initial commit

README.md

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 ---
+language: 
+- tr
+tags:
+- roberta
 license: cc-by-nc-sa-4.0
+datasets:
+- oscar
 ---
+
+# RoBERTa Turkish medium WordPiece 44k (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 WordPiece. Vocabulary size is 44.5k. 
+
+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{}
+```

config.json

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+{
+  "architectures": [
+    "RobertaForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 516,
+  "model_type": "roberta",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.10.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 44500
+}

pytorch_model.bin

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