Bge Base En V1.5 Course Recommender V5
這是一個從BAAI/bge-base-en-v1.5微調而來的sentence-transformers模型,能將句子和段落映射到768維的密集向量空間。
下載量 15.87k
發布時間 : 4/25/2025
模型概述
該模型主要用於語義文本相似度、語義搜索、複述挖掘、文本分類、聚類等任務。
模型特點
高維向量表示
能將句子和段落映射到768維的密集向量空間
多重負例排序損失
使用多重負例排序損失進行訓練,提高相似度計算準確性
長文本處理能力
最大序列長度為512個標記,適合處理較長文本
模型能力
計算句子相似度
語義搜索
文本分類
文本聚類
複述挖掘
使用案例
教育領域
課程推薦
基於課程描述的語義相似度進行課程推薦
信息檢索
語義搜索
基於語義而非關鍵詞匹配的搜索系統
🚀 基於BAAI/bge-base-en-v1.5的句子轉換器
本模型是基於 BAAI/bge-base-en-v1.5 微調的 sentence-transformers 模型。它可以將句子和段落映射到768維的密集向量空間,可用於語義文本相似度計算、語義搜索、釋義挖掘、文本分類、聚類等任務。
🚀 快速開始
直接使用(Sentence Transformers)
首先安裝Sentence Transformers庫:
pip install -U sentence-transformers
然後你可以加載這個模型並進行推理:
from sentence_transformers import SentenceTransformer
# 從🤗 Hub下載
model = SentenceTransformer("datasocietyco/bge-base-en-v1.5-course-recommender-v5")
# 進行推理
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]
# 獲取嵌入向量的相似度分數
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
✨ 主要特性
- 基於強大的 BAAI/bge-base-en-v1.5 模型進行微調。
- 能夠將句子和段落映射到768維的密集向量空間。
- 支持多種自然語言處理任務,如語義文本相似度計算、語義搜索、釋義挖掘、文本分類、聚類等。
📚 詳細文檔
模型詳情
模型描述
屬性 | 詳情 |
---|---|
模型類型 | 句子轉換器 |
基礎模型 | BAAI/bge-base-en-v1.5 |
最大序列長度 | 512個標記 |
輸出維度 | 768個標記 |
相似度函數 | 餘弦相似度 |
模型來源
- 文檔:Sentence Transformers Documentation
- 倉庫:Sentence Transformers on GitHub
- Hugging Face:Sentence Transformers on Hugging Face
完整模型架構
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()
)
訓練詳情
訓練數據集
未命名數據集
- 規模:45個訓練樣本
- 列:
anchor
和positive
- 基於前45個樣本的近似統計信息:
| | anchor | positive |
| ---- | ---- | ---- |
| 類型 | 字符串 | 字符串 |
| 詳情 |
- 最小值:143個標記
- 平均值:178.76個標記
- 最大值:258個標記
- 最小值:141個標記
- 平均值:176.76個標記
- 最大值:256個標記
- 樣本:
| 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
| - 損失函數:
MultipleNegativesRankingLoss
,參數如下:
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
評估數據集
未命名數據集
- 規模:5個評估樣本
- 列:
anchor
和positive
- 基於前5個樣本的近似統計信息:
| | anchor | positive |
| ---- | ---- | ---- |
| 類型 | 字符串 | 字符串 |
| 詳情 |
- 最小值:167個標記
- 平均值:211.2個標記
- 最大值:272個標記
- 最小值:165個標記
- 平均值:209.2個標記
- 最大值:270個標記
- 樣本:
| 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 requiredData 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 ROutlier 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
| - 損失函數:
MultipleNegativesRankingLoss
,參數如下:
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
訓練超參數
非默認超參數
eval_strategy
:stepsper_device_train_batch_size
:16per_device_eval_batch_size
:16learning_rate
:3e-06max_steps
:24warmup_ratio
:0.1batch_sampler
:no_duplicates
所有超參數
點擊展開
overwrite_output_dir
:Falsedo_predict
:Falseeval_strategy
:stepsprediction_loss_only
:Trueper_device_train_batch_size
:16per_device_eval_batch_size
:16per_gpu_train_batch_size
:Noneper_gpu_eval_batch_size
:Nonegradient_accumulation_steps
:1eval_accumulation_steps
:Nonetorch_empty_cache_steps
:Nonelearning_rate
:3e-06weight_decay
:0.0adam_beta1
:0.9adam_beta2
:0.999adam_epsilon
:1e-08max_grad_norm
:1.0num_train_epochs
:3.0max_steps
:24lr_scheduler_type
:linearlr_scheduler_kwargs
:{}warmup_ratio
:0.1warmup_steps
:0log_level
:passivelog_level_replica
:warninglog_on_each_node
:Truelogging_nan_inf_filter
:Truesave_safetensors
:Truesave_on_each_node
:Falsesave_only_model
:Falserestore_callback_states_from_checkpoint
:Falseno_cuda
:Falseuse_cpu
:Falseuse_mps_device
:Falseseed
:42data_seed
:Nonejit_mode_eval
:Falseuse_ipex
:Falsebf16
:Falsefp16
:Falsefp16_opt_level
:O1half_precision_backend
:autobf16_full_eval
:Falsefp16_full_eval
:Falsetf32
:Nonelocal_rank
:0ddp_backend
:Nonetpu_num_cores
:Nonetpu_metrics_debug
:Falsedebug
:[]dataloader_drop_last
:Falsedataloader_num_workers
:0dataloader_prefetch_factor
:Nonepast_index
:-1disable_tqdm
:Falseremove_unused_columns
:Truelabel_names
:Noneload_best_model_at_end
:Falseignore_data_skip
:Falsefsdp
:[]fsdp_min_num_params
:0fsdp_config
:{'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
:Noneaccelerator_config
:{'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
:Nonelabel_smoothing_factor
:0.0optim
:adamw_torchoptim_args
:Noneadafactor
:Falsegroup_by_length
:Falselength_column_name
:lengthddp_find_unused_parameters
:Noneddp_bucket_cap_mb
:Noneddp_broadcast_buffers
:Falsedataloader_pin_memory
:Truedataloader_persistent_workers
:Falseskip_memory_metrics
:Trueuse_legacy_prediction_loop
:Falsepush_to_hub
:Falseresume_from_checkpoint
:Nonehub_model_id
:Nonehub_strategy
:every_savehub_private_repo
:Falsehub_always_push
:Falsegradient_checkpointing
:Falsegradient_checkpointing_kwargs
:Noneinclude_inputs_for_metrics
:Falseeval_do_concat_batches
:Truefp16_backend
:autopush_to_hub_model_id
:Nonepush_to_hub_organization
:Nonemp_parameters
:auto_find_batch_size
:Falsefull_determinism
:Falsetorchdynamo
:Noneray_scope
:lastddp_timeout
:1800torch_compile
:Falsetorch_compile_backend
:Nonetorch_compile_mode
:Nonedispatch_batches
:Nonesplit_batches
:Noneinclude_tokens_per_second
:Falseinclude_num_input_tokens_seen
:Falseneftune_noise_alpha
:Noneoptim_target_modules
:Nonebatch_eval_metrics
:Falseeval_on_start
:Falseuse_liger_kernel
:Falseeval_use_gather_object
:Falsebatch_sampler
:no_duplicatesmulti_dataset_batch_sampler
:proportional
訓練日誌
輪次 | 步數 | 訓練損失 | 損失 |
---|---|---|---|
6.6667 | 20 | 0.0651 | 0.0005 |
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
📄 許可證
文檔中未提及相關許可證信息。
📖 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}
}
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