MiniLM-v31-SemanticEngine / README.md

LamaDiab

Updating model weights

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:989791
  - loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: turmeric
    sentences:
      - essential oils
      - joint comfort essential oil
      - bubble enigma
  - source_sentence: lavie naturelle sunscreen spf50
    sentences:
      - sunscreen
      - shields uvb sunscreen
      - smashbox 3 travel size box
  - source_sentence: cubs kids cloud slipper pink 25/26
    sentences:
      - monochrome duffle bag
      - ' slipper'
      - slipper
  - source_sentence: rhea glow face cleanser
    sentences:
      - face cleanser
      - ' glow face cleanser'
      - city girl collection lipstick lipstick extra creamy – no. 214
  - source_sentence: skinny royale
    sentences:
      - doughnuts blue icing
      - deli
      - poached eggs skinny royale
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy
model-index:
  - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
    results:
      - task:
          type: triplet
          name: Triplet
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy
            value: 0.9725362062454224
            name: Cosine Accuracy

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-v31-SemanticEngine")
# Run inference
sentences = [
    'skinny royale',
    'poached eggs skinny royale',
    'doughnuts blue icing',
]
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, 0.7055, 0.2723],
#         [0.7055, 1.0000, 0.2485],
#         [0.2723, 0.2485, 1.0000]])

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9725

Training Details

Training Dataset

Unnamed Dataset

Size: 989,791 training samples
Columns: anchor, positive, and itemCategory

Approximate statistics based on the first 1000 samples:

	anchor	positive	itemCategory
type	string	string	string
details	min: 3 tokens mean: 9.64 tokens max: 56 tokens	min: 3 tokens mean: 5.69 tokens max: 25 tokens	min: 3 tokens mean: 4.03 tokens max: 11 tokens

Samples:

anchor	positive	itemCategory
`restaurants`	`mineral water (s)`	`beverage`
`solodex anti age serum 30 ml`	`face serum`	`anti-aging`
`almond, cashew and cherry bar`	`cashew and cranberry almond, bar`	`snacks`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 9,467 evaluation samples
Columns: anchor, positive, negative, and itemCategory

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative	itemCategory
type	string	string	string	string
details	min: 3 tokens mean: 9.5 tokens max: 38 tokens	min: 3 tokens mean: 6.3 tokens max: 138 tokens	min: 3 tokens mean: 9.25 tokens max: 30 tokens	min: 3 tokens mean: 3.79 tokens max: 9 tokens

Samples:

anchor	positive	negative	itemCategory
`ritter sport smarties white chocolate`	`chocolate`	`small charcuterie tree`	`sweet`
`cordyline`	`reddish plant`	`table board`	`plant`
`gym strikers leggings purple`	`shape-retaining leggings`	`men's tennis t-shirt tts900 - red`	`trousers`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
learning_rate: 3e-05
weight_decay: 0.01
num_train_epochs: 4
warmup_ratio: 0.1
fp16: True
dataloader_num_workers: 1
dataloader_prefetch_factor: 2
dataloader_persistent_workers: True
push_to_hub: True
hub_model_id: LamaDiab/MiniLM-v31-SemanticEngine
hub_strategy: all_checkpoints

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 256
per_device_eval_batch_size: 256
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: 3e-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: 4
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
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: 1
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: True
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: True
resume_from_checkpoint: None
hub_model_id: LamaDiab/MiniLM-v31-SemanticEngine
hub_strategy: all_checkpoints
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: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy
0.0003	1	3.5134	-	-
0.2586	1000	2.5294	1.1220	0.9476
0.5172	2000	1.84	1.0357	0.9596
0.7758	3000	1.6007	0.9693	0.9656
1.0344	4000	2.0429	0.9276	0.9676
1.2928	5000	1.5438	0.8986	0.9688
1.5513	6000	1.5027	0.8980	0.9702
1.8098	7000	1.4302	0.9006	0.9708
2.0682	8000	1.4145	0.8990	0.9703
2.3267	9000	1.3572	0.8929	0.9706
2.5852	10000	1.3533	0.8818	0.9735
2.8436	11000	1.3183	0.8857	0.9726
3.1021	12000	1.3243	0.8805	0.9745
3.3606	13000	1.2964	0.8851	0.9734
3.6190	14000	1.2724	0.8803	0.9738
3.8775	15000	1.2631	0.8834	0.9725

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",
}