docs: Updated README.md

README.md

@@ -6,7 +6,7 @@ The default text embedding (`TextEmbedding`) model is Flag Embedding, presented
 
 ## 📈 Why FastEmbed?
 
-1. Light: FastEmbed is a lightweight library with few external dependencies. We don't require a GPU and don't download GBs of PyTorch dependencies, and instead use the ONNX Runtime. This makes it a great candidate for serverless runtimes like AWS Lambda. 
+1. Light: FastEmbed is a lightweight library with few external dependencies. We don't require a GPU and don't download GBs of PyTorch dependencies, and instead use the ONNX Runtime. This makes it a great candidate for serverless runtimes like AWS Lambda.
 
 2. Fast: FastEmbed is designed for speed. We use the ONNX Runtime, which is faster than PyTorch. We also use data parallelism for encoding large datasets.
 
@@ -48,6 +48,7 @@ len(embeddings_list[0]) # Vector of 384 dimensions
 
 Fastembed supports a variety of models for different tasks and modalities.
 The list of all the available models can be found [here](https://qdrant.github.io/fastembed/examples/Supported_Models/)
+
 ### 🎒 Dense text embeddings
 
 ```python
@@ -63,8 +64,6 @@ embeddings = list(model.embed(documents))
 
 ```
 
-
-
 ### 🔱 Sparse text embeddings
 
 * SPLADE++
@@ -81,10 +80,23 @@ embeddings = list(model.embed(documents))
 # ]
 ```
 
-<!--
-* BM42 - ([link](ToDo))
+* BM25
 
+```python
+from fastembed import SparseTextEmbedding
+
+model = SparseTextEmbedding(model_name="Qdrant/bm25")
+embeddings = list(model.embed(documents))
+
+# [
+#   SparseEmbedding(indices=[ 129793020, 1999429279,  819028769, ... ], values=[1.6477, 1.6327, 1.2377, ...]),
+#   SparseEmbedding(indices=[ 682147660, 1100855371,  339478471, ... ], values=[1.6741, 1.5432, 1.6741, ...])
+# ]
 ```
+
+* [BM42](https://qdrant.tech/articles/bm42/)
+
+```python
 from fastembed import SparseTextEmbedding
 
 model = SparseTextEmbedding(model_name="Qdrant/bm42-all-minilm-l6-v2-attentions")
@@ -95,11 +107,15 @@ embeddings = list(model.embed(documents))
 #   SparseEmbedding(indices=[ 38,  12,  91, ... ], values=[0.11, 0.22, 0.39, ...])
 # ]
 ```
--->
+
+You can install [PyStemmer](https://pypi.org/project/PyStemmer/) to improve the stemming performance when using BM25, BM42.
+
+```shell
+pip install fastembed[pystemmer]
+```
 
 ### 🦥 Late interaction models (aka ColBERT)
 
-
 ```python
 from fastembed import LateInteractionTextEmbedding
 
@@ -137,7 +153,6 @@ embeddings = list(model.embed(images))
 # ]
 ```
 
-
 ## ⚡️ FastEmbed on a GPU
 
 FastEmbed supports running on GPU devices.
@@ -168,7 +183,7 @@ Installation with Qdrant Client in Python:
 pip install qdrant-client[fastembed]
 ```
 
-or 
+or
 
 ```bash
 pip install qdrant-client[fastembed-gpu]

pyproject.toml

@@ -25,8 +25,11 @@ numpy = [
 ]
 pillow = "^10.3.0"
 snowballstemmer = "^2.2.0"
-PyStemmer = "^2.2.0"
 mmh3 = "^4.0"
+PyStemmer = { version = "^2.2.0", optional = true }
+
+[tool.poetry.extras]
+pystemmer = ["PyStemmer"]
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.2"