metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1056
  - loss:BatchSemiHardTripletLoss
base_model: BAAI/bge-base-en
widget:
  - source_sentence: |

      Name : LearnTech Innovations
      Category: Educational Software, Professional Development Solutions
      Department: IT Operations
      Location: Tokyo, Japan
      Amount: 1523.45
      Card: Technology Faculty Enhancement Series
      Trip Name: unknown
    sentences:
      - |

        Name : OptiNet Solutions
        Category: Strategic Infrastructure Consulting, Advanced Data Solutions
        Department: Engineering
        Location: London, UK
        Amount: 1289.99
        Card: Next-Gen Network Design
        Trip Name: unknown
      - |

        Name : SkillsBox Academy
        Category: Online Education, Employee Training Platforms
        Department: Engineering
        Location: Chicago, IL
        Amount: 1298.5
        Card: Skill Development for New Technologies
        Trip Name: unknown
      - |

        Name : Yue Hua
        Category: HR & Employment Services
        Department: Engineering
        Location: Berlin, Germany
        Amount: 3567.45
        Card: Talent Acquisition Enhancement
        Trip Name: unknown
  - source_sentence: |

      Name : Interlink Global Solutions
      Category: Training Platforms, Professional Networking Services
      Department: HR
      Location: San Francisco, CA
      Amount: 1342.55
      Card: Executive Leadership Development
      Trip Name: unknown
    sentences:
      - |

        Name : Quantifire Insights
        Category: Predictive Analytics Solutions
        Department: Marketing
        Location: Zurich, Switzerland
        Amount: 1275.58
        Card: Customer Engagement Enhancement
        Trip Name: unknown
      - |

        Name : Insight Analytics
        Category: Data Services & Analytics, Employee Training & Development
        Department: Marketing
        Location: Berlin, Germany
        Amount: 873.29
        Card: Market Research Initiative
        Trip Name: unknown
      - |

        Name : GlobalSafe Solutions
        Category: Risk Management Consultancy, International Insurance Brokerage
        Department: Finance
        Location: Zurich, Switzerland
        Amount: 1175.65
        Card: Annual Risk Assessment
        Trip Name: unknown
  - source_sentence: |

      Name : SkyScale Services
      Category: Global IT Solutions, Platform Integration
      Department: IT Operations
      Location: Dublin, Ireland
      Amount: 1492.54
      Card: Annual Platform Enhancement
      Trip Name: unknown
    sentences:
      - |

        Name : InterGlobe Connect
        Category: Financial Services, Data Connectivity
        Department: Finance
        Location: London, UK
        Amount: 152.79
        Card: Overseas Financial Operations
        Trip Name: unknown
      - |

        Name : SkyElevate Group
        Category: Luxury Travel Services, Corporate Event Planning
        Department: Executive
        Location: Dubai, UAE
        Amount: 2113.47
        Card: Executive Strategy Retreat
        Trip Name: Board of Directors Retreat
      - |

        Name : GlobalRes Workforce Solutions
        Category: Remote Work Platforms, HR Technology Vendors
        Department: Engineering
        Location: Barcelona, Spain
        Amount: 1894.27
        Card: Hybrid Work Enablement
        Trip Name: unknown
  - source_sentence: |

      Name : Nimbus Networks Inc.
      Category: Cloud Services, Application Hosting
      Department: Research & Development
      Location: Austin, TX
      Amount: 1134.67
      Card: NextGen Application Deployment
      Trip Name: unknown
    sentences:
      - >

        Name : Kaleidoscope Interactive

        Category: Interactive Software Platforms, Educational Content
        Distribution

        Department: Engineering

        Location: London, UK

        Amount: 1852.37

        Card: Innovative Education Initiative

        Trip Name: unknown
      - |

        Name : RBS
        Category: Financial Services, Business Consultancy
        Department: Finance
        Location: Toronto, Canada
        Amount: 1134.28
        Card: Cross-Border Transaction Facilitation
        Trip Name: unknown
      - |

        Name : HexaGuard Systems
        Category: Enterprise Software Solutions, Network Infrastructure Services
        Department: IT Operations
        Location: Toronto, Canada
        Amount: 1254.78
        Card: Integrated Security Enhancement Plan
        Trip Name: unknown
  - source_sentence: |

      Name : SMRT
      Category: Public Transportation, Transit Services
      Department: Sales
      Location: Los Angeles, CA
      Amount: 85.0
      Card: Sales Team Travel Budget
      Trip Name: unknown
    sentences:
      - |

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

        Name : Elastic Habitat Solutions
        Category: Cloud Enhancement Services, Data Analytics Tools
        Department: IT Operations
        Location: London, UK
        Amount: 1489.57
        Card: Scalable Data Initiative
        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
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: 0.9753787878787878
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.02462121212121212
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9753787878787878
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9753787878787878
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9753787878787878
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: bge base en eval
          type: bge-base-en-eval
        metrics:
          - type: cosine_accuracy
            value: 0.9813432835820896
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.018656716417910446
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9850746268656716
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9813432835820896
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9850746268656716
            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("llazio/finetuned-bge-base-en")
# Run inference
sentences = [
    '\nName : SMRT\nCategory: Public Transportation, Transit Services\nDepartment: Sales\nLocation: Los Angeles, CA\nAmount: 85.0\nCard: Sales Team Travel Budget\nTrip Name: unknown\n',
    '\nName : Elastic Habitat Solutions\nCategory: Cloud Enhancement Services, Data Analytics Tools\nDepartment: IT Operations\nLocation: London, UK\nAmount: 1489.57\nCard: Scalable Data Initiative\nTrip Name: unknown\n',
    '\nName : Wellness Haven\nCategory: Employee Health Programs, Professional Development\nDepartment: HR\nLocation: Munich, Germany\nAmount: 762.35\nCard: Corporate Wellness Initiatives\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	0.9754
dot_accuracy	0.0246
manhattan_accuracy	0.9754
euclidean_accuracy	0.9754
max_accuracy	0.9754

Triplet

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

Metric	Value
cosine_accuracy	0.9813
dot_accuracy	0.0187
manhattan_accuracy	0.9851
euclidean_accuracy	0.9813
max_accuracy	0.9851

Training Details

Training Dataset

Unnamed Dataset

Size: 1,056 training samples
Columns: sentence and label

Approximate statistics based on the first 1000 samples:

	sentence	label
type	string	int
details	min: 32 tokens mean: 39.65 tokens max: 49 tokens	0: ~4.80% 1: ~4.80% 2: ~5.60% 3: ~2.20% 4: ~5.10% 5: ~4.10% 6: ~3.70% 7: ~4.30% 8: ~4.80% 9: ~4.50% 10: ~1.80% 11: ~2.60% 12: ~4.50% 13: ~5.00% 14: ~4.20% 15: ~2.70% 16: ~3.50% 17: ~4.10% 18: ~2.70% 19: ~3.40% 20: ~3.80% 21: ~3.70% 22: ~4.20% 23: ~1.80% 24: ~2.60% 25: ~2.50% 26: ~3.00%

Samples:

sentence	label
`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`	`0`
`Name : Baku Category: Ride Sharing Department: Sales Location: Baku, Azerbaijan Amount: 1247.88 Card: Client Engagement Activities Trip Name: unknown`	`1`
`Name : Harris Consulting Group Category: Business Consulting, Legal Advisory Department: Finance Location: Toronto, Canada Amount: 1325.45 Card: Strategic Development Fund Trip Name: unknown`	`2`

Loss: BatchSemiHardTripletLoss

Evaluation Dataset

Unnamed Dataset

Size: 264 evaluation samples
Columns: sentence and label

Approximate statistics based on the first 264 samples:

	sentence	label
type	string	int
details	min: 34 tokens mean: 39.59 tokens max: 49 tokens	0: ~5.30% 1: ~4.92% 2: ~6.44% 3: ~2.27% 4: ~3.03% 5: ~4.55% 6: ~3.79% 7: ~4.17% 8: ~3.79% 9: ~3.41% 10: ~1.14% 11: ~1.89% 12: ~5.30% 13: ~4.92% 14: ~3.41% 15: ~2.27% 16: ~2.65% 17: ~2.65% 18: ~3.79% 19: ~3.79% 20: ~6.06% 21: ~6.44% 22: ~3.41% 23: ~1.89% 24: ~3.79% 25: ~2.65% 26: ~2.27%

Samples:

sentence	label
`Name : Fernández & Co. Services Category: Property Management, Facility Services Department: Office Administration Location: Madrid, Spain Amount: 1245.67 Card: Monthly Facility Operations Trip Name: unknown`	`18`
`Name : Habitat Solutions Category: Cloud Enhancement Services, Data Analytics Tools Department: IT Operations Location: London, UK Amount: 1489.57 Card: Scalable Data Initiative Trip Name: unknown`	`13`
`Name : Gandalf Category: Financial Services, Consulting Department: Finance Location: Singapore Amount: 457.29 Card: Financial Advisory Services Trip Name: unknown`	`21`

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	Training Loss	loss	bge-base-en-eval_max_accuracy	bge-base-en-train_max_accuracy
0	0	-	-	-	0.8428
1.5152	100	4.9094	4.7659	-	0.9688
3.0303	200	4.7495	4.6519	-	0.9725
4.5455	300	4.6271	4.5820	-	0.9754
5.0	330	-	-	0.9851	-

Framework Versions

Python: 3.9.6
Sentence Transformers: 3.1.1
Transformers: 4.45.2
PyTorch: 2.6.0
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}
}