transformers safetensors bert text-classification generated_from_trainer dataset:sst2 base_model:huawei-noah/TinyBERT_General_4L_312D base_model:finetune:huawei-noah/TinyBERT_General_4L_312D model-index autotrain_compatible endpoints_compatible region:us
---
base_model: huawei-noah/TinyBERT_General_4L_312D
datasets:
- sst2
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: TinyBERT_SST2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: sst2
      type: sst2
      config: default
      split: validation
      args: default
    metrics:
    - type: accuracy
      value: 0.8864678899082569
      name: Accuracy
---

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# TinyBERT_SST2

This model is a fine-tuned version of [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) on the sst2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5142
- Accuracy: 0.8865

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4686        | 0.06  | 500   | 0.4020          | 0.8337   |
| 0.384         | 0.12  | 1000  | 0.3666          | 0.8360   |
| 0.381         | 0.18  | 1500  | 0.3951          | 0.8337   |
| 0.3609        | 0.24  | 2000  | 0.4378          | 0.8555   |
| 0.3616        | 0.3   | 2500  | 0.3743          | 0.8475   |
| 0.3521        | 0.36  | 3000  | 0.3692          | 0.8589   |
| 0.3113        | 0.42  | 3500  | 0.5072          | 0.8486   |
| 0.319         | 0.48  | 4000  | 0.4212          | 0.8612   |
| 0.3034        | 0.53  | 4500  | 0.4555          | 0.8647   |
| 0.3098        | 0.59  | 5000  | 0.4163          | 0.8635   |
| 0.3113        | 0.65  | 5500  | 0.5226          | 0.8440   |
| 0.2949        | 0.71  | 6000  | 0.4137          | 0.875    |
| 0.2977        | 0.77  | 6500  | 0.4775          | 0.8486   |
| 0.3077        | 0.83  | 7000  | 0.4774          | 0.8693   |
| 0.2953        | 0.89  | 7500  | 0.4491          | 0.8589   |
| 0.2846        | 0.95  | 8000  | 0.5228          | 0.8784   |
| 0.292         | 1.01  | 8500  | 0.4801          | 0.8865   |
| 0.2185        | 1.07  | 9000  | 0.4889          | 0.8933   |
| 0.2343        | 1.13  | 9500  | 0.5862          | 0.8716   |
| 0.2667        | 1.19  | 10000 | 0.4796          | 0.8842   |
| 0.252         | 1.25  | 10500 | 0.5181          | 0.8842   |
| 0.2385        | 1.31  | 11000 | 0.5148          | 0.875    |
| 0.2144        | 1.37  | 11500 | 0.5345          | 0.8704   |
| 0.2348        | 1.43  | 12000 | 0.5073          | 0.8807   |
| 0.2166        | 1.48  | 12500 | 0.4885          | 0.8865   |
| 0.2104        | 1.54  | 13000 | 0.6118          | 0.8658   |
| 0.2145        | 1.6   | 13500 | 0.5091          | 0.8865   |
| 0.2098        | 1.66  | 14000 | 0.5221          | 0.8876   |
| 0.2111        | 1.72  | 14500 | 0.5031          | 0.8888   |
| 0.2042        | 1.78  | 15000 | 0.5257          | 0.8796   |
| 0.2091        | 1.84  | 15500 | 0.5175          | 0.8819   |
| 0.2027        | 1.9   | 16000 | 0.5528          | 0.8784   |
| 0.2173        | 1.96  | 16500 | 0.5142          | 0.8865   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
