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Alexander Huwiler 1 year ago
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README.md

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+# Semantic-Split
+
+![semantic-split-tests](https://github.com/agamm/semantic-split/actions/workflows/python-package.yml/badge.svg)
+
+A Python library to chunk/group your text based on semantic similarity - ideal for pre-processing data for Language Models or Vector Databases. Leverages [SentenceTransformers](https://github.com/UKPLab/sentence-transformers) and [spaCy](https://github.com/explosion/spaCy).
+
+## Why?
+
+1. **Better Context:** Providing more relevant context to your prompts enhances the LLM's performance ([arXiv:2005.14165](https://arxiv.org/abs/2005.14165) [cs.CL]). Semantic-Split groups related sentences together, ensuring your prompts have relevant context.
+
+2. **Improved Results:** Short, precise prompts often yield the best results from LLMs ([arXiv:2004.04906](https://arxiv.org/abs/2004.04906) [cs.CL]). By grouping semantically similar sentences, Semantic-Split helps you craft such efficient prompts.
+
+3. **Cost Savings:** LLMs like GPT-4 charge costs per token and have a token limit (e.g., 8K tokens). With Semantic-Split, you can make your prompts shorter and more meaningful, leading to potential cost savings.
+
+**Real world example**:
+
+Imagine you're building an application where users ask questions about articles:
+
+- A. We want to only add parts in the artcile that are relevant to our query (for better results).
+- B. We want to be able to query the article quickly (pre-processing).
+
+1. We want to pre-process the article - so each query is fast (point B). So we split it into semantic chunks using `semantic-split` and store it in a [Vector DB](https://unzip.dev/0x014-vector-databases/?ref=github-semantic-split) as embeddings.
+2. Each time the user asks something we calculate the embedding for their question and find the top 3 similar chunks in our Vector DB.
+3. We add those 3 chunks to our pompt, to get better results for our user's questions.
+
+As you can see, in part `1`, which involves semantic sentence splitting (grouping), is crucial. If we don't split or group the sentences semantically, we risk losing essential information. This can diminish the effectiveness of our Vector DB in identifying the most suitable chunks. Consequently, we may end up with poorer context for our prompts, negatively impacting the quality of our responses.
+
+## Install
+
+`python -m spacy download en_core_web_sm`  
+`pip install semantic-split`
+
+You might need to have CUDA for the `SentenceSimilarity` an easy fix is to install it via pytorch:
+`pip install torch` if you have a GPU.
+or (this requires python 3.8 for some reason)
+`pip3 install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html` if you don't.
+or `conda install cudatoolkit`
+
+## Development
+
+1. To use most of the functionality you will need to install some pre-requisists
+2. Spacy sm dataset: `python -m spacy download en_core_web_sm`
+3. `poetry install`
+4. See examples
+
+## ✂️ Splitters
+
+### 1. Semantic Similarity
+
+### Input
+
+```
+  I dogs are amazing.
+  Cats must be the easiest pets around.
+  Robots are advanced now with AI.
+  Flying in space can only be done by Artificial intelligence.
+```
+
+### Output
+
+`[ ["I dogs are amazing.", "Cats must be the easiest pets around."], `  
+`["Robots are advanced now with AI.", "Flying in space can only be done by Artificial intelligence."] ]`
+
+### Code
+
+```python
+from semantic_split import SimilarSentenceSplitter, SentenceTransformersSimilarity, SpacySentenceSplitter
+
+text = """
+  I dogs are amazing.
+  Cats must be the easiest pets around.
+  Robots are advanced now with AI.
+  Flying in space can only be done by Artificial intelligence."""
+
+model = SentenceTransformersSimilarity()
+sentence_splitter = SpacySentenceSplitter()
+splitter = SimilarSentenceSplitter(model, sentence_splitter)
+res = splitter.split(text)
+```
+
+## Tests
+
+`poetry run pytest`

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+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf (<=3.20.3)", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow", "accelerate (>=0.20.2)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.3)", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow", "accelerate (>=0.20.2)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.6.9)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf (<=3.20.3)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+fairscale = ["fairscale (>0.3)"]
+flax = ["flax (>=0.4.1,<=0.6.9)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune]", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune]"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf (<=3.20.3)", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.3)", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
+torch = ["accelerate (>=0.20.2)", "torch (>=1.9,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.14.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.3)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow"]
+
+[[package]]
+name = "typer"
+version = "0.7.0"
+description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "typer-0.7.0-py3-none-any.whl", hash = "sha256:b5e704f4e48ec263de1c0b3a2387cd405a13767d2f907f44c1a08cbad96f606d"},
+    {file = "typer-0.7.0.tar.gz", hash = "sha256:ff797846578a9f2a201b53442aedeb543319466870fbe1c701eab66dd7681165"},
+]
+
+[package.dependencies]
+click = ">=7.1.1,<9.0.0"
+
+[package.extras]
+all = ["colorama (>=0.4.3,<0.5.0)", "rich (>=10.11.0,<13.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
+dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2.17.0,<3.0.0)"]
+doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
+test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<13.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.6.3"
+description = "Backported and Experimental Type Hints for Python 3.7+"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "typing_extensions-4.6.3-py3-none-any.whl", hash = "sha256:88a4153d8505aabbb4e13aacb7c486c2b4a33ca3b3f807914a9b4c844c471c26"},
+    {file = "typing_extensions-4.6.3.tar.gz", hash = "sha256:d91d5919357fe7f681a9f2b5b4cb2a5f1ef0a1e9f59c4d8ff0d3491e05c0ffd5"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.0.3"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "urllib3-2.0.3-py3-none-any.whl", hash = "sha256:48e7fafa40319d358848e1bc6809b208340fafe2096f1725d05d67443d0483d1"},
+    {file = "urllib3-2.0.3.tar.gz", hash = "sha256:bee28b5e56addb8226c96f7f13ac28cb4c301dd5ea8a6ca179c0b9835e032825"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.1.0)", "urllib3-secure-extra"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "wasabi"
+version = "1.1.2"
+description = "A lightweight console printing and formatting toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "wasabi-1.1.2-py3-none-any.whl", hash = "sha256:0a3f933c4bf0ed3f93071132c1b87549733256d6c8de6473c5f7ed2e171b5cf9"},
+    {file = "wasabi-1.1.2.tar.gz", hash = "sha256:1aaef3aceaa32edb9c91330d29d3936c0c39fdb965743549c173cb54b16c30b5"},
+]
+
+[package.dependencies]
+colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python_version >= \"3.7\""}
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.9"
+content-hash = "ed986321552d32651d8cb73546c3acb1790b3f9134d78433e8cc0f51e54b65f0"

+ 25 - 0
pyproject.toml

@@ -0,0 +1,25 @@
+[tool.poetry]
+name = "semantic-split"
+version = "0.1.0"
+description = "A better way to split (chunk/group) your text before inserting them into an LLM/Vector DB."
+authors = ["Agam More <agam@agam.me>"]
+license = "MIT"
+readme = "README.md"
+packages = [{include = "semantic_split"}]
+
+[[tool.poetry.source]]
+name = "semnatic-split"
+url = "https://github.com/agamm/semantic-split"
+
+[tool.poetry.dependencies]
+python = "*"
+sentence-transformers = "^2.2.2"
+spacy = "^3.5.3"
+
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.3.1"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

+ 20 - 0
semantic_split/SentenceSimilarity.py

@@ -0,0 +1,20 @@
+from typing import List
+from sentence_transformers import SentenceTransformer, util
+
+class SentenceTransformersSimilarity():
+    def __init__(self, model='all-MiniLM-L6-v2', similarity_threshold=0.2):
+        self.model = SentenceTransformer(model)
+        self.similarity_threshold = similarity_threshold
+
+
+    def similarities(self, sentences: List[str]):
+        # Encode all sentences 
+        embeddings = self.model.encode(sentences)
+
+        # Calculate cosine similarities for neighboring sentences
+        similarities = []
+        for i in range(1, len(embeddings)):
+            sim = util.pytorch_cos_sim(embeddings[i-1], embeddings[i]).item()
+            similarities.append(sim)
+
+        return similarities

+ 11 - 0
semantic_split/__init__.py

@@ -0,0 +1,11 @@
+
+# Similarity models
+from .SentenceSimilarity import SentenceTransformersSimilarity
+
+# Splitters
+from .splitters.Splitter import Splitter
+from .splitters.SpacySentenceSplitter import SpacySentenceSplitter
+from .splitters.SimilarSentenceSplitter import SimilarSentenceSplitter
+
+__all__ = ['SentenceTransformersSimilarity', 'Splitter', 'SpacySentenceSplitter', 
+           'SimilarSentenceSplitter' ]

+ 39 - 0
semantic_split/splitters/SimilarSentenceSplitter.py

@@ -0,0 +1,39 @@
+
+
+from typing import List
+from .Splitter import Splitter
+
+
+
+class SimilarSentenceSplitter(Splitter):
+
+    def __init__(self, similarity_model, sentence_splitter: Splitter):
+        self.model = similarity_model
+        self.sentence_splitter = sentence_splitter
+
+    def split(self, text: str, group_max_sentences=5) -> List[str]:
+        '''
+            group_max_sentences: The maximum number of sentences in a group.
+        '''
+        sentences = self.sentence_splitter.split(text)
+
+        if len(sentences) == 0:
+            return []
+
+        
+        similarities = self.model.similarities(sentences)
+
+        # The first sentence is always in the first group.
+        groups = [[sentences[0]]]
+
+        # Using the group min/max sentences contraints, 
+        # group together the rest of the sentences.
+        for i in range(1, len(sentences)):
+            if len(groups[-1]) >= group_max_sentences:
+                groups.append([sentences[i]])
+            elif similarities[i-1] >= self.model.similarity_threshold:
+                groups[-1].append(sentences[i])
+            else:
+                groups.append([sentences[i]])
+
+        return groups

+ 15 - 0
semantic_split/splitters/SpacySentenceSplitter.py

@@ -0,0 +1,15 @@
+
+from typing import List
+from .Splitter import Splitter
+
+import spacy
+
+
+class SpacySentenceSplitter(Splitter):
+
+    def __init__(self):
+        self.nlp = spacy.load("en_core_web_sm")
+
+    def split(self, text: str) -> List[str]:
+        doc = self.nlp(text)
+        return [str(sent).strip() for sent in doc.sents]

+ 12 - 0
semantic_split/splitters/Splitter.py

@@ -0,0 +1,12 @@
+from abc import ABC, abstractmethod
+from typing import List
+
+
+class Splitter(ABC):
+
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def split(self, text: str) -> List[str]:
+        pass

+ 66 - 0
tests/splitters/test_similar_sentences_splitter.py

@@ -0,0 +1,66 @@
+import pytest
+from utls import load_testdata
+from semantic_split import SentenceTransformersSimilarity, \
+    SimilarSentenceSplitter, SpacySentenceSplitter
+
+
+splitter = None
+
+# Loading Spacy and The SentenceTransformer takes time, so we do it once for all tests.
+@pytest.fixture(autouse=True)
+def run_before_and_after_tests(tmpdir):
+    global splitter
+    model = SentenceTransformersSimilarity()
+    sentence_splitter = SpacySentenceSplitter()
+    splitter = SimilarSentenceSplitter(similarity_model = model, 
+                                       sentence_splitter=sentence_splitter)
+    yield  # this is where the testing happens
+
+
+def test_two_similar_sentences():
+
+    text = """I love dogs. I love cats."""
+    res = splitter.split(text)
+
+    assert res == [["I love dogs.", "I love cats."]]
+
+def test_similar_sentences():
+
+    text = """I dogs are amazing. 
+    Cats must be the easiest pets around. 
+    Robots are advanced now with AI. 
+    Flying in space can only be done by Artificial intelligence."""
+
+    res = splitter.split(text)
+
+    assert res == [
+        ["I dogs are amazing.", 
+         "Cats must be the easiest pets around."], 
+        ["Robots are advanced now with AI.", 
+         "Flying in space can only be done by Artificial intelligence."]]
+
+def test_different_sentences():
+
+    text = """I love dogs. He has flowers at home."""
+    res = splitter.split(text)
+
+    print(res)
+
+    assert res[0][0] == 'I love dogs.'
+    assert res[1][0] == 'He has flowers at home.'
+    assert res == [['I love dogs.'], ['He has flowers at home.']]
+
+
+def test_5th_sentences():
+    text = load_testdata('sentences.txt')
+    res = splitter.split(text)
+
+    assert len(res) == 5
+
+
+def test_max_group_sentences():
+    text = load_testdata('sentences.txt')
+    res = splitter.split(text, group_max_sentences=1)
+    
+    assert len(res) == 20
+

+ 20 - 0
tests/test_files/sentences.txt

@@ -0,0 +1,20 @@
+In the heart of the city, amidst the hustle and bustle, there was a tiny café that served the best croissants in town.
+Its owner, a kindly old woman named Madeleine, had learned the art of baking from her grandmother, a native of Paris.
+Every morning, as the sun rose, the aroma of freshly baked pastries would waft through the narrow streets, waking up the neighborhood with a promise of a delectable breakfast.
+Locals, tourists, and food critics alike were drawn to Madeleine's café, all eager for a taste of her legendary croissants.
+Shifting gears, on the edge of the observable universe, scientists discovered a galaxy far older than any previously known.
+This ancient celestial body, named Antiquus by its discoverers, seemed to defy many of our established notions about the lifespan and evolution of galaxies.
+The stars within Antiquus were dense and aged, glowing with the wisdom of the universe, a stark contrast to the young, bright stars of our own galaxy.
+Researchers, astronomers, and space enthusiasts around the world were entranced by the mysteries that Antiquus presented.
+Meanwhile, in the world of literature, a new author named Aravind Iyer was making waves with his debut novel.
+This evocative story, set against the backdrop of rural India, weaved a poignant narrative of love, loss, and the resilience of the human spirit.
+Readers were drawn in by Aravind's lyrical prose and the depth of his characters, feeling their joys and sorrows as if they were their own.
+Literary critics praised the novel for its authentic portrayal of rural life and the complexities of human relationships.
+In a completely different scenario, new advancements in the field of robotics were transforming the face of modern medicine.
+Surgical robots, guided by precision artificial intelligence, were performing procedures with a degree of accuracy that surpassed even the most skilled human surgeons.
+These innovations held the promise of reducing surgical errors and improving patient outcomes.
+Despite concerns about the role of AI in healthcare, most agreed that these technological advances marked a significant step forward in medical science.
+Over on Wall Street, investors were keeping a close eye on a new startup known as Ecolytics.
+Ecolytics had created a groundbreaking machine learning algorithm that could accurately predict the environmental impact of various industrial processes.
+As concerns about climate change became increasingly urgent, the technology offered by Ecolytics was seen as a game-changer.
+With their innovative approach, Ecolytics stood as a symbol of hope in the ongoing fight against global warming.

+ 13 - 0
tests/test_sentence_similarity.py

@@ -0,0 +1,13 @@
+from semantic_split import SentenceTransformersSimilarity
+
+
+def test_basic_usage():
+
+    sentence0 = "I love dogs." 
+    sentence1 = "I love cats."
+
+    model = SentenceTransformersSimilarity()
+    similarities = model.similarities([sentence0, sentence1])
+
+    assert similarities[0] - 0.7728430 < 0.0001
+

+ 14 - 0
tests/utls.py

@@ -0,0 +1,14 @@
+import os
+FIXTURE_DIR = os.path.join(
+    os.path.dirname(os.path.realpath(__file__)),
+    'test_files',
+)
+
+
+def test_path(file):
+    return os.path.join(FIXTURE_DIR, file)
+
+
+def load_testdata(file):
+    with open(test_path(file), 'r') as f:
+        return f.read()