ValentinaKim/bge-base-automobile-matryoshka

BGE base Financial Matryoshka

This is a sentence-transformers model finetuned from intfloat/e5-base. 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: intfloat/e5-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• 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': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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("ValentinaKim/bge-base-automobile-matryoshka")
# Run inference
sentences = [
    "클러스터 조명 밝기 조절은 시동 'ON' 상태에서 인포테인먼트 시스템의 설정> 클러스터/HUD > 화면 밝기를 차례로 선택하면 클러스터의 밝기를 조절할 수 있습니다. 인포테인먼트 시스템 화면에 표시되는 조명밝기 조절 정도를 참고하여 원하는 밝기로 조절하십시오.",
    '클러스터 조명 밝기 조절은 어떻게 하나요?',
    '하이브리드 자동차의 저압 타이어 경고등이 켜졌을 때의 조치는 무엇입니까?',
]
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

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5556
cosine_accuracy@3	0.8148
cosine_accuracy@5	0.8519
cosine_accuracy@10	0.9259
cosine_precision@1	0.5556
cosine_precision@3	0.2716
cosine_precision@5	0.1704
cosine_precision@10	0.0926
cosine_recall@1	0.5556
cosine_recall@3	0.8148
cosine_recall@5	0.8519
cosine_recall@10	0.9259
cosine_ndcg@10	0.7436
cosine_mrr@10	0.6849
cosine_map@100	0.689

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5556
cosine_accuracy@3	0.7778
cosine_accuracy@5	0.8519
cosine_accuracy@10	0.8889
cosine_precision@1	0.5556
cosine_precision@3	0.2593
cosine_precision@5	0.1704
cosine_precision@10	0.0889
cosine_recall@1	0.5556
cosine_recall@3	0.7778
cosine_recall@5	0.8519
cosine_recall@10	0.8889
cosine_ndcg@10	0.7335
cosine_mrr@10	0.6825
cosine_map@100	0.6897

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5926
cosine_accuracy@3	0.7778
cosine_accuracy@5	0.8148
cosine_accuracy@10	0.8889
cosine_precision@1	0.5926
cosine_precision@3	0.2593
cosine_precision@5	0.163
cosine_precision@10	0.0889
cosine_recall@1	0.5926
cosine_recall@3	0.7778
cosine_recall@5	0.8148
cosine_recall@10	0.8889
cosine_ndcg@10	0.7461
cosine_mrr@10	0.6997
cosine_map@100	0.7074

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5926
cosine_accuracy@3	0.7407
cosine_accuracy@5	0.8519
cosine_accuracy@10	0.8889
cosine_precision@1	0.5926
cosine_precision@3	0.2469
cosine_precision@5	0.1704
cosine_precision@10	0.0889
cosine_recall@1	0.5926
cosine_recall@3	0.7407
cosine_recall@5	0.8519
cosine_recall@10	0.8889
cosine_ndcg@10	0.7391
cosine_mrr@10	0.691
cosine_map@100	0.6993

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5926
cosine_accuracy@3	0.7407
cosine_accuracy@5	0.8519
cosine_accuracy@10	0.9259
cosine_precision@1	0.5926
cosine_precision@3	0.2469
cosine_precision@5	0.1704
cosine_precision@10	0.0926
cosine_recall@1	0.5926
cosine_recall@3	0.7407
cosine_recall@5	0.8519
cosine_recall@10	0.9259
cosine_ndcg@10	0.7456
cosine_mrr@10	0.6892
cosine_map@100	0.6933

Training Details

Training Dataset

Unnamed Dataset

• Size: 63 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 89 tokens mean: 181.76 tokens max: 365 tokens	min: 22 tokens mean: 46.21 tokens max: 72 tokens

• Samples:

  positive	anchor
  하이브리드 자동차의 전방 차량 출발 알림 기능의 제한 사항은 과격하게 운전할 경우, 빈번하게 차선을 침범할 경우, 차로 이탈방지 보조 등 다른 운전자 보조에 의해 차량이 제어될 경우 등입니다.	하이브리드 자동차의 전방 차량 출발 알림 기능의 제한 사항은 무엇입니까?
  파워 트렁크가 정상적으로 작동하지 않으면 무리한 힘을 가하지 마십시오. 파워 트렁크가 손상될 수 있습니다. 반드시 당사 직영 하이테크센터나 블루핸즈에서 점검을 받으십시오.	파워 트렁크가 정상적으로 작동하지 않으면 어떻게 해야 하나요?
  에어백 경고 라벨의 주의 사항은 13세 미만의 어린이는 에어백의 팽창 충격으로 다칠 수 있습니다. 어린이에게는 뒷좌석이 안전할 수 있습니다. 유아용 보조 좌석은 동승석에 설치하지 마십시오. 에어백에서 가능한 떨어져 앉으십시오. 좌석 안전벨트와 어린이 보호 장치를 사용하십시오.	에어백 경고 라벨의 주의 사항은 무엇입니까?

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• gradient_accumulation_steps: 64
• learning_rate: 2e-05
• num_train_epochs: 4
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• tf32: False
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 8
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 64
• 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: 4
• max_steps: -1
• lr_scheduler_type: cosine
• 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: False
• 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: True
• 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_fused
• 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: 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
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	dim_128_cosine_map@100	dim_256_cosine_map@100	dim_512_cosine_map@100	dim_64_cosine_map@100	dim_768_cosine_map@100
1.0	1	0.4923	0.5456	0.5549	0.4722	0.5450
2.0	2	0.6184	0.6751	0.7085	0.6313	0.7072
3.0	3	0.6810	0.6825	0.6916	0.6933	0.6840
4.0	4	0.6993	0.7074	0.6897	0.6933	0.689

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2+cu121
• Accelerate: 0.33.0
• Datasets: 2.19.1
• Tokenizers: 0.19.1

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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}