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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:221
- loss:BatchSemiHardTripletLoss
base_model: BAAI/bge-base-en
widget:
- source_sentence: '
Name : Vitality Systems
Category: Facility Management, Health Services
Department: Office Administration
Location: Chicago, IL
Amount: 347.29
Card: Office Wellness Initiative
Trip Name: unknown
'
sentences:
- '
Name : BizPro Connect
Category: Data Services & Analytics, Telecommunications
Department: Executive
Location: London, UK
Amount: 1249.67
Card: Q3 Strategic Enhancement Plan
Trip Name: unknown
'
- '
Name : Global Insights Group
Category: Subscriptions & Memberships, Data Services & Analytics
Department: Marketing
Location: London, UK
Amount: 1245.67
Card: Marketing Intelligence Fund
Trip Name: unknown
'
- '
Name : Allied Workplace Solutions
Category: Facility Management, Energy Services
Department: Office Administration
Location: New York, NY
Amount: 861.47
Card: Monthly Expenses Allocation
Trip Name: unknown
'
- source_sentence: '
Name : Café Del Mar
Category: Catering Services, Event Planning
Department: Sales
Location: Barcelona, ES
Amount: 578.29
Card: Q3 Client Engagement
Trip Name: unknown
'
sentences:
- '
Name : TranspoSolutions LLP
Category: Corporate Travel Analyst, Expense Management Services
Department: Executive
Location: New York, NY
Amount: 629.45
Card: Strategic Partnership Infrastructure
Trip Name: Tech Symposium NYC
'
- '
Name : Talent Scout Services
Category: Professional Services, Recruitment Solutions
Department: HR
Location: New York, NY
Amount: 3200.0
Card: Recruitment Excellence Fund
Trip Name: unknown
'
- '
Name : FastLane Transport
Category: Logistics & Transport, Vehicle Services
Department: Sales
Location: Miami, FL
Amount: 158.25
Card: Sales Travel Expenses
Trip Name: unknown
'
- source_sentence: '
Name : CleverCo
Category: Software & Licenses
Department: Customer Success
Location: Amsterdam, Netherlands
Amount: 2999.99
Card: Digital Engagement Tools
Trip Name: unknown
'
sentences:
- '
Name : Miller & Gartner
Category: Consulting, Business Expense
Department: Legal
Location: Chicago, IL
Amount: 1500.0
Card: Legal Fund
Trip Name: unknown
'
- '
Name : Urban Mobility Solutions
Category: Transportation Services, Leasing Services
Department: Executive
Location: Chicago, IL
Amount: 1023.45
Card: Strategic Partnership Building
Trip Name: Vendor Contract Negotiations
'
- '
Name : Tech Haven Solutions
Category: Integrated Systems Provider, Custom Hardware Solutions
Department: IT Operations
Location: Toronto, Canada
Amount: 1550.43
Card: Infrastructure Upgrades Project
Trip Name: unknown
'
- source_sentence: '
Name : Globex Solutions
Category: Financial Software, Data Management
Department: Finance
Location: New York, NY
Amount: 1324.57
Card: Global Revenue Enhancement Initiative
Trip Name: unknown
'
sentences:
- '
Name : CloudSync Security
Category: Cloud Solutions, Cybersecurity Services
Department: IT Operations
Location: Dublin, Ireland
Amount: 1239.45
Card: Integration Compliance Fund
Trip Name: unknown
'
- '
Name : Cirrus Insights
Category: Customer Engagement Platform, SaaS
Department: Sales
Location: Austin, TX
Amount: 1899.99
Card: Annual Software Licensing Fund
Trip Name: unknown
'
- '
Name : Innovative Patents Co.
Category: Intellectual Property Services, Legal Services
Department: Legal
Location: New York, NY
Amount: 3250.0
Card: Patent Acquisition Fund
Trip Name: unknown
'
- source_sentence: '
Name : TechSavvy Solutions
Category: Software Services, Online Subscription
Department: Engineering
Location: Austin, TX
Amount: 1200.0
Card: Annual Engineering Tools Budget
Trip Name: unknown
'
sentences:
- '
Name : Kanzan Solutions
Category: Consulting Services, Business Advisory
Department: Legal
Location: Tokyo, Japan
Amount: 3900.75
Card: Quarterly Compliance Review
Trip Name: unknown
'
- '
Name : Omachi Meitetsu
Category: Transportation Services, Travel Services
Department: Sales
Location: Hakkuba Japan
Amount: 120.0
Card: Quarterly Travel Expenses
Trip Name: unknown
'
- '
Name : Globex Tech Solutions
Category: Office Equipment Providers, IT Services & Solutions
Department: IT Operations
Location: New York, NY
Amount: 1589.75
Card: Annual IT Enhancement Budget
Trip Name: unknown
'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
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: 0.8280542986425339
name: Cosine Accuracy
- type: dot_accuracy
value: 0.17194570135746606
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.8280542986425339
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.8280542986425339
name: Euclidean Accuracy
- type: max_accuracy
value: 0.8280542986425339
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: bge base en eval
type: bge-base-en-eval
metrics:
- type: cosine_accuracy
value: 0.9714285714285714
name: Cosine Accuracy
- type: dot_accuracy
value: 0.02857142857142857
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9714285714285714
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9714285714285714
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9714285714285714
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("juanpprim/finetuned-bge-base-en")
# Run inference
sentences = [
'\nName : TechSavvy Solutions\nCategory: Software Services, Online Subscription\nDepartment: Engineering\nLocation: Austin, TX\nAmount: 1200.0\nCard: Annual Engineering Tools Budget\nTrip Name: unknown\n',
'\nName : Omachi Meitetsu\nCategory: Transportation Services, Travel Services\nDepartment: Sales\nLocation: Hakkuba Japan\nAmount: 120.0\nCard: Quarterly Travel Expenses\nTrip Name: unknown\n',
'\nName : Globex Tech Solutions\nCategory: Office Equipment Providers, IT Services & Solutions\nDepartment: IT Operations\nLocation: New York, NY\nAmount: 1589.75\nCard: Annual IT Enhancement Budget\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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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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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 | 0.8281 |
| dot_accuracy | 0.1719 |
| manhattan_accuracy | 0.8281 |
| euclidean_accuracy | 0.8281 |
| **max_accuracy** | **0.8281** |
#### 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 | 0.9714 |
| dot_accuracy | 0.0286 |
| manhattan_accuracy | 0.9714 |
| euclidean_accuracy | 0.9714 |
| **max_accuracy** | **0.9714** |
<!--
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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: 221 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 221 samples:
| | sentence | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 32 tokens</li><li>mean: 39.7 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~4.52%</li><li>1: ~3.17%</li><li>2: ~3.17%</li><li>3: ~4.52%</li><li>4: ~5.43%</li><li>5: ~4.07%</li><li>6: ~4.52%</li><li>7: ~4.07%</li><li>8: ~3.62%</li><li>9: ~3.62%</li><li>10: ~3.17%</li><li>11: ~2.71%</li><li>12: ~3.62%</li><li>13: ~3.17%</li><li>14: ~3.62%</li><li>15: ~2.26%</li><li>16: ~4.52%</li><li>17: ~4.07%</li><li>18: ~4.07%</li><li>19: ~3.17%</li><li>20: ~4.98%</li><li>21: ~3.17%</li><li>22: ~5.43%</li><li>23: ~3.62%</li><li>24: ~4.07%</li><li>25: ~1.81%</li><li>26: ~1.81%</li></ul> |
* Samples:
| sentence | label |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code><br>Name : Palace Suites<br>Category: Hotel Accommodation, Event Outsourcing<br>Department: Marketing<br>Location: Amsterdam, NL<br>Amount: 1278.64<br>Card: Annual Conference Stay<br>Trip Name: 2023 Innovation Summit<br></code> | <code>0</code> |
| <code><br>Name : BuroPro Services<br>Category: Facilities Management, Maintenance Solutions<br>Department: Office Administration<br>Location: Berlin, Germany<br>Amount: 879.99<br>Card: Monthly Equipment Oversight<br>Trip Name: unknown<br></code> | <code>1</code> |
| <code><br>Name : TechXperts Global<br>Category: IT Services, Consulting<br>Department: IT Operations<br>Location: Berlin, Germany<br>Amount: 987.49<br>Card: Quarterly System Assessment<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: 55 evaluation samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 55 samples:
| | sentence | label |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 34 tokens</li><li>mean: 39.07 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>0: ~1.82%</li><li>1: ~7.27%</li><li>2: ~1.82%</li><li>3: ~1.82%</li><li>4: ~1.82%</li><li>5: ~1.82%</li><li>6: ~10.91%</li><li>7: ~5.45%</li><li>8: ~3.64%</li><li>9: ~3.64%</li><li>10: ~14.55%</li><li>12: ~1.82%</li><li>13: ~3.64%</li><li>14: ~3.64%</li><li>15: ~3.64%</li><li>16: ~3.64%</li><li>19: ~9.09%</li><li>20: ~3.64%</li><li>22: ~1.82%</li><li>23: ~3.64%</li><li>24: ~3.64%</li><li>25: ~5.45%</li><li>26: ~1.82%</li></ul> |
* Samples:
| sentence | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------|
| <code><br>Name : NetSolve Consulting<br>Category: IT Consulting, Infrastructure Solutions<br>Department: IT Operations<br>Location: Berlin, Germany<br>Amount: 892.45<br>Card: Tech Infrastructure Enhancement<br>Trip Name: unknown<br></code> | <code>2</code> |
| <code><br>Name : Urban Mobility Solutions<br>Category: Transportation Services, Leasing Services<br>Department: Executive<br>Location: Chicago, IL<br>Amount: 1023.45<br>Card: Strategic Partnership Building<br>Trip Name: Vendor Contract Negotiations<br></code> | <code>10</code> |
| <code><br>Name : CloudSync Security<br>Category: Cloud Solutions, Cybersecurity Services<br>Department: IT Operations<br>Location: Dublin, Ireland<br>Amount: 1239.45<br>Card: Integration Compliance Fund<br>Trip Name: unknown<br></code> | <code>15</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
- `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
- `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 | - | 0.8281 |
| 5.0 | 70 | 0.9714 | - |
### Framework Versions
- Python: 3.9.22
- Sentence Transformers: 3.1.1
- Transformers: 4.42.2
- PyTorch: 2.7.0+cu126
- Accelerate: 1.3.0
- Datasets: 3.6.0
- 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",
}
```
#### 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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