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

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踏上一次丰富之旅，通过 Sentence Transformers 进行高级释义挖掘，并由 MLflow 增强。

学习目标​

  • 应用 sentence-transformers 进行高级释义挖掘。
  • 在 MLflow 中为这项任务开发自定义 PythonModel。
  • 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（如文档摘要和聊天机器人开发）的关键方面。

Sentence Transformers 在释义挖掘中的作用​

了解 Sentence Transformers（专门用于生成丰富的句子嵌入）如何用于捕获深层语义含义并比较文本内容。

MLflow：简化模型管理和部署​

深入了解 MLflow 如何简化 NLP 模型的管理和部署流程，重点关注高效跟踪和可自定义的模型实现。

加入我们，深入理解释义挖掘，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)

释义挖掘模型简介​

启动释义挖掘模型，集成 Sentence Transformers 和 MLflow 进行高级 NLP 任务。

模型结构概述​

  • 加载模型和语料库 load_context 方法：加载 Sentence Transformer 模型和用于释义识别的文本语料库必不可少。
  • 释义挖掘逻辑 predict 方法：集成用于输入验证和释义挖掘的自定义逻辑，提供可自定义的参数。
  • 排序和过滤匹配项 _sort_and_filter_matches 辅助方法：通过基于相似度分数进行排序和过滤，确保相关且唯一的释义识别。

主要特性​

  • 高级 NLP 技术：利用 Sentence Transformers 进行语义文本理解。
  • 自定义逻辑集成：展示模型行为自定义的灵活性。
  • 用户自定义选项：允许最终用户调整匹配标准以适应各种用例。
  • 处理效率：预编码语料库以进行高效的释义挖掘操作。
  • 强大的错误处理：包含验证以确保模型性能可靠。

实际应用​

该模型为各种应用中的释义检测提供了强大的工具， Exemplifies 了在 MLflow 框架内有效使用自定义模型的范例。

import warnings

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[float, int, int]],
      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​

通过创建和准备多样化的语料库来为释义挖掘奠定基础。

语料库创建​

  • 定义一个 corpus，其中包含来自不同主题的各种句子，包括太空探索、人工智能、园艺等。这种多样性使模型能够识别跨越广泛主题的释义。

将语料库写入文件​

  • 语料库被保存到名为 feedback.txt 的文件中，这反映了大规模数据处理中的常见做法。
  • 此步骤还为在释义挖掘模型中高效处理语料库做准备。

语料库的重要性​

语料库是模型查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用例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 + "
")

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 Sentence Transformer 模型​

  • 初始化 all-MiniLM-L6-v2 Sentence Transformer 模型，该模型非常适合生成适用于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，以便在 MLflow 部署期间可移植和易于加载。

定义工件和语料库路径​

  • 在 MLflow 中指定已保存模型和语料库的路径作为工件，这对于模型日志记录和可重现性至关重要。

为签名生成测试输出​

  • 生成一个示例输出，说明模型在释义挖掘方面的预期输出格式。

创建模型签名​

  • 使用 MLflow 的 infer_signature 定义模型的输入和输出模式，并添加 similarity_threshold 参数以实现推理的灵活性。
# 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)]

创建实验​

我们创建一个新的 MLflow 实验，以便我们要将模型记录到的运行不会记录到默认实验，而是具有其自己的上下文相关条目。

# 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 记录自定义释义挖掘模型，这是模型管理和部署的关键步骤。

启动 MLflow 运行​

  • 启动 MLflow 运行，以便在 MLflow 框架内全面记录模型日志和跟踪。

在 MLflow 中记录模型​

  • 使用 MLflow 的 Python 模型日志记录函数将自定义模型集成到 MLflow 生态系统中。
  • 为模型指定一个唯一名称，以便在 MLflow 中轻松识别。
  • 记录实例化的释义挖掘模型，以及一个输入示例、模型签名、工件和 Python 依赖项。

模型日志记录的成果和优势​

  • 将模型注册到 MLflow 中，以简化管理和部署，提高其可访问性和可追溯性。
  • 确保跨部署环境的模型可重现性和版本控制。
with mlflow.start_run() as run:
  model_info = mlflow.pyfunc.log_model(
      name="paraphrase_model",
      python_model=ParaphraseMiningModel(),
      input_example=input_example,
      signature=signature,
      artifacts=artifacts,
      pip_requirements=["sentence_transformers"],
  )
Downloading artifacts:   0%|          | 0/11 [00:00<?, ?it/s]
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
Downloading artifacts:   0%|          | 0/1 [00:00<?, ?it/s]

模型加载和释义挖掘预测​

通过使用 MLflow 加载释义挖掘模型并执行预测来演示其在实际中的应用。

加载模型进行推理​

  • 使用 MLflow 的 load_model 函数检索模型并为推理做好准备。
  • 使用模型在 MLflow 注册表中的唯一 URI 定位并加载模型。

执行释义挖掘预测​

  • 使用模型的 predict 方法进行预测，应用模型类中嵌入的释义挖掘逻辑。
  • 传递一个具有指定 similarity_threshold 的代表性查询，以在语料库中查找匹配的释义。

解释模型输出​

  • 审查与查询语义相似的句子列表，以突出模型在释义识别方面的能力。
  • 分析相似度分数，以了解查询与语料库句子之间语义相关性的程度。

结论​

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

# 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：见解和潜在增强​

在我们结束本教程时，让我们回顾一下使用 Sentence Transformers 和 MLflow 实现释义挖掘模型的历程。我们已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 PythonModel 实现的灵活性和强大功能。

主要收获​

  • 我们学习了如何将高级 NLP 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，还简化了部署和可扩展性。
  • 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将自定义逻辑集成到模型的 predict 函数中，从而满足释义挖掘等特定 NLP 任务的需求。
  • 通过我们的自定义模型，我们探索了句子嵌入、语义相似性以及语言理解细微差别的动态。这种理解在从内容推荐到对话式 AI 的各种应用中至关重要。

改进释义挖掘模型的想法​

虽然我们的模型是一个坚实的基础，但可以在 predict 函数中进行多项改进，使其更强大、功能更丰富。

  1. 上下文过滤器：引入基于上下文线索或特定关键字的过滤器，以进一步精炼搜索结果。此功能允许用户将释义缩小到与他们特定上下文或主题最相关的释义。

  2. 情感分析集成：集成情感分析，按情感基调对释义进行分组。这在客户反馈分析等应用中尤其有用，因为理解情感与内容同等重要。

  3. 多语言支持：扩展模型以支持多语言释义挖掘。此增强功能将大大拓宽模型在全局或多语言环境中的适用性。

使用向量数据库进行扩展​

  • 超越静态文本文件作为语料库，更具可扩展性和实际应用的方法将涉及将模型连接到外部向量数据库或内存存储。
  • 预计算的嵌入可以存储在这些数据库中并进行更新，以适应实时内容生成，而无需重新部署模型。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能力。

最后想法​

构建和部署释义挖掘模型的旅程既有启发性又有实践性。我们已经看到 MLflow 的 PythonModel 如何为创建自定义 NLP 解决方案提供了一个灵活的画布，以及如何利用 Sentence Transformers 来深入研究语言的语义。

本教程只是一个开始。在释义挖掘和整个 NLP 领域，还有巨大的探索和创新潜力。我们鼓励您在此基础上继续发展，尝试增强功能，并继续突破 MLflow 和高级 NLP 技术可能性的界限。