Bge Base En V1.5 Course Recommender V5

🚀 基于BAAI/bge-base-en-v1.5的句子转换器

本模型是基于 BAAI/bge-base-en-v1.5 微调的 sentence-transformers 模型。它可以将句子和段落映射到768维的密集向量空间，可用于语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等任务。

🚀 快速开始

直接使用（Sentence Transformers）

首先安装Sentence Transformers库：

pip install -U sentence-transformers

然后你可以加载这个模型并进行推理：

from sentence_transformers import SentenceTransformer

# 从🤗 Hub下载
model = SentenceTransformer("datasocietyco/bge-base-en-v1.5-course-recommender-v5")
# 进行推理
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 获取嵌入向量的相似度分数
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

✨ 主要特性

• 基于强大的 BAAI/bge-base-en-v1.5 模型进行微调。
• 能够将句子和段落映射到768维的密集向量空间。
• 支持多种自然语言处理任务，如语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等。

📚 详细文档

模型详情

模型描述

模型来源

• 文档：Sentence Transformers Documentation
• 仓库：Sentence Transformers on GitHub
• Hugging Face：Sentence Transformers on Hugging Face

完整模型架构

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

训练详情

训练数据集

未命名数据集

• 规模：45个训练样本
• 列：anchor 和 positive
• 基于前45个样本的近似统计信息： | | anchor | positive | | ---- | ---- | ---- | | 类型 | 字符串 | 字符串 | | 详情 |
  • 最小值：143个标记
  • 平均值：178.76个标记
  • 最大值：258个标记
  |
  • 最小值：141个标记
  • 平均值：176.76个标记
  • 最大值：256个标记
  |
• 样本： | anchor | positive | | ---- | ---- | | Creating and Querying Data in SQL. This course builds on foundational data skills to teach learners how to effectively manipulate data in Structured Query Language (SQL). By the end of this course, learners will be able to describe database structures, import data into a database, and combine and manipulate data within a single table. Learners will also have gained exposure to working with data of different types, including string, numerical, and temporal.. tags: 'DML', 'analytics', 'ERD', 'SQL', 'functions', 'DQL', 'DDL'. Languages: Course language: TBD. Prerequisites: No prerequisite course required. Target audience: Professionals with limited or no experience in SQL or similar languages. Junior analysts or analysts familiar with other similar programming languages and frameworks. | Course Name:Creating and Querying Data in SQL|Course Description:This course builds on foundational data skills to teach learners how to effectively manipulate data in Structured Query Language (SQL). By the end of this course, learners will be able to describe database structures, import data into a database, and combine and manipulate data within a single table. Learners will also have gained exposure to working with data of different types, including string, numerical, and temporal.|Tags:'DML', 'analytics', 'ERD', 'SQL', 'functions', 'DQL', 'DDL'|Course language: TBD|Target Audience:Professionals with limited or no experience in SQL or similar languages. Junior analysts or analysts familiar with other similar programming languages and frameworks|No prerequisite course required | | Clustering Categorical and Mixed Data in Python. In this course, learners will prepare data for, implement, and optimize three advanced clustering models in Python while comparing their different use cases. In particular, this course focuses on the suitability of different clustering methods for different kinds of data: numerical, categorical, and mixed. Learners will distinguish between k-modes, mean shift, and k-prototypes models, developing their understanding of when each model will best meet their needs.. tags: 'k-prototypes', 'mean-shift', 'clustering', 'k-modes'. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: This is an introductory level course for data scientists who want to learn to detect and visualize underlying patterns and groups in unlabelled data and how to handle different types of data.. | Course Name:Clustering Categorical and Mixed Data in Python|Course Description:In this course, learners will prepare data for, implement, and optimize three advanced clustering models in Python while comparing their different use cases. In particular, this course focuses on the suitability of different clustering methods for different kinds of data: numerical, categorical, and mixed. Learners will distinguish between k-modes, mean shift, and k-prototypes models, developing their understanding of when each model will best meet their needs.|Tags:'k-prototypes', 'mean-shift', 'clustering', 'k-modes'|Course language: Python|Target Audience:This is an introductory level course for data scientists who want to learn to detect and visualize underlying patterns and groups in unlabelled data and how to handle different types of data.|No prerequisite course required | | Hierarchical and Density-Based Clustering in Python. In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using Python. The first method, hierarchical clustering, creates easy-to-read, tree branch-like clusters in order of increasing specificity. The second method, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), creates groups based on the concentration of data points within a region, facilitating analysis of irregularly shaped data. By the end of this course, learners will prepare data for, implement, and optimize these models.. tags: 'Hierarchical', 'clustering', 'DBSCAN'. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: This is an introductory level course for data scientists who want to learn to detect and visualize underlying patterns and groups in unlabelled data and how to handle different types of data.. | Course Name:Hierarchical and Density-Based Clustering in Python|Course Description:In this course, learners will encounter more sophisticated methods for generating clusters within unlabeled data using Python. The first method, hierarchical clustering, creates easy-to-read, tree branch-like clusters in order of increasing specificity. The second method, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), creates groups based on the concentration of data points within a region, facilitating analysis of irregularly shaped data. By the end of this course, learners will prepare data for, implement, and optimize these models.|Tags:'Hierarchical', 'clustering', 'DBSCAN'|Course language: Python|Target Audience:This is an introductory level course for data scientists who want to learn to detect and visualize underlying patterns and groups in unlabelled data and how to handle different types of data.|No prerequisite course required |
• 损失函数：MultipleNegativesRankingLoss，参数如下：

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

评估数据集

未命名数据集

• 规模：5个评估样本
• 列：anchor 和 positive
• 基于前5个样本的近似统计信息： | | anchor | positive | | ---- | ---- | ---- | | 类型 | 字符串 | 字符串 | | 详情 |
  • 最小值：167个标记
  • 平均值：211.2个标记
  • 最大值：272个标记
  |
  • 最小值：165个标记
  • 平均值：209.2个标记
  • 最大值：270个标记
  |
• 样本： | anchor | positive | | ---- | ---- | | Interactive Visualization with Bokeh in Python. Bokeh is a Python library designed for building complex, multi-layered statistical plots. In this course, learners will discuss the advantages of interactive visualizations using this library. They will first generate a series of simple interactive plots, such as bar charts, scatter plots, and histograms. Then, they will clean and wrangle geospatial data to create an interactive choropleth map. They will also discuss how to export and integrate Bokeh visualizations into web sites as HTML widgets.
. tags: 'data story telling', 'analytics', 'interactive visualization', 'visualization', 'python', 'bokeh'. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.. | Course Name:Interactive Visualization with Bokeh in Python|Course Description:Bokeh is a Python library designed for building complex, multi-layered statistical plots. In this course, learners will discuss the advantages of interactive visualizations using this library. They will first generate a series of simple interactive plots, such as bar charts, scatter plots, and histograms. Then, they will clean and wrangle geospatial data to create an interactive choropleth map. They will also discuss how to export and integrate Bokeh visualizations into web sites as HTML widgets.
|Tags:'data story telling', 'analytics', 'interactive visualization', 'visualization', 'python', 'bokeh'|Course language: Python|Target Audience:Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.|No prerequisite course required | | Data Visualization with ggplot2 in R. In this course, learners will take their R skills to the next level by preparing data for exploratory analysis and creating basic, static visualizations. Learners will begin by discussing the purpose of conducting exploratory data analysis (EDA) and best practices for profiling a dataset. Then, using both base R and tidyverse packages, learners will generate bar charts, scatter plots, histograms, and other common visualizations to better understand the shape, structure, and features of a sample dataset.
. tags: 'ggplot', 'analytics', 'R', 'visualization', 'tidyverse', 'EDA'. Languages: Course language: TBD. Prerequisites: Prerequisite course required: Data Wrangling in R. Target audience: Creating visualizations is a critical means of exploring data and revealing insights. In this course, learners will take their R skills to the next level by preparing data for exploratory analysis and creating basic, static visualizations. Using both base R and tidyverse packages, learners will generate bar charts, scatter plots, histograms, and other common visualizations to better understand the shape, structure, and features of a sample dataset.. | Course Name:Data Visualization with ggplot2 in R|Course Description:In this course, learners will take their R skills to the next level by preparing data for exploratory analysis and creating basic, static visualizations. Learners will begin by discussing the purpose of conducting exploratory data analysis (EDA) and best practices for profiling a dataset. Then, using both base R and tidyverse packages, learners will generate bar charts, scatter plots, histograms, and other common visualizations to better understand the shape, structure, and features of a sample dataset.
|Tags:'ggplot', 'analytics', 'R', 'visualization', 'tidyverse', 'EDA'|Course language: TBD|Target Audience:Creating visualizations is a critical means of exploring data and revealing insights. In this course, learners will take their R skills to the next level by preparing data for exploratory analysis and creating basic, static visualizations. Using both base R and tidyverse packages, learners will generate bar charts, scatter plots, histograms, and other common visualizations to better understand the shape, structure, and features of a sample dataset.|Prerequisite course required: Data Wrangling in R | | Outlier Detection for Time Series in Python. While many outlier detection techniques are suitable for general-purpose datasets, time series data involves complex dependencies that, if properly modeled, allow past values to predict future values. In this course, learners will explore how to detect outliers in time series data based on their divergence from predicted values over a particular period. They will begin by developing an Autoregressive Integrated Moving Average (ARIMA) model to extrapolate key statistical properties used in forecasting based on a historical dataset. They will then identify outliers by comparing the model's predictions against actuals as the basis of an anomaly detection model.. tags: 'anomaly detection', 'outlier detection', 'AR', 'ARIMA', 'time series', 'MA'. Languages: Course language: Python. Prerequisites: No prerequisite course required. Target audience: Professionals with some Python experience who would like to expand their skills to learn about various outlier detection techniques. | Course Name:Outlier Detection for Time Series in Python|Course Description:While many outlier detection techniques are suitable for general-purpose datasets, time series data involves complex dependencies that, if properly modeled, allow past values to predict future values. In this course, learners will explore how to detect outliers in time series data based on their divergence from predicted values over a particular period. They will begin by developing an Autoregressive Integrated Moving Average (ARIMA) model to extrapolate key statistical properties used in forecasting based on a historical dataset. They will then identify outliers by comparing the model's predictions against actuals as the basis of an anomaly detection model.|Tags:'anomaly detection', 'outlier detection', 'AR', 'ARIMA', 'time series', 'MA'|Course language: Python|Target Audience:Professionals with some Python experience who would like to expand their skills to learn about various outlier detection techniques|No prerequisite course required |
• 损失函数：MultipleNegativesRankingLoss，参数如下：

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

训练超参数

非默认超参数

• eval_strategy：steps
• per_device_train_batch_size：16
• per_device_eval_batch_size：16
• learning_rate：3e-06
• max_steps：24
• warmup_ratio：0.1
• batch_sampler：no_duplicates

所有超参数

训练日志

框架版本

• Python：3.12.8
• Sentence Transformers：3.1.1
• Transformers：4.45.2
• PyTorch：2.2.2
• Accelerate：1.2.1
• Datasets：3.2.0
• Tokenizers：0.20.3

📄 许可证

文档中未提及相关许可证信息。

📖 引用

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}