---
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:3683
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Cost Accounting - A Comprehensive Study
sentences:
- Beginner Level
- Business Finance
- All Levels
- source_sentence: Build Financial Models & Value Companies The Easy Way
sentences:
- All Levels
- Business Finance
- All Levels
- source_sentence: build a solid foundation for trading options
sentences:
- Intermediate Level
- Business Finance
- All Levels
- source_sentence: Create Beautiful Image Maps for Your Website
sentences:
- Graphic Design
- Intermediate Level
- All Levels
- source_sentence: 'Multiply your returns using ''Value Investing",https://www.udemy.com/multiply-your-returns-using-value-investing/,true,20,1942,19,63,All
Levels,4.5 hours,2015-07-23T00:08:33Z
874284,Weekly Forex Analysis by Baraq FX"'
sentences:
- Beginner Level
- Business Finance
- All Levels
---
# 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)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### 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("sachin19566/bge-base-en-v1.5-udemy-fte")
# Run inference
sentences = [
'Multiply your returns using \'Value Investing",https://www.udemy.com/multiply-your-returns-using-value-investing/,true,20,1942,19,63,All Levels,4.5 hours,2015-07-23T00:08:33Z\n874284,Weekly Forex Analysis by Baraq FX"',
'All Levels',
'Business Finance',
]
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: 3,683 training samples
* Columns: course_title
, level
, and subject
* Approximate statistics based on the first 1000 samples:
| | course_title | level | subject |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | string | string | string |
| details |
Ultimate Investment Banking Course
| All Levels
| Business Finance
|
| Complete GST Course & Certification - Grow Your CA Practice
| All Levels
| Business Finance
|
| Financial Modeling for Business Analysts and Consultants
| Intermediate Level
| Business Finance
|
* Loss: [MultipleNegativesRankingLoss
](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: 100 evaluation samples
* Columns: course_title
, level
, and subject
* Approximate statistics based on the first 100 samples:
| | course_title | level | subject |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
| type | string | string | string |
| details | Learn to Use jQuery UI Widgets
| Beginner Level
| Web Development
|
| Financial Statements: Learn Accounting. Unlock the Numbers.
| Beginner Level
| Business Finance
|
| Trade Recap I - A Real Look at Futures Options Markets
| Beginner Level
| Business Finance
|
* Loss: [MultipleNegativesRankingLoss
](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`: 932
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters