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library_name: transformers
language:
- en
base_model: Hartunka/tiny_bert_km_50_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny_bert_km_50_v2_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5986574450772986
---
<!-- 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_km_50_v2_mnli
This model is a fine-tuned version of [Hartunka/tiny_bert_km_50_v2](https://huggingface.co/Hartunka/tiny_bert_km_50_v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8773
- Accuracy: 0.5987
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0026 | 1.0 | 1534 | 0.9472 | 0.5340 |
| 0.9203 | 2.0 | 3068 | 0.9027 | 0.5731 |
| 0.8675 | 3.0 | 4602 | 0.8775 | 0.5897 |
| 0.8211 | 4.0 | 6136 | 0.8704 | 0.5986 |
| 0.7773 | 5.0 | 7670 | 0.8703 | 0.6070 |
| 0.7352 | 6.0 | 9204 | 0.8939 | 0.6116 |
| 0.6939 | 7.0 | 10738 | 0.9022 | 0.6115 |
| 0.6533 | 8.0 | 12272 | 0.9490 | 0.6105 |
| 0.6136 | 9.0 | 13806 | 0.9655 | 0.6111 |
| 0.5745 | 10.0 | 15340 | 1.0082 | 0.6065 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|