ppuva1/finetuned-bge-base-en

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("ppuva1/finetuned-bge-base-en")
# Run inference
sentences = [
    '\nName : Allegro Integrations\nCategory: Payment Processing Solutions, Financial Technology Services\nDepartment: Finance\nLocation: Dublin, Ireland\nAmount: 1298.75\nCard: Bi-annual Financial Systems Audit\nTrip Name: unknown\n',
    '\nName : Banyan Tree Pte Ltd\nCategory: General Contractors - Residential and Commercial\nDepartment: Office Administration\nLocation: Houston, TX\nAmount: 987.65\nCard: Operational Infrastructure Management\nTrip Name: unknown\n',
    '\nName : ComplyTech Solutions\nCategory: Regulatory Software, Consultancy Services\nDepartment: Compliance\nLocation: Brussels, Belgium\nAmount: 1095.45\nCard: Regulatory Compliance Optimization Plan\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

Metric	bge-base-en-train	bge-base-en-eval
cosine_accuracy	0.476	0.0

Training Details

Training Dataset

Unnamed Dataset

• Size: 416 training samples
• Columns: sentence and label
• Approximate statistics based on the first 416 samples:

  	sentence	label
  type	string	int
  details	min: 32 tokens mean: 39.99 tokens max: 49 tokens	0: ~3.12% 1: ~3.12% 2: ~3.85% 3: ~4.81% 4: ~2.16% 5: ~4.33% 6: ~4.57% 7: ~3.85% 8: ~5.05% 9: ~4.09% 10: ~2.88% 11: ~4.33% 12: ~2.16% 13: ~4.09% 14: ~3.61% 15: ~5.77% 16: ~3.12% 17: ~6.01% 18: ~5.05% 19: ~2.64% 20: ~3.37% 21: ~2.88% 22: ~4.57% 23: ~2.64% 24: ~2.64% 25: ~3.85% 26: ~1.44%

• Samples:

  sentence	label
  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	0
  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	1
  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	2

• Loss: BatchSemiHardTripletLoss

Evaluation Dataset

Unnamed Dataset

• Size: 104 evaluation samples
• Columns: sentence and label
• Approximate statistics based on the first 104 samples:

  	sentence	label
  type	string	int
  details	min: 32 tokens mean: 39.19 tokens max: 46 tokens	0: ~1.92% 1: ~0.96% 2: ~4.81% 3: ~1.92% 5: ~5.77% 6: ~7.69% 7: ~4.81% 8: ~3.85% 9: ~5.77% 10: ~2.88% 11: ~4.81% 12: ~2.88% 13: ~1.92% 14: ~2.88% 15: ~0.96% 16: ~1.92% 17: ~3.85% 18: ~4.81% 19: ~3.85% 20: ~1.92% 21: ~0.96% 22: ~5.77% 23: ~7.69% 24: ~7.69% 25: ~4.81% 26: ~2.88%

• Samples:

  sentence	label
  Name : Aegis Risk Consultants Category: Executive Risk Management, Enterprise Solutions Department: Legal Location: London, UK Amount: 1743.56 Card: Leadership Liability Initiative Trip Name: unknown	11
  Name : Vinobia Lounge Category: Culinary Experiences, Networking Venues Department: Marketing Location: Dallas, TX Amount: 651.58 Card: Innovative Marketing Strategies Trip Name: Annual Marketing Event	8
  Name : Freenet AG Category: Telecommunication Services Department: IT Operations Location: Zurich, Switzerland Amount: 2794.37 Card: Infrastructure Support Services Trip Name: unknown	25

• 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

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	Training Loss	Validation Loss	bge-base-en-train_cosine_accuracy	bge-base-en-eval_cosine_accuracy
-1	-1	-	-	0.8510	-
3.8462	100	4.9979	5.0174	0.4760	-
-1	-1	-	-	-	0.0

Framework Versions

• Python: 3.11.8
• Sentence Transformers: 3.4.1
• Transformers: 4.48.2
• PyTorch: 2.6.0
• Accelerate: 1.3.0
• Datasets: 3.2.0
• Tokenizers: 0.21.0

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}
}