model: add octen_models

mteb/models/model_implementations/octen_models.py

@@ -0,0 +1,195 @@
+from mteb.models.instruct_wrapper import InstructSentenceTransformerModel
+from mteb.models.model_meta import ModelMeta
+from mteb.models.models_protocols import PromptType
+
+
+def instruction_template(
+    instruction: str, prompt_type: PromptType | None = None
+) -> str:
+    if (
+        prompt_type == PromptType.document
+    ):  # to avoid this issue: https://huggingface.co/Qwen/Qwen3-Embedding-8B/discussions/21
+        return " "
+    if not instruction:
+        return ""
+    if isinstance(instruction, dict):
+        if prompt_type is None:
+            instruction = next(iter(instruction.values()))  # TODO
+        else:
+            instruction = instruction[prompt_type]
+    return f"Instruct: {instruction}\nQuery:"
+
+
+multilingual_langs = [
+    "afr-Latn",
+    "ara-Arab",
+    "aze-Latn",
+    "bel-Cyrl",
+    "bul-Cyrl",
+    "ben-Beng",
+    "cat-Latn",
+    "ceb-Latn",
+    "ces-Latn",
+    "cym-Latn",
+    "dan-Latn",
+    "deu-Latn",
+    "ell-Grek",
+    "eng-Latn",
+    "spa-Latn",
+    "est-Latn",
+    "eus-Latn",
+    "fas-Arab",
+    "fin-Latn",
+    "fra-Latn",
+    "glg-Latn",
+    "guj-Gujr",
+    "heb-Hebr",
+    "hin-Deva",
+    "hrv-Latn",
+    "hat-Latn",
+    "hun-Latn",
+    "hye-Armn",
+    "ind-Latn",
+    "isl-Latn",
+    "ita-Latn",
+    "jpn-Jpan",
+    "jav-Latn",
+    "kat-Geor",
+    "kaz-Cyrl",
+    "khm-Khmr",
+    "kan-Knda",
+    "kor-Hang",
+    "kir-Cyrl",
+    "lao-Laoo",
+    "lit-Latn",
+    "lav-Latn",
+    "mkd-Cyrl",
+    "mal-Mlym",
+    "mon-Cyrl",
+    "mar-Deva",
+    "msa-Latn",
+    "mya-Mymr",
+    "nep-Deva",
+    "nld-Latn",
+    "nor-Latn",
+    "nob-Latn",
+    "nno-Latn",
+    "pan-Guru",
+    "pol-Latn",
+    "por-Latn",
+    "que-Latn",
+    "ron-Latn",
+    "rus-Cyrl",
+    "sin-Sinh",
+    "slk-Latn",
+    "slv-Latn",
+    "swa-Latn",
+    "tam-Taml",
+    "tel-Telu",
+    "tha-Thai",
+    "tgl-Latn",
+    "tur-Latn",
+    "ukr-Cyrl",
+    "urd-Arab",
+    "vie-Latn",
+    "yor-Latn",
+    "zho-Hans",
+]
+
+OCTEN_CITATION = """@misc{octen-embedding-2025,
+  title={Octen-Embedding-8B: A Fine-tuned Multilingual Text Embedding Model},
+  author={Octen Team},
+  year={2025},
+  url={https://huggingface.co/bflhc/bflhc/Octen-Embedding-8B}
+}"""
+
+training_data = {
+    "T2Retrieval",
+    "DuRetrieval",
+    "MMarcoReranking",
+    "CMedQAv2-reranking",
+    "NQ",
+    "MSMARCO",
+    "HotpotQA",
+    "FEVER",
+    "MrTidyRetrieval",
+    "MIRACLRetrieval",
+    "CodeSearchNet",
+}
+
+# Predefined prompts for various RTEB tasks
+_PREDEFINED_PROMPTS = {
+    # ========== Open Datasets ==========
+    # Legal domain
+    "AILACasedocs": "Given a legal case scenario, retrieve the most relevant case documents",
+    "AILAStatutes": "Given a legal scenario, retrieve the most relevant statute documents",
+    "LegalQuAD": "Given a legal question, retrieve relevant legal documents that answer the question",
+    "LegalSummarization": "Given a query, retrieve relevant legal documents for summarization",
+    # Code domain
+    "AppsRetrieval": "Given a query about mobile applications, retrieve relevant app information",
+    "HumanEvalRetrieval": "Given a code problem description, retrieve relevant code examples",
+    "MBPPRetrieval": "Given a programming problem description, retrieve relevant code solutions",
+    "DS1000Retrieval": "Given a data science problem, retrieve relevant code snippets",
+    "FreshStackRetrieval": "Given a programming question, retrieve relevant Stack Overflow posts",
+    # Finance domain
+    "FinQARetrieval": "Given a financial question, retrieve relevant financial documents",
+    "FinanceBenchRetrieval": "Given a financial query, retrieve relevant financial information",
+    "HC3FinanceRetrieval": "Given a finance-related query, retrieve relevant documents",
+    # Medical domain
+    "CUREv1": "Given a medical query, retrieve relevant clinical documents",
+    "ChatDoctorRetrieval": "Given a medical question, retrieve relevant medical information",
+    # SQL domain
+    "WikiSQLRetrieval": "Given a natural language query, retrieve relevant SQL examples",
+    # Multilingual
+    "MIRACLRetrievalHardNegatives": "Given a question, retrieve Wikipedia passages that answer the question",
+    # ========== Private/Closed Datasets ==========
+    # Code domain (Private)
+    "Code1Retrieval": "Given a code problem description, retrieve relevant code examples",
+    "JapaneseCode1Retrieval": "Given a code problem description, retrieve relevant code examples",
+    # Finance domain (Private)
+    "EnglishFinance1Retrieval": "Given a financial query, retrieve relevant financial documents",
+    "EnglishFinance2Retrieval": "Given a financial query, retrieve relevant financial documents",
+    "EnglishFinance3Retrieval": "Given a financial query, retrieve relevant financial documents",
+    "EnglishFinance4Retrieval": "Given a financial query, retrieve relevant financial documents",
+    # Healthcare domain (Private)
+    "EnglishHealthcare1Retrieval": "Given a medical question, retrieve relevant medical information",
+    "GermanHealthcare1Retrieval": "Given a medical question, retrieve relevant medical information",
+    # Legal domain (Private)
+    "FrenchLegal1Retrieval": "Given a legal query, retrieve relevant legal documents",
+    "GermanLegal1Retrieval": "Given a legal query, retrieve relevant legal documents",
+    "JapaneseLegal1Retrieval": "Given a legal query, retrieve relevant legal documents",
+    # General/Multilingual (Private)
+    "French1Retrieval": "Given a query, retrieve relevant passages",
+    "German1Retrieval": "Given a query, retrieve relevant passages",
+}
+
+
+Octen_Embedding_8B = ModelMeta(
+    loader=InstructSentenceTransformerModel,
+    loader_kwargs=dict(
+        instruction_template=instruction_template,
+        apply_instruction_to_passages=True,
+        prompts_dict=_PREDEFINED_PROMPTS,
+        max_seq_length=18480,
+        model_kwargs={"torch_dtype": "bfloat16"},
+    ),
+    name="bflhc/Octen-Embedding-8B",
+    languages=multilingual_langs,
+    open_weights=True,
+    revision="2030603c2926ab005fafd824fac5911e271be21f",
+    release_date="2025-12-23",
+    n_parameters=7567295488,
+    memory_usage_mb=14433,
+    embed_dim=4096,
+    max_tokens=32768,
+    license="apache-2.0",
+    reference="https://huggingface.co/bflhc/Octen-Embedding-8B",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=training_data,
+    citation=OCTEN_CITATION,
+    adapted_from="Qwen/Qwen3-Embedding-8B",
+)