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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:
- pear, blackberry, herbal notes, bamboo, clove, apple, guarana, green tree accord
- mace, hyrax, camellia, tea, akigalawood
- mandarin, lavender, green botanics, jasmine, basil, geranium, sage, sandalwood,
vetiver, rosewood, amber
- 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:
- cress, lantana, castoreum, parma violet, cotton flower, oud, hesperidic notes,
grape, olive tree, hyacinth, earthy notes, carambola, osmanthus, champaca, cypriol,
lemon blossom, rosewood
- yuzu, clary sage, balsam fir, cedar
- passionflower, red currant, rosehip, almond blossom, chocolate
- 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:
- honey, mahogany
- lychee, basil, rose, musk
- lemon, may rose, spices, peony, lily of the valley, blackcurrant, raspberry, peach,
musk, sandalwood, amber, heliotrope, oud
- 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:
- cabreuva, mate, bamboo leaf, black cardamom, orris root, camellia, oriental notes,
hibiscus, lily of the valley, lantana, wood notes
- sea salt, amberwood, marine notes, resins, clary sage, labdanum, white musk, blonde
woods
- nectarine, olive tree, grass, cress, clementine, red apple
- source_sentence: zara night eau de parfum envelops you in a captivating blend of
softness and elegance creating a rich floral experience that feels both fresh
and inviting this fragrance exudes a charming femininity where luscious floral
notes mingle seamlessly with a warm creamy essence that evokes a sense of comfort
users describe it as enchanting and seductive perfect for evening wear or special
occasions the scent captures the essence of a night blooming with possibilities
balancing the vibrancy of fresh petals with the alluring depth of sweet undertones
reviewers appreciate its ability to linger gracefully on the skin leaving a trail
of sophisticated allure without being overwhelming many find it to be a delightful
choice for those seeking a fragrance that is both versatile and memorable with
a touch of playfulness that hints at a romantic allure with a commendable rating
zara night is celebrated for its accessibility and charm making it a favored addition
to any perfume collection
sentences:
- whiskey, bellini, cognac, blackberry, juniper berry, iris root, aldehydes, red
currant, flint, cumin, mango, sea salt, sea notes, birch, bitter orange, marine
notes, grapefruit blossom, hawthorn, yuzu, clementine, cream, pineapple
- moss, sandalwood, mangosteen, cade oil
- bergamot, galbanum, petitgrain, jasmine, narcissus, violet, carnation, rose, spices,
blonde woods, iris, vanilla, amber
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: 0.8425746761744255
name: Pearson Cosine
- type: spearman_cosine
value: 0.718974393548417
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 = [
'zara night eau de parfum envelops you in a captivating blend of softness and elegance creating a rich floral experience that feels both fresh and inviting this fragrance exudes a charming femininity where luscious floral notes mingle seamlessly with a warm creamy essence that evokes a sense of comfort users describe it as enchanting and seductive perfect for evening wear or special occasions the scent captures the essence of a night blooming with possibilities balancing the vibrancy of fresh petals with the alluring depth of sweet undertones reviewers appreciate its ability to linger gracefully on the skin leaving a trail of sophisticated allure without being overwhelming many find it to be a delightful choice for those seeking a fragrance that is both versatile and memorable with a touch of playfulness that hints at a romantic allure with a commendable rating zara night is celebrated for its accessibility and charm making it a favored addition to any perfume collection',
'moss, sandalwood, mangosteen, cade oil',
'whiskey, bellini, cognac, blackberry, juniper berry, iris root, aldehydes, red currant, flint, cumin, mango, sea salt, sea notes, birch, bitter orange, marine notes, grapefruit blossom, hawthorn, yuzu, clementine, cream, pineapple',
]
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>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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 | 0.8426 |
| **spearman_cosine** | **0.719** |
<!--
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## 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: 33.83 tokens</li><li>max: 101 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, bay leaf, cedar needle, passionflower, black cardamom, flint, rice, teak wood, cedar leaf</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 | 0.5799 |
| 1.0870 | 50 | 0.6061 |
| 2.0 | 92 | 0.6940 |
| 2.1739 | 100 | 0.6940 |
| 3.0 | 138 | 0.7072 |
| 3.2609 | 150 | 0.7124 |
| 4.0 | 184 | 0.7150 |
| 4.3478 | 200 | 0.7177 |
| 5.0 | 230 | 0.7190 |
### 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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