aaa961/bge-base-en-cost-categories-3sets

BGE Base EN Cost Categories Fine-tuned

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
• Language: en
• License: apache-2.0

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, 'architecture': '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("aaa961/bge-base-en-cost-categories-3sets")
# Run inference
sentences = [
    '\nName : BlueWave Innovations\nCategory: Renewable Energy Solutions, Infrastructure Management\nDepartment: Office Administration\nLocation: Miami, FL\nAmount: 935.47\nCard: Building Energy Optimization\nTrip Name: unknown\n',
    'Office Rent & Utilities',
    'Data Services & Analytics',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.7791, 0.7019],
#         [0.7791, 1.0000, 0.7114],
#         [0.7019, 0.7114, 1.0000]])

Evaluation

Metrics

Information Retrieval

• Datasets: ir_eval_eval and ir_eval_test
• Evaluated with InformationRetrievalEvaluator

Metric	ir_eval_eval	ir_eval_test
cosine_accuracy@1	0.3333	0.5962
cosine_accuracy@3	0.6515	0.9038
cosine_accuracy@5	0.7273	0.9615
cosine_accuracy@10	0.8636	1.0
cosine_precision@1	0.3333	0.5962
cosine_precision@3	0.2172	0.3013
cosine_precision@5	0.1455	0.1923
cosine_precision@10	0.0864	0.1
cosine_recall@1	0.3333	0.5962
cosine_recall@3	0.6515	0.9038
cosine_recall@5	0.7273	0.9615
cosine_recall@10	0.8636	1.0
cosine_ndcg@10	0.5962	0.8208
cosine_mrr@10	0.5113	0.7611
cosine_map@100	0.5215	0.7611

Training Details

Training Dataset

Unnamed Dataset

• Size: 208 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 208 samples:

  	anchor	positive
  type	string	string
  details	min: 33 tokens mean: 39.84 tokens max: 49 tokens	min: 3 tokens mean: 5.62 tokens max: 7 tokens

• Samples:

  anchor	positive
  Name : Transcend Category: Upskilling Department: Human Resource Location: London, UK Amount: 859.47 Card: Technology Skills Enhancement Trip Name: unknown	Employee Training & Development
  Name : Ayden Category: Financial Software Department: Finance Location: Berlin, DE Amount: 1273.45 Card: Enterprise Technology Services Trip Name: unknown	Subscription & Revenue Infrastructure
  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	Office Rent & Utilities

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• num_train_epochs: 5
• learning_rate: 2e-05
• lr_scheduler_type: cosine
• warmup_steps: 0.1
• optim: adamw_torch_fused
• gradient_accumulation_steps: 4
• bf16: True
• eval_strategy: epoch
• per_device_eval_batch_size: 16
• load_best_model_at_end: True

All Hyperparameters

Click to expand

• per_device_train_batch_size: 16
• num_train_epochs: 5
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: None
• warmup_steps: 0.1
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 4
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: True
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: trackio
• eval_strategy: epoch
• per_device_eval_batch_size: 16
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: False
• hub_private_repo: None
• hub_model_id: None
• hub_strategy: every_save
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: True
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• warmup_ratio: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	ir_eval_eval_cosine_ndcg@10	ir_eval_test_cosine_ndcg@10
-1	-1	-	0.5962	-
1.0	4	-	-	0.8075
2.0	8	-	-	0.8413
2.6154	10	1.9420	-	-
3.0	12	-	-	0.8166
4.0	16	-	-	0.8205
5.0	20	1.3733	-	0.8208

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.12.11
• Sentence Transformers: 5.3.0
• Transformers: 5.3.0
• PyTorch: 2.5.1+cu121
• Accelerate: 1.13.0
• Datasets: 4.8.2
• Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}