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

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': 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

Dataset: custom-bge-dev
Evaluated with TripletEvaluator

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

Triplet

Dataset: custom-bge-test
Evaluated with TripletEvaluator

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}
}