---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SST2_ELECTRA_5E
  results: []
---

<!-- 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. -->

# SST2_ELECTRA_5E

This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3431
- Accuracy: 0.9267

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.667         | 0.12  | 50   | 0.5772          | 0.8533   |
| 0.4746        | 0.23  | 100  | 0.3421          | 0.9      |
| 0.3104        | 0.35  | 150  | 0.2948          | 0.9      |
| 0.2315        | 0.46  | 200  | 0.3269          | 0.8867   |
| 0.2675        | 0.58  | 250  | 0.2604          | 0.92     |
| 0.2467        | 0.69  | 300  | 0.2321          | 0.92     |
| 0.2013        | 0.81  | 350  | 0.2959          | 0.92     |
| 0.2165        | 0.92  | 400  | 0.2219          | 0.92     |
| 0.2524        | 1.04  | 450  | 0.2649          | 0.9133   |
| 0.1396        | 1.15  | 500  | 0.2985          | 0.9133   |
| 0.152         | 1.27  | 550  | 0.2766          | 0.9267   |
| 0.126         | 1.39  | 600  | 0.2657          | 0.9267   |
| 0.1545        | 1.5   | 650  | 0.2568          | 0.92     |
| 0.184         | 1.62  | 700  | 0.2916          | 0.92     |
| 0.198         | 1.73  | 750  | 0.2564          | 0.9267   |
| 0.1432        | 1.85  | 800  | 0.2669          | 0.9267   |
| 0.1405        | 1.96  | 850  | 0.2466          | 0.9333   |
| 0.0969        | 2.08  | 900  | 0.2213          | 0.9467   |
| 0.1055        | 2.19  | 950  | 0.2733          | 0.9333   |
| 0.0895        | 2.31  | 1000 | 0.3237          | 0.9333   |
| 0.118         | 2.42  | 1050 | 0.3666          | 0.9133   |
| 0.0775        | 2.54  | 1100 | 0.2783          | 0.94     |
| 0.1145        | 2.66  | 1150 | 0.2550          | 0.9267   |
| 0.1214        | 2.77  | 1200 | 0.2777          | 0.9267   |
| 0.1288        | 2.89  | 1250 | 0.2861          | 0.9267   |
| 0.076         | 3.0   | 1300 | 0.3194          | 0.9267   |
| 0.0865        | 3.12  | 1350 | 0.3391          | 0.9267   |
| 0.0626        | 3.23  | 1400 | 0.3133          | 0.9267   |
| 0.0657        | 3.35  | 1450 | 0.3322          | 0.9267   |
| 0.0858        | 3.46  | 1500 | 0.2799          | 0.94     |
| 0.0823        | 3.58  | 1550 | 0.2731          | 0.94     |
| 0.0739        | 3.7   | 1600 | 0.2822          | 0.9333   |
| 0.0911        | 3.81  | 1650 | 0.3264          | 0.9267   |
| 0.0808        | 3.93  | 1700 | 0.2388          | 0.9467   |
| 0.0509        | 4.04  | 1750 | 0.2740          | 0.94     |
| 0.0512        | 4.16  | 1800 | 0.3326          | 0.9267   |
| 0.0397        | 4.27  | 1850 | 0.3061          | 0.9333   |
| 0.0565        | 4.39  | 1900 | 0.2891          | 0.9333   |
| 0.0353        | 4.5   | 1950 | 0.3203          | 0.9333   |
| 0.0455        | 4.62  | 2000 | 0.3113          | 0.9333   |
| 0.0494        | 4.73  | 2050 | 0.3403          | 0.9267   |
| 0.0306        | 4.85  | 2100 | 0.3467          | 0.9267   |
| 0.0655        | 4.97  | 2150 | 0.3431          | 0.9267   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1