This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'cls', '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'compact garage storage shed under 3 feet wide',
    'outdoor 2 ft. 9 in . w x 2 ft. d plastic vertical tool shed Sheds with its tall profile , this 22 cubic foot vertical shed is great for storing ladders , long-handled tools , garden accessories , and more when space is at a premium . the durable , all-weather resin construction provides water resistance and uv protection , while the multi-wall panels are engineere interiorwidth-sidetoside:27|interiorheight-toptobottom:69|compatibleshelfpartnumber : xa1182|windrating:65|overallwidth-sidetoside:33|countryoforigin : united states|interiordepth-fronttoback:20.25|levelofassembly : full assembly needed|countryoforigin-additionaldetails : made in usa|doorwidth-sidet',
    'garden plastic/metal storage shed Sheds easily add storage space and value to your property with an outdoor garden shed . the metal shed has distinct advantages over traditional wooden models that make it a much more attractive choice for purchase . a metal shed is more affordable and being pre-fabricated significantly cuts down on expens dsmetallic : steel|shedtype : storage shed|color : grey|construction : traditional|overallheight-toptobottom:75.5|countryoforigin-additionaldetails : made in china|warrantylength:1 year|levelofassembly : full assembly needed|overallwidth-sidetoside:126.75|overalldepth-fronttoback:109|overallproductw',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.5560, 0.5587],
#         [0.5560, 1.0000, 0.5424],
#         [0.5587, 0.5424, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 284,573 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 100 samples:

	anchor	positive	negative
type	string	string	string
modality	text	text	text
details	min: 7 tokens mean: 10.2 tokens max: 16 tokens	min: 115 tokens mean: 173.42 tokens max: 196 tokens	min: 97 tokens mean: 168.43 tokens max: 205 tokens

Samples:

anchor	positive	negative
`modern 10 inch semi-flush mount light for entryway`	bucholz 1 -light 10 '' semi flush mount Flush Mount Lighting this 1-light semi-flush mount has a sleek , streamlined shape that gives a modern look to your hallway , entryway , or over your dining room table . it has a metal frame and downrod , and an egg-shaped glass shade with an opening at the bottom for light to shine through . the downrod retracts and ro shadematerial : glass\|dry , damporwetlocationlisted : dry\|whatisdrydamporwetlocationlisted : this indicates whether the fixture is safe to use in dry locations , damp locations ( moist environments ) , or wet locations ( direct exposure to water ) .\|estimatedtimetosetup:5\|bodyheight-toptobottom:10\|w	weatherford 1 - light 10 '' semi flush mount Flush Mount Lighting marrying modern minimalism and factory flair , this understated one-light flush mount brings a splash of industrial style as it shines a light down on your space . crafted of metal , this fixture features a round canopy and a conical shade with its upper portion left open to provide a peek at the so canopywidth-sidetoside:4.875\|recommendedbulbshapecode : a19\|warrantylength:1 year\|canopydepth-fronttoback:0.75\|powersource : hardwired\|dssecondaryproductstyle : nautical\|canopyheight-toptobottom:4.875\|recommendedbulbshape : standard\|producttype : flush mount\|whatisullisted : the underwriters laborat
`sleek glass pendant light for dining room`	bucholz 1 -light 10 '' semi flush mount Flush Mount Lighting this 1-light semi-flush mount has a sleek , streamlined shape that gives a modern look to your hallway , entryway , or over your dining room table . it has a metal frame and downrod , and an egg-shaped glass shade with an opening at the bottom for light to shine through . the downrod retracts and ro shadematerial : glass\|dry , damporwetlocationlisted : dry\|whatisdrydamporwetlocationlisted : this indicates whether the fixture is safe to use in dry locations , damp locations ( moist environments ) , or wet locations ( direct exposure to water ) .\|estimatedtimetosetup:5\|bodyheight-toptobottom:10\|w	`6 '' h glass sphere pendant shade ( screw on ) in smoke color : smoke\|shape : sphere\|overallproductweight:0.67\|overalldepth-fronttoback:6\|theme : no theme\|attachmenttype : screw on\|style : modern & contemporary\|overallwidth-sidetoside:6\|fittersize:3.25\|producttype : pendant shade\|primarymaterial : glass\|overallheight-toptobottom:6\| : no\|fitterincluded :`
`small metal frame flush mount light with glass shade`	bucholz 1 -light 10 '' semi flush mount Flush Mount Lighting this 1-light semi-flush mount has a sleek , streamlined shape that gives a modern look to your hallway , entryway , or over your dining room table . it has a metal frame and downrod , and an egg-shaped glass shade with an opening at the bottom for light to shine through . the downrod retracts and ro shadematerial : glass\|dry , damporwetlocationlisted : dry\|whatisdrydamporwetlocationlisted : this indicates whether the fixture is safe to use in dry locations , damp locations ( moist environments ) , or wet locations ( direct exposure to water ) .\|estimatedtimetosetup:5\|bodyheight-toptobottom:10\|w	`3 - light semi flush mount Flush Mount Lighting\|Commercial Ceiling Lighting it is perfect to transform your dining room or entryway with this charming transitional flush mount ceiling light . bodydepth-fronttoback:16.1\|producttype : semi flush mount\|recommendedbulbshape : candle\|bodydepth-fronttoback:12.2\|lightdirection : up\|warrantylength:2 years\|dssecondaryproductstyle : classic glam\|dsprimaryproductstyle : glam\|fixturedesign : unique/statement\|craftsmanshiptype : handcrafted / hand-fo`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Evaluation Dataset

Unnamed Dataset

Size: 14,978 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 100 samples:

	anchor	positive	negative
type	string	string	string
modality	text	text	text
details	min: 8 tokens mean: 11.03 tokens max: 18 tokens	min: 110 tokens mean: 159.21 tokens max: 193 tokens	min: 90 tokens mean: 168.18 tokens max: 201 tokens

Samples:

anchor	positive	negative
`quirky rooster shelf sitters`	4 piece rooster shelf sitter figurine set Decorative Objects add these colorful rooster sitters to shelves , windowsills , and more throughout your home . the set includes four hand-painted roosters with delightful floral accents . each cute sitter has dangling legs and plenty of charm . overallwidth-sidetoside:1.63\|geometricshapes : no geometric & shapes\|producttype : figurine\|subject : animals\|nature : no nature\|spiritualreligious : no spiritual & religious\|structuresbuildings : no structures & buildings\|wordstext : no words & text\|overallproductweight:0.25\|quantity:4\|entertainmen	dunellen farmhouse rooster 2 piece figurine set Decorative Objects be the early bird in grabbing these farmhouse silver metal and wood rooster sculpture by the head and complete your farmhouse set up now . this 2-piece set features two small and medium standing rooster sculptures with a gray finish on the body , and silver on the rest . this will look great as a ce overallwidth-sidetoside:13\|color : brown , silver\|purposefuldistressingtype : distressed metal\|style : farmhouse / country\|overallheight-toptobottom:5.1\|fantasysci-fi : no fantasy & sci-fi\|overallheight-toptobottom:16\|nature : no nature\|geometricshapes : no geometric & shapes\|spiritualreligious : no
`decorative rooster figurines with floral accents`	4 piece rooster shelf sitter figurine set Decorative Objects add these colorful rooster sitters to shelves , windowsills , and more throughout your home . the set includes four hand-painted roosters with delightful floral accents . each cute sitter has dangling legs and plenty of charm . overallwidth-sidetoside:1.63\|geometricshapes : no geometric & shapes\|producttype : figurine\|subject : animals\|nature : no nature\|spiritualreligious : no spiritual & religious\|structuresbuildings : no structures & buildings\|wordstext : no words & text\|overallproductweight:0.25\|quantity:4\|entertainmen	2 piece metal figurine set Decorative Objects the antique style of these two metal rooster decors will bring a fascinating appeal to any rustic , vintage , or antique accent table . with its iron construction , these rooster sculptures will continue to enhance the look of your home interior for years to come . featuring a decorative rustic bron geometricshapes : no geometric & shapes\|spiritualreligious : no spiritual & religious\|overallwidth-sidetoside:20.47\|producttype : figurine\|nature : no nature\|foodbeverage : no food & beverage\|fantasysci-fi : no fantasy & sci-fi\|people : no people\|agegroup : adult\|animals : rooster\|overallproductweig
`comfortable anti-fatigue mat for kitchen counter`	catrisha soothing foam comfort anti-fatigue mat Kitchen Mats this anti-fatigue mat provides a full inch of soothing foam comfort for your tired feet . perfect in front of a kitchen counter and has a skid-resistant backing . color : red\|pattern : solid color\|backingmaterial : polyester\|color : brown\|shape : rectangle\|supplierintendedandapproveduse : non residential use\|productcare : machine washable\|construction : machine made\|color : blue\|dssecondaryproductstyle : transitional modern\|overalllength-endtoend:30\|color : g	grano hello standing anti-fatigue mat Kitchen Mats use this mat in any part of your home . whether it be the kitchen , laundry room , bathroom , or doorway this elegant mat will bring luscious texture , enduring material , and comfortable steps for your convenience . this perfectly crafted pvc mat is joined with a rubber backing for long-lasting usa pattern : no pattern and not solid color\|overalllength-endtoend:30\|overalllength-endtoend:47\|overallproductweight:2.09\|holidayoccasion : no holiday\|dssecondaryproductstyle : modern farmhouse\|overallheight-toptobottom:0.47\|material : synthetics\|theme : text\|materialdetails:100 % pvc\|overallwidth-side

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
num_train_epochs: 1
learning_rate: 2e-05
warmup_steps: 0.1
bf16: True
per_device_eval_batch_size: 64
dataloader_num_workers: 4

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
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: 1
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: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 64
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: False
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: 4
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_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
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	Validation Loss
0.0225	100	1.1596	-
0.0450	200	0.6243	-
0.0675	300	0.5484	-
0.0899	400	0.4963	-
0.1124	500	0.4665	0.4796
0.1349	600	0.4426	-
0.1574	700	0.4353	-
0.1799	800	0.4284	-
0.2024	900	0.4111	-
0.2249	1000	0.3946	0.4274
0.2474	1100	0.3883	-
0.2698	1200	0.3806	-
0.2923	1300	0.3590	-
0.3148	1400	0.3661	-
0.3373	1500	0.3632	0.4086
0.3598	1600	0.3717	-
0.3823	1700	0.3517	-
0.4048	1800	0.3428	-
0.4273	1900	0.3574	-
0.4497	2000	0.3412	0.3882
0.4722	2100	0.3340	-
0.4947	2200	0.3356	-
0.5172	2300	0.3261	-
0.5397	2400	0.3358	-
0.5622	2500	0.3208	0.3833
0.5847	2600	0.3206	-
0.6072	2700	0.3254	-
0.6296	2800	0.3215	-
0.6521	2900	0.3082	-
0.6746	3000	0.3099	0.3684
0.6971	3100	0.3060	-
0.7196	3200	0.3009	-
0.7421	3300	0.3021	-
0.7646	3400	0.3163	-
0.7870	3500	0.3020	0.3652
0.8095	3600	0.3012	-
0.8320	3700	0.2927	-
0.8545	3800	0.2878	-
0.8770	3900	0.2974	-
0.8995	4000	0.3125	0.3584
0.9220	4100	0.2938	-
0.9445	4200	0.2997	-
0.9669	4300	0.2915	-
0.9894	4400	0.2923	-
1.0	4447	-	0.3572

Training Time

Training: 26.3 minutes
Evaluation: 3.2 minutes
Total: 29.5 minutes

Framework Versions

Python: 3.12.13
Sentence Transformers: 5.7.0
Transformers: 5.14.1
PyTorch: 2.13.0+cu130
Accelerate: 1.14.0
Datasets: 5.0.1
Tokenizers: 0.22.2

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@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},
}

EmiliaLee/k_model

SentenceTransformer based on BAAI/bge-m3

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Training Time

Framework Versions

Additional Resources

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

EmiliaLee/
k_model