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library_name: transformers
language:
- en
base_model: Hartunka/tiny_bert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny_bert_rand_20_v2_wnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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. -->
# tiny_bert_rand_20_v2_wnli
This model is a fine-tuned version of [Hartunka/tiny_bert_rand_20_v2](https://huggingface.co/Hartunka/tiny_bert_rand_20_v2) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6929
- Accuracy: 0.5634
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6975 | 1.0 | 3 | 0.6968 | 0.5493 |
| 0.6959 | 2.0 | 6 | 0.6929 | 0.5634 |
| 0.6935 | 3.0 | 9 | 0.7047 | 0.3239 |
| 0.6948 | 4.0 | 12 | 0.7106 | 0.3662 |
| 0.6912 | 5.0 | 15 | 0.7076 | 0.2817 |
| 0.6932 | 6.0 | 18 | 0.7093 | 0.3803 |
| 0.6926 | 7.0 | 21 | 0.7146 | 0.3944 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|