chore: prep release

README.md

@@ -14,9 +14,10 @@ Welcome to `rerankers`! Our goal is to provide users with a simple API to use an
 
 ## Updates
 
+- v0.5.0: Added support for the current state-of-the-art rerankers, BAAI's series of `BGE` layerwise LLM rerankers, based on [Gemma](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) and MiniCPM. These are different from RankGPT, as they're not listwise: the models are repurposed as "cross-encoders", and do output logit scores.
 - v0.4.0: ColBERT performance improvement! It should now be faster and result in stronger results following implementation of the JaColBERTv2.5 dynamic query length method. This version also now supports HuggingFace's Text-Embedding-Server (TEI) inference as an API reranker option, thanks to [@srisudarsan](https://github.com/srisudarsan).
 - v0.3.1: T5 bugfix and native default support for new Portuguese T5 rerankers.
-- v0.3.0: 🆕 Many changes! Experimental support for RankLLM, directly backed by the [rank-llm library](https://github.com/castorini/rank_llm). A new `Document` object, courtesy of joint-work by [@bclavie](https://github.com/bclavie) and [Anmol6](https://github.com/Anmol6). This object is transparent, but now offers support for `metadata` stored alongside each document. Many small QoL changes (RankedResults can be itered on directly...)
+- v0.3.0: Many changes! Experimental support for RankLLM, directly backed by the [rank-llm library](https://github.com/castorini/rank_llm). A new `Document` object, courtesy of joint-work by [@bclavie](https://github.com/bclavie) and [Anmol6](https://github.com/Anmol6). This object is transparent, but now offers support for `metadata` stored alongside each document. Many small QoL changes (RankedResults can be itered on directly...)
 - v0.2.0: [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) rerankers, Basic async support thanks to [@tarunamasa](https://github.com/tarunamasa), MixedBread.ai reranking API
 - v0.1.2: Voyage reranking API
 - v0.1.1: Langchain integration fixed!
@@ -198,6 +199,7 @@ Models:
 - ✅ Any standard SentenceTransformer or Transformers cross-encoder
 - ✅ RankGPT (Available both via the original RankGPT implementation and the improved RankLLM one)
 - ✅ T5-based pointwise rankers (InRanker, MonoT5...)
+- ✅ LLM-based pointwise rankers (BAAI/bge-reranker-v2.5-gemma2-lightweight, etc...)
 - ✅ Cohere, Jina, Voyage and MixedBread API rerankers
 - ✅ [FlashRank](https://github.com/PrithivirajDamodaran/FlashRank) rerankers (ONNX-optimised models, very fast on CPU)
 - ✅ ColBERT-based reranker - not a model initially designed for reranking, but does perform quite strongly in some cases. Implementation is lightweight, based only on transformers.

pyproject.toml

@@ -14,7 +14,7 @@ packages = [
 name = "rerankers" 
 
 
-version = "0.5.0rc1"
+version = "0.5.0"
 
 description = "A unified API for various document re-ranking models."
 

rerankers/__init__.py

@@ -2,4 +2,4 @@
 from rerankers.documents import Document
 
 __all__ = ["Reranker", "Document"]
-__version__ = "0.5.0rc1"
+__version__ = "0.5.0"