custom-bge / README.md
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Add new SentenceTransformer model.
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metadata
base_model: BAAI/bge-large-en
datasets: []
language: []
library_name: sentence-transformers
metrics:
  - cosine_accuracy
  - dot_accuracy
  - manhattan_accuracy
  - euclidean_accuracy
  - max_accuracy
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:22604
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218
      Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations -
      QC Lab
    sentences:
      - 'mat-3783s5 : 3783 Seq 5 - Material Order'
      - '21-1313-2.0 : Layout Drawings'
      - '26-0500-1.0a : Breakers (2P 20A)'
  - source_sentence: >-
      23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218
      Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations -
      QC Lab
    sentences:
      - '26-0500-1.3 : Cabling / Wiring'
      - '26-0500-1.0a : Breakers (2P 20A)'
      - '23-2000-1.1 : HWR and HWS Pipe, Valves and Fittings'
  - source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 5-P-3783
    sentences:
      - 'mat-3783s8 : 3783 Seq 8 - Material Order'
      - 'mat-3783s5 : 3783 Seq 5 - Material Order'
      - 'mat-3786s18 : 3786 Seq 18 - Material Order'
  - source_sentence: 3786 Rady (Pacific - JD Hudson)->Seq 18-P-3786
    sentences:
      - '26-0500-1.0a : Breakers (2P 20A)'
      - 'dwg-3786s18 : 3786 Seq 18 - Drawings'
      - '23-7000-4.0b : EAV-91623'
  - source_sentence: 3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783
    sentences:
      - 'mat-3783s5 : 3783 Seq 5 - Material Order'
      - 'dwg-3783s8 : 3783 Seq 8 - Drawings'
      - 'dwg-3783s18 : 3783 Seq 18 - Drawings'
model-index:
  - name: SentenceTransformer based on BAAI/bge-large-en
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: custom bge dev
          type: custom-bge-dev
        metrics:
          - type: cosine_accuracy
            value: 0.9838187702265372
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.016181229773462782
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9838187702265372
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9838187702265372
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9838187702265372
            name: Max Accuracy
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: custom bge test
          type: custom-bge-test
        metrics:
          - type: cosine_accuracy
            value: 0.9838187702265372
            name: Cosine Accuracy
          - type: dot_accuracy
            value: 0.016181229773462782
            name: Dot Accuracy
          - type: manhattan_accuracy
            value: 0.9838187702265372
            name: Manhattan Accuracy
          - type: euclidean_accuracy
            value: 0.9838187702265372
            name: Euclidean Accuracy
          - type: max_accuracy
            value: 0.9838187702265372
            name: Max Accuracy

SentenceTransformer based on BAAI/bge-large-en

This is a sentence-transformers model finetuned from BAAI/bge-large-en. It maps sentences & paragraphs to a 1024-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-large-en
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 1024 tokens
  • Similarity Function: Cosine Similarity

Model Sources

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 1024, '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("rnbokade/custom-bge")
# Run inference
sentences = [
    '3783 UC Davis (Northern Cal - Jon Sanguinetti)->Seq 18-P-3783',
    'dwg-3783s18 : 3783 Seq 18 - Drawings',
    'mat-3783s5 : 3783 Seq 5 - Material Order',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Triplet

Metric Value
cosine_accuracy 0.9838
dot_accuracy 0.0162
manhattan_accuracy 0.9838
euclidean_accuracy 0.9838
max_accuracy 0.9838

Triplet

Metric Value
cosine_accuracy 0.9838
dot_accuracy 0.0162
manhattan_accuracy 0.9838
euclidean_accuracy 0.9838
max_accuracy 0.9838

Training Details

Training Dataset

Unnamed Dataset

  • Size: 22,604 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 22 tokens
    • mean: 25.35 tokens
    • max: 27 tokens
    • min: 15 tokens
    • mean: 18.84 tokens
    • max: 24 tokens
    • min: 6 tokens
    • mean: 16.74 tokens
    • max: 38 tokens
  • Samples:
    anchor positive negative
    MOD 1- Metal Decking - Floor
    Stud Wall Panels
    Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines
    EW1001-125 : Door Slabs / Frames / Hardware dwg-3783s16 : 3783 Seq 16 - Drawings
    MOD 1- Metal Decking - Floor
    Stud Wall Panels
    Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines
    EW1001-125 : Door Slabs / Frames / Hardware mat-3783s16 : 3783 Seq 16 - Material Order
    MOD 1- Metal Decking - Floor
    Stud Wall Panels
    Floor Sheathing (Megaboard) Layout of Dirtt Frame Centerlines
    EW1001-125 : Door Slabs / Frames / Hardware dwg-3786s292 : 3786 Seq 292 - Drawings
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 618 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 22 tokens
    • mean: 33.18 tokens
    • max: 45 tokens
    • min: 13 tokens
    • mean: 17.48 tokens
    • max: 22 tokens
    • min: 13 tokens
    • mean: 17.48 tokens
    • max: 22 tokens
  • Samples:
    anchor positive negative
    23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab 26-0500-1.0 : Breakers (3P 20A) dwg-3786s17 : 3786 Seq 17 - Drawings
    23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab 26-0500-1.0 : Breakers (3P 20A) mat-3786s17 : 3786 Seq 17 - Material Order
    23-0125 - Crispr mRNA Fume Hood Installations->Construction->QC Lab 1218 Fume Hood Install->Electrical - Fume Hood Power/Grounding Terminations - QC Lab 26-0500-1.0 : Breakers (3P 20A) 09-9000-2.0 : Paint and Coatings
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
        "scale": 20.0,
        "similarity_fct": "cos_sim"
    }
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 16
  • per_device_eval_batch_size: 16
  • num_train_epochs: 1
  • warmup_ratio: 0.1
  • fp16: True
  • 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: 5e-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: 1
  • 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: True
  • 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: 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 Training Loss loss custom-bge-dev_max_accuracy custom-bge-test_max_accuracy
0 0 - - 0.8463 -
0.0708 100 0.5651 0.6065 0.9919 -
0.1415 200 0.168 0.4217 0.9935 -
0.2123 300 0.0499 0.6747 0.9951 -
0.2831 400 0.2205 0.8112 0.9951 -
0.3539 500 0.1167 0.7040 0.9903 -
0.4246 600 0.0968 0.7364 0.9822 -
0.4954 700 0.1704 0.5540 0.9968 -
0.5662 800 0.1104 0.7266 0.9951 -
0.6369 900 0.1698 1.1020 0.9725 -
0.7077 1000 0.1077 0.9028 0.9790 -
0.7785 1100 0.1667 0.8478 0.9757 -
0.8493 1200 0.0707 0.7629 0.9887 -
0.9200 1300 0.0299 0.8024 0.9871 -
0.9908 1400 0.0005 0.8161 0.9838 -
1.0 1413 - - - 0.9838

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.0.1
  • Transformers: 4.42.4
  • PyTorch: 2.3.1+cu121
  • Accelerate: 0.32.1
  • Datasets: 2.21.0
  • 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",
}

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}
}