bge base en v1.5 course recommender v5
Overview
The bge-base-en-v1.5-course-recommender-v5 is a specialized sentence-similarity model fine-tuned for educational content mapping. Built on the BGE (Baaiv General Embedding) architecture, it converts course descriptions and learner queries into dense vectors to facilitate high-precision semantic search. Unlike general-purpose embedding models, this version is optimized for the nuances of academic and professional training taxonomies, making it ideal for building course recommendation engines or automated curriculum mapping tools. It integrates seamlessly into any RAG (Retrieval-Augmented Generation) pipeline via standard embedding libraries, providing a lightweight yet performant alternative for developers needing domain-specific retrieval without the overhead of massive LLMs.
Highlights
- Optimized for educational content and course semantic similarity
- Apache-2.0 licensed for flexible commercial integration
- High-efficiency dense vector embeddings for fast retrieval
- Ideal for RAG pipelines and academic recommendation systems
Usage
Install
# Install Hugging Face transformers
pip install transformers torchSDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("datasocietyco/bge-base-en-v1.5-course-recommender-v5")
tokenizer = AutoTokenizer.from_pretrained("datasocietyco/bge-base-en-v1.5-course-recommender-v5")Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance：Before downloading, install huggingface_hub with:
Guidance
pip install -U huggingface_hubCLI Download
Download the full repository
Download the full repository
huggingface-cli download datasocietyco/bge-base-en-v1.5-course-recommender-v5Download a single file to a local folder (e.g. config.json into ./dir)
Download a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download datasocietyco/bge-base-en-v1.5-course-recommender-v5 config.json --local-dir ./dirSee the official docs for more CLI options
SDK Download
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('datasocietyco/bge-base-en-v1.5-course-recommender-v5')Git Download
Make sure git-lfs is installed first
Git Download
git lfs install
git clone https://huggingface.co/datasocietyco/bge-base-en-v1.5-course-recommender-v5To skip LFS large-file downloads, use:
Skip LFS
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasocietyco/bge-base-en-v1.5-course-recommender-v5Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.
PyTorch / Transformers Usage
Install Transformers
Install Transformers
pip install -U transformers torchLoad the model and run inference
Load the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('datasocietyco/bge-base-en-v1.5-course-recommender-v5')
tokenizer = AutoTokenizer.from_pretrained('datasocietyco/bge-base-en-v1.5-course-recommender-v5')Full Documentation
来源: HuggingFace
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:45
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: BAAI/bge-base-en-v1.5 <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
code
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()
)Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
bash
pip install -U sentence-transformersThen you can load this model and run inference.
python
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]
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Training Details
Training Dataset
#### Unnamed Dataset
- Size: 45 training samples
- Columns: <code>anchor</code> and <code>positive</code>
- Approximate statistics based on the first 45 samples:
- Samples: