metadata

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
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 1
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 1
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 1
            name: Max Accuracy
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 1
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 1
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 1
            name: Max Accuracy

SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a sentence-transformers model finetuned from 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
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]

Evaluation

Metrics

Triplet

Dataset: bge-base-en-v1.5-train
Evaluated with 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 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 TripletEvaluator

Metric	Value
cosine_accuracy	1.0
dot_accuracy	0.0
manhattan_accuracy	1.0
euclidean_accuracy	1.0
max_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 208 training samples
Columns: sentence and label

Approximate statistics based on the first 208 samples:

	sentence	label
type	string	int
details	min: 33 tokens mean: 39.62 tokens max: 49 tokens	0: ~3.37% 1: ~3.85% 2: ~3.85% 3: ~3.37% 4: ~6.25% 5: ~4.81% 6: ~3.85% 7: ~3.37% 8: ~4.33% 9: ~3.85% 10: ~2.40% 11: ~1.92% 12: ~3.37% 13: ~3.85% 14: ~2.88% 15: ~2.40% 16: ~5.29% 17: ~5.77% 18: ~5.29% 19: ~4.33% 20: ~1.92% 21: ~4.81% 22: ~2.40% 23: ~2.40% 24: ~2.88% 25: ~4.33% 26: ~2.88%

Samples:

sentence	label
`Name : FTC Category: Regulatory Compliance Services, Business Consulting Department: Legal Location: Toronto, Canada Amount: 3594.76 Card: Annual Compliance Assessment Trip Name: unknown`	`0`
`Name : IntelliSync Integration Category: Connectivity Services, Enterprise Solutions Department: IT Operations Location: San Francisco, CA Amount: 1387.42 Card: Global Connectivity Suite Trip Name: unknown`	`1`
`Name : Omachi Meitetsu Category: Transportation Services, Travel Services Department: Sales Location: Hakkuba Japan Amount: 120.0 Card: Quarterly Travel Expenses Trip Name: unknown`	`2`

Loss: BatchSemiHardTripletLoss

Evaluation Dataset

Unnamed Dataset

Size: 52 evaluation samples
Columns: sentence and label

Approximate statistics based on the first 52 samples:

	sentence	label
type	string	int
details	min: 32 tokens mean: 39.12 tokens max: 46 tokens	0: ~3.85% 1: ~1.92% 2: ~9.62% 3: ~5.77% 4: ~3.85% 5: ~3.85% 7: ~3.85% 8: ~3.85% 9: ~3.85% 10: ~3.85% 11: ~3.85% 12: ~7.69% 13: ~7.69% 14: ~1.92% 15: ~3.85% 17: ~1.92% 18: ~1.92% 19: ~3.85% 21: ~1.92% 23: ~9.62% 24: ~1.92% 25: ~1.92% 26: ~7.69%

Samples:

sentence	label
`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`	`15`
`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`	`5`
`Name : Zero One Category: Media Production Department: Marketing Location: New York, NY Amount: 7500.0 Card: Sales Operating Budget Trip Name: unknown`	`13`

Loss: 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

Click to expand

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

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

@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

@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}
}