finetuned-bge-base-en / README.md

lzwcv

Add new SentenceTransformer model.

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metadata

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 : Baku
      Category: Ride Sharing
      Department: Sales
      Location: Baku, Azerbaijan
      Amount: 1247.88
      Card: Client Engagement Activities
      Trip Name: unknown
    sentences:
      - |

        Name : Dome Interactive Designs
        Category: Digital Display Solutions, Event Technology Rentals
        Department: Sales
        Location: Kyoto, Japan
        Amount: 1832.34
        Card: Virtual Reality Experience Stand
        Trip Name: Global Tech Expo 2023
      - |

        Name : Il Vino e L'Arte
        Category: Culinary Experience, Cultural Event Venue
        Department: Marketing
        Location: Rome, Italy
        Amount: 748.32
        Card: Cultural Engagement Dinner
        Trip Name: unknown
      - |

        Name : Nordic Assurance Group
        Category: Insurance Consulting, Risk Management Services
        Department: Legal
        Location: Oslo, Norway
        Amount: 1225.75
        Card: Annual Risk Assessment
        Trip Name: unknown
  - source_sentence: |

      Name : Omni Utility Services
      Category: Facility Management, Environmental Consulting
      Department: Office Administration
      Location: Melbourne, Australia
      Amount: 1421.59
      Card: Bi-monthly Utility Management
      Trip Name: unknown
    sentences:
      - |

        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
      - |

        Name : Aperio Global Insights
        Category: Strategic Business Consulting, Data Analytics Services
        Department: Finance
        Location: Chicago, IL
        Amount: 3456.78
        Card: Global Market Expansion Evaluation
        Trip Name: unknown
      - |

        Name : NetWise Solutions
        Category: Data Transfer Services, Digital Infrastructure
        Department: Product
        Location: Singapore
        Amount: 1579.42
        Card: Global Network Enhancement
        Trip Name: unknown
  - source_sentence: |

      Name : Sphere Financial Systems
      Category: Financial Management Services, International Billing Solutions
      Department: Finance
      Location: London, United Kingdom
      Amount: 856.47
      Card: Cross-Border Transaction Reconciliation
      Trip Name: unknown
    sentences:
      - |

        Name : Telestream Innovations
        Category: Subscription Services, Internet & Network Services
        Department: IT Operations
        Location: Amsterdam, Netherlands
        Amount: 1389.54
        Card: Unified Communications Platform
        Trip Name: unknown
      - |

        Name : Guava Growth Solutions
        Category: Employee Engagement Platform, Team Building Activities
        Department: HR
        Location: San Francisco, USA
        Amount: 1346.75
        Card: Annual Team Cohesion Initiative
        Trip Name: unknown
      - |

        Name : Anthro Insights
        Category: Talent Acquisition Services, Corporate Education Programs
        Department: Human Resource
        Location: London, UK
        Amount: 1440.75
        Card: Diversity & Inclusion
        Trip Name: unknown
  - source_sentence: |

      Name : NexGen Comms
      Category: Telecom Services, Communications Solutions
      Department: Sales
      Location: Berlin, Germany
      Amount: 879.45
      Card: Q2 Client Outreach Program
      Trip Name: unknown
    sentences:
      - |

        Name : Kreutz & Partners
        Category: Strategic Consulting
        Department: Marketing
        Location: Zurich, Switzerland
        Amount: 982.75
        Card: Digital Growth Strategy
        Trip Name: unknown
      - |

        Name : Vigilant Protec
        Category: Consulting Services, Cybersecurity Solutions
        Department: Legal
        Location: London, UK
        Amount: 1987.65
        Card: Global Compliance Enhancement
        Trip Name: unknown
      - |

        Name : HelioNet Interactive
        Category: Customer Engagement Platforms, Software Development Tools
        Department: Product
        Location: Vancouver, Canada
        Amount: 1367.29
        Card: Product Improvement Initiative
        Trip Name: unknown
  - source_sentence: |

      Name : Apex Innovations Group
      Category: Business Consulting, Training Services
      Department: Executive
      Location: Sydney, Australia
      Amount: 1575.34
      Card: Leadership Development Program
      Trip Name: unknown
    sentences:
      - |

        Name : Freenet AG
        Category: Telecommunication Services
        Department: IT Operations
        Location: Zurich, Switzerland
        Amount: 2794.37
        Card: Infrastructure Support Services
        Trip Name: unknown
      - |

        Name : CloudFlare Inc.
        Category: Internet & Network Services, SaaS
        Department: IT Operations
        Location: New York, NY
        Amount: 2000.0
        Card: Annual Cloud Services Budget
        Trip Name: unknown
      - |

        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
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: 0.8371040723981901
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.16289592760180996
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.8280542986425339
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.8371040723981901
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.8371040723981901
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: bge base en eval
          type: bge-base-en-eval
        metrics:
          - type: cosine_accuracy
            value: 1
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9714285714285714
            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

This is a sentence-transformers model finetuned from 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
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("lzwcv/finetuned-bge-base-en")
# Run inference
sentences = [
    '\nName : Apex Innovations Group\nCategory: Business Consulting, Training Services\nDepartment: Executive\nLocation: Sydney, Australia\nAmount: 1575.34\nCard: Leadership Development Program\nTrip Name: unknown\n',
    '\nName : CloudFlare Inc.\nCategory: Internet & Network Services, SaaS\nDepartment: IT Operations\nLocation: New York, NY\nAmount: 2000.0\nCard: Annual Cloud Services Budget\nTrip Name: unknown\n',
    '\nName : EcoClean Systems\nCategory: Environmental Services, Industrial Equipment Care\nDepartment: Office Administration\nLocation: San Francisco, CA\nAmount: 952.63\nCard: Essential Facility Sustainability\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-train
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.8371
dot_accuracy	0.1629
manhattan_accuracy	0.8281
euclidean_accuracy	0.8371
max_accuracy	0.8371

Triplet

Dataset: bge-base-en-eval
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	1.0
dot_accuracy	0.0
manhattan_accuracy	0.9714
euclidean_accuracy	1.0
max_accuracy	1.0

Training Details

Training Dataset

Unnamed Dataset

Size: 221 training samples
Columns: sentence and label

Approximate statistics based on the first 221 samples:

	sentence	label
type	string	int
details	min: 33 tokens mean: 39.6 tokens max: 49 tokens	0: ~4.52% 1: ~4.52% 2: ~5.43% 3: ~2.26% 4: ~2.26% 5: ~2.71% 6: ~3.17% 7: ~3.62% 8: ~2.71% 9: ~5.43% 10: ~2.71% 11: ~4.07% 12: ~1.81% 13: ~4.52% 14: ~4.98% 15: ~3.62% 16: ~4.52% 17: ~4.98% 18: ~4.52% 19: ~2.71% 20: ~2.71% 21: ~4.52% 22: ~3.62% 23: ~4.07% 24: ~3.17% 25: ~4.98% 26: ~1.81%

Samples:

sentence	label
`Name : Quantifire Insights Category: Predictive Analytics Solutions Department: Marketing Location: Zurich, Switzerland Amount: 1275.58 Card: Customer Engagement Enhancement Trip Name: unknown`	`0`
`Name : ElevateLearning Solutions Category: E-Learning Platforms, Collaborative Software Department: Engineering Location: Toronto, Canada Amount: 1523.89 Card: Dev Team Skill Boosting Initiative Trip Name: unknown`	`1`
`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`	`2`

Loss: BatchSemiHardTripletLoss

Evaluation Dataset

Unnamed Dataset

Size: 55 evaluation samples
Columns: sentence and label

Approximate statistics based on the first 55 samples:

	sentence	label
type	string	int
details	min: 32 tokens mean: 39.73 tokens max: 47 tokens	0: ~1.82% 1: ~5.45% 2: ~9.09% 3: ~3.64% 4: ~5.45% 5: ~1.82% 6: ~1.82% 7: ~5.45% 10: ~5.45% 11: ~5.45% 12: ~3.64% 13: ~1.82% 14: ~3.64% 15: ~3.64% 16: ~7.27% 17: ~1.82% 18: ~5.45% 19: ~5.45% 20: ~1.82% 21: ~1.82% 22: ~3.64% 23: ~1.82% 24: ~7.27% 25: ~3.64% 26: ~1.82%

Samples:

sentence	label
`Name : CyberGuard Provisions Category: Security Software Solutions, Data Protection Services Department: Information Security Location: San Francisco, CA Amount: 879.92 Card: Digital Fortress Action Plan Trip Name: unknown`	`17`
`Name : Sphere Financial Systems Category: Financial Management Services, International Billing Solutions Department: Finance Location: London, United Kingdom Amount: 856.47 Card: Cross-Border Transaction Reconciliation Trip Name: unknown`	`7`
`Name : RBC Category: Transaction Processing, Financial Services Department: Finance Location: Limassol, Cyprus Amount: 843.56 Card: Quarterly Financial Management Trip Name: unknown`	`7`

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
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: 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

Training Logs

Epoch	Step	bge-base-en-eval_max_accuracy	bge-base-en-train_max_accuracy
0	0	-	0.8371
5.0	35	1.0	-

Framework Versions

Python: 3.10.0
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}
}