--- 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 : Vigilant Protec Category: Consulting Services, Cybersecurity Solutions Department: Legal Location: London, UK Amount: 1987.65 Card: Global Compliance Enhancement Trip Name: unknown ' sentences: - ' Name : Rosetta Tech Category: Technology Supplies, Software Solutions Department: Research & Development Location: Hamburg, Germany Amount: 2129.49 Card: Advanced Research Toolkit Acquisition Trip Name: unknown ' - ' Name : Ikebana Studio Category: Office Decor Services, Art Supplies Department: All Departments Location: Kyoto, Japan Amount: 789.45 Card: Creative Work Environment Initiative 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 ' - source_sentence: ' Name : La Gourmanderie Collective Category: Culinary Consulting, Team Building Activities Department: Marketing Location: Paris, France Amount: 1468.77 Card: Innovative Cuisine Workshop Trip Name: unknown ' sentences: - ' Name : Gandalf Category: Financial Services, Consulting Department: Finance Location: Singapore Amount: 457.29 Card: Financial Advisory Services 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 ' - ' Name : Baku Category: Ride Sharing Department: Sales Location: Baku, Azerbaijan Amount: 1247.88 Card: Client Engagement Activities 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 : CleverInsight Solutions Category: Business Process Optimization Department: Finance Location: Toronto, Canada Amount: 2127.45 Card: Quarterly Insights & Efficiency Project Trip Name: unknown ' - ' Name : SynergyBridge Category: Customer Experience Software, Revenue Growth Tools Department: Sales Location: San Francisco, CA Amount: 1558.72 Card: Customer Relationship Enhancement Trip Name: unknown ' - ' Name : CloudArc Category: Cloud Storage Solutions, Internet Services Department: Engineering Location: Toronto, Canada Amount: 1573.63 Card: Infrastructure Scaling Trip Name: unknown ' - source_sentence: ' Name : GigaTrend Category: Data Services, Cloud Software Solutions Department: Research & Development Location: London, UK Amount: 1345.67 Card: Data-Driven Innovation Project Trip Name: unknown ' sentences: - ' Name : Global Wellness Network Category: Corporate Wellness Programs, Employee Engagement Department: HR Location: Berlin, Germany Amount: 1285.75 Card: Wellness and Engagement Program Trip Name: unknown ' - ' Name : TechXperts Global Category: IT Services, Consulting Department: IT Operations Location: Berlin, Germany Amount: 987.49 Card: Quarterly System Assessment Trip Name: unknown ' - ' Name : InterStep Insight Reports Category: Data Services, Research Publications Department: Marketing Location: Toronto, Canada Amount: 1248.76 Card: Strategic Market Research Trip Name: unknown ' - source_sentence: ' Name : Viacom Solutions Category: Telecom Hardware, Network Architecture Department: Engineering Location: Tokyo, Japan Amount: 1450.67 Card: Global Network Optimization Project Trip Name: unknown ' sentences: - ' Name : CloudMetric Solutions Category: Data Analytics, Virtual Infrastructure Management Department: Engineering Location: Toronto, Canada Amount: 1644.75 Card: Real-Time Resource Monitoring Trip Name: unknown ' - ' 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 : Pardalis Digital Category: Data Analytics Platform, Professional Networking Service Department: Sales Location: Dublin, Ireland Amount: 1456.75 Card: Sales Intelligence & Networking Platform 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: 0.8461538461538461 name: Cosine Accuracy - type: dot_accuracy value: 0.15384615384615385 name: Dot Accuracy - type: manhattan_accuracy value: 0.8557692307692307 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.8461538461538461 name: Euclidean Accuracy - type: max_accuracy value: 0.8557692307692307 name: Max Accuracy - task: type: triplet name: Triplet dataset: name: bge base en eval type: bge-base-en-eval metrics: - type: cosine_accuracy value: 0.9545454545454546 name: Cosine Accuracy - type: dot_accuracy value: 0.045454545454545456 name: Dot Accuracy - type: manhattan_accuracy value: 0.9545454545454546 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.9545454545454546 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("ivanleomk/finetuned-bge-base-en") # Run inference sentences = [ '\nName : Viacom Solutions\nCategory: Telecom Hardware, Network Architecture\nDepartment: Engineering\nLocation: Tokyo, Japan\nAmount: 1450.67\nCard: Global Network Optimization Project\nTrip Name: unknown\n', '\nName : Pardalis Digital\nCategory: Data Analytics Platform, Professional Networking Service\nDepartment: Sales\nLocation: Dublin, Ireland\nAmount: 1456.75\nCard: Sales Intelligence & Networking Platform\nTrip Name: unknown\n', "\nName : Il Vino e L'Arte\nCategory: Culinary Experience, Cultural Event Venue\nDepartment: Marketing\nLocation: Rome, Italy\nAmount: 748.32\nCard: Cultural Engagement Dinner\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 | 0.8462 | | dot_accuracy | 0.1538 | | manhattan_accuracy | 0.8558 | | euclidean_accuracy | 0.8462 | | **max_accuracy** | **0.8558** | #### 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 | 0.9545 | | dot_accuracy | 0.0455 | | manhattan_accuracy | 0.9545 | | euclidean_accuracy | 0.9545 | | **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 : 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](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 : Viacom Solutions
Category: Telecom Hardware, Network Architecture
Department: Engineering
Location: Tokyo, Japan
Amount: 1450.67
Card: Global Network Optimization Project
Trip Name: unknown
 | 9 | |
Name : Vista Cascades Resort
Category: Hospitality, Event Hosting
Department: Sales
Location: Orlando, FL
Amount: 1823.45
Card: Annual Sales Retreat
Trip Name: Q3 Strategy Session
 | 12 | |
Name : ActiveHealth CoLab
Category: Health Services, Wellness Solutions
Department: HR
Location: Amsterdam, Netherlands
Amount: 745.32
Card: Corporate Wellness Partnership
Trip Name: unknown
 | 23 | * 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 - `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`: 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`: 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 - `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 | - | 0.8558 | | 5.0 | 65 | 0.9545 | - | ### Framework Versions - Python: 3.11.10 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 3.1.0 - 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} } ```