datasocietyco/bge-base-en-v1.5-course-recommender-v1

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-v1")
# Run inference
sentences = [
    'Clustering in NLP',
    'This course covers the clustering concepts of natural language processing, equipping learners with the ability to cluster text data into groups and topics by finding similarities between different documents.',
    '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: 183 training samples
• Columns: name, description, languages, prerequisites, target_audience, and merged
• Approximate statistics based on the first 183 samples:

  	name	description	languages	prerequisites	target_audience	merged
  type	string	string	string	string	string	string
  details	min: 3 tokens mean: 7.06 tokens max: 16 tokens	min: 13 tokens mean: 40.5 tokens max: 117 tokens	min: 6 tokens mean: 6.66 tokens max: 10 tokens	min: 8 tokens mean: 12.56 tokens max: 21 tokens	min: 5 tokens mean: 23.2 tokens max: 54 tokens	min: 45 tokens mean: 81.98 tokens max: 174 tokens

• Samples:

  name	description	languages	prerequisites	target_audience	merged
  Foundations of Big Data	A theoretical course covering topics on how to handle data at scale and the different tools needed for distributed data storage, analysis, and management. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of distributed computing.	Course language: TBD	Prerequisite course required: Optimizing Ensemble Methods	Professionals who would like to learn the core concepts of big data and understand data at scale	Foundations of Big Data A theoretical course covering topics on how to handle data at scale and the different tools needed for distributed data storage, analysis, and management. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of distributed computing. Course language: TBD Prerequisite course required: Optimizing Ensemble Methods Professionals who would like to learn the core concepts of big data and understand data at scale
  Big Data Orchestration & Workflow Management	A theoretical course covering topics on how to handle data at scale and the different tools needed for orchestrating big data systems and manage the workflow. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of the distributed resource management ecosystem.	Course language: TBD	Prerequisite course required: Foundations of Big Data	Professionals who would like to learn the core concepts of distributed system orchestration and workflow management tools.	Big Data Orchestration & Workflow Management A theoretical course covering topics on how to handle data at scale and the different tools needed for orchestrating big data systems and manage the workflow. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of the distributed resource management ecosystem. Course language: TBD Prerequisite course required: Foundations of Big Data Professionals who would like to learn the core concepts of distributed system orchestration and workflow management tools.
  Distributed Data Storage (Hadoop)	A course that covers theory and implementation on a specific cloud platform covering topics on distributed data storage systems. Learners will be able to dive into the nature of storing and processing data at scale using tools like Hadoop on a selected cloud platform. This course will allow students to get a great foundation for creating and managing distributed data storage resources.	Course language: Java, Python	Prerequisite course required: Foundations of Big Data	Professionals who have coding knowledge and want to learn to create a scalable data storage solution using cloud services.	Distributed Data Storage (Hadoop) A course that covers theory and implementation on a specific cloud platform covering topics on distributed data storage systems. Learners will be able to dive into the nature of storing and processing data at scale using tools like Hadoop on a selected cloud platform. This course will allow students to get a great foundation for creating and managing distributed data storage resources. Course language: Java, Python Prerequisite course required: Foundations of Big Data Professionals who have coding knowledge and want to learn to create a scalable data storage solution using cloud services.

• 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, target_audience, and merged
• Approximate statistics based on the first 50 samples:

  	name	description	languages	prerequisites	target_audience	merged
  type	string	string	string	string	string	string
  details	min: 3 tokens mean: 6.98 tokens max: 15 tokens	min: 16 tokens mean: 39.66 tokens max: 83 tokens	min: 6 tokens mean: 6.66 tokens max: 10 tokens	min: 8 tokens mean: 12.58 tokens max: 21 tokens	min: 5 tokens mean: 24.06 tokens max: 54 tokens	min: 47 tokens mean: 81.94 tokens max: 139 tokens

• Samples:

  name	description	languages	prerequisites	target_audience	merged
  Word Embeddings in NLP	This course covers the intermediate concepts of natural language processing like creating word embeddings, feature engineering and word embeddings for finding text features for model development.	Course language: Python	Prerequisite course required: Topic Modeling in NLP	This is an intermediate level course for data scientists who have experience in NLP and want to learn to process and mine natural language and text data.	Word Embeddings in NLP This course covers the intermediate concepts of natural language processing like creating word embeddings, feature engineering and word embeddings for finding text features for model development. Course language: Python Prerequisite course required: Topic Modeling in NLP This is an intermediate level course for data scientists who have experience in NLP and want to learn to process and mine natural language and text data.
  Big Data Orchestration & Workflow Management	A theoretical course covering topics on how to handle data at scale and the different tools needed for orchestrating big data systems and manage the workflow. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of the distributed resource management ecosystem.	Course language: TBD	Prerequisite course required: Foundations of Big Data	Professionals who would like to learn the core concepts of distributed system orchestration and workflow management tools.	Big Data Orchestration & Workflow Management A theoretical course covering topics on how to handle data at scale and the different tools needed for orchestrating big data systems and manage the workflow. Learners will be able to dive into the vast world of data and computing at scale and get a comprehensive overview of the distributed resource management ecosystem. Course language: TBD Prerequisite course required: Foundations of Big Data Professionals who would like to learn the core concepts of distributed system orchestration and workflow management tools.
  Accelerating Data Engineering Pipelines	Explore how to employ advanced data engineering tools and techniques with GPUs to significantly improve data engineering pipelines	Course language: Python	No prerequisite course required	Professionals who wants to learn the foundation of data science and lays the groundwork for analysis and modeling.	Accelerating Data Engineering Pipelines Explore how to employ advanced data engineering tools and techniques with GPUs to significantly improve data engineering pipelines Course language: Python No prerequisite course required Professionals who wants to learn the foundation of data science and lays the groundwork for analysis and modeling.

• 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
1.6667	20	1.4345	1.0243
3.3333	40	0.9835	0.7613
5.0	60	0.7294	0.6593

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}
}