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LamaDiab/MiniLM-v2-v28-SemanticEngine

sourceHugging Faceupdated 10mo agoView on Hugging Face

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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:

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("LamaDiab/MiniLM-v2-v28-SemanticEngine")
# Run inference
sentences = [
    'xbase 100 kids swimming goggles -  clear lenses - blue / yellow',
    'blue and yellow swimming goggles',
    'snuggs wearable blanket monk red christmas deer',
]
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, 0.7809, 0.0460],
#         [0.7809, 1.0000, 0.0060],
#         [0.0460, 0.0060, 1.0000]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Triplet

—Evaluated with <code>TripletEvaluator</code>

Metric	Value
cosine_accuracy	0.98

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Training Details

Training Dataset

Unnamed Dataset

—Size: 798,551 training samples
—Columns: <code>anchor</code>, <code>positive</code>, <code>itemCategory</code>, <code>shoppingCategory</code>, and <code>shoppingCategory_normalized</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | itemCategory | shoppingCategory | shoppingCategory_normalized | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | type | string | string | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 11.21 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.92 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.94 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.38 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.47 tokens</li><li>max: 5 tokens</li></ul> |
—Samples: | anchor | positive | itemCategory | shoppingCategory | shoppingCategorynormalized | |:------------------------------------------------|:-----------------------------------|:--------------------|:-----------------------------|:----------------------------| | <code>sky blue dotted t-chemise</code> | <code>t-shirt sky dotted</code> | <code>top</code> | <code>fashion</code> | <code>fashion</code> | | <code>coffee - set of scented candles</code> | <code>pleasant aroma candle</code> | <code>candle</code> | <code>home and garden</code> | <code>homegarden</code> | | <code>nasturtium passion fruit chocolate</code> | <code>chocolate</code> | <code>sweet</code> | <code>restaurants</code> | <code>food_dining</code> |
—Loss: <code>MultipleNegativesSymmetricRankingLoss</code> with these parameters:

json

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

Evaluation Dataset

Unnamed Dataset

—Size: 9,509 evaluation samples
—Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>itemCategory</code>, <code>shoppingCategory</code>, and <code>shoppingCategory_normalized</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | itemCategory | shoppingCategory | shoppingCategory_normalized | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:--------------------------------------------------------------------------------| | type | string | string | string | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 9.63 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 6.52 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.5 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.86 tokens</li><li>max: 9 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.36 tokens</li><li>max: 5 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.44 tokens</li><li>max: 5 tokens</li></ul> |
—Samples: | anchor | positive | negative | itemCategory | shoppingCategory | shoppingCategorynormalized | |:---------------------------------------------------------------------|:----------------------------------|:----------------------------------------------------------------------------------------|:------------------------------------|:---------------------------|:----------------------------------| | <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code> | <code>0.7 mm pencil</code> | <code>rush tip marker set, 6 pastel colors, carton box, black edition no. 116453</code> | <code>pencil</code> | <code>stationary</code> | <code>officeschool</code> | | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code> marker pen set </code> | <code>palestine scarf notebook (spirals)</code> | <code>marker</code> | <code>stationary</code> | <code>officeschool</code> | | <code>first person singular author: haruki murakami</code> | <code>haruki murakami book</code> | <code>chocolate flakes mocha</code> | <code>literature and fiction</code> | <code>entertainment</code> | <code>sportsentertainment</code> |
—Loss: <code>MultipleNegativesSymmetricRankingLoss</code> with these parameters:

json

  {
      "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: 2e-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-v28-SemanticEngine
—hub_strategy: all_checkpoints

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: 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: 2e-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: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
—fsdp_transformer_layer_cls_to_wrap: None
—accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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-v28-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: {}

</details>

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy
0.0003	1	2.1128	-	-
0.3205	1000	1.6867	0.7481	0.9634
0.6410	2000	1.1579	0.6880	0.9722
0.9615	3000	1.894	0.6943	0.9689
1.2819	4000	1.3968	0.6312	0.9768
1.6022	5000	1.2426	0.6099	0.9789
1.9225	6000	1.1736	0.6118	0.9785
2.2428	7000	1.1514	0.6085	0.9788
2.5631	8000	1.1096	0.6068	0.9797
2.8834	9000	1.1285	0.6031	0.9800

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

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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