metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:208
- loss:BatchSemiHardTripletLoss
base_model: BAAI/bge-base-en
widget:
- 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 : Gartner & Associates
Category: Consulting, Business Services
Department: Legal
Location: San Francisco, CA
Amount: 5000.0
Card: Legal Consultation Fund
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 : 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
- source_sentence: |
Name : Miller & Gartner
Category: Consulting, Business Expense
Department: Legal
Location: Chicago, IL
Amount: 1500.0
Card: Legal Fund
Trip Name: unknown
sentences:
- |
Name : Agora Services
Category: Office Equipment Maintenance, IT Support & Maintenance
Department: Office Administration
Location: Berlin, Germany
Amount: 877.29
Card: Quarterly Equipment Evaluation
Trip Name: unknown
- |
Name : InsightReports Group
Category: Research and Insights, Consulting Services
Department: Marketing
Location: New York, NY
Amount: 1499.89
Card: Market Research
Trip Name: unknown
- |
Name : Mosaic Technologies
Category: Cloud Solutions Provider, Data Analytics Platforms
Department: R&D
Location: Berlin, Germany
Amount: 1785.45
Card: AI Model Enhancement Project
Trip Name: unknown
- source_sentence: |
Name : Café Del Mar
Category: Catering Services, Event Planning
Department: Sales
Location: Barcelona, ES
Amount: 578.29
Card: Q3 Client Engagement
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 : Staton Morgan
Category: Recruitment Services, Consulting
Department: HR
Location: Melbourne, Australia
Amount: 1520.67
Card: New Hires
Trip Name: unknown
- |
Name : Palace Suites
Category: Hotel Accommodation, Event Outsourcing
Department: Marketing
Location: Amsterdam, NL
Amount: 1278.64
Card: Annual Conference Stay
Trip Name: 2023 Innovation Summit
- 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 : 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 : Omachi Meitetsu
Category: Transportation Services, Travel Services
Department: Sales
Location: Hakkuba Japan
Amount: 120.0
Card: Quarterly Travel Expenses
Trip Name: unknown
- |
Name : Clarion Data Solutions
Category: Cloud Computing & Data Storage Solutions, Consulting Services
Department: Engineering
Location: Berlin, Germany
Amount: 756.49
Card: Data Management Initiatives
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 : Zero One
Category: Media Production
Department: Marketing
Location: New York, NY
Amount: 7500.0
Card: Sales Operating Budget
Trip Name: unknown
- |
Name : Vitality Systems
Category: Facility Management, Health Services
Department: Office Administration
Location: Chicago, IL
Amount: 347.29
Card: Office Wellness Initiative
Trip Name: unknown
- |
Name : TechSavvy Solutions
Category: Software Services, Online Subscription
Department: Engineering
Location: Austin, TX
Amount: 1200.0
Card: Annual Engineering Tools Budget
Trip Name: unknown
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_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: 0.8461538553237915
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: bge base en eval
type: bge-base-en-eval
metrics:
- type: cosine_accuracy
value: 0.39393940567970276
name: Cosine 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 dimensions
- 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("ppuva1/finetuned-bge-base-en")
# 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 : TechSavvy Solutions\nCategory: Software Services, Online Subscription\nDepartment: Engineering\nLocation: Austin, TX\nAmount: 1200.0\nCard: Annual Engineering Tools Budget\nTrip Name: unknown\n',
'\nName : Vitality Systems\nCategory: Facility Management, Health Services\nDepartment: Office Administration\nLocation: Chicago, IL\nAmount: 347.29\nCard: Office Wellness Initiative\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
- Datasets:
bge-base-en-trainandbge-base-en-eval - Evaluated with
TripletEvaluator
| Metric | bge-base-en-train | bge-base-en-eval |
|---|---|---|
| cosine_accuracy | 0.8462 | 0.3939 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 208 training samples
- Columns:
sentenceandlabel - Approximate statistics based on the first 208 samples:
sentence label type string int details - min: 33 tokens
- mean: 39.81 tokens
- max: 49 tokens
- 0: ~3.85%
- 1: ~3.37%
- 2: ~3.85%
- 3: ~2.40%
- 4: ~5.29%
- 5: ~4.33%
- 6: ~4.33%
- 7: ~3.37%
- 8: ~3.85%
- 9: ~4.33%
- 10: ~3.37%
- 11: ~3.85%
- 12: ~2.40%
- 13: ~5.29%
- 14: ~3.37%
- 15: ~5.77%
- 16: ~4.33%
- 17: ~2.40%
- 18: ~2.88%
- 19: ~3.37%
- 20: ~3.85%
- 21: ~4.33%
- 22: ~2.88%
- 23: ~4.33%
- 24: ~4.81%
- 25: ~1.92%
- 26: ~1.92%
- Samples:
sentence label
Name : Transcend
Category: Upskilling
Department: Human Resource
Location: London, UK
Amount: 859.47
Card: Technology Skills Enhancement
Trip Name: unknown0
Name : Ayden
Category: Financial Software
Department: Finance
Location: Berlin, DE
Amount: 1273.45
Card: Enterprise Technology Services
Trip Name: unknown1
Name : Urban Sphere
Category: Utilities Management, Facility Services
Department: Office Administration
Location: New York, NY
Amount: 937.32
Card: Monthly Operations Budget
Trip Name: unknown2 - Loss:
BatchSemiHardTripletLoss
Evaluation Dataset
Unnamed Dataset
- Size: 52 evaluation samples
- Columns:
sentenceandlabel - Approximate statistics based on the first 52 samples:
sentence label type string int details - min: 32 tokens
- mean: 38.37 tokens
- max: 43 tokens
- 0: ~1.92%
- 4: ~1.92%
- 5: ~11.54%
- 7: ~5.77%
- 8: ~5.77%
- 10: ~7.69%
- 11: ~3.85%
- 12: ~3.85%
- 13: ~1.92%
- 16: ~3.85%
- 17: ~1.92%
- 18: ~13.46%
- 19: ~5.77%
- 20: ~3.85%
- 21: ~3.85%
- 22: ~7.69%
- 23: ~3.85%
- 24: ~5.77%
- 25: ~5.77%
- Samples:
sentence label
Name : Tooly
Category: Survey Software, SaaS
Department: Marketing
Location: San Francisco, CA
Amount: 2000.0
Card: Annual Marketing Technology Budget
Trip Name: unknown10
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: unknown21
Name : Gartner & Associates
Category: Consulting, Business Services
Department: Legal
Location: San Francisco, CA
Amount: 5000.0
Card: Legal Consultation Fund
Trip Name: unknown5 - Loss:
BatchSemiHardTripletLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 16per_device_eval_batch_size: 16learning_rate: 2e-05num_train_epochs: 5warmup_ratio: 0.1batch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 2e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 5max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional
Training Logs
| Epoch | Step | bge-base-en-train_cosine_accuracy | bge-base-en-eval_cosine_accuracy |
|---|---|---|---|
| -1 | -1 | 0.8462 | 0.3939 |
Framework Versions
- Python: 3.11.8
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.4.1
- Accelerate: 0.34.2
- Datasets: 3.2.0
- Tokenizers: 0.21.0
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}
}