--- 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 : EconoTransfer Services Category: Financial Services, Digital Payment Solutions Department: Finance Location: Dublin, Ireland Amount: 1523.47 Card: Expense Reconciliation Project Trip Name: unknown ' sentences: - ' Name : SecureStream Analytics Category: Data Processing Services, IT Security Solutions Department: Information Security Location: Chicago, IL Amount: 1345.67 Card: Integrated Systems Analysis Trip Name: unknown ' - ' Name : ActiveHealth CoLab Category: Health Services, Wellness Solutions Department: HR Location: Amsterdam, Netherlands Amount: 745.32 Card: Corporate Wellness Partnership Trip Name: unknown ' - ' Name : InfoQuest Global Ltd. Category: Business Intelligence Platforms, IT Consulting Solutions Department: Marketing Location: Zurich, Switzerland Amount: 1550.75 Card: Market Penetration Analysis Tool Trip Name: unknown ' - source_sentence: ' Name : CloudBridge Associates Category: Cloud Solutions, Data Management Services Department: Research & Development Location: Berlin, Germany Amount: 1942.37 Card: Next-Gen Architecture Design Trip Name: unknown ' sentences: - ' Name : GlobalCert Systems Category: Certification Services, Regulatory Consultants Department: Compliance Location: Geneva, Switzerland Amount: 1302.57 Card: Annual Compliance Certification Trip Name: unknown ' - ' Name : Streamline Financial Systems Category: Financial Management Software, Subscription Analytics Department: Finance Location: San Francisco, USA Amount: 1389.56 Card: Integrated Revenue Solutions Trip Name: unknown ' - ' Name : Caspian Treasury Services Category: Financial Consulting, International Banking Department: Finance Location: Sydney, Australia Amount: 1580.67 Card: Global Financial Strategy Alignment Trip Name: unknown ' - source_sentence: ' 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 ' sentences: - ' Name : CustomerFirst Solutions Category: Customer Engagement Platforms, Subscription Management Tools Department: Sales Location: New York, USA Amount: 1587.92 Card: Customer Outreach Enhancement Trip Name: unknown ' - ' Name : EquipTech International Category: Technical Services, Office Solutions Department: IT Operations Location: Berlin, Germany Amount: 1459.95 Card: Multi-Functional Device Optimization Trip Name: unknown ' - ' Name : Vitality Wellness Network Category: Health & Wellness, Employee Engagement Services Department: HR Location: Toronto, Canada Amount: 304.78 Card: Quarterly Employee Well-Being Package Trip Name: unknown ' - source_sentence: ' Name : InterStep Insight Reports Category: Data Services, Research Publications Department: Marketing Location: Toronto, Canada Amount: 1248.76 Card: Strategic Market Research Trip Name: unknown ' sentences: - ' 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 ' - ' Name : Connexis Group Category: Venue Logistics Services, Corporate Membership Consultancy Department: Sales Location: Berlin, Germany Amount: 1478.55 Card: International Trade Show Engagement Trip Name: unknown ' - ' Name : GlobalFitness Unity Category: Community Health Programs, Corporate Wellness Solutions Department: Office Administration Location: Copenhagen, Denmark Amount: 987.56 Card: Office Health and Wellness Partnership Trip Name: unknown ' - source_sentence: ' Name : BlueHorizon Retreats Category: Event Planning Services, Executive Travel Arrangements Department: Executive Location: Maui, Hawaii Amount: 2356.5 Card: Annual Leadership Summit Trip Name: Executive Team Building Retreat ' sentences: - ' Name : InteractivoPlus Category: Communication Platforms, Subscription Services Department: Customer Success Location: Barcelona, Spain Amount: 1157.49 Card: Enhanced Communication Channel Trip Name: unknown ' - ' Name : Skyline Digital Solutions Category: Cloud Management Services, Internet & Network Services Department: IT Operations Location: Sydney, Australia Amount: 1128.37 Card: Global Networking Project Trip Name: unknown ' - ' Name : TransDia Solutions Category: Automotive Services, Logistics Consulting Department: Sales Location: Bordeaux, France Amount: 1326.47 Card: Client Distribution Coordination Trip Name: Quarterly Regional Visits ' 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: 0.8653846153846154 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.8605769230769231 name: Euclidean Accuracy - type: max_accuracy value: 0.8653846153846154 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("curiousabhinav/finetuned-bge-base-en") # Run inference sentences = [ '\nName : BlueHorizon Retreats\nCategory: Event Planning Services, Executive Travel Arrangements\nDepartment: Executive\nLocation: Maui, Hawaii\nAmount: 2356.5\nCard: Annual Leadership Summit\nTrip Name: Executive Team Building Retreat\n', '\nName : InteractivoPlus\nCategory: Communication Platforms, Subscription Services\nDepartment: Customer Success\nLocation: Barcelona, Spain\nAmount: 1157.49\nCard: Enhanced Communication Channel\nTrip Name: unknown\n', '\nName : TransDia Solutions\nCategory: Automotive Services, Logistics Consulting\nDepartment: Sales\nLocation: Bordeaux, France\nAmount: 1326.47\nCard: Client Distribution Coordination\nTrip Name: Quarterly Regional Visits\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 | 0.8654 | | euclidean_accuracy | 0.8606 | | **max_accuracy** | **0.8654** | #### 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 |

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: 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: 66 evaluation samples * Columns: sentence and label * Approximate statistics based on the first 66 samples: | | sentence | label | |:--------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | type | string | int | | details |

min: 35 tokens
mean: 40.83 tokens
max: 46 tokens

0: ~3.03%
1: ~1.52%
2: ~1.52%
4: ~3.03%
5: ~4.55%
6: ~1.52%
7: ~9.09%
8: ~6.06%
9: ~3.03%
10: ~4.55%
11: ~3.03%
12: ~3.03%
13: ~3.03%
14: ~3.03%
15: ~4.55%
16: ~4.55%
18: ~3.03%
19: ~3.03%
20: ~7.58%
21: ~1.52%
22: ~6.06%
23: ~4.55%
24: ~4.55%
25: ~3.03%
26: ~7.58%


Name : Aperio Global Insights
Category: Strategic Business Consulting, Data Analytics Services
Department: Finance
Location: Chicago, IL
Amount: 3456.78
Card: Global Market Expansion Evaluation
Trip Name: unknown

| 5 | |


Name : InfiniTech Systems
Category: Technical Hardware Management, Software Solutions
Department: IT Operations
Location: New York, USA
Amount: 1099.47
Card: Integrated Support Package
Trip Name: unknown

| 12 | |


Name : Mosaic Labour Mitachi
Category: Labour Consultancy, Professional Workforce Assessment
Department: Human Resource
Location: Tokyo, Japan
Amount: 2985.65
Card: Global Workforce Optimization
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 - `dataloader_pin_memory`: False - `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 - `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`: False - `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 - `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 | - | 0.8654 | | 5.0 | 65 | 0.9545 | - | ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 3.1.1 - Transformers: 4.42.4 - PyTorch: 2.7.0 - Accelerate: 1.3.0 - Datasets: 3.6.0 - Tokenizers: 0.19.1 ## 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} } ```