Add new models nvidia, gte, linq

mteb/models/bge_models.py

@@ -7,7 +7,7 @@
 model_prompts = {"query": "Represent this sentence for searching relevant passages: "}
 
 bge_small_en_v1_5 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="BAAI/bge-small-en-v1.5",
         revision="5c38ec7c405ec4b44b94cc5a9bb96e735b38267a",
@@ -30,7 +30,7 @@
 )
 
 bge_base_en_v1_5 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="BAAI/bge-base-en-v1.5",
         revision="a5beb1e3e68b9ab74eb54cfd186867f64f240e1a",
@@ -53,7 +53,7 @@
 )
 
 bge_large_en_v1_5 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="BAAI/bge-large-en-v1.5",
         revision="d4aa6901d3a41ba39fb536a557fa166f842b0e09",

mteb/models/cohere_models.py

@@ -216,7 +216,7 @@ def encode(
 }
 
 cohere_mult_3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         CohereTextEmbeddingModel,
         model_name="embed-multilingual-v3.0",
         model_prompts=model_prompts,
@@ -238,7 +238,7 @@ def encode(
 )
 
 cohere_eng_3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         CohereTextEmbeddingModel,
         model_name="embed-english-v3.0",
         model_prompts=model_prompts,

mteb/models/e5_instruct.py

@@ -12,15 +12,14 @@
 MISTRAL_LANGUAGES = ["eng_Latn", "fra_Latn", "deu_Latn", "ita_Latn", "spa_Latn"]
 
 
-def e5_instruction(instruction: str) -> str:
-    return f"Instruct: {instruction}\nQuery: "
+E5_INSTRUCTION = "Instruct: {instruction}\nQuery: "
 
 
 e5_instruct = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="intfloat/multilingual-e5-large-instruct",
-        instruction_template=e5_instruction,
+        instruction_template=E5_INSTRUCTION,
         attn="cccc",
         pooling_method="mean",
         mode="embedding",
@@ -44,10 +43,10 @@ def e5_instruction(instruction: str) -> str:
 )
 
 e5_mistral = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="intfloat/e5-mistral-7b-instruct",
-        instruction_template=e5_instruction,
+        instruction_template=E5_INSTRUCTION,
         attn="cccc",
         pooling_method="lasttoken",
         mode="embedding",

mteb/models/e5_models.py

@@ -114,7 +114,7 @@
 }
 
 e5_mult_small = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/multilingual-e5-small",
         revision="fd1525a9fd15316a2d503bf26ab031a61d056e98",
@@ -137,7 +137,7 @@
 )
 
 e5_mult_base = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/multilingual-e5-base",
         model_prompts=model_prompts,
@@ -159,7 +159,7 @@
 )
 
 e5_mult_large = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/multilingual-e5-large",
         revision="ab10c1a7f42e74530fe7ae5be82e6d4f11a719eb",
@@ -182,7 +182,7 @@
 )
 
 e5_eng_small_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-small-v2",
         model_prompts=model_prompts,
@@ -204,7 +204,7 @@
 )
 
 e5_eng_small = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-small",
         revision="e272f3049e853b47cb5ca3952268c6662abda68f",
@@ -227,7 +227,7 @@
 )
 
 e5_eng_base_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-base-v2",
         revision="1c644c92ad3ba1efdad3f1451a637716616a20e8",
@@ -252,7 +252,7 @@
 )
 
 e5_eng_large_v2 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="intfloat/e5-large-v2",
         revision="b322e09026e4ea05f42beadf4d661fb4e101d311",

mteb/models/gritlm_models.py

@@ -17,7 +17,7 @@ def gritlm_instruction(instruction: str = "") -> str:
 
 
 gritlm7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="GritLM/GritLM-7B",
         instruction_template=gritlm_instruction,
@@ -40,7 +40,7 @@ def gritlm_instruction(instruction: str = "") -> str:
     use_instructions=True,
 )
 gritlm8x7b = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="GritLM/GritLM-8x7B",
         instruction_template=gritlm_instruction,

mteb/models/gte_models.py

@@ -3,14 +3,18 @@
 from functools import partial
 
 from mteb.model_meta import ModelMeta
+from mteb.models.instruct_wrapper import instruct_wrapper
+
+
+def instruction_template(instruction: str) -> str:
+    return f"Instruct: {instruction}\nQuery: " if instruction else ""
 
-from .instruct_wrapper import instruct_wrapper
 
 gte_Qwen2_7B_instruct = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         instruct_wrapper,
         model_name_or_path="Alibaba-NLP/gte-Qwen2-7B-instruct",
-        instruction_template="Instruct: {instruction}\nQuery: ",
+        instruction_template=instruction_template,
         attn="cccc",
         pooling_method="lasttoken",
         mode="embedding",
@@ -33,3 +37,59 @@
     framework=["Sentence Transformers", "PyTorch"],
     use_instructions=True,
 )
+
+
+gte_Qwen1_5_7B_instruct = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Alibaba-NLP/gte-Qwen1.5-7B-instruct",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype="auto",
+        normalized=True,
+    ),
+    name="Alibaba-NLP/gte-Qwen1.5-7B-instruct",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="07d27e5226328010336563bc1b564a5e3436a298",
+    release_date="2024-04-20",  # initial commit of hf model.
+    n_parameters=7_720_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="apache-2.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)
+
+
+gte_Qwen2_1_5B_instruct = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype="auto",
+        normalized=True,
+    ),
+    name="Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="c6c1b92f4a3e1b92b326ad29dd3c8433457df8dd",
+    release_date="2024-07-29",  # initial commit of hf model.
+    n_parameters=1_780_000_000,
+    memory_usage=None,
+    embed_dim=8960,
+    license="apache-2.0",
+    max_tokens=131072,
+    reference="https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)

mteb/models/instruct_wrapper.py

@@ -47,6 +47,11 @@ def __init__(
                     "No instruction template provided. Instructions will be used as-is."
                 )
 
+            if "gte-Qwen" in model_name_or_path:
+                logger.warning(
+                    "Instructions are used in both query and docs, which may cause performance discrepancies from the original implementation."
+                )
+
             self.instruction_template = instruction_template
             super().__init__(model_name_or_path=model_name_or_path, mode=mode, **kwargs)
 

mteb/models/jina_models.py

@@ -191,7 +191,7 @@ def encode(
 
 
 jina_embeddings_v3 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         JinaWrapper,
         model="jinaai/jina-embeddings-v3",
         revision="215a6e121fa0183376388ac6b1ae230326bfeaed",

mteb/models/linq_models.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+from functools import partial
+
+import torch
+
+from mteb.model_meta import ModelMeta
+from mteb.models.instruct_wrapper import instruct_wrapper
+
+
+def instruction_template(instruction: str) -> str:
+    return f"Instruct: {instruction}\nQuery: " if instruction else ""
+
+
+Linq_Embed_Mistral = ModelMeta(
+    loader=partial(  # type: ignore
+        instruct_wrapper,
+        model_name_or_path="Linq-AI-Research/Linq-Embed-Mistral",
+        instruction_template=instruction_template,
+        attn="cccc",
+        pooling_method="lasttoken",
+        mode="embedding",
+        torch_dtype=torch.bfloat16,
+        normalized=True,
+    ),
+    name="Linq-AI-Research/Linq-Embed-Mistral",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="0c1a0b0589177079acc552433cad51d7c9132379",
+    release_date="2024-05-29",  # initial commit of hf model.
+    n_parameters=7_110_000_000,
+    memory_usage=None,
+    embed_dim=4096,
+    license="cc-by-nc-4.0",
+    max_tokens=32768,
+    reference="https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+)

mteb/models/mxbai_models.py

@@ -5,7 +5,7 @@
 from mteb.model_meta import ModelMeta, sentence_transformers_loader
 
 mxbai_embed_large_v1 = ModelMeta(
-    loader=partial(
+    loader=partial(  # type: ignore
         sentence_transformers_loader,
         model_name="mixedbread-ai/mxbai-embed-large-v1",
         revision="990580e27d329c7408b3741ecff85876e128e203",