sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:3683 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: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) <!-- 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("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]
```

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 3,683 training samples
* Columns: <code>course_title</code>, <code>level</code>, and <code>subject</code>
* Approximate statistics based on the first 1000 samples:
  |         | course_title                                                                      | level                                                                           | subject                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                          | string                                                                         |
  | details | <ul><li>min: 4 tokens</li><li>mean: 11.02 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.27 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> |
* Samples:
  | course_title                                                             | level                           | subject                       |
  |:-------------------------------------------------------------------------|:--------------------------------|:------------------------------|
  | <code>Ultimate Investment Banking Course</code>                          | <code>All Levels</code>         | <code>Business Finance</code> |
  | <code>Complete GST Course & Certification - Grow Your CA Practice</code> | <code>All Levels</code>         | <code>Business Finance</code> |
  | <code>Financial Modeling for Business Analysts and Consultants</code>    | <code>Intermediate Level</code> | <code>Business Finance</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: 100 evaluation samples
* Columns: <code>course_title</code>, <code>level</code>, and <code>subject</code>
* Approximate statistics based on the first 100 samples:
  |         | course_title                                                                      | level                                                                           | subject                                                                        |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                          | string                                                                         |
  | details | <ul><li>min: 4 tokens</li><li>mean: 12.63 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.42 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 4.0 tokens</li><li>max: 4 tokens</li></ul> |
* Samples:
  | course_title                                                             | level                       | subject                       |
  |:-------------------------------------------------------------------------|:----------------------------|:------------------------------|
  | <code>Learn to Use jQuery UI Widgets</code>                              | <code>Beginner Level</code> | <code>Web Development</code>  |
  | <code>Financial Statements: Learn Accounting. Unlock the Numbers.</code> | <code>Beginner Level</code> | <code>Business Finance</code> |
  | <code>Trade Recap I - A Real Look at Futures Options Markets</code>      | <code>Beginner Level</code> | <code>Business Finance</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`: 932
- `warmup_ratio`: 0.1
- `fp16`: True
- `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`: 932
- `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`: True
- `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
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   |
|:------:|:----:|:-------------:|:------:|
| 0.0866 | 20   | 2.2161        | 1.7831 |
| 0.1732 | 40   | 1.9601        | 1.5400 |
| 0.2597 | 60   | 1.6253        | 1.1987 |
| 0.3463 | 80   | 1.2393        | 1.0009 |
| 0.4329 | 100  | 1.1817        | 0.9073 |
| 0.5195 | 120  | 1.0667        | 0.8817 |
| 0.6061 | 140  | 1.258         | 0.8282 |
| 0.6926 | 160  | 1.2375        | 0.7618 |
| 0.7792 | 180  | 1.0925        | 0.7274 |
| 0.8658 | 200  | 1.0823        | 0.7101 |
| 0.9524 | 220  | 0.8789        | 0.7056 |
| 1.0390 | 240  | 0.9597        | 0.7107 |
| 1.1255 | 260  | 0.8427        | 0.7221 |
| 1.2121 | 280  | 0.8612        | 0.7287 |
| 1.2987 | 300  | 0.8428        | 0.7275 |
| 1.3853 | 320  | 0.6426        | 0.7451 |
| 1.4719 | 340  | 0.709         | 0.7642 |
| 1.5584 | 360  | 0.6602        | 0.7851 |
| 1.6450 | 380  | 0.7356        | 0.8244 |
| 1.7316 | 400  | 0.7633        | 0.8310 |
| 1.8182 | 420  | 0.9592        | 0.8185 |
| 1.9048 | 440  | 0.6715        | 0.8094 |
| 1.9913 | 460  | 0.7926        | 0.8103 |
| 2.0779 | 480  | 0.7703        | 0.8011 |
| 2.1645 | 500  | 0.6287        | 0.8266 |
| 2.2511 | 520  | 0.5481        | 0.8536 |
| 2.3377 | 540  | 0.7101        | 0.8679 |
| 2.4242 | 560  | 0.423         | 0.9025 |
| 2.5108 | 580  | 0.6814        | 0.9197 |
| 2.5974 | 600  | 0.5879        | 0.9492 |
| 2.6840 | 620  | 0.537         | 0.9861 |
| 2.7706 | 640  | 0.5107        | 1.0179 |
| 2.8571 | 660  | 0.6164        | 1.0413 |
| 2.9437 | 680  | 0.6582        | 1.0710 |
| 3.0303 | 700  | 0.4553        | 1.1001 |
| 3.1169 | 720  | 0.3649        | 1.1416 |
| 3.2035 | 740  | 0.9273        | 1.1142 |
| 3.2900 | 760  | 0.8816        | 1.0694 |
| 3.3766 | 780  | 0.7005        | 1.0481 |
| 3.4632 | 800  | 1.9002        | 1.0289 |
| 3.5498 | 820  | 1.4467        | 1.0141 |
| 3.6364 | 840  | 1.5564        | 1.0023 |
| 3.7229 | 860  | 1.2316        | 0.9961 |
| 3.8095 | 880  | 1.0549        | 0.9931 |
| 3.8961 | 900  | 1.2359        | 0.9913 |
| 3.9827 | 920  | 1.3568        | 0.9897 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.33.0
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## 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}
}
```

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