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("apostolosfilippas/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

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 tokensmean: 39.81 tokensmax: 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 : TranscendCategory: UpskillingDepartment: Human ResourceLocation: London, UKAmount: 859.47Card: Technology Skills EnhancementTrip Name: unknown	0
  Name : AydenCategory: Financial SoftwareDepartment: FinanceLocation: Berlin, DEAmount: 1273.45Card: Enterprise Technology ServicesTrip Name: unknown	1
  Name : Urban SphereCategory: Utilities Management, Facility ServicesDepartment: Office AdministrationLocation: New York, NYAmount: 937.32Card: Monthly Operations BudgetTrip Name: unknown	2

• Loss: 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	min: 32 tokensmean: 38.37 tokensmax: 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 : ToolyCategory: Survey Software, SaaSDepartment: MarketingLocation: San Francisco, CAAmount: 2000.0Card: Annual Marketing Technology BudgetTrip Name: unknown	10
  Name : CloudFlare Inc.Category: Internet & Network Services, SaaSDepartment: IT OperationsLocation: New York, NYAmount: 2000.0Card: Annual Cloud Services BudgetTrip Name: unknown	21
  Name : Gartner & AssociatesCategory: Consulting, Business ServicesDepartment: LegalLocation: San Francisco, CAAmount: 5000.0Card: Legal Consultation FundTrip Name: unknown	5

• Loss: 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

• 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}
• tp_size: 0
• 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

Framework Versions

• Python: 3.11.7
• Sentence Transformers: 3.4.1
• Transformers: 4.50.1
• PyTorch: 2.6.0
• Accelerate: 1.5.2
• Datasets: 3.4.1
• Tokenizers: 0.21.1

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}
}