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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,24 @@
 ---
 license: apache-2.0
 ---
+
+# ChineseModernBert
+
+在自然语言处理领域，[BERT](https://huggingface.co/papers/1810.04805)自 2018 年发布以来，凭借其在语言理解和生成任务上的卓越表现，一直被广泛应用。然而，随着时间的推移，无论是中文还是英文的常用 Bert 模型，由于其发布时间距今已有 5 - 6 年，在模型架构和训练数据方面都逐渐显露出陈旧的问题。
+
+近期，**ModernBERT**在 12 月19日左右发布，为自然语言处理领域带来了新的活力。但目前该模型主要面向英文用户，对于中文用户来说，其适用性存在一定的局限性。为了填补这一空白，满足中文自然语言处理任务的需求，笔者精心构建了基于中文语料的 ModelBert 中文版本模型。
+
+ModelBert 是一个专门基于中文预训练语料进行训练的预训练模型。在训练过程中，选用了高质量的[C](https://huggingface.co/datasets/BAAI/CCI3-HQ)[CI3-](https://huggingface.co/datasets/BAAI/CCI3-HQ)[HQ](https://huggingface.co/datasets/BAAI/CCI3-HQ)数据集进行 1epoch 的预训练。CCI3-HQ 数据集包含了丰富多样的中文文本，涵盖了新闻资讯、文学作品、学术论文、社交媒体内容等多个领域，这使得 ModelBert 能够学习到全面且深入的中文语言特征和语义信息。
+
+## 训练细节
+
+- **硬件配置**：笔者训练资源有限，本次训练使用了2\*8\*A100，预训练时间为58小时左右。
+- **优化器与学习率**：优化器采用adamw，初始学习率设置为 1e-4。learning rate scheduler 采用余弦退火。后续可能采用WSD等学习率计划从新优化。
+- **Tokenizer**：Tokenizer 选用了 Qwen2.5 系列。
+- **Batch Size**：单卡Batch Size 设置为4，总Batch size为64 。
+- **上下文长度**：上下文长度设置为 4096，并采用packing等策略。
+- **训练策略**：采用了 Packing 等策略。MLM比率设置为0.3.
+
+## 开源协议
+
+ModelBert 遵循 Apache 2.0 开源协议发布。这意味着你可以自由地使用、修改和分发该模型，但请务必遵守开源协议中的相关规定，并在使用过程中保留模型的版权声明。

added_tokens.json

@@ -0,0 +1,25 @@
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+  "<tool_call>": 151657,
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+  "[MASK]": 151665
+}

config.json

@@ -0,0 +1,46 @@
+{
+  "_name_or_path": "ModernChineseBertLarge",
+  "architectures": [
+    "ModernBertForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 151643,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "embedding_dropout": 0.0,
+  "eos_token_id": 151645,
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+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 151643,
+  "reference_compile": true,
+  "repad_logits_with_grad": false,
+  "sep_token_id": 151643,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_size": 151936
+}

model.safetensors

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+size 1999973744

special_tokens_map.json

@@ -0,0 +1,38 @@
+{
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+    "<|object_ref_start|>",
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+    "lstrip": false,
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+    "single_word": false
+  }
+}

tokenizer.json

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+size 11422079

tokenizer_config.json

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+  "add_prefix_space": false,
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+    "<|vision_pad|>",
+    "<|image_pad|>",
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+  "bos_token": null,
+  "chat_template": "{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
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+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen2VLProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}