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
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

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:

pip install -U sentence-transformers

Then 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-v2")
# Run inference
sentences = [
    'Visualizing Data with Matplotlib in Python',
    'This course covers the basics of data visualization and exploratory data analysis. It helps students learn different plots and their use cases.',
    'Course language: Python',
]
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]

Training Details

Training Dataset

Unnamed Dataset

Size: 143 training samples
Columns: name, description, languages, prerequisites, target_audience, and combined

Approximate statistics based on the first 143 samples:

	name	description	languages	prerequisites	target_audience	combined
type	string	string	string	string	string	string
details	min: 3 tokens mean: 7.82 tokens max: 17 tokens	min: 13 tokens mean: 39.24 tokens max: 117 tokens	min: 6 tokens mean: 6.57 tokens max: 10 tokens	min: 8 tokens mean: 12.85 tokens max: 22 tokens	min: 12 tokens mean: 23.02 tokens max: 54 tokens	min: 58 tokens mean: 94.5 tokens max: 187 tokens

Samples:

name	description	languages	prerequisites	target_audience	combined
`Reinforcement Learning`	`This course covers the specialized branch of machine learning and deep learning called reinforcement learning (RL). By the end of this course students will be able to define RL use cases and real world scenarios where RL models are used, they will be able to create a simple RL model and evaluate its performance.`	`Course language: Python`	`Prerequisite course required: Working with Complex Pre-trained CNNs in Python`	`Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for reinforcement learning.`	`Course Name:Reinforcement Learning`
`Optimizing Ensemble Methods in Python`	`This course covers advanced topics in optimizing ensemble learning methods – specifically random forest and gradient boosting. Students will learn to implement base models and perform hyperparameter tuning to enhance the performance of models.`	`Course language: Python`	`Prerequisite course required: Ensemble Methods in Python`	`Professionals experience in ensemble methods and who want to enhance their skill set in advanced Python classification techniques.`	`Course Name:Optimizing Ensemble Methods in Python`
`Fundamentals of Accelerated Computing with OpenACC`	`Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace Heat Equation to achieve performance gains.`	`Course language: Python`	`No prerequisite course required`	`Professionals who want to learn how to write code, configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and implement the workflow learnt for massive performance gains.`	`Course Name:Fundamentals of Accelerated Computing with OpenACC`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

Unnamed Dataset

Size: 36 evaluation samples
Columns: name, description, languages, prerequisites, target_audience, and combined

Approximate statistics based on the first 36 samples:

	name	description	languages	prerequisites	target_audience	combined
type	string	string	string	string	string	string
details	min: 3 tokens mean: 7.92 tokens max: 13 tokens	min: 13 tokens mean: 46.39 tokens max: 92 tokens	min: 6 tokens mean: 6.75 tokens max: 10 tokens	min: 8 tokens mean: 13.47 tokens max: 20 tokens	min: 5 tokens mean: 23.75 tokens max: 54 tokens	min: 61 tokens mean: 103.28 tokens max: 165 tokens

Samples:

name	description	languages	prerequisites	target_audience	combined
`Intro to CSS, Part 2`	`A course that continues to build on the foundational understanding of CSS syntax and allows students to work with responsive design and media queries.`	`Course language: CSS, HTML`	`Prerequisite course required: Intro to CSS, Part 1`	`Professionals who would like to continue learning the core concepts of CSS and be able to style simple web pages.`	`Course Name:Intro to CSS, Part 2`
`Foundations of Statistics in Python`	`This course is designed for learners who would like to learn about statistics and apply it for decision-making. This course is a comprehensive review of statistical terms ranging from foundational (mean, median, mode, standard deviation, variance, covariance, correlation) to more complex concepts such as normality in data, confidence intervals, and p-values. Additional topics include how to calculate summary statistics and how to carry out hypothesis testing to inform decisions.`	`Course language: Python`	`Prerequisite course required: Intro to Visualization in Python`	`Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.`	`Course Name:Foundations of Statistics in Python`
`Spherical k-Means and Hierarchical Clustering in R`	`This course covers the unsupervised learning method called clustering which is used to find patterns or groups in data without the need for labelled data. This course includes different methods of clustering on numerical data including density-based and hierarchical-based clustering and how to build, evaluate and interpret these models.`	`Course language: R`	`Prerequisite course required: Intro to Clustering in R`	`Professionals with some R experience who would like to expand their skillset to more clustering techniques like hierarchical clustering and DBSCAN.`	`Course Name:Spherical k-Means and Hierarchical Clustering in R`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 3e-06
max_steps: 64
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 3e-06
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 3.0
max_steps: 64
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
2.2222	20	1.5188	1.1718
4.4444	40	1.0652	0.8327
6.6667	60	0.677	0.7192

Framework Versions

Python: 3.9.13
Sentence Transformers: 3.1.1
Transformers: 4.45.1
PyTorch: 2.2.2
Accelerate: 0.34.2
Datasets: 3.0.0
Tokenizers: 0.20.0

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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}
}