metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:7022
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: >-
      nike indigo man presents an invigorating blend that captures the essence
      of modern masculinity with a fresh and woody character launched in 2015
      this fragrance seamlessly weaves together vibrant citrus notes and
      aromatic spices offering an energizing experience that is both elegant and
      bold users describe it as sophisticated yet approachable making it perfect
      for daily wear or special occasions the fragrance elicits feelings of
      confidence and freshness with many appreciating its unique combination of
      floral nuances balanced by warm earthy undertones reviewers frequently
      note its impressively longlasting quality allowing its captivating scent
      to linger throughout the day with a solid rating of 388 nike indigo man is
      lauded not only for its wellrounded olfactory profile but also for its
      ability to leave a lasting impression embodying both vigor and refinement
    sentences:
      - ozonic notes
      - cedar
      - earthy notes
  - source_sentence: >-
      nike indigo man presents an invigorating blend that captures the essence
      of modern masculinity with a fresh and woody character launched in 2015
      this fragrance seamlessly weaves together vibrant citrus notes and
      aromatic spices offering an energizing experience that is both elegant and
      bold users describe it as sophisticated yet approachable making it perfect
      for daily wear or special occasions the fragrance elicits feelings of
      confidence and freshness with many appreciating its unique combination of
      floral nuances balanced by warm earthy undertones reviewers frequently
      note its impressively longlasting quality allowing its captivating scent
      to linger throughout the day with a solid rating of 388 nike indigo man is
      lauded not only for its wellrounded olfactory profile but also for its
      ability to leave a lasting impression embodying both vigor and refinement
    sentences:
      - amber
      - cypress
      - ivy
  - source_sentence: >-
      named after the novel of the same name by poet and aviator antoine de
      saintexupery the perfume is a tribute to women who like to take risks
    sentences:
      - pear
      - fruity notes
      - woody notes
  - source_sentence: >-
      basi femme by armand basi exudes a playful yet sophisticated charm that
      captures the essence of femininity launched in 2000 this fragrance
      envelops wearers in a delightful bouquet of floral and fruity elements
      balanced by warm and comforting undertones users describe it as sweet and
      aromatic with hints of soft spice that create a captivating allure the
      scent is often praised for its versatility making it suitable for both day
      and evening wear feedback highlights its elegance and freshness which
      evoke a sense of confidence and joy many appreciate its soft yet distinct
      presence noting how it leaves a lasting impression without overwhelming
      the senses the overall mood is uplifting and approachable perfect for the
      modern woman who seeks to express her individuality while remaining
      effortlessly chic with a solid rating from its community basi femme
      resonates as a timeless fragrance choice celebrated for its inviting and
      warm character
    sentences:
      - citrus notes
      - fig leaf
      - jasmine petal
  - source_sentence: >-
      lalibela by memo paris is an enchanting fragrance that transports you to a
      lush garden awash with the vibrancy of blooming flowers this scent
      harmoniously blends rich floral notes with a warm sweet undertone creating
      a captivating atmosphere that feels both opulent and inviting the essence
      of tender blooms is beautifully complemented by a hint of creamy sweetness
      enveloping the wearer in a soft and sensuous embrace users describe
      lalibela as a sophisticated and luxurious perfume often noting its unique
      complexity that unfolds throughout the day with a cozy warmth reminiscent
      of cherished memories it evokes feelings of comfort and elegance many
      appreciate its longlasting presence making it a favorite for both day and
      evening wear reviewers highlight the fragrances ability to turn heads
      leaving a memorable impression without being overwhelming overall lalibela
      is a masterful olfactory experience that embodies the spirit of femininity
      perfect for those seeking a signature scent infused with both richness and
      charm
    sentences:
      - vanilla
      - cypress
      - green notes
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: pearson_cosine
            value: 0.9221809753640012
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8309208495457832
            name: Spearman Cosine

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}) with Transformer model: 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'lalibela by memo paris is an enchanting fragrance that transports you to a lush garden awash with the vibrancy of blooming flowers this scent harmoniously blends rich floral notes with a warm sweet undertone creating a captivating atmosphere that feels both opulent and inviting the essence of tender blooms is beautifully complemented by a hint of creamy sweetness enveloping the wearer in a soft and sensuous embrace users describe lalibela as a sophisticated and luxurious perfume often noting its unique complexity that unfolds throughout the day with a cozy warmth reminiscent of cherished memories it evokes feelings of comfort and elegance many appreciate its longlasting presence making it a favorite for both day and evening wear reviewers highlight the fragrances ability to turn heads leaving a memorable impression without being overwhelming overall lalibela is a masterful olfactory experience that embodies the spirit of femininity perfect for those seeking a signature scent infused with both richness and charm',
    'vanilla',
    'green notes',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9222
spearman_cosine	0.8309

Training Details

Training Dataset

Unnamed Dataset

Size: 7,022 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string float
details
min: 12 tokens
mean: 178.91 tokens
max: 256 tokens

min: 3 tokens
mean: 3.93 tokens
max: 7 tokens

min: 0.0
mean: 0.51
max: 1.0

	sentence_0	sentence_1	label
type	string	string	float
details	min: 12 tokens mean: 178.91 tokens max: 256 tokens	min: 3 tokens mean: 3.93 tokens max: 7 tokens	min: 0.0 mean: 0.51 max: 1.0

Samples:

sentence_0	sentence_1	label
americas by mahogany is an invigorating fragrance crafted for the modern man embodying the spirit of adventure and exploration this scent opens with a refreshing burst that hints at invigorating elements balanced with a spicy warmth that captivates the senses users often describe it as a harmonious blend of fresh and woody accords creating an atmosphere that is both vibrant and grounding this perfumes unique composition has garnered praise for its longevity and versatility making it suitable for both day and evening wear reviewers highlight its ability to evoke a sense of confidence and sophistication likening its character to a breezy stroll through verdant landscapes complemented by hints of rich aromatic woods the overall mood is one of refined masculinity inviting wearers to embrace their individuality while leaving a memorable impression with a commendable rating of 426 out of 5 americas resonates with those seeking a fragrance that is fresh yet complex encapsulating the essence o...	`black pepper`	`1.0`
rosa gallica by brecourt is an exquisite unisex fragrance that encapsulates a warm and inviting aura perfect for those who appreciate the elegance of nature intertwined with a touch of mystique with its alluring blend of floral and woody elements this fragrance wraps the wearer in a soft balsamic embrace that evokes a sense of serene sophistication crafted by renowned perfumer emilie bouge rosa gallica has garnered a favorable reception from users who describe it as both captivating and refined reviewers often highlight its unique combination of spicy warmth and delicate floral notes creating an atmosphere that is both romantic and grounded its celebrated for its longevity allowing the wearer to carry its gentle yet complex character throughout the day fans of this fragrance appreciate its versatility noting it as suitable for both day and evening wear with many finding it perfect for cooler weather the overall sentiment points to a fragrance that feels timeless and elegant evoking the...	`pimento`	`0.0`
roberto cavalli nero assoluto is a mesmerizing fragrance that embodies a rich and sensual experience perfect for the confident modern woman with its intriguing blend of velvety florals and warm undertones this perfume radiates an aura of elegance and sophistication users frequently describe it as both alluring and comforting invoking sensations of nightfall enhanced by an embrace of soft sweetness the fragrance opens with captivating floral notes that invite curiosity and provide a fresh inviting introduction as it evolves deeper and warmer accords emerge creating a luxurious complexity that many wearers find addictive the interplay between the sweet and powdery elements gives it a creamy enveloping quality that makes it an excellent choice for evening wear or special occasions with an impressive rating from fragrance enthusiasts many reviewers appreciate its longevity and sillage stating that it lingers beautifully without being overpowering the overall impression of nero assoluto is ...	`blonde woods`	`1.0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 4
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: None
hub_always_push: False
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss	spearman_cosine
0.2273	50	-	0.2856
0.4545	100	-	0.7237
0.6818	150	-	0.7814
0.9091	200	-	0.8037
1.0	220	-	0.8096
1.1364	250	-	0.8067
1.3636	300	-	0.8130
1.5909	350	-	0.8195
1.8182	400	-	0.8221
2.0	440	-	0.8222
2.0455	450	-	0.8229
2.2727	500	0.0988	0.8177
2.5	550	-	0.8277
2.7273	600	-	0.8273
2.9545	650	-	0.8287
3.0	660	-	0.8287
3.1818	700	-	0.8269
3.4091	750	-	0.8295
3.6364	800	-	0.8301
3.8636	850	-	0.8306
4.0	880	-	0.8309

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.5.1+cu124
Accelerate: 1.2.1
Datasets: 3.2.0
Tokenizers: 0.21.0

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",
}