Add pipeline tag

README.md

@@ -1,9 +1,11 @@
 ---
-license: apache-2.0
 base_model:
 - Qwen/Qwen3-8B-Base
 library_name: transformers
+license: apache-2.0
+pipeline_tag: text-ranking
 ---
+
 # Qwen3-Reranker-8B
 
 <p align="center">
@@ -46,7 +48,7 @@ For more details, including benchmark evaluation, hardware requirements, and inf
 > **Note**:
 > - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding. 
 > - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
-> - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.
+> - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.
 
 
 ## Usage
@@ -66,7 +68,9 @@ from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM
 def format_instruction(instruction, query, doc):
     if instruction is None:
         instruction = 'Given a web search query, retrieve relevant passages that answer the query'
-    output = "<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}".format(instruction=instruction,query=query, doc=doc)
+    output = "<Instruct>: {instruction}
+<Query>: {query}
+<Document>: {doc}".format(instruction=instruction,query=query, doc=doc)
     return output
 
 def process_inputs(pairs):
@@ -101,8 +105,17 @@ token_false_id = tokenizer.convert_tokens_to_ids("no")
 token_true_id = tokenizer.convert_tokens_to_ids("yes")
 max_length = 8192
 
-prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
-suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
+prefix = "<|im_start|>system
+Judge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>
+<|im_start|>user
+"
+suffix = "<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+"
 prefix_tokens = tokenizer.encode(prefix, add_special_tokens=False)
 suffix_tokens = tokenizer.encode(suffix, add_special_tokens=False)
         
@@ -150,7 +163,7 @@ If you find our work helpful, feel free to give us a cite.
 ```
 @misc{qwen3-embedding,
     title  = {Qwen3-Embedding},
-    url    = {https://qwenlm.github.io/blog/qwen3/},
+    url    = {https://qwenlm.github.io/blog/qwen3-embedding/},
     author = {Qwen Team},
     month  = {May},
     year   = {2025}