MiniLM-v2-v32-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:902990
  - loss:MultipleNegativesSymmetricRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: dove deodorant stick fresh
    sentences:
      - women's deodorant
      - antiperspirant deodorant stick
      - bubbles natur.bottle w.hand pink(6)m280m
  - source_sentence: avrelle travel size olive oil & rocca set
    sentences:
      - haircare set
      - shampoo
      - maldives waterfall body splash
  - source_sentence: not not donner
    sentences:
      - mix chocolate pie
      - >-
        stuffed dough with donner, mozzarella cheese, tomatoes, lettuce, onion
        with ranch sauce.
      - deli
  - source_sentence: pink n' yellow
    sentences:
      - mirror
      - yellow mirror
      - watercolor pan 2.5 ml scarlet semi white nights no 1911318
  - source_sentence: ice cream roll machine for kids
    sentences:
      - horse pink
      - pretend play
      - chocolates game
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: 0.9764419794082642
            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-v2-v32-SemanticEngine")
# Run inference
sentences = [
    'ice cream roll machine for kids',
    'chocolates game',
    'horse pink',
]
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,  0.3436, -0.0405],
#         [ 0.3436,  1.0000,  0.0382],
#         [-0.0405,  0.0382,  1.0000]])

Evaluation

Metrics

Triplet

Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9764

Training Details

Training Dataset

Unnamed Dataset

Size: 902,990 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: 10.31 tokens max: 103 tokens	min: 3 tokens mean: 5.38 tokens max: 22 tokens	min: 3 tokens mean: 4.01 tokens max: 9 tokens

Samples:

anchor	positive	itemCategory
`adult skipping rope - petrol`	`skipping rope`	`gym equipment`
`bambino rainbow gold & sliver sage`	`shisha`	`home decor accessory`
`joobran’s minimalist clutch – evergreen`	`crossbody bag`	`bag`

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

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

Evaluation Dataset

Unnamed Dataset

Size: 9,466 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: 9.65 tokens max: 32 tokens	min: 2 tokens mean: 5.75 tokens max: 131 tokens	min: 3 tokens mean: 9.32 tokens max: 36 tokens	min: 3 tokens mean: 3.82 tokens max: 9 tokens

Samples:

anchor	positive	negative	itemCategory
`extra bubblemint sugar free chewing gum`	`gum`	`dried mangoes`	`sweet`
`golden pothos`	`indoor plant`	`fire truck gam`	`plant`
`effortless style slit linen pants - beige`	`beige pants`	`light grey keep going stone washed t-shirt`	`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
gradient_accumulation_steps: 2
learning_rate: 3e-05
weight_decay: 0.01
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-v2-v32-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: 2
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: 3
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-v2-v32-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
0.0006	1	2.7497	-	-
0.5669	1000	1.9615	1.0099	0.9696
1.1337	2000	1.7676	0.8953	0.9739
1.7003	3000	1.4662	0.8705	0.9748
2.2669	4000	1.3918	0.8650	0.9761
2.8334	5000	1.3306	0.8527	0.9764

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",
}