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language:
- en
base_model: Hartunka/tiny_bert_km_50_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny_bert_km_50_v1_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6413751017087063
---
<!-- 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_v1_mnli
This model is a fine-tuned version of [Hartunka/tiny_bert_km_50_v1](https://huggingface.co/Hartunka/tiny_bert_km_50_v1) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8205
- Accuracy: 0.6414
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0082 | 1.0 | 1534 | 0.9426 | 0.5438 |
| 0.9147 | 2.0 | 3068 | 0.8890 | 0.5845 |
| 0.8604 | 3.0 | 4602 | 0.8646 | 0.6063 |
| 0.8145 | 4.0 | 6136 | 0.8447 | 0.6156 |
| 0.7652 | 5.0 | 7670 | 0.8321 | 0.6299 |
| 0.7172 | 6.0 | 9204 | 0.8490 | 0.6406 |
| 0.6722 | 7.0 | 10738 | 0.8362 | 0.6512 |
| 0.6289 | 8.0 | 12272 | 0.8635 | 0.6460 |
| 0.5887 | 9.0 | 13806 | 0.9014 | 0.6464 |
| 0.5489 | 10.0 | 15340 | 0.9343 | 0.6449 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.19.1
|