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https://github.com/hiyouga/LLaMA-Factory.git
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[webui] support escape html (#7190)
Former-commit-id: abb23f767351098a926202ea4edc94d9e9a4681c
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@ -126,6 +126,7 @@ class HuggingfaceEngine(BaseEngine):
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num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
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repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
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length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
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skip_special_tokens: Optional[bool] = input_kwargs.pop("skip_special_tokens", None)
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max_length: Optional[int] = input_kwargs.pop("max_length", None)
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max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
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stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
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@ -145,6 +146,9 @@ class HuggingfaceEngine(BaseEngine):
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if repetition_penalty is not None
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else generating_args["repetition_penalty"],
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length_penalty=length_penalty if length_penalty is not None else generating_args["length_penalty"],
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skip_special_tokens=skip_special_tokens
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if skip_special_tokens is not None
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else generating_args["skip_special_tokens"],
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eos_token_id=template.get_stop_token_ids(tokenizer),
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pad_token_id=tokenizer.pad_token_id,
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)
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@ -241,7 +245,9 @@ class HuggingfaceEngine(BaseEngine):
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response_ids = generate_output[:, prompt_length:]
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response = tokenizer.batch_decode(
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response_ids, skip_special_tokens=generating_args["skip_special_tokens"], clean_up_tokenization_spaces=True
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response_ids,
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skip_special_tokens=getattr(gen_kwargs["generation_config"], "skip_special_tokens", True),
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clean_up_tokenization_spaces=True,
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)
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results = []
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for i in range(len(response)):
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@ -289,7 +295,9 @@ class HuggingfaceEngine(BaseEngine):
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input_kwargs,
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)
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streamer = TextIteratorStreamer(
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tokenizer, skip_prompt=True, skip_special_tokens=generating_args["skip_special_tokens"]
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tokenizer,
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skip_prompt=True,
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skip_special_tokens=getattr(gen_kwargs["generation_config"], "skip_special_tokens", True),
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)
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gen_kwargs["streamer"] = streamer
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thread = Thread(target=model.generate, kwargs=gen_kwargs, daemon=True)
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@ -139,6 +139,7 @@ class VllmEngine(BaseEngine):
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num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1)
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repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None)
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length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None)
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skip_special_tokens: Optional[bool] = input_kwargs.pop("skip_special_tokens", None)
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max_length: Optional[int] = input_kwargs.pop("max_length", None)
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max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None)
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stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
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@ -172,7 +173,9 @@ class VllmEngine(BaseEngine):
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stop=stop,
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stop_token_ids=self.template.get_stop_token_ids(self.tokenizer),
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max_tokens=max_tokens,
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skip_special_tokens=self.generating_args["skip_special_tokens"],
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skip_special_tokens=skip_special_tokens
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if skip_special_tokens is not None
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else self.generating_args["skip_special_tokens"],
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)
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if images is not None: # add image features
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@ -36,14 +36,21 @@ if is_gradio_available():
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import gradio as gr
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def _format_response(text: str, lang: str, thought_words: Tuple[str, str] = ("<think>", "</think>")) -> str:
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def _escape_html(text: str) -> str:
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r"""
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Escapes HTML characters.
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"""
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return text.replace("<", "<").replace(">", ">")
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def _format_response(text: str, lang: str, escape_html: bool, thought_words: Tuple[str, str]) -> str:
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r"""
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Post-processes the response text.
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Based on: https://huggingface.co/spaces/Lyte/DeepSeek-R1-Distill-Qwen-1.5B-Demo-GGUF/blob/main/app.py
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"""
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if thought_words[0] not in text:
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return text
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return _escape_html(text) if escape_html else text
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text = text.replace(thought_words[0], "")
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result = text.split(thought_words[1], maxsplit=1)
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@ -54,6 +61,9 @@ def _format_response(text: str, lang: str, thought_words: Tuple[str, str] = ("<t
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summary = ALERTS["info_thought"][lang]
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thought, answer = result
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if escape_html:
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thought, answer = _escape_html(thought), _escape_html(answer)
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return (
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f"<details open><summary class='thinking-summary'><span>{summary}</span></summary>\n\n"
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f"<div class='thinking-container'>\n{thought}\n</div>\n</details>{answer}"
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@ -154,14 +164,19 @@ class WebChatModel(ChatModel):
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messages: List[Dict[str, str]],
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role: str,
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query: str,
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escape_html: bool,
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) -> Tuple[List[Dict[str, str]], List[Dict[str, str]], str]:
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r"""
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Adds the user input to chatbot.
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Inputs: infer.chatbot, infer.messages, infer.role, infer.query
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Output: infer.chatbot, infer.messages
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Inputs: infer.chatbot, infer.messages, infer.role, infer.query, infer.escape_html
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Output: infer.chatbot, infer.messages, infer.query
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"""
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return chatbot + [{"role": "user", "content": query}], messages + [{"role": role, "content": query}], ""
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return (
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chatbot + [{"role": "user", "content": _escape_html(query) if escape_html else query}],
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messages + [{"role": role, "content": query}],
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"",
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)
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def stream(
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self,
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@ -176,6 +191,8 @@ class WebChatModel(ChatModel):
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max_new_tokens: int,
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top_p: float,
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temperature: float,
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skip_special_tokens: bool,
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escape_html: bool,
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) -> Generator[Tuple[List[Dict[str, str]], List[Dict[str, str]]], None, None]:
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r"""
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Generates output text in stream.
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@ -195,6 +212,7 @@ class WebChatModel(ChatModel):
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max_new_tokens=max_new_tokens,
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top_p=top_p,
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temperature=temperature,
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skip_special_tokens=skip_special_tokens,
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):
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response += new_text
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if tools:
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@ -209,7 +227,7 @@ class WebChatModel(ChatModel):
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bot_text = "```json\n" + tool_calls + "\n```"
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else:
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output_messages = messages + [{"role": Role.ASSISTANT.value, "content": result}]
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bot_text = _format_response(result, lang, self.engine.template.thought_words)
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bot_text = _format_response(result, lang, escape_html, self.engine.template.thought_words)
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chatbot[-1] = {"role": "assistant", "content": bot_text}
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yield chatbot, output_messages
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@ -79,17 +79,33 @@ def create_chat_box(
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max_new_tokens = gr.Slider(minimum=8, maximum=8192, value=1024, step=1)
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top_p = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.01)
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temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01)
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skip_special_tokens = gr.Checkbox(value=True)
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escape_html = gr.Checkbox(value=True)
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clear_btn = gr.Button()
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tools.input(check_json_schema, inputs=[tools, engine.manager.get_elem_by_id("top.lang")])
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submit_btn.click(
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engine.chatter.append,
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[chatbot, messages, role, query],
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[chatbot, messages, role, query, escape_html],
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[chatbot, messages, query],
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).then(
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engine.chatter.stream,
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[chatbot, messages, lang, system, tools, image, video, audio, max_new_tokens, top_p, temperature],
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[
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chatbot,
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messages,
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lang,
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system,
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tools,
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image,
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video,
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audio,
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max_new_tokens,
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top_p,
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temperature,
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skip_special_tokens,
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escape_html,
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],
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[chatbot, messages],
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)
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clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
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@ -111,6 +127,8 @@ def create_chat_box(
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max_new_tokens=max_new_tokens,
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top_p=top_p,
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temperature=temperature,
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skip_special_tokens=skip_special_tokens,
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escape_html=escape_html,
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clear_btn=clear_btn,
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),
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)
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@ -2412,6 +2412,40 @@ LOCALES = {
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"label": "温度",
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},
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},
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"skip_special_tokens": {
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"en": {
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"label": "Skip special tokens",
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},
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"ru": {
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"label": "Пропустить специальные токены",
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},
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"zh": {
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"label": "跳过特殊 token",
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},
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"ko": {
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"label": "스페셜 토큰을 건너뛰기",
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},
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"ja": {
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"label": "スペシャルトークンをスキップ",
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},
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},
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"escape_html": {
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"en": {
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"label": "Escape HTML tags",
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},
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"ru": {
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"label": "Исключить HTML теги",
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},
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"zh": {
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"label": "转义 HTML 标签",
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},
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"ko": {
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"label": "HTML 태그 이스케이프",
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},
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"ja": {
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"label": "HTML タグをエスケープ",
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},
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},
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"clear_btn": {
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"en": {
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"value": "Clear history",
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