Hugging Face's logo

labdmitriy
/
finetuned-bge-base-en-v1.5

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:208
loss:BatchSemiHardTripletLoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community
finetuned-bge-base-en-v1.5 / README.md
labdmitriy's picture
labdmitriy
Add new SentenceTransformer model.
7c7e274 verified
preview code
|
raw
history blame contribute delete
25.5 kB
 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:208
- loss:BatchSemiHardTripletLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: '
Name : ArgoMaintenance
Category: Office Equipment Repair, Consulting Services
Department: Office Administration
Location: Lisbon, Portugal
Amount: 345.67
Card: Quarterly Equipment Review
Trip Name: unknown
'
sentences:
- '
Name : EcoClean Systems
Category: Environmental Services, Industrial Equipment Care
Department: Office Administration
Location: San Francisco, CA
Amount: 952.63
Card: Essential Facility Sustainability
Trip Name: unknown
'
- '
Name : Midnight Brasserie
Category: Culinary Experience, Event Catering
Department: Marketing
Location: Paris, France
Amount: 456.87
Card: Quarterly Team Building
Trip Name: Summer Collaboration Retreat
'
- '
Name : ComplyTech Solutions
Category: Regulatory Software, Consultancy Services
Department: Compliance
Location: Brussels, Belgium
Amount: 1095.45
Card: Regulatory Compliance Optimization Plan
Trip Name: unknown
'
- source_sentence: '
Name : Casa del Camino
Category: Boutique Hotel, Travel Services
Department: Marketing
Location: Laguna Beach, CA
Amount: 842.67
Card: Team Retreat Planning
Trip Name: Annual Strategy Offsite
'
sentences:
- '
Name : Yue Hua
Category: HR & Employment Services
Department: Engineering
Location: Berlin, Germany
Amount: 3567.45
Card: Talent Acquisition Enhancement
Trip Name: unknown
'
- '
Name : Global Insight Seminars
Category: Corporate Training, Event Management Services
Department: HR
Location: London, UK
Amount: 2499.95
Card: Leadership Development Conference
Trip Name: unknown
'
- '
Name : Globetrotter Partners
Category: Lodging Services, Corporate Retreat Planning
Department: Executive
Location: Banff, Canada
Amount: 1559.75
Card: Leadership Development Seminar
Trip Name: unknown
'
- source_sentence: '
Name : TechSupply Inc.
Category: Electronics Retail, Supply Chain
Department: Research & Development
Location: Berlin, Germany
Amount: 742.45
Card: New Prototype Equipment
Trip Name: unknown
'
sentences:
- '
Name : Stanford Graphics
Category: Printed Materials, Corporate Identity Design
Department: Office Administration
Location: London, UK
Amount: 348.79
Card: Annual Business Stationery Restock
Trip Name: unknown
'
- '
Name : SkillAdvance Academy
Category: Online Learning Platform, Professional Development
Department: Engineering
Location: Austin, TX
Amount: 1875.67
Card: Continuous Improvement Initiative
Trip Name: unknown
'
- '
Name : ZetaCore Dynamics
Category: Enterprise Workflow Solutions, Cloud Computing Services
Department: Engineering
Location: Toronto, Canada
Amount: 2984.37
Card: Operational Efficiency Suite
Trip Name: unknown
'
- source_sentence: '
Name : Gandalf
Category: Financial Services, Consulting
Department: Finance
Location: Singapore
Amount: 457.29
Card: Financial Advisory Services
Trip Name: unknown
'
sentences:
- '
Name : City Shuttle Services
Category: Transportation, Logistics
Department: Sales
Location: San Francisco, CA
Amount: 85.0
Card: Sales Team Travel Fund
Trip Name: Client Meeting in Bay Area
'
- '
Name : RBC
Category: Transaction Processing, Financial Services
Department: Finance
Location: Limassol, Cyprus
Amount: 843.56
Card: Quarterly Financial Management
Trip Name: unknown
'
- '
Name : BuroPro Services
Category: Facilities Management, Maintenance Solutions
Department: Office Administration
Location: Berlin, Germany
Amount: 879.99
Card: Monthly Equipment Oversight
Trip Name: unknown
'
- source_sentence: '
Name : Otter.ai
Category: Software and Subscriptions
Department: Customer Success
Location: Toronto, ON
Amount: 1289.75
Card: Sales Team Software Budget
Trip Name: unknown
'
sentences:
- '
Name : TechSavvy Solutions
Category: Software Services, Online Subscription
Department: Engineering
Location: Austin, TX
Amount: 1200.0
Card: Annual Engineering Tools Budget
Trip Name: unknown
'
- '
Name : Baku
Category: Ride Sharing
Department: Sales
Location: Baku, Azerbaijan
Amount: 1247.88
Card: Client Engagement Activities
Trip Name: unknown
'
- '
Name : Willink Labs
Category: Consulting Services, Professional Services
Department: Engineering
Location: San Francisco, CA
Amount: 4500.0
Card: Backend Systems Upgrade Analysis
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-v1.5
results:
- task:
type: triplet
name: Triplet
dataset:
name: bge base en v1.5 train
type: bge-base-en-v1.5-train
metrics:
- type: cosine_accuracy
value: 0.8461538461538461
name: Cosine Accuracy
- type: dot_accuracy
value: 0.15384615384615385
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.8509615384615384
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.8461538461538461
name: Euclidean Accuracy
- type: max_accuracy
value: 0.8509615384615384
name: Max Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: bge base en v1.5 eval
type: bge-base-en-v1.5-eval
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- type: dot_accuracy
value: 0.0
name: Dot Accuracy
- type: manhattan_accuracy
value: 1.0
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 1.0
name: Euclidean Accuracy
- type: max_accuracy
value: 1.0
name: Max Accuracy
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- type: dot_accuracy
value: 0.0
name: Dot Accuracy
- type: manhattan_accuracy
value: 1.0
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 1.0
name: Euclidean Accuracy
- type: max_accuracy
value: 1.0
name: Max Accuracy
---
# 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("labdmitriy/finetuned-bge-base-en-v1.5")
# Run inference
sentences = [
'\nName : Otter.ai\nCategory: Software and Subscriptions\nDepartment: Customer Success\nLocation: Toronto, ON\nAmount: 1289.75\nCard: Sales Team Software Budget\nTrip Name: unknown\n',
'\nName : Willink Labs\nCategory: Consulting Services, Professional Services\nDepartment: Engineering\nLocation: San Francisco, CA\nAmount: 4500.0\nCard: Backend Systems Upgrade Analysis\nTrip Name: unknown\n',
'\nName : Baku\nCategory: Ride Sharing\nDepartment: Sales\nLocation: Baku, Azerbaijan\nAmount: 1247.88\nCard: Client Engagement Activities\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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `bge-base-en-v1.5-train`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:----------|
| cosine_accuracy | 0.8462 |
| dot_accuracy | 0.1538 |
| manhattan_accuracy | 0.851 |
| euclidean_accuracy | 0.8462 |
| **max_accuracy** | **0.851** |
#### Triplet
* Dataset: `bge-base-en-v1.5-eval`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:--------|
| cosine_accuracy | 1.0 |
| dot_accuracy | 0.0 |
| manhattan_accuracy | 1.0 |
| euclidean_accuracy | 1.0 |
| **max_accuracy** | **1.0** |
#### Triplet
* Dataset: `bge-base-en-v1.5-eval`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:--------|
| cosine_accuracy | 1.0 |
| dot_accuracy | 0.0 |
| manhattan_accuracy | 1.0 |
| euclidean_accuracy | 1.0 |
| **max_accuracy** | **1.0** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## 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: 33 tokens</li><li>mean: 39.62 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>0: ~3.37%</li><li>1: ~3.85%</li><li>2: ~3.85%</li><li>3: ~3.37%</li><li>4: ~6.25%</li><li>5: ~4.81%</li><li>6: ~3.85%</li><li>7: ~3.37%</li><li>8: ~4.33%</li><li>9: ~3.85%</li><li>10: ~2.40%</li><li>11: ~1.92%</li><li>12: ~3.37%</li><li>13: ~3.85%</li><li>14: ~2.88%</li><li>15: ~2.40%</li><li>16: ~5.29%</li><li>17: ~5.77%</li><li>18: ~5.29%</li><li>19: ~4.33%</li><li>20: ~1.92%</li><li>21: ~4.81%</li><li>22: ~2.40%</li><li>23: ~2.40%</li><li>24: ~2.88%</li><li>25: ~4.33%</li><li>26: ~2.88%</li></ul> |
* Samples:
| sentence | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code><br>Name : FTC<br>Category: Regulatory Compliance Services, Business Consulting<br>Department: Legal<br>Location: Toronto, Canada<br>Amount: 3594.76<br>Card: Annual Compliance Assessment<br>Trip Name: unknown<br></code> | <code>0</code> |
| <code><br>Name : IntelliSync Integration<br>Category: Connectivity Services, Enterprise Solutions<br>Department: IT Operations<br>Location: San Francisco, CA<br>Amount: 1387.42<br>Card: Global Connectivity Suite<br>Trip Name: unknown<br></code> | <code>1</code> |
| <code><br>Name : Omachi Meitetsu<br>Category: Transportation Services, Travel Services<br>Department: Sales<br>Location: Hakkuba Japan<br>Amount: 120.0<br>Card: Quarterly Travel Expenses<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: 32 tokens</li><li>mean: 39.12 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>0: ~3.85%</li><li>1: ~1.92%</li><li>2: ~9.62%</li><li>3: ~5.77%</li><li>4: ~3.85%</li><li>5: ~3.85%</li><li>7: ~3.85%</li><li>8: ~3.85%</li><li>9: ~3.85%</li><li>10: ~3.85%</li><li>11: ~3.85%</li><li>12: ~7.69%</li><li>13: ~7.69%</li><li>14: ~1.92%</li><li>15: ~3.85%</li><li>17: ~1.92%</li><li>18: ~1.92%</li><li>19: ~3.85%</li><li>21: ~1.92%</li><li>23: ~9.62%</li><li>24: ~1.92%</li><li>25: ~1.92%</li><li>26: ~7.69%</li></ul> |
* Samples:
| sentence | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------|
| <code><br>Name : NexGen Fiscal Systems<br>Category: Financial Software Solutions, Revenue Management Services<br>Department: Finance<br>Location: San Francisco, CA<br>Amount: 2749.95<br>Card: Q4 Revenue Optimization Initiative<br>Trip Name: unknown<br></code> | <code>15</code> |
| <code><br>Name : Midnight Brasserie<br>Category: Culinary Experience, Event Catering<br>Department: Marketing<br>Location: Paris, France<br>Amount: 456.87<br>Card: Quarterly Team Building<br>Trip Name: Summer Collaboration Retreat<br></code> | <code>5</code> |
| <code><br>Name : Zero One<br>Category: Media Production<br>Department: Marketing<br>Location: New York, NY<br>Amount: 7500.0<br>Card: Sales Operating Budget<br>Trip Name: unknown<br></code> | <code>13</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
- `bf16`: 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`: 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`: True
- `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-v1.5-eval_max_accuracy | bge-base-en-v1.5-train_max_accuracy |
|:-----:|:----:|:----------------------------------:|:-----------------------------------:|
| 0 | 0 | - | 0.8510 |
| 5.0 | 65 | 1.0 | - |
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.6.0+cu124
- 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->