |
| 0.0028 | 11.0 | 2530 | 0.3619 | 0.8946 | 0.9234 | 0.9090 |
| 0.0957 | 12.0 | 2760 | 0.3871 | 0.8971 | 0.9258 | 0.9114 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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