bge base en v1.5 course recommender v5
简介
这是一个基于 BGE-base-en-v1.5 微调的特定领域向量模型，专门针对课程推荐场景进行了优化。它属于句子相似度模型，能够将课程描述或用户需求转化为高维向量，通过计算余弦相似度实现精准的课程匹配。相比通用 embedding 模型，它在学术或培训内容的语义检索上更具针对性。对于开发者而言，该模型上手简单，可直接集成到 RAG（检索增强生成）流水线中，作为向量数据库的索引模型，提升课程搜索的召回率。
核心亮点
- 基于 BGE v1.5 底座，语义表征能力强
- 专为课程推荐优化，提升匹配精准度
- 适用于 RAG 架构中的知识检索环节
- Apache-2.0 协议，商业部署无压力
使用方法
安装依赖
# 安装 Hugging Face transformers
pip install transformers torchSDK 使用
# 使用 transformers 加载模型
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 下载
我们推荐使用命令行或者 Hugging Face Hub SDK 来进行模型的下载。
操作指引：在下载前，请先通过如下命令安装 huggingface_hub：
操作指引
pip install -U huggingface_hub命令行下载
下载完整模型库
下载完整模型库
huggingface-cli download datasocietyco/bge-base-en-v1.5-course-recommender-v5下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
下载单个文件到指定本地文件夹（以下载 config.json 到当前路径下 ./dir 目录为例）
huggingface-cli download datasocietyco/bge-base-en-v1.5-course-recommender-v5 config.json --local-dir ./dirSDK 下载
SDK 下载
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('datasocietyco/bge-base-en-v1.5-course-recommender-v5')Git 下载
请确保 lfs 已经被正确安装
Git 下载
git lfs install
git clone https://huggingface.co/datasocietyco/bge-base-en-v1.5-course-recommender-v5如果您希望跳过 lfs 大文件下载，可以使用如下命令
跳过 LFS
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasocietyco/bge-base-en-v1.5-course-recommender-v5模型文件托管在 Hugging Face Hub，使用 HF CLI / SDK / Git 直接下载，不经过本站。
PyTorch / Transformers 使用
安装 Transformers
安装 Transformers
pip install -U transformers torch模型加载和推理
模型加载和推理
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')完整文档
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:
