1st commit
Browse files- README.md +23 -0
- config.json +29 -0
- special_tokens_map.json +1 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1 -0
- vocab.txt +0 -0
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
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## 模型
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* `base模型`:12-layer, 768-hidden, 12-heads, 110M parameters
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| 模型简称 | 语料 | 京盘下载 |
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| - | - | - |
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| fin-roberta-wwm | 金融语料 | - |
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## 快速加载
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### 使用Huggingface-Transformers
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依托于[Huggingface-Transformers](https://github.com/huggingface/transformers),可轻松调用以上模型。
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```
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tokenizer = BertTokenizer.from_pretrained("MODEL_NAME")
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model = BertModel.from_pretrained("MODEL_NAME")
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```
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**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!**
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其中`MODEL_NAME`对应列表如下:
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| 模型名 | MODEL_NAME |
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| - | - |
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| fin-roberta-wwm | wangfan/jdt-fin-roberta-wwm |
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config.json
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{
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"architectures": [
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"BertForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"directionality": "bidi",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"transformers_version": "4.6.1",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 21128
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}
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special_tokens_map.json
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{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
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tokenizer.json
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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}
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vocab.txt
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