metadata

base_model: BAAI/bge-base-en-v1.5
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:50
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: Statistics & Probability
    sentences:
      - 'Course language: Python'
      - 'Prerequisite course required: Intermediate Statistics'
      - >-
        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 advanced statistics topics on probability like
        permutations and combinations, joint probability, conditional
        probability, marginal probability, and Bayes' theorem that provides a
        way to revise existing predictions or update probabilities given new or
        additional evidence.
      - >-
        Professionals some Python experience who would like to expand their
        skill set to more advanced Python visualization techniques and tools.
  - source_sentence: Intermediate Statistics
    sentences:
      - 'Course language: Python'
      - >-
        Professionals some Python experience who would like to expand their
        skill set to more advanced Python visualization techniques and tools.
      - >-
        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 intermediate statistics topics like t-value,
        t-distribution, chi-square distribution, f-statistic, and f-distribution
        that enable us to compare observed and expected frequencies objectively.
      - 'Prerequisite course required: Introduction to Statistics'
  - source_sentence: Cypress
    sentences:
      - >-
        Cypress is an end-to-end testing framework for your web application.
        This course explores its features, core concepts, its ecosystem, and how
        to write tests.
      - 'Course language: TBD'
      - 'Prerequisite course required: Unit Testing in Jest'
      - >-
        Professionals who would like to explore the world of testing web
        applications
  - source_sentence: Intermediate Outlier Detection
    sentences:
      - 'Prerequisite course required: Intro to Outlier Detection'
      - >-
        Detecting outlier data points are powerful machine learning techniques.
        This course covers how techniques like Local Outlier Factor and
        Isolation Forest play a role in anomaly and outlier detection. By the
        end of the course, students will learn to implement these techniques to
        identify anomalous data points
      - 'Course language: Python'
      - >-
        Professionals with some Python experience who would like to expand their
        skills to learn about various outlier detection techniques
  - source_sentence: 'React Ecosystem: Forms'
    sentences:
      - 'Course language: JavaScript'
      - 'Prerequisite course required: React Ecosystem: API Calls'
      - >-
        Professionals who would like to learn about advanced concepts that would
        allow them to build interactive websites with React.
      - >-
        A course that builds on the foundations of React framework and expands
        learners' skills to more advanced concepts.

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
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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("datasocietyco/bge-base-en-v1.5-course-recommender-v2")
# Run inference
sentences = [
    'React Ecosystem: Forms',
    "A course that builds on the foundations of React framework and expands learners' skills to more advanced concepts.",
    'Course language: JavaScript',
]
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: 50 training samples
Columns: name, description, languages, prerequisites, and target_audience

Approximate statistics based on the first 50 samples:

	name	description	languages	prerequisites	target_audience
type	string	string	string	string	string
details	min: 3 tokens mean: 7.0 tokens max: 16 tokens	min: 16 tokens mean: 43.96 tokens max: 117 tokens	min: 6 tokens mean: 6.6 tokens max: 10 tokens	min: 8 tokens mean: 12.32 tokens max: 21 tokens	min: 12 tokens mean: 22.74 tokens max: 54 tokens

Samples:

name	description	languages	prerequisites	target_audience
`Autoencoders`	`This course takes students through a journey into the world od autoencoders - a set of powerful deep learning models that have a special place in the world of image analysis. By the end of this course students will be able to navigate through the application space of autoencoders and implement autoencoders to perform tasks such as image denoising and more.`	`Course language: Python`	`Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition`	`Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for image processing and computer vision.`
`Advanced CNN`	`This course build on the subject of Convolutional Neural Networks and dives into the complex pre-trained state-of-the-art CNN architectures. It also gives students a preview of what transfer learning is and why it is such a powerful concept in Deep Learning. By the end of this course students will be able to have implemented and explored pre-trained models such as ResNet, VGG16, and Inception3.`	`Course language: Python`	`Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition`	`Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for image processing, computer vision, and transfer learning.`
`Advanced 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 application of different methods of clustering on categorical or mixed data, equipping learners to build, evaluate, and interpret these models.`	`Course language: R`	`Prerequisite course required: Intermediate Clustering in R`	`Professionals with some R experience who would like to expand their skillset to learn the core unsupervised learning techniques. Analysts with experience in another similar programming language who would like to learn core unsupervised learning frameworks and packages in R.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

Unnamed Dataset

Size: 50 evaluation samples
Columns: name, description, languages, prerequisites, and target_audience

Approximate statistics based on the first 50 samples:

	name	description	languages	prerequisites	target_audience
type	string	string	string	string	string
details	min: 3 tokens mean: 7.0 tokens max: 16 tokens	min: 16 tokens mean: 43.96 tokens max: 117 tokens	min: 6 tokens mean: 6.6 tokens max: 10 tokens	min: 8 tokens mean: 12.32 tokens max: 21 tokens	min: 12 tokens mean: 22.74 tokens max: 54 tokens

Samples:

name	description	languages	prerequisites	target_audience
`Autoencoders`	`This course takes students through a journey into the world od autoencoders - a set of powerful deep learning models that have a special place in the world of image analysis. By the end of this course students will be able to navigate through the application space of autoencoders and implement autoencoders to perform tasks such as image denoising and more.`	`Course language: Python`	`Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition`	`Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for image processing and computer vision.`
`Advanced CNN`	`This course build on the subject of Convolutional Neural Networks and dives into the complex pre-trained state-of-the-art CNN architectures. It also gives students a preview of what transfer learning is and why it is such a powerful concept in Deep Learning. By the end of this course students will be able to have implemented and explored pre-trained models such as ResNet, VGG16, and Inception3.`	`Course language: Python`	`Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition`	`Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for image processing, computer vision, and transfer learning.`
`Advanced 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 application of different methods of clustering on categorical or mixed data, equipping learners to build, evaluate, and interpret these models.`	`Course language: R`	`Prerequisite course required: Intermediate Clustering in R`	`Professionals with some R experience who would like to expand their skillset to learn the core unsupervised learning techniques. Analysts with experience in another similar programming language who would like to learn core unsupervised learning frameworks and packages in R.`

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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
5.0	20	1.0201	0.7447
5.5	40	0.6132	0.5379
6.0	60	0.5127	0.4702

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

@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}
}