initial release

README.md

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+---
+language:
+- "zh"
+tags:
+- "chinese"
+- "masked-lm"
+- "wikipedia"
+license: "cc-by-sa-4.0"
+pipeline_tag: "fill-mask"
+mask_token: "[MASK]"
+---
+
+# roberta-base-chinese
+
+## Model Description
+
+This is a RoBERTa model pre-trained on Chinese Wikipedia texts (both simplified and traditional). You can fine-tune `roberta-base-chinese` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/roberta-base-chinese-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/roberta-base-chinese-ud-goeswith), and so on.
+
+## How to Use
+
+```py
+from transformers import AutoTokenizer,AutoModelForMaskedLM
+tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-chinese")
+model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/roberta-base-chinese")
+```
+

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,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "BertTokenizerFast",
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 26582
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a67dcef87fec9d56698b921ab879921d3b6ac770783c5e746a79da2e176aeed7
+size 426006827

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": [
+    "[CLS]",
+    "[PAD]",
+    "[SEP]",
+    "[UNK]",
+    "[MASK]"
+  ],
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": false,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizerFast",
+  "unk_token": "[UNK]"
+}