---
datasets:
- scoup123/AffixChecker
language:
- tr
metrics:
- accuracy
pipeline_tag: text-classification
---
# Model Card for Model ID

### Model Description
Given 2 words in Turkish, the model predicts whether they share an affix or not. Fine-tuned on dbmdz/bert-base-turkish-cased,
fine-tuned on a task similar to NLI, but on word level and with 2 labels. It was created as a final project for one of my classes.



- **Developed by:** Scoup123
- **Model type:** BERT
- **Language(s) (NLP):** Turkish
- **Finetuned from model [optional]:** dbmdz/bert-base-turkish-cased

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** in-works
-

## Uses

It can be used in morphological analyzing tasks.
### Direct Use

It can probably be used without additional finetuning on Turkish.

## Training Details

### Training Data

scoup123/affixfinder

The dataset used was generated from a generated dataset mentioned in the paper titled Turkish language resources: Morphological parser, morphological disambiguator and web corpus.


## Evaluation

Test Accuracy: 0.9874
Precision: 0.9874
Recall: 0.9874
F1 Score: 0.9874

**It should be used with caution as these scores are too high.

### Testing Data, Factors & Metrics

#### Testing Data

A testing split data was created from the training data

#### Summary

This model aims to create an affix identifier for Turkish. 

## Model Examination [optional]

I have just created it, so further testing needed to check if it actually works. Additionally, you should check it if it works before using it.

[More Information Needed]

## Environmental Impact



Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** Free Colab T4 GPU
- **Hours used:** ~2.5 hours
- **Cloud Provider:** Google
- **Compute Region:** Europe
- **Carbon Emitted:** [More Information Needed]


## Citation [optional]

**APA:**

Sak, H., Güngör, T., & Saraçlar, M. (2008). Turkish language resources: Morphological parser, morphological disambiguator and web corpus. 
In Advances in natural language processing (pp. 417-427). Springer Berlin Heidelberg.




## Model Card Authors [optional]

Kaan Bayar

## Model Card Contact

kaan.bayar13@gmail.com