使用 Sentence Transformers 和 MLflow 进行高级释义挖掘

通过使用 Sentence Transformers 进行高级释义挖掘，并结合 MLflow 增强功能，开启一段充实的旅程。

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学习目标

• 应用 sentence-transformers 进行高级释义挖掘。

• 为 MLflow 开发一个定制的 PythonModel ，专门针对此任务。

• 在 MLflow 生态系统中有效管理和跟踪模型。

• 使用 MLflow 的部署功能部署释义挖掘模型。

探索释义挖掘

探索识别语义相似但文本不同的句子的过程，这是各种NLP应用中的一个关键方面，如文档摘要和聊天机器人开发。

句子转换器在释义挖掘中的作用

了解如何使用专门用于生成丰富句子嵌入的 Sentence Transformers 来捕捉深层次的语义含义并比较文本内容。

MLflow: 简化模型管理和部署

深入探讨 MLflow 如何简化管理和部署 NLP 模型的过程，重点在于高效的跟踪和可定制的模型实现。

加入我们，深入理解释义挖掘的细微差别，并掌握使用 MLflow 管理和部署 NLP 模型的艺术。

import warnings

# Disable a few less-than-useful UserWarnings from setuptools and pydantic
warnings.filterwarnings("ignore", category=UserWarning)

Paraphrase Mining 模型简介

启动释义挖掘模型，整合 Sentence Transformers 和 MLflow 以进行高级自然语言处理任务。

模型结构的概述

• 加载模型和语料库 ``load_context`` 方法：对于加载用于释义识别的 Sentence Transformer 模型和文本语料库至关重要。

• 释义挖掘逻辑 ``predict`` 方法: 整合了输入验证和释义挖掘的自定义逻辑，提供可定制的参数。

• 排序和过滤匹配 ``_sort_and_filter_matches`` 辅助方法：通过基于相似度分数的排序和过滤，确保相关且唯一的释义识别。

主要特点

• 高级NLP技术：利用句子转换器进行语义文本理解。

• 自定义逻辑集成：展示了模型行为定制的灵活性。

• 用户自定义选项：允许终端用户根据不同使用场景调整匹配标准。

• 处理效率：预编码语料库以提高释义挖掘操作的效率。

• 稳健的错误处理：包含验证以确保模型的可靠性能。

实际影响

该模型为各种应用中的释义检测提供了一个强大的工具，展示了在 MLflow 框架内有效使用自定义模型的实例。

import warnings
from typing import List

import pandas as pd
from sentence_transformers import SentenceTransformer, util

import mlflow
from mlflow.models.signature import infer_signature
from mlflow.pyfunc import PythonModel


class ParaphraseMiningModel(PythonModel):
    def load_context(self, context):
        """Load the model context for inference, including the customer feedback corpus."""
        try:
            # Load the pre-trained sentence transformer model
            self.model = SentenceTransformer.load(context.artifacts["model_path"])

            # Load the customer feedback corpus from the specified file
            corpus_file = context.artifacts["corpus_file"]
            with open(corpus_file) as file:
                self.corpus = file.read().splitlines()

        except Exception as e:
            raise ValueError(f"Error loading model and corpus: {e}")

    def _sort_and_filter_matches(
        self, query: str, paraphrase_pairs: List[tuple], similarity_threshold: float
    ):
        """Sort and filter the matches by similarity score."""

        # Convert to list of tuples and sort by score
        sorted_matches = sorted(paraphrase_pairs, key=lambda x: x[1], reverse=True)

        # Filter and collect paraphrases for the query, avoiding duplicates
        query_paraphrases = {}
        for score, i, j in sorted_matches:
            if score < similarity_threshold:
                continue

            paraphrase = self.corpus[j] if self.corpus[i] == query else self.corpus[i]
            if paraphrase == query:
                continue

            if paraphrase not in query_paraphrases or score > query_paraphrases[paraphrase]:
                query_paraphrases[paraphrase] = score

        return sorted(query_paraphrases.items(), key=lambda x: x[1], reverse=True)

    def predict(self, context, model_input, params=None):
        """Predict method to perform paraphrase mining over the corpus."""

        # Validate and extract the query input
        if isinstance(model_input, pd.DataFrame):
            if model_input.shape[1] != 1:
                raise ValueError("DataFrame input must have exactly one column.")
            query = model_input.iloc[0, 0]
        elif isinstance(model_input, dict):
            query = model_input.get("query")
            if query is None:
                raise ValueError("The input dictionary must have a key named 'query'.")
        else:
            raise TypeError(
                f"Unexpected type for model_input: {type(model_input)}. Must be either a Dict or a DataFrame."
            )

        # Determine the minimum similarity threshold
        similarity_threshold = params.get("similarity_threshold", 0.5) if params else 0.5

        # Add the query to the corpus for paraphrase mining
        extended_corpus = self.corpus + [query]

        # Perform paraphrase mining
        paraphrase_pairs = util.paraphrase_mining(
            self.model, extended_corpus, show_progress_bar=False
        )

        # Convert to list of tuples and sort by score
        sorted_paraphrases = self._sort_and_filter_matches(
            query, paraphrase_pairs, similarity_threshold
        )

        # Warning if no paraphrases found
        if not sorted_paraphrases:
            warnings.warn("No paraphrases found above the similarity threshold.", UserWarning)

        return {sentence[0]: str(sentence[1]) for sentence in sorted_paraphrases}

准备用于释义挖掘的语料库

通过创建和准备一个多样化的语料库，为释义挖掘打下基础。

语料库创建

• 定义一个包含各种主题句子的 语料库 ，包括太空探索、人工智能、园艺等。这种多样性使模型能够在广泛的学科范围内识别释义。

将语料库写入文件

• 语料库被保存到一个名为 feedback.txt 的文件中，这与大规模数据处理中的常见做法相呼应。

• 此步骤还为 Paraphrase Mining Model 内的有效处理准备语料库。

语料库的重要性

语料库作为模型寻找语义相似句子的关键数据集。其多样性确保了模型在不同使用场景中的适应性和有效性。

corpus = [
    "Exploring ancient cities in Europe offers a glimpse into history.",
    "Modern AI technologies are revolutionizing industries.",
    "Healthy eating contributes significantly to overall well-being.",
    "Advancements in renewable energy are combating climate change.",
    "Learning a new language opens doors to different cultures.",
    "Gardening is a relaxing hobby that connects you with nature.",
    "Blockchain technology could redefine digital transactions.",
    "Homemade Italian pasta is a delight to cook and eat.",
    "Practicing yoga daily improves both physical and mental health.",
    "The art of photography captures moments in time.",
    "Baking bread at home has become a popular quarantine activity.",
    "Virtual reality is creating new experiences in gaming.",
    "Sustainable travel is becoming a priority for eco-conscious tourists.",
    "Reading books is a great way to unwind and learn.",
    "Jazz music provides a rich tapestry of sound and rhythm.",
    "Marathon training requires discipline and perseverance.",
    "Studying the stars helps us understand our universe.",
    "The rise of electric cars is an important environmental development.",
    "Documentary films offer deep insights into real-world issues.",
    "Crafting DIY projects can be both fun and rewarding.",
    "The history of ancient civilizations is fascinating to explore.",
    "Exploring the depths of the ocean reveals a world of marine wonders.",
    "Learning to play a musical instrument can be a rewarding challenge.",
    "Artificial intelligence is shaping the future of personalized medicine.",
    "Cycling is not only a great workout but also eco-friendly transportation.",
    "Home automation with IoT devices is enhancing living experiences.",
    "Understanding quantum computing requires a grasp of complex physics.",
    "A well-brewed cup of coffee is the perfect start to the day.",
    "Urban farming is gaining popularity as a sustainable food source.",
    "Meditation and mindfulness can lead to a more balanced life.",
    "The popularity of podcasts has revolutionized audio storytelling.",
    "Space exploration continues to push the boundaries of human knowledge.",
    "Wildlife conservation is essential for maintaining biodiversity.",
    "The fusion of technology and fashion is creating new trends.",
    "E-learning platforms have transformed the educational landscape.",
    "Dark chocolate has surprising health benefits when enjoyed in moderation.",
    "Robotics in manufacturing is leading to more efficient production.",
    "Creating a personal budget is key to financial well-being.",
    "Hiking in nature is a great way to connect with the outdoors.",
    "3D printing is innovating the way we create and manufacture objects.",
    "Sommeliers can identify a wine's characteristics with just a taste.",
    "Mind-bending puzzles and riddles are great for cognitive exercise.",
    "Social media has a profound impact on communication and culture.",
    "Urban sketching captures the essence of city life on paper.",
    "The ethics of AI is a growing field in tech philosophy.",
    "Homemade skincare remedies are becoming more popular.",
    "Virtual travel experiences can provide a sense of adventure at home.",
    "Ancient mythology still influences modern storytelling and literature.",
    "Building model kits is a hobby that requires patience and precision.",
    "The study of languages opens windows into different worldviews.",
    "Professional esports has become a major global phenomenon.",
    "The mysteries of the universe are unveiled through space missions.",
    "Astronauts' experiences in space stations offer unique insights into life beyond Earth.",
    "Telescopic observations bring distant galaxies within our view.",
    "The study of celestial bodies helps us understand the cosmos.",
    "Space travel advancements could lead to interplanetary exploration.",
    "Observing celestial events provides valuable data for astronomers.",
    "The development of powerful rockets is key to deep space exploration.",
    "Mars rover missions are crucial in searching for extraterrestrial life.",
    "Satellites play a vital role in our understanding of Earth's atmosphere.",
    "Astrophysics is central to unraveling the secrets of space.",
    "Zero gravity environments in space pose unique challenges and opportunities.",
    "Space tourism might soon become a reality for many.",
    "Lunar missions have contributed significantly to our knowledge of the moon.",
    "The International Space Station is a hub for groundbreaking space research.",
    "Studying comets and asteroids reveals information about the early solar system.",
    "Advancements in space technology have implications for many scientific fields.",
    "The possibility of life on other planets continues to intrigue scientists.",
    "Black holes are among the most mysterious phenomena in space.",
    "The history of space exploration is filled with remarkable achievements.",
    "Future space missions could unlock the mysteries of dark matter.",
]

# Write out the corpus to a file
corpus_file = "/tmp/feedback.txt"
with open(corpus_file, "w") as file:
    for sentence in corpus:
        file.write(sentence + "\n")

设置释义挖掘模型

准备 Sentence Transformer 模型以与 MLflow 集成，以利用其释义挖掘功能。

加载句子转换器模型

• 初始化 all-MiniLM-L6-v2 句子转换器模型，非常适合生成适合释义挖掘的句子嵌入。

准备输入示例

• 创建一个 DataFrame 作为输入示例，以说明模型将处理的查询类型，有助于定义模型的输入结构。

保存模型

• 将模型保存到 /tmp/paraphrase_search_model 以便于在部署期间使用 MLflow 进行加载和便携性。

定义工件和语料库路径

• 在MLflow中将保存的模型和语料库的路径指定为工件，这对模型记录和再现至关重要。

生成签名测试输出

• 生成一个示例输出，展示模型在释义挖掘中的预期输出格式。

创建模型签名

• 使用 MLflow 的 infer_signature 来定义模型的输入和输出模式，为推理灵活性添加 similarity_threshold 参数。

# Load a pre-trained sentence transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")

# Create an input example DataFrame
input_example = pd.DataFrame({"query": ["This product works well. I'm satisfied."]})

# Save the model in the /tmp directory
model_directory = "/tmp/paraphrase_search_model"
model.save(model_directory)

# Define the path for the corpus file
corpus_file = "/tmp/feedback.txt"

# Define the artifacts (paths to the model and corpus file)
artifacts = {"model_path": model_directory, "corpus_file": corpus_file}

# Generate test output for signature
# Sample output for paraphrase mining could be a list of tuples (paraphrase, score)
test_output = [{"This product is satisfactory and functions as expected.": "0.8"}]

# Define the signature associated with the model
# The signature includes the structure of the input and the expected output, as well as any parameters that
# we would like to expose for overriding at inference time (including their default values if they are not overridden).
signature = infer_signature(
    model_input=input_example, model_output=test_output, params={"similarity_threshold": 0.5}
)

# Visualize the signature, showing our overridden inference parameter and its default.
signature

inputs:
  ['query': string]
outputs:
  ['This product is satisfactory and functions as expected.': string]
params:
  ['similarity_threshold': double (default: 0.5)]

创建一个实验

我们创建一个新的 MLflow 实验，这样我们将要记录模型的运行不会记录到默认实验中，而是拥有其上下文相关条目。

# If you are running this tutorial in local mode, leave the next line commented out.
# Otherwise, uncomment the following line and set your tracking uri to your local or remote tracking server.

# mlflow.set_tracking_uri("http://127.0.0.1:8080")

mlflow.set_experiment("Paraphrase Mining")

<Experiment: artifact_location='file:///Users/benjamin.wilson/repos/mlflow-fork/mlflow/docs/source/llms/sentence-transformers/tutorials/paraphrase-mining/mlruns/380691166097743403', creation_time=1701282619556, experiment_id='380691166097743403', last_update_time=1701282619556, lifecycle_stage='active', name='Paraphrase Mining', tags={}>

使用 MLflow 记录释义挖掘模型

使用 MLflow 记录自定义的释义挖掘模型，这是模型管理和部署的关键步骤。

启动一个 MLflow 运行

• 启动一个 MLflow 运行，以在 MLflow 框架内创建模型日志记录和跟踪的全面记录。

在 MLflow 中记录模型

• 使用 MLflow 的 Python 模型日志记录功能将自定义模型集成到 MLflow 生态系统中。

• 为模型提供一个唯一的名称，以便在MLflow中易于识别。

• 记录实例化的 Paraphrase Mining 模型，连同输入示例、模型签名、工件和 Python 依赖项。

模型日志的结果与益处

• 在MLflow中注册模型，以实现简化的管理和部署，增强其可访问性和可追踪性。

• 确保模型在不同部署环境中的可重复性和版本控制。

with mlflow.start_run() as run:
    model_info = mlflow.pyfunc.log_model(
        "paraphrase_model",
        python_model=ParaphraseMiningModel(),
        input_example=input_example,
        signature=signature,
        artifacts=artifacts,
        pip_requirements=["sentence_transformers"],
    )

2023/11/30 15:41:39 INFO mlflow.store.artifact.artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false

模型加载与释义挖掘预测

通过使用MLflow加载并执行预测，展示Paraphrase Mining模型的实际应用。

加载模型以进行推理

• 使用 MLflow 的 load_model 函数来检索并准备模型以进行推理。

• 使用其唯一的URI在MLflow注册表中定位并加载模型。

执行一个释义挖掘预测

• 使用模型的 predict 方法进行预测，应用嵌入在模型类中的释义挖掘逻辑。

• 传递一个带有设定 similarity_threshold 的代表性查询，以在语料库中找到匹配的释义。

解释模型输出

• 回顾与查询语义相似的句子列表，突出显示模型的释义识别能力。

• 分析相似度分数以理解查询与语料库句子之间的语义相关程度。

结论

此演示验证了释义挖掘模型在实际场景中的有效性，强调了其在内容推荐、信息检索和对话式AI中的实用性。

# Load our model by supplying the uri that was used to save the model artifacts
loaded_dynamic = mlflow.pyfunc.load_model(model_info.model_uri)

# Perform a quick validation that our loaded model is performing adequately
loaded_dynamic.predict(
    {"query": "Space exploration is fascinating."}, params={"similarity_threshold": 0.65}
)

{'Studying the stars helps us understand our universe.': '0.8207424879074097',
 'The history of space exploration is filled with remarkable achievements.': '0.7770636677742004',
 'Exploring ancient cities in Europe offers a glimpse into history.': '0.7461957335472107',
 'Space travel advancements could lead to interplanetary exploration.': '0.7090306282043457',
 'Space exploration continues to push the boundaries of human knowledge.': '0.6893945932388306',
 'The mysteries of the universe are unveiled through space missions.': '0.6830739974975586',
 'The study of celestial bodies helps us understand the cosmos.': '0.671358048915863'}

结论：见解与潜在改进

随着本教程的结束，让我们回顾一下使用 Sentence Transformers 和 MLflow 实现 Paraphrase Mining 模型的旅程。我们已经成功构建并部署了一个能够识别语义相似句子的模型，展示了 MLflow 的 PythonModel 实现的灵活性和强大功能。

关键要点

• 我们学习了如何将高级自然语言处理技术，特别是释义挖掘，与MLflow集成。这种集成不仅增强了模型管理，还简化了部署和扩展性。

• MLflow 中 PythonModel 实现的灵活性是一个核心主题。我们亲眼目睹了它如何允许在模型的预测函数中加入自定义逻辑，以适应特定的 NLP 任务，如释义挖掘。

• 通过我们的自定义模型，我们探索了句子嵌入的动态、语义相似性以及语言理解的细微差别。这种理解在从内容推荐到对话AI的广泛应用中至关重要。

增强释义挖掘模型的想法

虽然我们的模型作为一个强大的起点，但在 predict 函数中可以进行几项改进，使其更加强大和功能丰富：

1. 上下文过滤器：基于上下文线索或特定关键词引入过滤器，以进一步细化搜索结果。此功能将允许用户将释义缩小到与其特定上下文或主题最相关的那些。

2. 情感分析集成: 结合情感分析，根据情感色调对释义进行分组。这在客户反馈分析等应用中尤其有用，因为在这些应用中，理解情感与理解内容同样重要。

3. 多语言支持：扩展模型以支持多种语言的释义挖掘。这一增强将显著扩大模型在全球或多语言环境中的适用性。

向量数据库的可扩展性

• 超越将静态文本文件作为语料库，一个更具扩展性和现实世界的方法是连接模型到一个外部向量数据库或内存存储。

• 预计算的嵌入可以在这些数据库中存储和更新，适应实时内容生成而不需要重新部署模型。这种方法将显著提高模型在实际应用中的可扩展性和响应性。

最后的思考

构建和部署Paraphrase Mining模型的旅程既富有启发性又具有实用性。我们看到了MLflow的``PythonModel``如何提供了一个灵活的画布来打造定制的NLP解决方案，以及如何利用句子转换器深入挖掘语言的语义。

本教程只是一个开始。在释义挖掘和整个自然语言处理领域中，还有巨大的潜力等待进一步探索和创新。我们鼓励您在此基础上进行构建，尝试增强功能，并继续推动MLflow和先进NLP技术的可能性边界。