Update README.md

README.md

@@ -1,41 +1,49 @@
----
-language: 
-- tr
-tags:
-- roberta
-license: cc-by-nc-sa-4.0
-datasets:
-- oscar
----
-
-# RoBERTa Turkish medium BPE 28k (uncased)
-
-Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. 
-The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. 
-
-Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 28.6k. 
-
-The details can be found at this paper: 
-https://arxiv.org/...
-
-The following code can be used for model loading and tokenization, example max length (514) can be changed:
-```
-	model = AutoModel.from_pretrained([model_path])
-	#for sequence classification:
-	#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
-
-	tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
-	tokenizer.mask_token = "[MASK]"
-	tokenizer.cls_token = "[CLS]"
-	tokenizer.sep_token = "[SEP]"
-	tokenizer.pad_token = "[PAD]"
-	tokenizer.unk_token = "[UNK]"
-	tokenizer.bos_token = "[CLS]"
-	tokenizer.eos_token = "[SEP]"
-	tokenizer.model_max_length = 514
-```
-
-### BibTeX entry and citation info
-```bibtex
-@article{}
-```
+---
+language: 
+- tr
+tags:
+- roberta
+license: cc-by-nc-sa-4.0
+datasets:
+- oscar
+---
+
+# RoBERTa Turkish medium BPE 28k (uncased)
+
+Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. 
+The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. 
+
+Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 28.6k. 
+
+The details and performance comparisons can be found at this paper: 
+https://arxiv.org/abs/2204.08832
+
+The following code can be used for model loading and tokenization, example max length (514) can be changed:
+```
+	model = AutoModel.from_pretrained([model_path])
+	#for sequence classification:
+	#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])
+
+	tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
+	tokenizer.mask_token = "[MASK]"
+	tokenizer.cls_token = "[CLS]"
+	tokenizer.sep_token = "[SEP]"
+	tokenizer.pad_token = "[PAD]"
+	tokenizer.unk_token = "[UNK]"
+	tokenizer.bos_token = "[CLS]"
+	tokenizer.eos_token = "[SEP]"
+	tokenizer.model_max_length = 514
+```
+### BibTeX entry and citation info
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2204.08832,
+  doi = {10.48550/ARXIV.2204.08832},
+  url = {https://arxiv.org/abs/2204.08832},
+  author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
+}
+```