Bge Base En V1.5 Course Recommender V5

🚀 SentenceTransformer based on BAAI/bge-base-en-v1.5

This model is a finetuned version of BAAI/bge-base-en-v1.5 using sentence-transformers. It maps sentences and paragraphs to a 768-dimensional dense vector space, which can be applied in semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and other tasks.

🚀 Quick Start

First, you need to install the Sentence Transformers library:

pip install -U sentence-transformers

Then, you can load this model and perform inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("datasocietyco/bge-base-en-v1.5-course-recommender-v5")
# Run inference
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]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

✨ Features

• Semantic Mapping: Maps sentences and paragraphs to a 768 - dimensional dense vector space.
• Multiple Applications: Can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, etc.

📦 Installation

pip install -U sentence-transformers

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("datasocietyco/bge-base-en-v1.5-course-recommender-v5")
# Run inference
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]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

📚 Documentation

Model Details

Model Description

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

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()
)

Training Details

Training Dataset

Unnamed Dataset

• Size: 45 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 45 samples:

  	anchor	positive
  type	string	string
  details	min: 143 tokens mean: 178.76 tokens max: 258 tokens	min: 141 tokens mean: 176.76 tokens max: 256 tokens

• Samples:

  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
  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
  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...	...