Bertopic ArXiv

🚀 BERTopic_ArXiv

BERTopic_ArXiv是一個基於BERTopic的模型。BERTopic是一個靈活且模塊化的主題建模框架，可從大型數據集中生成易於解釋的主題。此預訓練模型展示了BERTopic中可使用的幾種表示模型，它在約30000篇ArXiv摘要上進行訓練，採用了多種主題表示方法。

🚀 快速開始

本模型的使用步驟如下：

1. 安裝BERTopic：

pip install -U bertopic
pip install -U safetensors

2. 使用模型：

from bertopic import BERTopic
topic_model = BERTopic.load("MaartenGr/BERTopic_ArXiv")

topic_model.get_topic_info()

3. 查看不同的主題表示：

>>> topic_model.get_topic(0, full=True)
{'Main': [['dialogue', 0.02704485163341523],
  ['dialog', 0.01677038224466311],
  ['response', 0.011692640237477233],
  ['responses', 0.01002788412923778],
  ['intent', 0.00990720856306287],
  ['oriented', 0.009217253131615378],
  ['slot', 0.009177118721490055],
  ['conversational', 0.009129311385144046],
  ['systems', 0.009101146153425574],
  ['conversation', 0.008845392252307181]],
 'POS': [['dialogue', 0.02704485163341523],
  ['dialog', 0.01677038224466311],
  ['response', 0.011692640237477233],
  ['responses', 0.01002788412923778],
  ['intent', 0.00990720856306287],
  ['slot', 0.009177118721490055],
  ['conversational', 0.009129311385144046],
  ['systems', 0.009101146153425574],
  ['conversation', 0.008845392252307181],
  ['user', 0.008753551043296965]],
 'KeyBERTInspired': [['task oriented dialogue', 0.6559894680976868],
  ['dialogue systems', 0.6249060034751892],
  ['oriented dialogue', 0.5788208246231079],
  ['dialog systems', 0.530449628829956],
  ['dialogue state', 0.5167528390884399],
  ['response generation', 0.5143576860427856],
  ['spoken language understanding', 0.46739083528518677],
  ['oriented dialog', 0.4600704610347748],
  ['dialog', 0.4534587264060974],
  ['dialogues', 0.44082391262054443]],
 'MMR': [['dialogue', 0.02704485163341523],
  ['dialog', 0.01677038224466311],
  ['response', 0.011692640237477233],
  ['responses', 0.01002788412923778],
  ['intent', 0.00990720856306287],
  ['oriented', 0.009217253131615378],
  ['slot', 0.009177118721490055],
  ['conversational', 0.009129311385144046],
  ['systems', 0.009101146153425574],
  ['conversation', 0.008845392252307181]],
 'KeyBERT + MMR': [['task oriented dialogue', 0.6559894680976868],
  ['dialogue systems', 0.6249060034751892],
  ['oriented dialogue', 0.5788208246231079],
  ['dialog systems', 0.530449628829956],
  ['dialogue state', 0.5167528390884399],
  ['response generation', 0.5143576860427856],
  ['spoken language understanding', 0.46739083528518677],
  ['oriented dialog', 0.4600704610347748],
  ['dialog', 0.4534587264060974],
  ['dialogues', 0.44082391262054443]],
 'OpenAI_Label': [['Challenges and Approaches in Developing Task-oriented Dialogue Systems',
   1]],
 'OpenAI_Summary': [['Task-oriented dialogue systems and their components, such as dialogue policy, natural language understanding, dialogue state tracking, response generation, and end-to-end training using neural networks. These components are crucial in assisting users to complete various activities such as booking tickets and restaurant reservations through spoken language understanding dialogue. The challenge lies in tracking dialogue states of multiple domains and obtaining annotations for training. Effective SLU is achieved by utilizing context from the prior dialogue history.',
   1]]}

✨ 主要特性

• 多種主題表示方法：使用了POS、KeyBERTInspired、MaximalMarginalRelevance、KeyBERT + MaximalMarginalRelevance、ChatGPT labels、ChatGPT summaries等多種主題表示方法。
• 可視化示例：提供了默認c-TF-IDF表示和ChatGPT生成標籤的可視化示例。
• 詳細的主題信息：可以查看每個主題的關鍵詞、頻率、標籤等信息。

📦 安裝指南

要使用此模型，請安裝BERTopic：

pip install -U bertopic
pip install -U safetensors

💻 使用示例

基礎用法

from bertopic import BERTopic
topic_model = BERTopic.load("MaartenGr/BERTopic_ArXiv")

topic_model.get_topic_info()

高級用法

查看所有不同的主題表示（關鍵詞、標籤、摘要等）：

>>> topic_model.get_topic(0, full=True)
{'Main': [['dialogue', 0.02704485163341523],
  ['dialog', 0.01677038224466311],
  ['response', 0.011692640237477233],
  ['responses', 0.01002788412923778],
  ['intent', 0.00990720856306287],
  ['oriented', 0.009217253131615378],
  ['slot', 0.009177118721490055],
  ['conversational', 0.009129311385144046],
  ['systems', 0.009101146153425574],
  ['conversation', 0.008845392252307181]],
 'POS': [['dialogue', 0.02704485163341523],
  ['dialog', 0.01677038224466311],
  ['response', 0.011692640237477233],
  ['responses', 0.01002788412923778],
  ['intent', 0.00990720856306287],
  ['slot', 0.009177118721490055],
  ['conversational', 0.009129311385144046],
  ['systems', 0.009101146153425574],
  ['conversation', 0.008845392252307181],
  ['user', 0.008753551043296965]],
 'KeyBERTInspired': [['task oriented dialogue', 0.6559894680976868],
  ['dialogue systems', 0.6249060034751892],
  ['oriented dialogue', 0.5788208246231079],
  ['dialog systems', 0.530449628829956],
  ['dialogue state', 0.5167528390884399],
  ['response generation', 0.5143576860427856],
  ['spoken language understanding', 0.46739083528518677],
  ['oriented dialog', 0.4600704610347748],
  ['dialog', 0.4534587264060974],
  ['dialogues', 0.44082391262054443]],
 'MMR': [['dialogue', 0.02704485163341523],
  ['dialog', 0.01677038224466311],
  ['response', 0.011692640237477233],
  ['responses', 0.01002788412923778],
  ['intent', 0.00990720856306287],
  ['oriented', 0.009217253131615378],
  ['slot', 0.009177118721490055],
  ['conversational', 0.009129311385144046],
  ['systems', 0.009101146153425574],
  ['conversation', 0.008845392252307181]],
 'KeyBERT + MMR': [['task oriented dialogue', 0.6559894680976868],
  ['dialogue systems', 0.6249060034751892],
  ['oriented dialogue', 0.5788208246231079],
  ['dialog systems', 0.530449628829956],
  ['dialogue state', 0.5167528390884399],
  ['response generation', 0.5143576860427856],
  ['spoken language understanding', 0.46739083528518677],
  ['oriented dialog', 0.4600704610347748],
  ['dialog', 0.4534587264060974],
  ['dialogues', 0.44082391262054443]],
 'OpenAI_Label': [['Challenges and Approaches in Developing Task-oriented Dialogue Systems',
   1]],
 'OpenAI_Summary': [['Task-oriented dialogue systems and their components, such as dialogue policy, natural language understanding, dialogue state tracking, response generation, and end-to-end training using neural networks. These components are crucial in assisting users to complete various activities such as booking tickets and restaurant reservations through spoken language understanding dialogue. The challenge lies in tracking dialogue states of multiple domains and obtaining annotations for training. Effective SLU is achieved by utilizing context from the prior dialogue history.',
   1]]}

📚 詳細文檔

主題概述

• 主題數量：107
• 訓練文檔數量：33189

訓練過程

模型的訓練過程如下：

from cuml.manifold import UMAP
from cuml.cluster import HDBSCAN
from bertopic import BERTopic
from sklearn.feature_extraction.text import CountVectorizer
from bertopic.representation import PartOfSpeech, KeyBERTInspired, MaximalMarginalRelevance, OpenAI

# 準備子模型
embedding_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
umap_model = UMAP(n_components=5, n_neighbors=50, random_state=42, metric="cosine", verbose=True)
hdbscan_model = HDBSCAN(min_samples=20, gen_min_span_tree=True, prediction_data=False, min_cluster_size=20, verbose=True)
vectorizer_model = CountVectorizer(stop_words="english", ngram_range=(1, 3), min_df=5)

# 使用ChatGPT進行摘要
summarization_prompt = """
I have a topic that is described by the following keywords: [KEYWORDS]
In this topic, the following documents are a small but representative subset of all documents in the topic:
[DOCUMENTS]

Based on the information above, please give a description of this topic in the following format:
topic: <description>
"""
summarization_model = OpenAI(model="gpt-3.5-turbo", chat=True, prompt=summarization_prompt, nr_docs=5, exponential_backoff=True, diversity=0.1)

# 表示模型
representation_models = {
    "POS": PartOfSpeech("en_core_web_lg"),
    "KeyBERTInspired": KeyBERTInspired(),
    "MMR": MaximalMarginalRelevance(diversity=0.3),
    "KeyBERT + MMR": [KeyBERTInspired(), MaximalMarginalRelevance(diversity=0.3)],
    "OpenAI_Label": OpenAI(model="gpt-3.5-turbo", exponential_backoff=True, chat=True, diversity=0.1),
    "OpenAI_Summary": [KeyBERTInspired(), summarization_model],
}

# 擬合BERTopic
topic_model= BERTopic(
        embedding_model=embedding_model,
        umap_model=umap_model,
        hdbscan_model=hdbscan_model,
        vectorizer_model=vectorizer_model,
        representation_model=representation_models,
        verbose=True
).fit(docs)

訓練超參數

• calculate_probabilities：False
• language：None
• low_memory：False
• min_topic_size：10
• n_gram_range：(1, 1)
• nr_topics：None
• seed_topic_list：None
• top_n_words：10
• verbose：True

框架版本