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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1459
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: still popular today this fresh fougere fragrance inspired many
wannabes
sentences:
- fruity notes, fig leaf, cedar needle, resins
- ginger, mandarin, green tea, hazelnut, amberwood, daffodil, tangerine, metallic
effect
- mandarin, lavender, green botanics, jasmine, basil, geranium, sage, sandalwood,
vetiver, rosewood, amber
- source_sentence: rose blush cologne 2023 by jo malone london rose blush cologne
presents an enchanting bouquet that captures the essence of blooming romance and
tropical vitality with an initial sweet hint of luscious litchi and a refreshing
touch of herbs this fragrance unfolds into a heart of delicate rose showcasing
a radiant femininity the composition is beautifully rounded off with soft musky
undertones adding an elegant warmth that lingers on the skin users describe rose
blush as vibrant and joyful perfect for both everyday wear and special occasions
reviewers appreciate its fresh appeal heralding it as an uplifting scent that
evokes feelings of spring and renewal many highlight its moderate longevity making
it suitable for those who desire a fragrance that gently permeates without overwhelming
whether youre seeking a burst of floral energy or a subtle whisper of sophistication
this perfume is sure to leave a delightful impression
sentences:
- blonde woods, fresh ginger, carnation, green botanics, clover, green tea, white
tea, clary sage, mahogany, ambergris, vetiver, fruits, pink grapefruit, frangipani,
myrtle, darjeeling tea, mint
- yuzu, clary sage, balsam fir, cedar
- lychee, basil, rose, musk
- source_sentence: thank u next by ariana grande is a playful and modern fragrance
that captures the essence of youthful exuberance and selfempowerment this charming
scent exudes a vibrant sweetness that dances between fruity and creamy notes creating
an inviting aura that is both uplifting and comforting users often describe this
perfume as deliciously sweet and fun making it perfect for casual wear or a spirited
night out the blend is frequently noted for its warm inviting quality evoking
a sense of cheerful nostalgia many reviewers highlight its longlasting nature
and delightful sillage ensuring that its fragrant embrace stays with you throughout
the day perfect for the confident contemporary woman thank u next effortlessly
combines the spirited essence of fresh berries with a creamy tropical nuance which
is masterfully balanced by an undercurrent of sweet indulgence overall this fragrance
is celebrated for its delightful charm and is sure to make a memorable impression
wherever you go
sentences:
- clary sage, citruses
- tagetes, gingerbread, white wood, red fruit, spices, creme brulee
- cashmeran, lime, myrtle, metallic effect, vetiver, nasturtium, pimento, resins
- source_sentence: little black dress eau fraiche by avon exudes a lively and refreshing
spirit that captivates effortlessly this fragrance opens with a bright burst of
citrus that instantly uplifts the mood reminiscent of sunkissed afternoons as
it unfolds delicate floral notes weave through creating an elegant bouquet that
embodies femininity and charm the scent is anchored by a subtle musk that rounds
out the experience providing a warm and inviting backdrop users have praised this
fragrance for its fresh and invigorating essence making it perfect for daytime
wear many appreciate its lightness and airy quality which is ideal for those seeking
a scent that is both playful and sophisticated with a commendable rating of 375
out of 5 it has earned accolades for its delightful character and versatility
appealing to a broad audience who value a fragrance that feels both chic and approachable
overall little black dress eau fraiche is described as an essential contemporary
scent for the modern woman effortlessly enhancing any occasion with its vibrant
charm
sentences:
- floral notes, citruses, water lily, lady of the night flower, white musk, clove,
creme brulee, mango, fruits, clover, hinoki wood
- lemon, may rose, spices, peony, lily of the valley, blackcurrant, raspberry, peach,
musk, sandalwood, amber, heliotrope, oud
- cyclamen, petitgrain, sesame, thyme, myrrh
- source_sentence: indulge your senses with comme une evidence limited edition 2008
by yves rocher a sophisticated floral fragrance that captures the essence of tranquility
and elegance this scent harmoniously blends delicate floral notes with hints of
earthy moss creating a fresh and uplifting experience reminiscent of a serene
garden in full bloom users describe it as both refreshing and subtle ideal for
those seeking a signature scent that exudes femininity without overwhelming presence
the composition is said to invoke feelings of serenity and poise making it perfect
for daytime wear or special occasions when one desires a touch of grace with an
overall rating of 376 the fragrance has garnered appreciation for its longevity
and ability to evoke memories of blooming florals intertwined with natural sweetness
it strikes a perfect balance appealing to those who cherish a scent that is both
light and intricately layered whether strolling through sunlit paths or enjoying
quiet moments inside comme une evidence limited edition envelops the wearer in
a soothing embrace leaving a lasting impression of refined simplicity
sentences:
- papyrus, ginger, spices, herbal notes, lemon blossom, green tree accord, ambertonic,
lemon leaf oil, cassis, pimento, acacia, citron, gardenia, elemi, black amber,
clove, clary sage, ambergris, lime, darjeeling tea, cashmeran, blonde woods
- oud, ginger, sea salt, lily, resins
- ambertonic, lemon leaf oil, resins, white wood, woody notes, sweet pea, ambergris
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: 0.9339541699697309
name: Pearson Cosine
- type: spearman_cosine
value: 0.733406361302126
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/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](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
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 = [
'indulge your senses with comme une evidence limited edition 2008 by yves rocher a sophisticated floral fragrance that captures the essence of tranquility and elegance this scent harmoniously blends delicate floral notes with hints of earthy moss creating a fresh and uplifting experience reminiscent of a serene garden in full bloom users describe it as both refreshing and subtle ideal for those seeking a signature scent that exudes femininity without overwhelming presence the composition is said to invoke feelings of serenity and poise making it perfect for daytime wear or special occasions when one desires a touch of grace with an overall rating of 376 the fragrance has garnered appreciation for its longevity and ability to evoke memories of blooming florals intertwined with natural sweetness it strikes a perfect balance appealing to those who cherish a scent that is both light and intricately layered whether strolling through sunlit paths or enjoying quiet moments inside comme une evidence limited edition envelops the wearer in a soothing embrace leaving a lasting impression of refined simplicity',
'oud, ginger, sea salt, lily, resins',
'papyrus, ginger, spices, herbal notes, lemon blossom, green tree accord, ambertonic, lemon leaf oil, cassis, pimento, acacia, citron, gardenia, elemi, black amber, clove, clary sage, ambergris, lime, darjeeling tea, cashmeran, blonde woods',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.934 |
| **spearman_cosine** | **0.7334** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 1,459 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 12 tokens</li><li>mean: 182.01 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 33.09 tokens</li><li>max: 88 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.25</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>today tomorrow always in love by avon embodying a sense of timeless romance today tomorrow always in love is an enchanting fragrance that strikes a perfect balance between freshness and warmth this captivating scent opens with bright effervescent notes that evoke images of blooming gardens and sunlit moments as the fragrance unfolds it reveals a charming bouquet that celebrates femininity featuring delicate floral elements that wrap around the wearer like a cherished embrace users describe this perfume as uplifting and evocative making it an ideal companion for both everyday wear and special occasions many reviewers appreciate its elegant character highlighting its multifaceted nature that seamlessly transitions from day to night while some find it subtly sweet and playful others cherish its musky undertones which lend a depth that enhances its allure overall with a moderate rating that suggests a solid appreciation among wearers today tomorrow always in love captures the essence of ro...</code> | <code>lotus, neroli, carambola, pomegranate, tuberose, gardenia, tuberose, pepper, musk, woody notes, amber</code> | <code>1.0</code> |
| <code>mankind hero by kenneth cole encapsulates a vibrant and adventurous spirit designed for the modern man who embraces both freshness and sophistication this fragrance unfolds with an invigorating burst reminiscent of a brisk mountain breeze seamlessly paired with a zesty hint of citrus the aromatic heart introduces a soothing edginess where lavender and warm vanilla intertwine creating a balanced yet captivating profile as it settles an inviting warmth emerges enriched by woody undertones that linger pleasantly on the skin users have praised mankind hero for its versatile character suitable for both casual outings and formal occasions many describe it as longlasting and unique appreciating the balanced blend that feels both refreshing and comforting the overall sentiment reflects a sense of confidence and elegance making this scent a cherished addition to a mans fragrance collection it has garnered favorable reviews boasting a solid rating that underscores its appeal embrace the essence ...</code> | <code>mountain air, lemon, coriander, lavender, vanilla, clary sage, plum, musk, coumarin, amberwood, oak moss</code> | <code>1.0</code> |
| <code>black essential dark by avon immerse yourself in the captivating allure of black essential dark a fragrance that elegantly marries the depth of aromatic woods with a touch of leathers sensuality this modern scent envelops the wearer in a rich and sophisticated aura exuding confidence and a hint of mystery users describe it as both refreshing and spicy with an invigorating blend that feels perfect for the urban man who embraces lifes more daring adventures crafted with meticulous attention by perfumer mike parrot this fragrance has garnered a solid reputation amongst enthusiasts resulting in a commendable 405 rating from its admirers many find it to be versatile enough for both day and night wear making it an essential companion for various occasions reviewers frequently highlight its longlasting presence creating an inviting and memorable impression with a delicate yet commanding presence black essential dark is ideal for those looking to leave a mark without overpowering the senses wh...</code> | <code>mint, allspice, white tea, amber, herbal notes, pear blossom, armoise, gurgum wood, creme brulee</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"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`: 5
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `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`: 5
- `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
</details>
### Training Logs
| Epoch | Step | spearman_cosine |
|:------:|:----:|:---------------:|
| 1.0 | 46 | 0.6586 |
| 1.0870 | 50 | 0.6783 |
| 2.0 | 92 | 0.7334 |
| 2.1739 | 100 | 0.7268 |
| 3.0 | 138 | 0.7400 |
| 3.2609 | 150 | 0.7400 |
| 4.0 | 184 | 0.7426 |
| 4.3478 | 200 | 0.7387 |
| 5.0 | 230 | 0.7400 |
| 1.0 | 46 | 0.7387 |
| 1.0870 | 50 | 0.7387 |
| 2.0 | 92 | 0.7295 |
| 2.1739 | 100 | 0.7255 |
| 3.0 | 138 | 0.7242 |
| 3.2609 | 150 | 0.7255 |
| 4.0 | 184 | 0.7124 |
| 4.3478 | 200 | 0.7216 |
| 5.0 | 230 | 0.7334 |
### 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
```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",
}
```
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