sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:146 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
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:146
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Intro to CSS, Part 2
  sentences:
  - Professionals who would like to continue learning the core concepts of CSS and
    be able to style simple web pages.
  - 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'
  - '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.'
  - 'Prerequisite course required: Intro to CSS, Part 1'
- source_sentence: Reinforcement Learning
  sentences:
  - '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: Advanced CNN
    Professionals some Python experience who would like to expand their skillset to
    more advanced machine learning algorithms for reinforcement learning.'
  - 'Prerequisite course required: Advanced CNN'
  - Professionals some Python experience who would like to expand their skillset to
    more advanced machine learning algorithms for 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'
- source_sentence: 'Intro to JavaScript: Fetch Async Await'
  sentences:
  - A course that dives into the exploration of the frontend APIs, asynchronous calls
    and the concepts of modular JavaScript.
  - 'Course language: HTML, JavaScript'
  - Professionals who would like to learn the core concepts of frontend APIs, asynchronous
    calls, and the concepts of modular JavaScript.
  - 'Intro to JavaScript: Fetch Async Await A course that dives into the exploration
    of the frontend APIs, asynchronous calls and the concepts of modular JavaScript.
    Course language: HTML, JavaScript Prerequisite course required: Intro to JavaScript:
    the DOM Professionals who would like to learn the core concepts of frontend APIs,
    asynchronous calls, and the concepts of modular JavaScript.'
  - 'Prerequisite course required: Intro to JavaScript: the DOM'
- source_sentence: React Testing Library
  sentences:
  - A course that introduces to testing simple and complex React applications with
    React Testing Library.
  - Professionals who would like to explore the world of testing react applications
  - 'Course language: React'
  - 'Prerequisite course required: React Ecosystem: Styling'
  - 'React Testing Library A course that introduces to testing simple and complex
    React applications with React Testing Library. Course language: React Prerequisite
    course required: React Ecosystem: Styling Professionals who would like to explore
    the world of testing react applications'
- source_sentence: 'Intro to JavaScript: Basic Concepts'
  sentences:
  - 'Course language: HTML, JavaScript'
  - 'Prerequisite course required: Intro to JavaScript: Fetch Async Await'
  - 'Intro to JavaScript: Basic Concepts A course that finalizes the series of introductory
    JavaScript courses and introduces the students to the basic concepts in the JavaScript
    ecosystem. Course language: HTML, JavaScript Prerequisite course required: Intro
    to JavaScript: Fetch Async Await Professionals who would like to learn the basic
    concepts of JavaScript and be able to create modern JS driven websites.'
  - A course that finalizes the series of introductory JavaScript courses and introduces
    the students to the basic concepts in the JavaScript ecosystem.
  - Professionals who would like to learn the basic concepts of JavaScript and be
    able to create modern JS driven websites.
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/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](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
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-v3")
# Run inference
sentences = [
    'Intro to JavaScript: Basic Concepts',
    'A course that finalizes the series of introductory JavaScript courses and introduces the students to the basic concepts in the JavaScript ecosystem.',
    'Course language: HTML, 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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 146 training samples
* Columns: <code>name</code>, <code>description</code>, <code>languages</code>, <code>prerequisites</code>, <code>target_audience</code>, and <code>merged</code>
* Approximate statistics based on the first 146 samples:
  |         | name                                                                             | description                                                                         | languages                                                                        | prerequisites                                                                     | target_audience                                                                   | merged                                                                              |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                           | string                                                                            | string                                                                            | string                                                                              |
  | details | <ul><li>min: 3 tokens</li><li>mean: 7.12 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 41.41 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 6.65 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.69 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 23.17 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 45 tokens</li><li>mean: 83.04 tokens</li><li>max: 174 tokens</li></ul> |
* Samples:
  | name                                        | description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | languages                            | prerequisites                                                               | target_audience                                                                                                                                        | merged                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
  |:--------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|:----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Introduction to Statistics</code>     | <code>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.</code> | <code>Course language: Python</code> | <code>Prerequisite course required: Intro to Visualization in Python</code> | <code>Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.</code> | <code>Introduction to 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 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.</code> |
  | <code>Statistics & Probability</code>       | <code>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.</code>                                                            | <code>Course language: Python</code> | <code>Prerequisite course required: Intermediate Statistics</code>          | <code>Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.</code> | <code>Statistics & Probability 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. Course language: Python Prerequisite course required: Intermediate Statistics Professionals some Python experience who would like to expand their skill set to more advanced Python visualization techniques and tools.</code>                                                                       |
  | <code>Databases: Advanced Relational</code> | <code>A deeper dive into the many capabilities of a relational database, how to optimize usage and make sure that your are getting the most use out of your database so that you have a strong base for your applications.</code>                                                                                                                                                                                                                                                                                | <code>Course language: SQL</code>    | <code>Prerequisite course required: Databases: Relational</code>            | <code>Professionals who would like to improve on their knowledge of relational databases.</code>                                                       | <code>Databases: Advanced Relational A deeper dive into the many capabilities of a relational database, how to optimize usage and make sure that your are getting the most use out of your database so that you have a strong base for your applications. Course language: SQL Prerequisite course required: Databases: Relational Professionals who would like to improve on their knowledge of relational databases.</code>                                                                                                                                                                                                                                                                                                                                                |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 37 evaluation samples
* Columns: <code>name</code>, <code>description</code>, <code>languages</code>, <code>prerequisites</code>, <code>target_audience</code>, and <code>merged</code>
* Approximate statistics based on the first 37 samples:
  |         | name                                                                             | description                                                                        | languages                                                                       | prerequisites                                                                     | target_audience                                                                   | merged                                                                              |
  |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                             | string                                                                          | string                                                                            | string                                                                            | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 6.84 tokens</li><li>max: 13 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 36.92 tokens</li><li>max: 84 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 6.7 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.05 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 23.3 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 47 tokens</li><li>mean: 77.81 tokens</li><li>max: 124 tokens</li></ul> |
* Samples:
  | name                                             | description                                                                                                                                                                                                                                                      | languages                                | prerequisites                                                              | target_audience                                                                                                                                                       | merged                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:-------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------|:---------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Understanding Different OS Concepts</code> | <code>A course that builds foundational knowledge of what an operating system is. It walks through the different core concepts of OS and its inner workings.</code>                                                                                              | <code>Course language: TBD</code>        | <code>Prerequisite course required: Domain & Hosting</code>                | <code>Professionals who would like to learn the core concepts of Operating system</code>                                                                              | <code>Understanding Different OS Concepts A course that builds foundational knowledge of what an operating system is. It walks through the different core concepts of OS and its inner workings. Course language: TBD Prerequisite course required: Domain & Hosting Professionals who would like to learn the core concepts of Operating system</code>                                                                                                                                                                                         |
  | <code>Basic GraphQL: Node.js</code>              | <code>An introduction to GraphQL, what it is good for and how to use it to query or change data.</code>                                                                                                                                                          | <code>Course language: JavaScript</code> | <code>Prerequisite course required: JSON APIs: Node.js</code>              | <code>Professionals who would like to learn the core concepts of GraphQL, using Node.js</code>                                                                        | <code>Basic GraphQL: Node.js An introduction to GraphQL, what it is good for and how to use it to query or change data. Course language: JavaScript Prerequisite course required: JSON APIs: Node.js Professionals who would like to learn the core concepts of GraphQL, using Node.js</code>                                                                                                                                                                                                                                                   |
  | <code>Deep Learning for Text Analysis</code>     | <code>This course continues on tackling topics in deep learning that address specific problem types. In this course students will be getting to know RNNs and LSTMs - types of neural networks that are often used for solving problems in text analysis.</code> | <code>Course language: Python</code>     | <code>Prerequisite course required: Neural Networks & Deep Learning</code> | <code>Professionals who would like to get a base-level understanding of the recurrent neural networks, their subtypes, and their application in text analysis.</code> | <code>Deep Learning for Text Analysis This course continues on tackling topics in deep learning that address specific problem types. In this course students will be getting to know RNNs and LSTMs - types of neural networks that are often used for solving problems in text analysis. Course language: Python Prerequisite course required: Neural Networks & Deep Learning Professionals who would like to get a base-level understanding of the recurrent neural networks, their subtypes, and their application in text analysis.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "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
<details><summary>Click to expand</summary>

- `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

</details>

### Training Logs
| Epoch | Step | Training Loss | loss   |
|:-----:|:----:|:-------------:|:------:|
| 2.0   | 20   | 1.4618        | 1.0396 |
| 4.0   | 40   | 0.8698        | 0.8235 |
| 6.0   | 60   | 0.8096        | 0.7544 |


### Framework Versions
- Python: 3.11.7
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->