chore: import upstream snapshot with attribution

docs/source/bge/bge_reranker_v2.rst

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+BGE-Reranker-v2
+===============
+
++------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
+|                                                      Model                                                       |        Language       | Parameters  |  Model Size  |                                                                       Description                                                                       |
++==================================================================================================================+=======================+=============+==============+=========================================================================================================================================================+
+| `BAAI/bge-reranker-v2-m3 <https://huggingface.co/BAAI/bge-reranker-v2-m3>`_                                      |      Multilingual     |    568M     |    2.27 GB   | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference.                                            |
++------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
+| `BAAI/bge-reranker-v2-gemma <https://huggingface.co/BAAI/bge-reranker-v2-gemma>`_                                |      Multilingual     |    2.51B    |     10 GB    | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities.                                            |
++------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
+| `BAAI/bge-reranker-v2-minicpm-layerwise <https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise>`_        |      Multilingual     |    2.72B    |    10.9 GB   | Suitable for multilingual contexts, allows freedom to select layers for output, facilitating accelerated inference.                                     |
++------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
+| `BAAI/bge-reranker-v2.5-gemma2-lightweight <https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight>`_  |      Multilingual     |    2.72B    |    10.9 GB   | Suitable for multilingual contexts, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. |
++------------------------------------------------------------------------------------------------------------------+-----------------------+-------------+--------------+---------------------------------------------------------------------------------------------------------------------------------------------------------+
+
+
+.. tip::
+
+    You can select the model according your senario and resource:
+
+    - For multilingual, utilize :code:`BAAI/bge-reranker-v2-m3`, :code:`BAAI/bge-reranker-v2-gemma` and :code:`BAAI/bge-reranker-v2.5-gemma2-lightweight`.
+    - For Chinese or English, utilize :code:`BAAI/bge-reranker-v2-m3` and :code:`BAAI/bge-reranker-v2-minicpm-layerwise`.
+    - For efficiency, utilize :code:`BAAI/bge-reranker-v2-m3` and the low layer of :code:`BAAI/bge-reranker-v2-minicpm-layerwise`.
+    - For better performance, recommand :code:`BAAI/bge-reranker-v2-minicpm-layerwise` and :code:`BAAI/bge-reranker-v2-gemma`.
+
+    Make sure always test on your real use case and choose the one with best speed-quality balance!
+
+Usage
+-----
+
+**bge-reranker-v2-m3**
+
+Use :code:`bge-reranker-v2-m3` in the same way as bge-reranker-base and bge-reranker-large.
+
+.. code:: python
+
+    from FlagEmbedding import FlagReranker
+
+    # Setting use_fp16 to True speeds up computation with a slight performance degradation
+    reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True)
+
+    score = reranker.compute_score(['query', 'passage'])
+    # or set "normalize=True" to apply a sigmoid function to the score for 0-1 range
+    score = reranker.compute_score(['query', 'passage'], normalize=True)
+
+    print(score)
+
+**bge-reranker-v2-gemma**
+
+Use the :code:`FlagLLMReranker` class for bge-reranker-v2-gemma.
+
+.. code:: python
+
+    from FlagEmbedding import FlagLLMReranker
+
+    # Setting use_fp16 to True speeds up computation with a slight performance degradation
+    reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True)
+
+    score = reranker.compute_score(['query', 'passage'])
+    print(score)
+
+**bge-reranker-v2-minicpm-layerwise**
+
+Use the :code:`LayerWiseFlagLLMReranker` class for bge-reranker-v2-minicpm-layerwise.
+
+.. code:: python
+
+    from FlagEmbedding import LayerWiseFlagLLMReranker
+
+    # Setting use_fp16 to True speeds up computation with a slight performance degradation
+    reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True)
+
+    # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
+    score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) 
+    print(score)
+
+**bge-reranker-v2.5-gemma2-lightweight**
+
+Use the :code:`LightWeightFlagLLMReranker` class for bge-reranker-v2.5-gemma2-lightweight.
+
+.. code:: python
+
+    from FlagEmbedding import LightWeightFlagLLMReranker
+
+    # Setting use_fp16 to True speeds up computation with a slight performance degradation
+    reranker = LightWeightFlagLLMReranker('BAAI/bge-reranker-v2.5-gemma2-lightweight', use_fp16=True)
+
+    # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
+    score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28], compress_ratio=2, compress_layer=[24, 40])
+    print(score)