Update README.md

README.md

@@ -1,3 +1,77 @@
 ---
-license: mit
+widget:
+- text: "gelirken bir litre [MASK] aldım."
+  example_title: "Örnek 1"
 ---
+
+# turkish-small-bert-uncased
+
+This is a Turkish Small uncased BERT model, developed to fill the gap for small-sized BERT models for Turkish. Since this model is uncased: it does not make a difference between turkish and Turkish. 
+
+#### ⚠ Uncased use requires manual lowercase conversion
+
+ 
+**Don't** use the `do_lower_case = True` flag with the tokenizer. Instead, convert your text to lower case as follows: 
+```python
+text.replace("I", "ı").lower()
+```
+This is due to a [known issue](https://github.com/huggingface/transformers/issues/6680) with the tokenizer.
+
+Be aware that this model may exhibit biased predictions as it was trained primarily on crawled data, which inherently can contain various biases.
+
+Other relevant information can be found in the [paper](https://arxiv.org/abs/2307.14134). 
+
+
+## Example Usage
+```python
+from transformers import AutoTokenizer, BertForMaskedLM
+from transformers import pipeline
+
+model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-small-bert-uncased")
+# or
+# model = BertForMaskedLM.from_pretrained("ytu-ce-cosmos/turkish-small-bert-uncased", from_tf = True)
+
+tokenizer = AutoTokenizer.from_pretrained("ytu-ce-cosmos/turkish-small-bert-uncased")
+
+unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
+unmasker("gelirken bir litre [MASK] aldım.")
+[{'score': 0.3692811131477356,
+  'token': 2417,
+  'token_str': 'su',
+  'sequence': 'gelirken bir litre su aldım.'},
+ {'score': 0.2551537752151489,
+  'token': 11818,
+  'token_str': 'benzin',
+  'sequence': 'gelirken bir litre benzin aldım.'},
+ {'score': 0.036265160888433456,
+  'token': 29480,
+  'token_str': 'mazot',
+  'sequence': 'gelirken bir litre mazot aldım.'},
+ {'score': 0.03350532799959183,
+  'token': 4521,
+  'token_str': 'süt',
+  'sequence': 'gelirken bir litre süt aldım.'},
+ {'score': 0.02558029256761074,
+  'token': 2168,
+  'token_str': 'daha',
+  'sequence': 'gelirken bir litre daha aldım.'}]
+```
+
+
+# Acknowledgments
+- Research supported with Cloud TPUs from [Google's TensorFlow Research Cloud](https://sites.research.google/trc/about/) (TFRC). Thanks for providing access to the TFRC ❤️
+- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
+
+# Citations
+```bibtex
+@article{kesgin2023developing,
+  title={Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models},
+  author={Kesgin, Himmet Toprak and Yuce, Muzaffer Kaan and Amasyali, Mehmet Fatih},
+  journal={arXiv preprint arXiv:2307.14134},
+  year={2023}
+}
+```
+
+# License
+
+MIT