🤖 **模型加载与初始化**: 使用Hugging Face Transformers加载bigscience/T0pp开源LLM，并初始化tokenizer进行文本预处理，使模型能够执行文本生成任务，例如回答法律问题。

📝 **法律文本预处理**: 利用spaCy和正则表达式对法律文本进行预处理，包括转换为小写、去除多余空格和特殊字符，以及使用spaCy的NLP流程进行分词和词形还原。过滤停用词以保留有意义的术语，提高法律聊天机器人响应的准确性。

🏢 **法律实体提取**: 使用spaCy的命名实体识别（NER）功能从文本中提取法律实体，如组织、日期和法律术语。返回包含识别的实体及其类别（如组织、日期或法律相关术语）的元组列表。

🔍 **法律文档检索系统构建**: 使用FAISS构建法律文档检索系统，实现高效的语义搜索。加载Hugging Face的MiniLM嵌入模型，生成文本的数值表示，并存储在FAISS向量索引中，实现快速相似性搜索。

💬 **法律AI聊天机器人定义**: 通过使用预训练的语言模型生成对法律查询的响应来定义法律AI聊天机器人。该函数使用tokenizer处理用户查询，并使用模型生成响应，然后将响应解码为可读文本，删除任何特殊token。

In this tutorial, we will build an efficient Legal AI CHatbot using open-source tools. It provides a step-by-step guide to creating a chatbot using bigscience/T0pp LLM, Hugging Face Transformers, and PyTorch. We will walk you through setting up the model, optimizing performance using PyTorch, and ensuring an efficient and accessible AI-powered legal assistant.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizermodel_name = "bigscience/T0pp"  # Open-source and availabletokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

First, we load bigscience/T0pp, an open-source LLM, using Hugging Face Transformers. It initializes a tokenizer for text preprocessing and loads the AutoModelForSeq2SeqLM, enabling the model to perform text generation tasks such as answering legal queries.

import spacyimport renlp = spacy.load("en_core_web_sm")def preprocess_legaltext(text):    text = text.lower()    text = re.sub(r'\s+', ' ', text)  # Remove extra spaces    text = re.sub(r'[^a-zA-Z0-9\s]', '', text)  # Remove special characters    doc = nlp(text)    tokens = [token.lemma for token in doc if not token.is_stop]  # Lemmatization    return " ".join(tokens)sample_text = "The contract is valid for 5 years, terminating on December 31, 2025."print(preprocess_legal_text(sample_text))

Then, we preprocess legal text using spaCy and regular expressions to ensure cleaner and more structured input for NLP tasks. It first converts text to lowercase, removes extra spaces and special characters using regex, and then tokenizes and lemmatizes the text using spaCy’s NLP pipeline. Additionally, it filters out stop words to retain only meaningful terms, making it ideal for legal text processing in AI applications. The cleaned text is more efficient for machine learning and language models like bigscience/T0pp, improving accuracy in legal chatbot responses.

def extract_legalentities(text):    doc = nlp(text)    entities = [(ent.text, ent.label) for ent in doc.ents]    return entitiessample_text = "Apple Inc. signed a contract with Microsoft on June 15, 2023."print(extract_legal_entities(sample_text))

Here, we extract legal entities from text using spaCy’s Named Entity Recognition (NER) capabilities. The function processes the input text with spaCy’s NLP model, identifying and extracting key entities such as organizations, dates, and legal terms. It returns a list of tuples, each containing the recognized entity and its category (e.g., organization, date, or law-related term).

import faissimport numpy as npimport torchfrom transformers import AutoModel, AutoTokenizerembedding_model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")embedding_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")def embed_text(text):    inputs = embedding_tokenizer(text, return_tensors="pt", padding=True, truncation=True)    with torch.no_grad():        output = embedding_model(inputs)    embedding = output.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()  # Ensure 1D vector    return embeddinglegal_docs = [    "A contract is legally binding if signed by both parties.",    "An NDA prevents disclosure of confidential information.",    "A non-compete agreement prohibits working for a competitor."]doc_embeddings = np.array([embed_text(doc) for doc in legal_docs])print("Embeddings Shape:", doc_embeddings.shape)  # Should be (num_samples, embedding_dim)index = faiss.IndexFlatL2(doc_embeddings.shape[1])  # Dimension should match embedding sizeindex.add(doc_embeddings)query = "What happens if I break an NDA?"query_embedding = embedtext(query).reshape(1, -1)  # Reshape for FAISS, retrieved_indices = index.search(query_embedding, 1)print(f"Best matching legal text: {legal_docs[retrieved_indices[0][0]]}")

With the above code, we build a legal document retrieval system using FAISS for efficient semantic search. It first loads the MiniLM embedding model from Hugging Face to generate numerical representations of text. The embed_text function processes legal documents and queries by computing contextual embeddings using MiniLM. These embeddings are stored in a FAISS vector index, allowing fast similarity searches.

def legal_chatbot(query):    inputs = tokenizer(query, return_tensors="pt", padding=True, truncation=True)    output = model.generate(inputs, max_length=100)    return tokenizer.decode(output[0], skip_special_tokens=True)query = "What happens if I break an NDA?"print(legal_chatbot(query))

Finally, we define a Legal AI Chatbot as generating responses to legal queries using a pre-trained language model. The legal_chatbot function takes a user query, processes it using the tokenizer, and generates a response with the model. The response is then decoded into readable text, removing any special tokens. When a query like “What happens if I break an NDA?” is input, the chatbot provides a relevant AI-generated legal response.

In conclusion, by integrating bigscience/T0pp LLM, Hugging Face Transformers, and PyTorch, we have demonstrated how to build a powerful and scalable Legal AI Chatbot using open-source resources. This project is a solid foundation for creating reliable AI-powered legal tools, making legal assistance more accessible and automated.

Here is the Colab Notebook for the above project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 80k+ ML SubReddit.

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The post Building a Legal AI Chatbot: A Step-by-Step Guide Using bigscience/T0pp LLM, Open-Source NLP Models, Streamlit, PyTorch, and Hugging Face Transformers appeared first on MarkTechPost.