sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:2441 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 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:2441
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Show me list of applicant
  sentences:
  - What is the company's policy on leave for military family care
  - Display emergency contact information for team members with a specific termination
    date
  - Show applicant skills
- source_sentence: show me list of holiday
  sentences:
  - Update team member availability for a specific task and project
  - Open expense report for approval
  - Show me the annual leave calendar
- source_sentence: show me list of Department
  sentences:
  - What is the total employee count for all sub-departments under the finance department
  - Submit expense report for fuel expense
  - Update department travel request emergency contact details
- source_sentence: show me list of employee
  sentences:
  - Display employee onboarding custom fields
  - Show the department-wise employee count with parent departments
  - Show me the list of employees whose employment type is 'Direct Hire'.
- source_sentence: show me list of Employee department details
  sentences:
  - Update the employee dependent marital status to Married
  - Display employee count for permanent employees
  - Display the average performance rating of employees in the marketing department
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.9259259259259259
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9629629629629629
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9629629629629629
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9629629629629629
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.9259259259259259
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.9135802469135802
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.9185185185185184
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.9111111111111112
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.0491695853675358
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1401488320760167
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.23395729689149355
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.45382552985732183
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9288881439330929
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9444444444444444
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9023082326274489
      name: Cosine Map@100
---

# 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 dimensions
- **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("sentence_transformers_model_id")
# Run inference
sentences = [
    'show me list of Employee department details',
    'Update the employee dependent marital status to Married',
    'Display the average performance rating of employees in the marketing department',
]
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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## Evaluation

### Metrics

#### Information Retrieval

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9259     |
| cosine_accuracy@3   | 0.963      |
| cosine_accuracy@5   | 0.963      |
| cosine_accuracy@10  | 0.963      |
| cosine_precision@1  | 0.9259     |
| cosine_precision@3  | 0.9136     |
| cosine_precision@5  | 0.9185     |
| cosine_precision@10 | 0.9111     |
| cosine_recall@1     | 0.0492     |
| cosine_recall@3     | 0.1401     |
| cosine_recall@5     | 0.234      |
| cosine_recall@10    | 0.4538     |
| **cosine_ndcg@10**  | **0.9289** |
| cosine_mrr@10       | 0.9444     |
| cosine_map@100      | 0.9023     |

<!--
## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 2,441 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | sentence_1                                                                       |
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                           |
  | details | <ul><li>min: 7 tokens</li><li>mean: 7.37 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.75 tokens</li><li>max: 18 tokens</li></ul> |
* Samples:
  | sentence_0                             | sentence_1                                   |
  |:---------------------------------------|:---------------------------------------------|
  | <code>Show me list of applicant</code> | <code>Show applicant rating</code>           |
  | <code>Show me list of applicant</code> | <code>Update my objective statement</code>   |
  | <code>Show me list of applicant</code> | <code>Update my job title preferences</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`: 30
- `per_device_eval_batch_size`: 30
- `num_train_epochs`: 111
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 30
- `per_device_eval_batch_size`: 30
- `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`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 111
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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
- `include_for_metrics`: []
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch   | Step | Training Loss | cosine_ndcg@10 |
|:-------:|:----:|:-------------:|:--------------:|
| 0.6098  | 50   | -             | 0.5941         |
| 1.0     | 82   | -             | 0.6506         |
| 1.2195  | 100  | -             | 0.6689         |
| 1.8293  | 150  | -             | 0.6894         |
| 2.0     | 164  | -             | 0.6963         |
| 2.4390  | 200  | -             | 0.7201         |
| 3.0     | 246  | -             | 0.7488         |
| 3.0488  | 250  | -             | 0.7519         |
| 3.6585  | 300  | -             | 0.7863         |
| 4.0     | 328  | -             | 0.8176         |
| 4.2683  | 350  | -             | 0.8238         |
| 4.8780  | 400  | -             | 0.8320         |
| 5.0     | 410  | -             | 0.8471         |
| 5.4878  | 450  | -             | 0.8525         |
| 6.0     | 492  | -             | 0.8568         |
| 6.0976  | 500  | 1.6965        | 0.8533         |
| 6.7073  | 550  | -             | 0.8646         |
| 7.0     | 574  | -             | 0.8631         |
| 7.3171  | 600  | -             | 0.8676         |
| 7.9268  | 650  | -             | 0.8718         |
| 8.0     | 656  | -             | 0.8765         |
| 8.5366  | 700  | -             | 0.8764         |
| 9.0     | 738  | -             | 0.8845         |
| 9.1463  | 750  | -             | 0.8778         |
| 9.7561  | 800  | -             | 0.8894         |
| 10.0    | 820  | -             | 0.8848         |
| 10.3659 | 850  | -             | 0.9048         |
| 10.9756 | 900  | -             | 0.9029         |
| 11.0    | 902  | -             | 0.9026         |
| 11.5854 | 950  | -             | 0.8995         |
| 12.0    | 984  | -             | 0.8956         |
| 12.1951 | 1000 | 1.0614        | 0.8922         |
| 12.8049 | 1050 | -             | 0.9043         |
| 13.0    | 1066 | -             | 0.9103         |
| 13.4146 | 1100 | -             | 0.9057         |
| 14.0    | 1148 | -             | 0.9097         |
| 14.0244 | 1150 | -             | 0.9096         |
| 14.6341 | 1200 | -             | 0.9223         |
| 15.0    | 1230 | -             | 0.9258         |
| 15.2439 | 1250 | -             | 0.9118         |
| 15.8537 | 1300 | -             | 0.9207         |
| 16.0    | 1312 | -             | 0.9239         |
| 16.4634 | 1350 | -             | 0.9250         |
| 17.0    | 1394 | -             | 0.9161         |
| 17.0732 | 1400 | -             | 0.9203         |
| 17.6829 | 1450 | -             | 0.9146         |
| 18.0    | 1476 | -             | 0.9198         |
| 18.2927 | 1500 | 0.9705        | 0.9197         |
| 18.9024 | 1550 | -             | 0.9250         |
| 19.0    | 1558 | -             | 0.9248         |
| 19.5122 | 1600 | -             | 0.9289         |


### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.5.1
- Accelerate: 0.26.0
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## 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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