Create README.md

README.md

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+---
+language: "tr"
+tags:
+- sentiment
+- twitter
+- turkish
+---
+
+This Turkish Sentiment Analysis model is a fine-tuned checkpoint of pretrained [BERTurk model 128k uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) with [BounTi dataset](https://ieeexplore.ieee.org/document/9477814).
+## Usage in Hugging Face Pipeline
+```
+from transformers import pipeline
+bounti = pipeline("sentiment-analysis",model="akoksal/bounti")
+print(bounti("Bu yemeği pek sevmedim"))
+>> [{'label': 'negative', 'score': 0.8012508153915405}]
+```
+
+## Results
+The scores of the finetuned model with BERTurk:
+
+||Accuracy|Precision|Recall|F1|
+|-------------|:---------:|:---------:|:------:|:-----:|
+|Validation|0.745|0.706|0.730|0.715|
+|Test|0.723|0.692|0.729|0.701|
+
+
+
+## Dataset
+You can find the dataset in [our Github repo](https://github.com/boun-tabi/BounTi-Turkish-Sentiment-Analysis) with the training, validation, and test splits.
+
+Due to Twitter copyright, we cannot release the full text of the tweets. We share the tweet IDs, and the full text can be downloaded through official Twitter API.
+
+|          | Training | Validation | Test |
+|----------|:--------:|:----------:|:----:|
+| Positive | 1691     |     188    |  469 |
+| Neutral  | 3034     |     338    | 843  |
+| Negative | 1008     |     113    | 280  |
+| Total    | 5733     |     639    | 1592 |
+
+## Citation
+You can cite the following paper if you use our work:
+```
+@INPROCEEDINGS{BounTi,
+  author={Köksal, Abdullatif and Özgür, Arzucan},
+  booktitle={2021 29th Signal Processing and Communications Applications Conference (SIU)}, 
+  title={Twitter Dataset and Evaluation of Transformers for Turkish Sentiment Analysis}, 
+  year={2021},
+  volume={},
+  number={}
+  }
+```
+---