metadata

base_model: BAAI/bge-base-en-v1.5
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 : Nordiska Hosting Collective
      Category: Cloud Storage Solutions, Data Security Services
      Department: IT Operations
      Location: Helsinki, Finland
      Amount: 1439.57
      Card: Annual Data Management Plan
      Trip Name: unknown
    sentences:
      - |

        Name : Allegro Integrations
        Category: Payment Processing Solutions, Financial Technology Services
        Department: Finance
        Location: Dublin, Ireland
        Amount: 1298.75
        Card: Bi-annual Financial Systems Audit
        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
      - |

        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
  - source_sentence: |

      Name : Pixwise Interactive Solutions
      Category: Creative Services, Technology Solutions
      Department: Marketing
      Location: Munich, Germany
      Amount: 1052.75
      Card: Digital Innovation Campaign
      Trip Name: unknown
    sentences:
      - |

        Name : GlobalFitness Unity
        Category: Community Health Programs, Corporate Wellness Solutions
        Department: Office Administration
        Location: Copenhagen, Denmark
        Amount: 987.56
        Card: Office Health and Wellness Partnership
        Trip Name: unknown
      - |

        Name : Prometeo Analytics
        Category: Data Analysis Services, Video Production Agency
        Department: Marketing
        Location: Buenos Aires, Argentina
        Amount: 1263.89
        Card: Influencer Content Strategy
        Trip Name: unknown
      - |

        Name : Global Talent Network
        Category: HR Consultancy Services, Corporate Event Organizers
        Department: HR
        Location: Los Angeles, CA
        Amount: 1375.65
        Card: Leadership Summit Coordination
        Trip Name: unknown
  - source_sentence: |

      Name : Wellness Haven
      Category: Employee Health Programs, Professional Development
      Department: HR
      Location: Munich, Germany
      Amount: 762.35
      Card: Corporate Wellness Initiatives
      Trip Name: unknown
    sentences:
      - |

        Name : Wong & Lim
        Category: Technical Equipment Services, Facility Services
        Department: Office Administration
        Location: Berlin, Germany
        Amount: 458.29
        Card: Monthly Equipment Care Program
        Trip Name: unknown
      - |

        Name : Infinity Creations
        Category: Design Services, Promotional Materials
        Department: Sales
        Location: Toronto, Canada
        Amount: 1583.47
        Card: Quarterly Sales Campaign
        Trip Name: unknown
      - |

        Name : Gartner & Associates
        Category: Consulting, Business Services
        Department: Legal
        Location: San Francisco, CA
        Amount: 5000.0
        Card: Legal Consultation Fund
        Trip Name: unknown
  - source_sentence: |

      Name : Valiant Solutions
      Category: Workshop Coordination, Training Services
      Department: Engineering
      Location: Lisbon, Portugal
      Amount: 499.75
      Card: Quarterly Skill Development
      Trip Name: unknown
    sentences:
      - |

        Name : TransLogix Solutions
        Category: Logistics Services, Corporate Travel Management
        Department: Sales
        Location: Berlin, Germany
        Amount: 485.67
        Card: Quarterly Client Visit and Logistics Coordination
        Trip Name: unknown
      - |

        Name : NexaCloud Technologies
        Category: Implement Services, Cloud Solutions
        Department: IT Operations
        Location: Berlin, Germany
        Amount: 1490.65
        Card: Cloud Optimization Initiative
        Trip Name: unknown
      - |

        Name : SecureStream Analytics
        Category: Data Processing Services, IT Security Solutions
        Department: Information Security
        Location: Chicago, IL
        Amount: 1345.67
        Card: Integrated Systems Analysis
        Trip Name: unknown
  - source_sentence: |

      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
    sentences:
      - |

        Name : TelecomMastery Solutions
        Category: Cloud Infrastructure & Hosting, Telecommunications Services
        Department: IT Operations
        Location: Zurich, Switzerland
        Amount: 1583.45
        Card: Global Connectivity Enhancement
        Trip Name: unknown
      - |

        Name : Versatile Systems Ltd.
        Category: Office Management Solutions, Software Solutions
        Department: Office Administration
        Location: Tokyo, Japan
        Amount: 845.67
        Card: Integrated Office Infrastructure
        Trip Name: unknown
      - |

        Name : Nimbus Streamline
        Category: Cloud Services, Internet Infrastructure
        Department: IT Operations
        Location: Berlin, Germany
        Amount: 1376.49
        Card: Distributed Server Management
        Trip Name: unknown
model-index:
  - name: SentenceTransformer based on BAAI/bge-base-en-v1.5
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: ramp finetune eval
          type: ramp-finetune-eval
        metrics:
          - type: cosine_accuracy
            value: 0
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0
            name: Max Accuracy
          - type: cosine_accuracy
            value: 0
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: ramp finetune test
          type: ramp-finetune-test
        metrics:
          - type: cosine_accuracy
            value: 0
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0
            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("ivanleomk/finetuned-bge-bai")
# Run inference
sentences = [
    '\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 : TelecomMastery Solutions\nCategory: Cloud Infrastructure & Hosting, Telecommunications Services\nDepartment: IT Operations\nLocation: Zurich, Switzerland\nAmount: 1583.45\nCard: Global Connectivity Enhancement\nTrip Name: unknown\n',
    '\nName : Nimbus Streamline\nCategory: Cloud Services, Internet Infrastructure\nDepartment: IT Operations\nLocation: Berlin, Germany\nAmount: 1376.49\nCard: Distributed Server Management\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: ramp-finetune-eval
Evaluated with TripletEvaluator

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

Triplet

Dataset: ramp-finetune-eval
Evaluated with TripletEvaluator

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

Triplet

Dataset: ramp-finetune-test
Evaluated with TripletEvaluator

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

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: 39.66 tokens max: 48 tokens	0: ~4.81% 1: ~5.29% 2: ~6.25% 3: ~2.40% 4: ~3.85% 5: ~4.33% 6: ~3.85% 7: ~2.40% 8: ~4.81% 9: ~3.37% 10: ~3.85% 11: ~3.85% 12: ~4.81% 13: ~4.81% 14: ~5.29% 15: ~3.37% 16: ~4.81% 17: ~4.33% 18: ~3.85% 19: ~1.92% 20: ~2.88% 21: ~2.88% 22: ~3.37% 23: ~0.96% 24: ~4.33% 25: ~2.40% 26: ~0.96%

Samples:

sentence	label
`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`	`0`
`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`	`1`
`Name : Apex Innovations Group Category: Business Consulting, Training Services Department: Executive Location: Sydney, Australia Amount: 1575.34 Card: Leadership Development Program Trip Name: unknown`	`2`

Loss: BatchSemiHardTripletLoss

Evaluation Dataset

Unnamed Dataset

Size: 66 evaluation samples
Columns: sentence and label

Approximate statistics based on the first 66 samples:

	sentence	label
type	string	int
details	min: 35 tokens mean: 39.89 tokens max: 45 tokens	0: ~1.52% 1: ~4.55% 2: ~4.55% 3: ~7.58% 5: ~6.06% 6: ~4.55% 7: ~1.52% 8: ~3.03% 9: ~1.52% 10: ~6.06% 11: ~1.52% 13: ~4.55% 14: ~4.55% 17: ~6.06% 18: ~4.55% 19: ~6.06% 20: ~3.03% 21: ~1.52% 22: ~7.58% 23: ~7.58% 24: ~3.03% 25: ~4.55% 26: ~4.55%

Samples:

sentence	label
`Name : Skyline Digital Solutions Category: Cloud Management Services, Internet & Network Services Department: IT Operations Location: Sydney, Australia Amount: 1128.37 Card: Global Networking Project Trip Name: unknown`	`14`
`Name : Global Assurance Solutions Category: Enterprise Risk Management, Strategic Business Advisory Department: Finance Location: Zurich, Switzerland Amount: 1358.92 Card: Comprehensive Risk Assessment Framework Trip Name: unknown`	`6`
`Name : Nihon Global Ventures Category: Consulting Services, Technology Implementation Department: IT Operations Location: Tokyo, Japan Amount: 1453.17 Card: Network Optimization Program Trip Name: unknown`	`18`

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: 1
warmup_ratio: 0.1
fp16: 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: 1
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: True
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
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	ramp-finetune-eval_max_accuracy	ramp-finetune-test_max_accuracy
0	0	0.0	-
1.0	13	-	0.0

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.2.1
Transformers: 4.44.2
PyTorch: 2.5.0+cu121
Accelerate: 0.34.2
Datasets: 3.1.0
Tokenizers: 0.19.1

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}
}