# Fine-Tuning Accuracy for Multiclass Classification - Ada model

**URL:** <https://community.openai.com/t/fine-tuning-accuracy-for-multiclass-classification-ada-model/409236>\
**Category:** API\
**Tags:** fine-tuning\
**Created:** [October 2, 2023, 5:14pm UTC](https://community.openai.com/t/fine-tuning-accuracy-for-multiclass-classification-ada-model/409236 "2023-10-02T17:14:18Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Raviteja](https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/raviteja/32/452840_2.png) [@Raviteja](https://community.openai.com/u/Raviteja)\
**Post date:** [October 2, 2023, 5:14pm UTC](https://community.openai.com/t/fine-tuning-accuracy-for-multiclass-classification-ada-model/409236/1 "2023-10-02T17:14:18Z")

</div>

Hello all,

I employed the ‘Ada’ model for a multiclass classification task with 40 unique labels. My training dataset consists of 40,000 incident descriptions and their corresponding incident labels. I performed fine-tuning 9 times on this training dataset. Each time, I achieved an accuracy of 76% when compared to the actual test dataset. Surprisingly, the accuracy remained consistent, without any noticeable increase or decrease. I’m perplexed about why I consistently obtain a 76% accuracy and would appreciate any insights or assistance in understanding this phenomenon. Thank you in advance for your help.