--- 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 : Gandalf Category: Financial Services, Consulting Department: Finance Location: Singapore Amount: 457.29 Card: Financial Advisory Services Trip Name: unknown ' sentences: - ' Name : InterGlobal Tech Category: Business Software Solutions, Data Processing Services Department: Marketing Location: New York, NY Amount: 1249.95 Card: Marketing Automation Tools Trip Name: unknown ' - ' Name : Nuvotek Solutions Category: Consulting Services, Managed IT Services Department: Information Security Location: Berlin, Germany Amount: 879.65 Card: Annual Cybersecurity Resilience Program Trip Name: unknown ' - ' Name : Omega Systems Inc. Category: Integrated Business Solutions, Enterprise Software Sales Department: Research & Development Location: Oslo, Norway Amount: 1943.75 Card: AI Development Suite Trip Name: unknown ' - source_sentence: ' 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 ' sentences: - ' 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 ' - ' Name : InterLang Solutions Category: Language Interpretation Services, Remote Collaboration Tools Department: HR Location: Tokyo, Japan Amount: 1642.59 Card: Diversity & Inclusion Initiatives Trip Name: unknown ' - ' Name : CovaRisk Consulting Category: Risk Advisory, Financial Services Department: Legal Location: Toronto, Canada Amount: 1124.37 Card: Assurance Payment Trip Name: unknown ' - source_sentence: ' Name : Optix Global Category: Digital Storage Solutions, Office Essentials Provider Department: All Departments Location: Tokyo, Japan Amount: 568.77 Card: Monthly Office Needs Trip Name: unknown ' sentences: - ' Name : Digital Wave Solutions Category: IT Infrastructure Services, Data Analytic Platforms Department: Finance Location: San Francisco, CA Amount: 1748.92 Card: Annual Data Management & Reporting Trip Name: unknown ' - ' Name : Analytix Global Solutions Category: Business Intelligence Services, Regulatory Compliance Tools Department: Finance Location: London, UK Amount: 1323.67 Card: Financial Compliance Enhancement Trip Name: unknown ' - ' Name : Daesung Enterprises Category: Catering Services, Event Management Department: Sales Location: Lisbon, Portugal Amount: 375.45 Card: Q4 Client Engagement Events Trip Name: unknown ' - source_sentence: ' Name : Kanzan Solutions Category: Consulting Services, Business Advisory Department: Legal Location: Tokyo, Japan Amount: 3900.75 Card: Quarterly Compliance Review Trip Name: unknown ' sentences: - ' Name : Alta Via Mix Category: Airline Catering, Luxury Travel Services Department: Executive Location: Milan, Italy Amount: 1925.49 Card: Executive Incentive Program Trip Name: Annual Leadership Summit ' - ' Name : RBS Category: Financial Services, Business Consultancy Department: Finance Location: Toronto, Canada Amount: 1134.28 Card: Cross-Border Transaction Facilitation Trip Name: unknown ' - ' 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 ' - source_sentence: ' Name : NetWise Solutions Category: Data Transfer Services, Digital Infrastructure Department: Product Location: Singapore Amount: 1579.42 Card: Global Network Enhancement Trip Name: unknown ' sentences: - ' 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 ' - ' Name : AeroDyn Research Category: Research Services, Data Analysis Department: Research & Development Location: Amsterdam, Netherlands Amount: 2457.42 Card: Annual Innovation Assessment Trip Name: unknown ' - ' Name : Global Horizon Travel Category: Travel Services, Package Deals Department: Sales Location: Tokyo, Japan Amount: 1199.75 Card: Annual Sales Retreat Trip Name: Sales Strategy Summit ' 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: 0.8605769230769231 name: Cosine Accuracy - type: dot_accuracy value: 0.13942307692307693 name: Dot Accuracy - type: manhattan_accuracy value: 0.8413461538461539 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.8605769230769231 name: Euclidean Accuracy - type: max_accuracy value: 0.8605769230769231 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: bge base en eval type: bge-base-en-eval metrics: - type: cosine_accuracy value: 0.9242424242424242 name: Cosine Accuracy - type: dot_accuracy value: 0.07575757575757576 name: Dot Accuracy - type: manhattan_accuracy value: 0.9545454545454546 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9242424242424242 name: Euclidean Accuracy - type: max_accuracy value: 0.9545454545454546 name: Max Accuracy --- # SentenceTransformer based on BAAI/bge-base-en This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en](https://huggingface.co/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](https://huggingface.co/BAAI/bge-base-en) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("dshvadskiy/finetuned-bge-base-en") # Run inference sentences = [ '\nName : NetWise Solutions\nCategory: Data Transfer Services, Digital Infrastructure\nDepartment: Product\nLocation: Singapore\nAmount: 1579.42\nCard: Global Network Enhancement\nTrip Name: unknown\n', '\nName : Global Horizon Travel\nCategory: Travel Services, Package Deals\nDepartment: Sales\nLocation: Tokyo, Japan\nAmount: 1199.75\nCard: Annual Sales Retreat\nTrip Name: Sales Strategy Summit\n', '\nName : AeroDyn Research\nCategory: Research Services, Data Analysis\nDepartment: Research & Development\nLocation: Amsterdam, Netherlands\nAmount: 2457.42\nCard: Annual Innovation Assessment\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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.8606 | | dot_accuracy | 0.1394 | | manhattan_accuracy | 0.8413 | | euclidean_accuracy | 0.8606 | | **max_accuracy** | **0.8606** | #### Triplet * Dataset: `bge-base-en-eval` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:-----------| | cosine_accuracy | 0.9242 | | dot_accuracy | 0.0758 | | manhattan_accuracy | 0.9545 | | euclidean_accuracy | 0.9242 | | **max_accuracy** | **0.9545** | ## 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 | | | * Samples: | sentence | label | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
Name : Yijie Logistics
Category: Logistics Services
Department: Sales
Location: Berlin, Germany
Amount: 485.67
Card: Quarterly Client Visit and Logistics Coordination
Trip Name: unknown
 | 0 | |
Name : Serenity Solutions
Category: Office Wellness Solutions
Department: Office Administration
Location: Munich, Germany
Amount: 772.58
Card: Ergonomic Office Enhancements
Trip Name: unknown
 | 1 | |
Name : Cortec International
Category: Event Management Services, Business Solutions
Department: Sales
Location: London, UK
Amount: 1337.25
Card: Global Sales Summit Participation
Trip Name: unknown
 | 2 | * Loss: [BatchSemiHardTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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 | | | * Samples: | sentence | label | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------| |
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
 | 21 | |
Name : NexGen Comms
Category: Telecom Services, Communications Solutions
Department: Sales
Location: Berlin, Germany
Amount: 879.45
Card: Q2 Client Outreach Program
Trip Name: unknown
 | 23 | |
Name : Digital Wave Solutions
Category: IT Infrastructure Services, Data Analytic Platforms
Department: Finance
Location: San Francisco, CA
Amount: 1748.92
Card: Annual Data Management & Reporting
Trip Name: unknown
 | 18 | * Loss: [BatchSemiHardTripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#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 | - | 0.8606 | | 5.0 | 65 | 0.9545 | - | ### Framework Versions - Python: 3.9.16 - 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 ```bibtex @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 ```bibtex @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} } ```