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README.md

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+在众多业务中，越来越频繁的使用预训练语言模型（Pre-trained Language Models），为了在金融场景下各任务中取得更好效果，我们发布了jdt-fin-roberta-wwm模型
+
+## 模型
+* `base模型`：12-layer, 768-hidden, 12-heads, 110M parameters  
+
+| 模型简称 | 语料 | 京盘下载 |
+| - | - | - |
+| fin-roberta-wwm | 金融语料 | - |
+
+## 快速加载
+### 使用Huggingface-Transformers
+依托于[Huggingface-Transformers](https://github.com/huggingface/transformers)，可轻松调用以上模型。
+```
+tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
+model = BertModel.from_pretrained("MODEL_NAME")
+```
+**注意：本目录中的所有模型均使用BertTokenizer以及BertModel加载，请勿使用RobertaTokenizer/RobertaModel！**
+其中`MODEL_NAME`对应列表如下：
+
+| 模型名 | MODEL_NAME |
+| - | - |
+| fin-roberta-wwm | wangfan/jdt-fin-roberta-wwm |
+

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "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": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.6.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

special_tokens_map.json

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

tokenizer_config.json

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+{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null}