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LamaDiab/FinetunningMiniLM-V18Data-256ConstantBATCH-SemanticEngine

sourceHugging Faceupdated 10mo agoView on Hugging Face

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Model Card

SentenceTransformer based on LamaDiab/v4MiniLM-V18Data-256ConstantBATCH-SemanticEngine

This is a sentence-transformers model finetuned from LamaDiab/v4MiniLM-V18Data-256ConstantBATCH-SemanticEngine. 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: LamaDiab/v4MiniLM-V18Data-256ConstantBATCH-SemanticEngine
—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/FinetunningMiniLM-V18Data-256ConstantBATCH-SemanticEngine")
# Run inference
sentences = [
    'italian dolce provolone',
    'experience the authentic taste of italy with our italian dolce provolone. indulge in its creamy texture, delicate flavors, and versatility in both simple and sophisticated culinary creations.',
    'trident - gum strawberry flavor - 5 per pack',
]
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, 0.8072, 0.1772],
#         [0.8072, 1.0000, 0.2218],
#         [0.1772, 0.2218, 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	0.9706

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Training Details

Training Dataset

Unnamed Dataset

—Size: 705,905 training samples
—Columns: <code>anchor</code>, <code>positive</code>, and <code>itemCategory</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | itemCategory | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 13.19 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.46 tokens</li><li>max: 93 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.91 tokens</li><li>max: 11 tokens</li></ul> |
—Samples: | anchor | positive | itemCategory | |:-----------------------------------------------|:-----------------------------------------|:-------------------------------| | <code>mango nos nos small</code> | <code>milk chocolate ganache cake</code> | <code>sweet</code> | | <code>lux soap creamy perfection 165 gm</code> | <code>soap</code> | <code>hand soap</code> | | <code>grey deo original</code> | <code>classic deodrant</code> | <code>women's deodorant</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>, and <code>itemCategory</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | itemCategory | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | 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: 2 tokens</li><li>mean: 6.53 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.52 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.88 tokens</li><li>max: 10 tokens</li></ul> |
—Samples: | anchor | positive | negative | itemCategory | |:---------------------------------------------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------|:------------------------------------| | <code>pilot mechanical pencil progrex h-127 - 0.7 mm</code> | <code>office supplies</code> | <code>scary halloween skull mask</code> | <code>pencil</code> | | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code>superior </code> | <code>coloring and writing book 21 x 29.7 cm 100 gsm 18 pages number subtraction ma4014</code> | <code>marker</code> | | <code>first person singular author: haruki murakami</code> | <code>haruki murakami book</code> | <code>buried secrets</code> | <code>literature and fiction</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: 0.001
—num_train_epochs: 4
—warmup_ratio: 0.2
—fp16: True
—dataloader_num_workers: 1
—dataloader_prefetch_factor: 2
—dataloader_persistent_workers: True
—push_to_hub: True
—hub_model_id: LamaDiab/FinetunningMiniLM-V18Data-256ConstantBATCH-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: 0.001
—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: 0.2
—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/FinetunningMiniLM-V18Data-256ConstantBATCH-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
0.0004	1	1.2042	-	-
0.3626	1000	1.1885	0.3903	0.9712
0.7252	2000	0.8207	0.3788	0.9699
1.0877	3000	0.7257	0.3854	0.9716
1.45	4000	0.9126	0.3814	0.9709
1.8123	5000	0.8911	0.3810	0.9711
2.1746	6000	0.8678	0.3830	0.9716
2.5370	7000	0.8551	0.3813	0.9721
2.8993	8000	0.8576	0.3842	0.9715
3.2616	9000	0.8325	0.3848	0.9713
3.6239	10000	0.8453	0.3842	0.9710
3.9862	11000	0.817	0.3848	0.9706

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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