sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:208 loss:BatchSemiHardTripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:BAAI/bge-base-en base_model:finetune:BAAI/bge-base-en model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:208
- loss:BatchSemiHardTripletLoss
widget:
- source_sentence: '

    Name : Gandalf

    Category: Financial Services, Consulting

    Department: Finance

    Location: Singapore

    Amount: 457.29

    Card: Financial Advisory Services

    Trip Name: unknown

    '
  sentences:
  - '

    Name : InterGlobal Tech

    Category: Business Software Solutions, Data Processing Services

    Department: Marketing

    Location: New York, NY

    Amount: 1249.95

    Card: Marketing Automation Tools

    Trip Name: unknown

    '
  - '

    Name : Nuvotek Solutions

    Category: Consulting Services, Managed IT Services

    Department: Information Security

    Location: Berlin, Germany

    Amount: 879.65

    Card: Annual Cybersecurity Resilience Program

    Trip Name: unknown

    '
  - '

    Name : Omega Systems Inc.

    Category: Integrated Business Solutions, Enterprise Software Sales

    Department: Research & Development

    Location: Oslo, Norway

    Amount: 1943.75

    Card: AI Development Suite

    Trip Name: unknown

    '
- source_sentence: '

    Name : NexGen Fiscal Systems

    Category: Financial Software Solutions, Revenue Management Services

    Department: Finance

    Location: San Francisco, CA

    Amount: 2749.95

    Card: Q4 Revenue Optimization Initiative

    Trip Name: unknown

    '
  sentences:
  - '

    Name : GlobalRes Workforce Solutions

    Category: Remote Work Platforms, HR Technology Vendors

    Department: Engineering

    Location: Barcelona, Spain

    Amount: 1894.27

    Card: Hybrid Work Enablement

    Trip Name: unknown

    '
  - '

    Name : InterLang Solutions

    Category: Language Interpretation Services, Remote Collaboration Tools

    Department: HR

    Location: Tokyo, Japan

    Amount: 1642.59

    Card: Diversity & Inclusion Initiatives

    Trip Name: unknown

    '
  - '

    Name : CovaRisk Consulting

    Category: Risk Advisory, Financial Services

    Department: Legal

    Location: Toronto, Canada

    Amount: 1124.37

    Card: Assurance Payment

    Trip Name: unknown

    '
- source_sentence: '

    Name : Optix Global

    Category: Digital Storage Solutions, Office Essentials Provider

    Department: All Departments

    Location: Tokyo, Japan

    Amount: 568.77

    Card: Monthly Office Needs

    Trip Name: unknown

    '
  sentences:
  - '

    Name : Digital Wave Solutions

    Category: IT Infrastructure Services, Data Analytic Platforms

    Department: Finance

    Location: San Francisco, CA

    Amount: 1748.92

    Card: Annual Data Management & Reporting

    Trip Name: unknown

    '
  - '

    Name : Analytix Global Solutions

    Category: Business Intelligence Services, Regulatory Compliance Tools

    Department: Finance

    Location: London, UK

    Amount: 1323.67

    Card: Financial Compliance Enhancement

    Trip Name: unknown

    '
  - '

    Name : Daesung Enterprises

    Category: Catering Services, Event Management

    Department: Sales

    Location: Lisbon, Portugal

    Amount: 375.45

    Card: Q4 Client Engagement Events

    Trip Name: unknown

    '
- source_sentence: '

    Name : Kanzan Solutions

    Category: Consulting Services, Business Advisory

    Department: Legal

    Location: Tokyo, Japan

    Amount: 3900.75

    Card: Quarterly Compliance Review

    Trip Name: unknown

    '
  sentences:
  - '

    Name : Alta Via Mix

    Category: Airline Catering, Luxury Travel Services

    Department: Executive

    Location: Milan, Italy

    Amount: 1925.49

    Card: Executive Incentive Program

    Trip Name: Annual Leadership Summit

    '
  - '

    Name : RBS

    Category: Financial Services, Business Consultancy

    Department: Finance

    Location: Toronto, Canada

    Amount: 1134.28

    Card: Cross-Border Transaction Facilitation

    Trip Name: unknown

    '
  - '

    Name : InnovaThink Global

    Category: Management Consultancy, Technical Training Services

    Department: HR

    Location: Zurich, Switzerland

    Amount: 1675.32

    Card: Innovation and Efficiency Program

    Trip Name: unknown

    '
- source_sentence: '

    Name : NetWise Solutions

    Category: Data Transfer Services, Digital Infrastructure

    Department: Product

    Location: Singapore

    Amount: 1579.42

    Card: Global Network Enhancement

    Trip Name: unknown

    '
  sentences:
  - '

    Name : Fernández & Co. Services

    Category: Property Management, Facility Services

    Department: Office Administration

    Location: Madrid, Spain

    Amount: 1245.67

    Card: Monthly Facility Operations

    Trip Name: unknown

    '
  - '

    Name : AeroDyn Research

    Category: Research Services, Data Analysis

    Department: Research & Development

    Location: Amsterdam, Netherlands

    Amount: 2457.42

    Card: Annual Innovation Assessment

    Trip Name: unknown

    '
  - '

    Name : Global Horizon Travel

    Category: Travel Services, Package Deals

    Department: Sales

    Location: Tokyo, Japan

    Amount: 1199.75

    Card: Annual Sales Retreat

    Trip Name: Sales Strategy Summit

    '
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: bge base en train
      type: bge-base-en-train
    metrics:
    - type: cosine_accuracy
      value: 0.8605769230769231
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.13942307692307693
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8413461538461539
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8605769230769231
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8605769230769231
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: bge base en eval
      type: bge-base-en-eval
    metrics:
    - type: cosine_accuracy
      value: 0.9242424242424242
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.07575757575757576
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.9545454545454546
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.9242424242424242
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.9545454545454546
      name: Max Accuracy
---

# SentenceTransformer based on BAAI/bge-base-en

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en). 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](https://huggingface.co/BAAI/bge-base-en) <!-- at revision b737bf5dcc6ee8bdc530531266b4804a5d77b5d8 -->
- **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("dshvadskiy/finetuned-bge-base-en")
# Run inference
sentences = [
    '\nName : NetWise Solutions\nCategory: Data Transfer Services, Digital Infrastructure\nDepartment: Product\nLocation: Singapore\nAmount: 1579.42\nCard: Global Network Enhancement\nTrip Name: unknown\n',
    '\nName : Global Horizon Travel\nCategory: Travel Services, Package Deals\nDepartment: Sales\nLocation: Tokyo, Japan\nAmount: 1199.75\nCard: Annual Sales Retreat\nTrip Name: Sales Strategy Summit\n',
    '\nName : AeroDyn Research\nCategory: Research Services, Data Analysis\nDepartment: Research & Development\nLocation: Amsterdam, Netherlands\nAmount: 2457.42\nCard: Annual Innovation Assessment\nTrip Name: unknown\n',
]
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

#### Triplet
* Dataset: `bge-base-en-train`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.8606     |
| dot_accuracy       | 0.1394     |
| manhattan_accuracy | 0.8413     |
| euclidean_accuracy | 0.8606     |
| **max_accuracy**   | **0.8606** |

#### Triplet
* Dataset: `bge-base-en-eval`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric             | Value      |
|:-------------------|:-----------|
| cosine_accuracy    | 0.9242     |
| dot_accuracy       | 0.0758     |
| manhattan_accuracy | 0.9545     |
| euclidean_accuracy | 0.9242     |
| **max_accuracy**   | **0.9545** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 208 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 208 samples:
  |         | sentence                                                                          | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | type    | string                                                                            | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
  | details | <ul><li>min: 32 tokens</li><li>mean: 39.5 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~5.29%</li><li>1: ~4.81%</li><li>2: ~3.37%</li><li>3: ~3.85%</li><li>4: ~3.85%</li><li>5: ~5.77%</li><li>6: ~1.92%</li><li>7: ~2.88%</li><li>8: ~5.29%</li><li>9: ~5.29%</li><li>10: ~4.33%</li><li>11: ~2.40%</li><li>12: ~3.85%</li><li>13: ~2.88%</li><li>14: ~4.33%</li><li>15: ~3.37%</li><li>16: ~3.37%</li><li>17: ~1.44%</li><li>18: ~4.33%</li><li>19: ~4.81%</li><li>20: ~3.85%</li><li>21: ~2.88%</li><li>22: ~5.77%</li><li>23: ~3.37%</li><li>24: ~2.88%</li><li>25: ~0.96%</li><li>26: ~2.88%</li></ul> |
* Samples:
  | sentence                                                                                                                                                                                                                                     | label          |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
  | <code><br>Name : Yijie Logistics<br>Category: Logistics Services<br>Department: Sales<br>Location: Berlin, Germany<br>Amount: 485.67<br>Card: Quarterly Client Visit and Logistics Coordination<br>Trip Name: unknown<br></code>             | <code>0</code> |
  | <code><br>Name : Serenity Solutions<br>Category: Office Wellness Solutions<br>Department: Office Administration<br>Location: Munich, Germany<br>Amount: 772.58<br>Card: Ergonomic Office Enhancements<br>Trip Name: unknown<br></code>       | <code>1</code> |
  | <code><br>Name : Cortec International<br>Category: Event Management Services, Business Solutions<br>Department: Sales<br>Location: London, UK<br>Amount: 1337.25<br>Card: Global Sales Summit Participation<br>Trip Name: unknown<br></code> | <code>2</code> |
* Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss)

### Evaluation Dataset

#### Unnamed Dataset


* Size: 52 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 52 samples:
  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
  | details | <ul><li>min: 34 tokens</li><li>mean: 39.62 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>0: ~3.85%</li><li>3: ~1.92%</li><li>4: ~5.77%</li><li>5: ~5.77%</li><li>6: ~3.85%</li><li>7: ~1.92%</li><li>8: ~1.92%</li><li>9: ~1.92%</li><li>10: ~3.85%</li><li>11: ~9.62%</li><li>12: ~5.77%</li><li>13: ~3.85%</li><li>14: ~1.92%</li><li>15: ~9.62%</li><li>17: ~1.92%</li><li>18: ~3.85%</li><li>20: ~1.92%</li><li>21: ~9.62%</li><li>22: ~1.92%</li><li>23: ~3.85%</li><li>24: ~1.92%</li><li>25: ~5.77%</li><li>26: ~7.69%</li></ul> |
* Samples:
  | sentence                                                                                                                                                                                                                                                         | label           |
  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------|
  | <code><br>Name : Versatile Systems Ltd.<br>Category: Office Management Solutions, Software Solutions<br>Department: Office Administration<br>Location: Tokyo, Japan<br>Amount: 845.67<br>Card: Integrated Office Infrastructure<br>Trip Name: unknown<br></code> | <code>21</code> |
  | <code><br>Name : NexGen Comms<br>Category: Telecom Services, Communications Solutions<br>Department: Sales<br>Location: Berlin, Germany<br>Amount: 879.45<br>Card: Q2 Client Outreach Program<br>Trip Name: unknown<br></code>                                   | <code>23</code> |
  | <code><br>Name : Digital Wave Solutions<br>Category: IT Infrastructure Services, Data Analytic Platforms<br>Department: Finance<br>Location: San Francisco, CA<br>Amount: 1748.92<br>Card: Annual Data Management & Reporting<br>Trip Name: unknown<br></code>   | <code>18</code> |
* Loss: [<code>BatchSemiHardTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `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 | bge-base-en-eval_max_accuracy | bge-base-en-train_max_accuracy |
|:-----:|:----:|:-----------------------------:|:------------------------------:|
| 0     | 0    | -                             | 0.8606                         |
| 5.0   | 65   | 0.9545                        | -                              |


### Framework Versions
- Python: 3.9.16
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.6.0
- Accelerate: 1.3.0
- Datasets: 3.2.0
- 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",
}
```

#### BatchSemiHardTripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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