---
language:
- en
base_model: Hartunka/tiny_bert_rand_100_v1
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny_bert_rand_100_v1_sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE SST2
      type: glue
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8096330275229358
---

<!-- 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_100_v1_sst2

This model is a fine-tuned version of [Hartunka/tiny_bert_rand_100_v1](https://huggingface.co/Hartunka/tiny_bert_rand_100_v1) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4504
- Accuracy: 0.8096

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4222        | 1.0   | 264  | 0.4504          | 0.8096   |
| 0.2388        | 2.0   | 528  | 0.5902          | 0.7798   |
| 0.1889        | 3.0   | 792  | 0.5326          | 0.7936   |
| 0.1546        | 4.0   | 1056 | 0.5878          | 0.8039   |
| 0.1291        | 5.0   | 1320 | 0.7141          | 0.7890   |
| 0.1088        | 6.0   | 1584 | 0.8532          | 0.7833   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.19.1