--- 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](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 dimensions - **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("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-train` and `bge-base-en-eval` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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: sentence and label * Approximate statistics based on the first 208 samples: | | sentence | label | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | int | | details | | | * Samples: | sentence | label | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| |
Name : Transcend
Category: Upskilling
Department: Human Resource
Location: London, UK
Amount: 859.47
Card: Technology Skills Enhancement
Trip Name: unknown
 | 0 | |
Name : Ayden
Category: Financial Software
Department: Finance
Location: Berlin, DE
Amount: 1273.45
Card: Enterprise Technology Services
Trip Name: unknown
 | 1 | |
Name : Urban Sphere
Category: Utilities Management, Facility Services
Department: Office Administration
Location: New York, NY
Amount: 937.32
Card: Monthly Operations Budget
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 : Tooly
Category: Survey Software, SaaS
Department: Marketing
Location: San Francisco, CA
Amount: 2000.0
Card: Annual Marketing Technology Budget
Trip Name: unknown
 | 10 | |
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
 | 21 | |
Name : Gartner & Associates
Category: Consulting, Business Services
Department: Legal
Location: San Francisco, CA
Amount: 5000.0
Card: Legal Consultation Fund
Trip Name: unknown
 | 5 | * 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`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `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 - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_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 ```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} } ```