model: Add CoDi-Embedding-V1

mteb/models/codi_models.py

@@ -0,0 +1,141 @@
+from __future__ import annotations
+
+import logging
+from functools import partial
+
+from mteb.model_meta import ModelMeta
+from mteb.encoder_interface import PromptType
+from mteb.models.instruct_wrapper import InstructSentenceTransformerWrapper
+
+logger = logging.getLogger(__name__)
+
+codi_instruction = {
+    "CmedqaRetrieval": {
+        "query": "Given a Chinese community medical question, retrieve replies that best answer the question",
+        "passage": "",
+    },
+    "CovidRetrieval": {
+        "query": "Given a question on COVID-19, retrieve news articles that answer the question",
+        "passage": "",
+    },
+    "DuRetrieval": {
+        "query": "Given a Chinese search query, retrieve web passages that answer the question",
+        "passage": "",
+    },
+    "EcomRetrieval": {
+        "query": "Given a user query from an e-commerce website, retrieve description sentences of relevant products",
+        "passage": "",
+    },
+    "MedicalRetrieval": {
+        "query": "Given a medical question, retrieve user replies that best answer the question",
+        "passage": "",
+    },
+    "MMarcoRetrieval": {
+        "query": "Given a web search query, retrieve relevant passages that answer the query",
+        "passage": "",
+    },
+    "T2Retrieval": {
+        "query": "Given a Chinese search query, retrieve web passages that answer the question",
+        "passage": "",
+    },
+    "VideoRetrieval": {
+        "query": "Given a video search query, retrieve the titles of relevant videos",
+        "passage": "",
+    },
+    "AFQMC": "Represent the text in conversations between users and financial customer service, retrieve semantically similar text",
+    "ATEC": "Represent the text in conversations between users and financial customer service, retrieve semantically similar text",
+    "BQ": "Represent the user problem descriptions when handling bank credit business, retrieve semantically similar text",
+    "LCQMC": "Represent the user question descriptions on general question-answering platforms, retrieve semantically similar text",
+    "PAWSX": "Represent the Chinese Translations of English Encyclopedias, retrieve semantically similar text",
+    "QBQTC": "Represent the web search query, retrieve semantically similar text",
+    "STSB": "Represent the short general domain sentences, retrieve semantically similar text",
+    "T2Reranking": {
+        "query": "Given a Chinese search query, retrieve web passages that answer the question",
+        "passage": "",
+    },
+    "MMarcoReranking": {
+        "query": "Given a web search query, retrieve relevant passages that answer the query",
+        "passage": "",
+    },
+    "CMedQAv1-reranking": {
+        "query": "Given a Chinese community medical question, retrieve replies that best answer the question",
+        "passage": "",
+    },
+    "CMedQAv2-reranking": {
+        "query": "Given a Chinese community medical question, retrieve replies that best answer the question",
+        "passage": "",
+    },
+    "Ocnli": "Retrieve semantically similar text",
+    "Cmnli": "Retrieve semantically similar text",
+    "TNews": "Classify the fine-grained category of the given news title",
+    "IFlyTek": "Given an App description text, find the appropriate fine-grained category",
+    "Waimai": "Classify the customer review from a food takeaway platform into positive or negative",
+    "OnlineShopping": "Classify the customer review for online shopping into positive or negative",
+    "JDReview": "Classify the customer review for iPhone on e-commerce platform into positive or negative",
+    "MultilingualSentiment": "Classify sentiment of the customer review into positive, neutral, or negative",
+    "CLSClusteringS2S": "Identify the main category of scholar papers based on the titles",
+    "CLSClusteringP2P": "Identify the main category of scholar papers based on the titles and abstracts",
+    "ThuNewsClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
+    "ThuNewsClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
+}
+
+
+def instruction_template(
+    instruction: str, prompt_type: PromptType | None = None
+) -> str:
+    if not instruction or prompt_type == PromptType.passage:
+        return "<s>"
+    if isinstance(instruction, dict):
+        if prompt_type is None:
+            instruction = list(instruction.values())[0]
+        else:
+            instruction = instruction[prompt_type]
+    return f"<s>Instruction: {instruction} \nQuery: "
+
+
+training_data = {
+    "T2Retrieval": ["train"],
+    "DuRetrieval": ["train"],
+    "T2Reranking": ["train"],
+    "MMarcoReranking": ["train"],
+    "CMedQAv2-reranking": ["train"],
+    "BQ": ["train"],
+    "LCQMC": ["train"],
+    "PAWSX": ["train"],
+    "STS-B": ["train"],
+    "AFQMC": ["train"],
+    "Cmnli": ["train"],
+    "Ocnli": ["train"],
+}
+
+model_name_or_path = "Youtu-RAG/CoDi-Embedding-V1"
+
+CoDiEmb_Embedding_V1 = ModelMeta(
+    name="Youtu-RAG/CoDi-Embedding-V1",
+    languages=["zho-Hans"],
+    revision="9ee4337715ce337f12b8d30f20e87e8528ccedd6",
+    release_date="2025-08-20",
+    loader=partial(
+        InstructSentenceTransformerWrapper,
+        model_name_or_path,
+        revision="9ee4337715ce337f12b8d30f20e87e8528ccedd6",
+        instruction_template=instruction_template,
+        apply_instruction_to_passages=True,
+        prompts_dict=codi_instruction,
+        trust_remote_code=True,
+        max_seq_length=4096,
+    ),
+    open_weights=True,
+    n_parameters=2724880896,
+    memory_usage_mb=None,
+    embed_dim=2304,
+    license="apache-2.0",
+    max_tokens=4096,
+    reference="https://huggingface.co/CoDiEmb/CoDi-MiniCPM",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=training_data,
+)

mteb/models/overview.py

@@ -104,6 +104,7 @@
     voyage_models,
     voyage_v,
     xyz_models,
+    codi_models,
 )
 
 logger = logging.getLogger(__name__)
@@ -201,6 +202,7 @@
     geogpt_models,
     mcinext_models,
     vi_vn_models,
+    codi_models,
 ]
 MODEL_REGISTRY = {}