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library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_50_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: bert_base_rand_50_v1_mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MRPC
type: glue
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.7009803921568627
- name: F1
type: f1
value: 0.7867132867132867
---
<!-- 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_rand_50_v1_mrpc
This model is a fine-tuned version of [Hartunka/bert_base_rand_50_v1](https://huggingface.co/Hartunka/bert_base_rand_50_v1) on the GLUE MRPC dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6031
- Accuracy: 0.7010
- F1: 0.7867
- Combined Score: 0.7438
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
| 0.6274 | 1.0 | 15 | 0.6055 | 0.6740 | 0.7816 | 0.7278 |
| 0.57 | 2.0 | 30 | 0.6031 | 0.7010 | 0.7867 | 0.7438 |
| 0.4881 | 3.0 | 45 | 0.6269 | 0.6863 | 0.7673 | 0.7268 |
| 0.3638 | 4.0 | 60 | 0.7925 | 0.6544 | 0.7394 | 0.6969 |
| 0.2309 | 5.0 | 75 | 0.9225 | 0.6667 | 0.7606 | 0.7136 |
| 0.1597 | 6.0 | 90 | 1.0316 | 0.6789 | 0.7706 | 0.7247 |
| 0.0979 | 7.0 | 105 | 1.3663 | 0.6471 | 0.75 | 0.6985 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|