Add giga embeddings

mteb/models/jasper_models.py

@@ -44,7 +44,7 @@ def encode(
         instruction = self.get_task_instruction(task_name, prompt_type)
 
         # to passage prompts won't be applied to passages
-        if prompt_type == PromptType.passage and task.metadata.type == "s2p":
+        if prompt_type == PromptType.passage and task.metadata.category == "s2p":
             instruction = None
 
         embeddings = self.model.encode(

mteb/models/ru_sentence_models.py

@@ -4,9 +4,12 @@
 
 from functools import partial
 
+import torch
+
 from mteb.encoder_interface import PromptType
 from mteb.model_meta import ModelMeta, sentence_transformers_loader
 from mteb.models.bge_models import bge_m3_training_data
+from mteb.models.instruct_wrapper import InstructSentenceTransformerWrapper
 
 rubert_tiny = ModelMeta(
     name="cointegrated/rubert-tiny",
@@ -559,3 +562,34 @@
     public_training_code=None,
     framework=["Sentence Transformers", "PyTorch"],
 )
+
+giga_embeddings = ModelMeta(
+    loader=partial(
+        InstructSentenceTransformerWrapper,
+        model_name="ai-sage/Giga-Embeddings-instruct",
+        revision="646f5ff3587e74a18141c8d6b60d1cffd5897b92",
+        trust_remote_code=True,
+        instruction_template="Instruct: {instruction}\nQuery: ",
+        apply_instruction_to_passages=False,
+        model_kwargs={
+            "torch_dtype": torch.bfloat16,
+        },
+    ),
+    name="ai-sage/Giga-Embeddings-instruct",
+    languages=["eng_Latn", "rus_Cyrl"],
+    open_weights=True,
+    revision="646f5ff3587e74a18141c8d6b60d1cffd5897b92",
+    release_date="2024-12-13",
+    n_parameters=2_530_000_000,
+    memory_usage_mb=9649,
+    embed_dim=2048,
+    license="mit",
+    max_tokens=32768,
+    reference="https://huggingface.co/ai-sage/Giga-Embeddings-instruct",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=None,
+)