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---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_50_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_50_v2_rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5379061371841155
---
<!-- 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_v2_rte
This model is a fine-tuned version of [Hartunka/bert_base_rand_50_v2](https://huggingface.co/Hartunka/bert_base_rand_50_v2) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6894
- Accuracy: 0.5379
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7076 | 1.0 | 10 | 0.6894 | 0.5379 |
| 0.6783 | 2.0 | 20 | 0.7175 | 0.5235 |
| 0.6307 | 3.0 | 30 | 0.7913 | 0.5343 |
| 0.5137 | 4.0 | 40 | 1.0965 | 0.5090 |
| 0.372 | 5.0 | 50 | 1.2044 | 0.4801 |
| 0.2719 | 6.0 | 60 | 1.6171 | 0.4838 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
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