SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
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': 256, 'do_lower_case': False, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 384, '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("LamaDiab/MiniLM-SemanticEngine")
# Run inference
sentences = [
    'hiit biker shorts - black',
    'black shorts',
    'winter slippers for ladies christmas themed',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.7103, -0.0705],
#         [ 0.7103,  1.0000, -0.0356],
#         [-0.0705, -0.0356,  1.0000]])

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9472

Training Details

Training Dataset

Unnamed Dataset

Size: 169,967 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 3 tokens
mean: 8.82 tokens
max: 237 tokens

min: 3 tokens
mean: 14.99 tokens
max: 256 tokens

	anchor	positive
type	string	string
details	min: 3 tokens mean: 8.82 tokens max: 237 tokens	min: 3 tokens mean: 14.99 tokens max: 256 tokens

Samples:

anchor	positive
`orasi barista almond milk is a premium, plant-based milk designed specifically for coffee lovers. crafted to create the perfect froth, it delivers a smooth and creamy texture that enhances the flavor of your lattes, cappuccinos, and other coffee drinks.`	`groceries`
`this toy is a "modern fashion" doll, combining beauty and innovation in its design. the doll has long and pink hair that adds a modern and attractive character to it. it comes with a wide variety of clothes and cool accessories that allow children to switch outfits and try different looks.`
features:
modern and attractive design: the doll has a stylish and modern design that suits the tastes of children of different ages.
long and colorful hair: long and colorful hair gives the doll a distinctive and beautiful look, enhancing the possibilities of play and creativity.
wide range of clothes: the game has a large assortment of clothes that allow children to choose the appropriate outfits for the doll character according to their imagination.
multiple accessories: it comes with various accessories that add a touch of distinction and elegance to the doll, allowing to experiment with different styles.
stimulate creativity and imagination: the game helps enhance children's imagination by...	`kids`
`zinnia ice box vivid gen.2 - blue`	`blue ice box`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": true
}

Evaluation Dataset

Unnamed Dataset

Size: 16,216 evaluation samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 3 tokens mean: 9.79 tokens max: 52 tokens	min: 2 tokens mean: 19.21 tokens max: 256 tokens	min: 3 tokens mean: 9.76 tokens max: 67 tokens

Samples:

anchor	positive	negative
`dosado ring`	`dosado or dos- à- dos: a wavy movement of two people around eachother, without turning & facing the same direction. material: 18k gold plated hammered brass. size: one size, adjustable. care instructions: to keep the jewelry pieces looking as good as new, please make sure that you store them in an airtight container. they should not come in contact with sweat, water or pefume, alcohol, sanitizers etc. polish with a microfiber cloth.`	`kiprun ks light men's running shoes - black`
`puzzle city of fog`	this amazing puzzle offers a unique opportunity to explore the beauty of san francisco, also known as the "city by the bay," through assembling a 2000-piece jigsaw. you'll immerse yourself in a world full of colors and details, as your eyes wander across the iconic golden gate bridge, towering buildings, distinctive hilly streets, and sailing ships in the harbor. it’s a panoramic depiction of san francisco, providing a comprehensive view of the city and its landmarks. features: explore san francisco: enjoy a virtual exploration of san francisco without leaving your home. get up close with famous landmarks such as the golden gate bridge and the harbor. improves cognitive skills: assembling the puzzle enhances focus, memory, and fine motor skills while boosting problem-solving and decision-making abilities. relaxation and stress relief: puzzle assembly is a fun and engaging activity that helps to relax and reduce stress, especially when concentrating on the appealing details of san franc...	`unicorn`
`my fault series`	`mercedes ron book`	`sophie's world`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": true
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
weight_decay: 0.01
num_train_epochs: 5
warmup_ratio: 0.2
fp16: True
dataloader_num_workers: 2
dataloader_prefetch_factor: 2
push_to_hub: True
hub_model_id: LamaDiab/MiniLM-SemanticEngine
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: 64
per_device_eval_batch_size: 64
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.2
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: 2
dataloader_prefetch_factor: 2
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: True
resume_from_checkpoint: None
hub_model_id: LamaDiab/MiniLM-SemanticEngine
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy
0.0004	1	1.6989	-	-
0.1883	500	1.6103	1.4441	0.9124
0.3765	1000	1.1942	1.3155	0.9233
0.5648	1500	0.9831	1.2584	0.9257
0.7530	2000	0.8867	1.2368	0.9254
0.9413	2500	0.8094	1.1874	0.9274
1.1295	3000	0.5818	1.1431	0.9348
1.3178	3500	0.6978	1.1291	0.9374
1.5060	4000	0.6652	1.0936	0.9389
1.6943	4500	0.6287	1.0889	0.9369
1.8825	5000	0.5986	1.0780	0.9404
2.0708	5500	0.4376	1.0783	0.9386
2.2590	6000	0.511	1.0674	0.9405
2.4473	6500	0.4997	1.0412	0.9427
2.6355	7000	0.4985	1.0160	0.9441
2.8238	7500	0.4798	1.0264	0.9434
3.0120	8000	0.3477	1.0153	0.9455
3.2003	8500	0.4117	1.0177	0.9461
3.3886	9000	0.4302	1.0071	0.9451
3.5768	9500	0.4046	1.0171	0.9460
3.7651	10000	0.414	0.9819	0.9474
3.9533	10500	0.3786	0.9982	0.9463
4.1416	11000	0.2952	0.9920	0.9461
4.3298	11500	0.3655	0.9959	0.9455
4.5181	12000	0.3655	0.9961	0.9464
4.7063	12500	0.3662	0.9826	0.9467
4.8946	13000	0.3545	0.9864	0.9472

Framework Versions

Python: 3.11.13
Sentence Transformers: 5.1.2
Transformers: 4.53.3
PyTorch: 2.6.0+cu124
Accelerate: 1.9.0
Datasets: 4.4.1
Tokenizers: 0.21.2

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",
}

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Safetensors

Model size

22.7M params

Tensor type

F32

Model tree for LamaDiab/MiniLM-SemanticEngine

Base model

sentence-transformers/all-MiniLM-L6-v2

Finetuned

(608)

this model

Evaluation results

Cosine Accuracy on Unknown
self-reported

0.947

View on Papers With Code