Duplicate from tsukumijima/chinese-roberta-wwm-ext-large-onnx

Co-authored-by: tsukumi <[email protected]>

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- zh
+base_model:
+- hfl/chinese-roberta-wwm-ext-large
+---
+
+Style-Bert-VITS2 の ONNX 推論コード向けに ONNX 形式へ変換した [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) です。  
+変換コードは https://github.com/tsukumijima/Style-Bert-VITS2/blob/master/convert_bert_onnx.py にあります。
+
+> [!NOTE]
+> モデルの軽量化のため、BERT の隠れ層 (hidden states) のうち、Style-Bert-VITS2 で用いる最後から3番目の隠れ層のみを言語特徴量として抽出する ONNX モデルとして変換しています。  
+> そのため、事実上 Style-Bert-VITS2 の ONNX 推論以外での使い道はないと思います。

added_tokens.json

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+{}

config.json

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+{
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "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",
+  "type_vocab_size": 2,
+  "vocab_size": 21128
+}

model.onnx

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model_fp16.onnx

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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]"}

tokenizer_config.json

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+{"init_inputs": []}