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--- |
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datasets: |
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- scoup123/AffixChecker |
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language: |
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- tr |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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--- |
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# Model Card for Model ID |
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### Model Description |
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Given 2 words in Turkish, the model predicts whether they share an affix or not. Fine-tuned on dbmdz/bert-base-turkish-cased, |
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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. |
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- **Developed by:** Scoup123 |
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- **Model type:** BERT |
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- **Language(s) (NLP):** Turkish |
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- **Finetuned from model [optional]:** dbmdz/bert-base-turkish-cased |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** in-works |
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## Uses |
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It can be used in morphological analyzing tasks. |
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### Direct Use |
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It can probably be used without additional finetuning on Turkish. |
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## Training Details |
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### Training Data |
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scoup123/affixfinder |
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The dataset used was generated from a generated dataset mentioned in the paper titled Turkish language resources: Morphological parser, morphological disambiguator and web corpus. |
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## Evaluation |
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Test Accuracy: 0.9874 |
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Precision: 0.9874 |
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Recall: 0.9874 |
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F1 Score: 0.9874 |
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**It should be used with caution as these scores are too high. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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A testing split data was created from the training data |
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#### Summary |
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This model aims to create an affix identifier for Turkish. |
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## Model Examination [optional] |
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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. |
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[More Information Needed] |
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## Environmental Impact |
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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). |
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- **Hardware Type:** Free Colab T4 GPU |
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- **Hours used:** ~2.5 hours |
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- **Cloud Provider:** Google |
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- **Compute Region:** Europe |
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- **Carbon Emitted:** [More Information Needed] |
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## Citation [optional] |
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**APA:** |
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Sak, H., Güngör, T., & Saraçlar, M. (2008). Turkish language resources: Morphological parser, morphological disambiguator and web corpus. |
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In Advances in natural language processing (pp. 417-427). Springer Berlin Heidelberg. |
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## Model Card Authors [optional] |
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Kaan Bayar |
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## Model Card Contact |
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kaan.bayar13@gmail.com |
