Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,107 +1,118 @@
----
-license: mit
-language:
-- en
-- zh
-base_model:
-- Qwen/Qwen2.5-7B
----
-
-# 对数据任务类型分类，比如"情感分析"、"文本分类"、"翻译"，"总结"、"数学问答"....
-
-
-
-    import os
-    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-    import json
-    import torch
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    from tqdm import tqdm
-    from loguru import logger
-    
-    model_name = "Laurie/Qwen2.5-7b-data-classification"
-    
-    # 加载模型和 tokenizer，同时调整 padding_side 为 left（适用于 decoder-only 模型）
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        torch_dtype="auto",
-        device_map="auto",
-        # attn_implementation="flash_attention_2"
-    )
-    tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left") # batch 推理时要左填充
-    
-    # 对话模板
-    system_message = [{"role": "system", "content": "你是一个数据分类专家，请根据对话内容判断其所属的类别。"}]
-    last_query = [{"role": "user", "content": "现在请输出你的判断结果："}]
-    
-    def prepare_text(messages: list[dict]) -> str:
-        """
-        将 messages 中的 "from"/"value" 键转为 "role"/"content"，并构造完整对话文本
-        """
-        messages = [{"role": item["from"], "content": item["value"]} for item in messages]
-        messages = system_message + messages + last_query
-        text = tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
-        )
-        return text
-    
-    def generate_task_types_batch(messages_batch: list[list[dict]]) -> list[str]:
-        """
-        对一个 batch 的对话列表进行推理生成，并返回每个对话中 assistant 的回答部分
-        """
-        # 将每个消息列表转换为完整文本
-        texts = [prepare_text(messages) for messages in messages_batch]
-        
-        # 使用批量编码，并进行 padding 以适应批量输入
-        model_inputs = tokenizer(
-            texts, 
-            return_tensors="pt", 
-            padding=True, 
-            truncation=True
-        ).to(model.device)
-        
-        with torch.no_grad():
-            generated_ids = model.generate(
-                **model_inputs,
-                max_new_tokens=32,
-                eos_token_id=[151643, 151645],
-                pad_token_id=151643,
-                do_sample=True,
-                repetition_penalty=1.05, 
-                temperature=0.7,
-                top_p=0.8,
-                top_k=20
-            )
-        
-        generated_ids = [
-            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-        ]
-    
-        task_types = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
-        return task_types
-    
-    def process_json(json_path: str, save_path: str, batch_size: int = 8):
-        """
-        读取 JSON 文件，对数据进行批量推理处理，
-        并将结果写回保存。
-        """
-        with open(json_path, "r", encoding="utf-8") as f:
-            data = json.load(f)
-        
-        # 分批处理，batch_size 可根据 GPU 显存情况进行调整
-        for i in tqdm(range(0, len(data_slice), batch_size)):
-            batch = data_slice[i : i + batch_size]
-            conversations_batch = [item["conversations"] for item in batch]
-            task_types = generate_task_types_batch(conversations_batch)
-            for item, answer in zip(batch, task_types):
-                item["task_type"] = answer
-    
-        with open(save_path, "w", encoding="utf-8") as f:
-            json.dump(data_slice, f, ensure_ascii=False, indent=4)
-        logger.info(f"已处理 {len(data_slice)} 条数据，保存到 {save_path}")
-    if __name__ == "__main__":
-        json_path = "./qwen_bench_300k.json"
-        save_path = "./qwen_bench_300k_cls.json"
+---
+license: mit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B
+---
+
+# 对数据任务类型分类，比如"情感分析"、"文本分类"、"翻译"，"总结"、"数学问答"....
+
+
+
+    import os
+    os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+    import json
+    import torch
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    from tqdm import tqdm
+    from loguru import logger
+    
+    model_name = "Laurie/Qwen2.5-7b-data-classification"
+    
+    # 加载模型和 tokenizer，同时调整 padding_side 为 left（适用于 decoder-only 模型）
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype="auto",
+        device_map="auto",
+        # attn_implementation="flash_attention_2"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left") # batch 推理时要左填充
+    
+    # 对话模板
+    system_message = [{"role": "system", "content": "你是一个数据分类专家，请根据对话内容判断其所属的类别。"}]
+    last_query = [{"role": "user", "content": "现在请输出你的判断结果："}]
+    
+    def prepare_text(messages: list[dict]) -> str:
+        """
+        将 messages 中的 "from"/"value" 键转为 "role"/"content"，并构造完整对话文本
+        """
+        messages = [{"role": item["from"], "content": item["value"]} for item in messages]
+        messages = system_message + messages + last_query
+        text = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+        return text
+    
+    def generate_task_types_batch(messages_batch: list[list[dict]]) -> list[str]:
+        """
+        对一个 batch 的对话列表进行推理生成，并返回每个对话中 assistant 的回答部分
+        """
+        # 将每个消息列表转换为完整文本
+        texts = [prepare_text(messages) for messages in messages_batch]
+        
+        # 使用批量编码，并进行 padding 以适应批量输入
+        model_inputs = tokenizer(
+            texts, 
+            return_tensors="pt", 
+            padding=True, 
+            truncation=True
+        ).to(model.device)
+        
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **model_inputs,
+                max_new_tokens=32,
+                eos_token_id=[151643, 151645],
+                pad_token_id=151643,
+                do_sample=True,
+                repetition_penalty=1.05, 
+                temperature=0.7,
+                top_p=0.8,
+                top_k=20
+            )
+        
+        generated_ids = [
+            output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+        ]
+    
+        task_types = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+        return task_types
+    
+    def process_json(json_path: str, save_path: str, batch_size: int = 8):
+        """
+        读取 JSON 文件，对数据进行批量推理处理，
+        并将结果写回保存。
+        """
+        with open(json_path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        
+        # 分批处理，batch_size 可根据 GPU 显存情况进行调整
+        for i in tqdm(range(0, len(data_slice), batch_size)):
+            batch = data_slice[i : i + batch_size]
+            conversations_batch = [item["conversations"] for item in batch]
+            task_types = generate_task_types_batch(conversations_batch)
+            for item, answer in zip(batch, task_types):
+                item["task_type"] = answer
+    
+        with open(save_path, "w", encoding="utf-8") as f:
+            json.dump(data_slice, f, ensure_ascii=False, indent=4)
+        logger.info(f"已处理 {len(data_slice)} 条数据，保存到 {save_path}")
+    if __name__ == "__main__":
+        json_path = "./qwen_bench_300k.json"
+        save_path = "./qwen_bench_300k_cls.json"
         process_json(json_path, save_path, batch_size=16)