|
--- |
|
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](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-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] |
|
``` |
|
|
|
<!-- |
|
### 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: 50 training samples |
|
* Columns: <code>name</code>, <code>description</code>, <code>languages</code>, <code>prerequisites</code>, and <code>target_audience</code> |
|
* Approximate statistics based on the first 50 samples: |
|
| | name | description | languages | prerequisites | target_audience | |
|
|:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | string | string | string | |
|
| details | <ul><li>min: 3 tokens</li><li>mean: 7.0 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 43.96 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 6.6 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.32 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 22.74 tokens</li><li>max: 54 tokens</li></ul> | |
|
* Samples: |
|
| name | description | languages | prerequisites | target_audience | |
|
|:--------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Autoencoders</code> | <code>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.</code> | <code>Course language: Python</code> | <code>Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition</code> | <code>Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for image processing and computer vision.</code> | |
|
| <code>Advanced CNN</code> | <code>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.</code> | <code>Course language: Python</code> | <code>Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition</code> | <code>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.</code> | |
|
| <code>Advanced Clustering in R</code> | <code>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.</code> | <code>Course language: R</code> | <code>Prerequisite course required: Intermediate Clustering in R</code> | <code>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.</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: 50 evaluation samples |
|
* Columns: <code>name</code>, <code>description</code>, <code>languages</code>, <code>prerequisites</code>, and <code>target_audience</code> |
|
* Approximate statistics based on the first 50 samples: |
|
| | name | description | languages | prerequisites | target_audience | |
|
|:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
|
| type | string | string | string | string | string | |
|
| details | <ul><li>min: 3 tokens</li><li>mean: 7.0 tokens</li><li>max: 16 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 43.96 tokens</li><li>max: 117 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 6.6 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.32 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 22.74 tokens</li><li>max: 54 tokens</li></ul> | |
|
* Samples: |
|
| name | description | languages | prerequisites | target_audience | |
|
|:--------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>Autoencoders</code> | <code>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.</code> | <code>Course language: Python</code> | <code>Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition</code> | <code>Professionals some Python experience who would like to expand their skillset to more advanced machine learning algorithms for image processing and computer vision.</code> | |
|
| <code>Advanced CNN</code> | <code>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.</code> | <code>Course language: Python</code> | <code>Prerequisite course required: Convolutional Neural Networks (CNN) for Image Recognition</code> | <code>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.</code> | |
|
| <code>Advanced Clustering in R</code> | <code>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.</code> | <code>Course language: R</code> | <code>Prerequisite course required: Intermediate Clustering in R</code> | <code>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.</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 | |
|
|:-----:|:----:|:-------------:|:------:| |
|
| 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 |
|
```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.* |
|
--> |
