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LamaDiab/NewMiniLM-V25Data-256BATCH-SemanticEngine

sourceHugging Faceupdated 10mo agoView on Hugging Face

0likes73downloads

Model Card

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/NewMiniLM-V25Data-256BATCH-SemanticEngine")
# Run inference
sentences = [
    'golden olive pouch',
    'pouch',
    'trio kaftan',
]
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, 0.8075, 0.1342],
#         [0.8075, 1.0000, 0.1006],
#         [0.1342, 0.1006, 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	0.9704

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Training Details

Training Dataset

Unnamed Dataset

—Size: 1,006,385 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: 11.94 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 4.57 tokens</li><li>max: 83 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.89 tokens</li><li>max: 9 tokens</li></ul> |
—Samples: | anchor | positive | itemCategory | |:-------------------------------------------------------------------------------------------------|:---------------------------------|:------------------------| | <code>lice repellent serum</code> | <code>hair serum</code> | <code>hair serum</code> | | <code>vanilla sponge cake with fresh moisturizer and strawberry pieces.<br>serve person.</code> | <code>vanilla tres leches</code> | <code>sweet</code> | | <code>wyl chips - kettle cooked sea salt & balsamic vinegar potato chips - gr</code> | <code>snacks</code> | <code>snacks</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: 3 tokens</li><li>mean: 6.3 tokens</li><li>max: 150 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.5 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.87 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> pilot pencil </code> | <code>lunch bag colors 22 × 16 × 28 cm must shark 000586181</code> | <code>pencil</code> | | <code>superior drawing marker -pen - set of 12 colors - 2 nib</code> | <code> marker pen </code> | <code>staedtler triplus fineliner 10 + 3 pack</code> | <code>marker</code> | | <code>first person singular author: haruki murakami</code> | <code> first person singular book</code> | <code>misty grater stainless steel</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
—gradient_accumulation_steps: 2
—learning_rate: 3e-05
—weight_decay: 0.001
—num_train_epochs: 5
—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/NewMiniLM-V25Data-256BATCH-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: 2
—eval_accumulation_steps: None
—torch_empty_cache_steps: None
—learning_rate: 3e-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: 5
—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/NewMiniLM-V25Data-256BATCH-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
0.0005	1	3.5148	-	-
0.5086	1000	2.3332	0.4183	0.9514
1.0173	2000	1.1848	0.3631	0.9598
1.5257	3000	1.2233	0.3502	0.9645
2.0341	4000	1.0983	0.3489	0.9663
2.5425	5000	0.9919	0.3366	0.9687
3.0508	6000	0.9487	0.3369	0.9696
3.5592	7000	0.8887	0.3311	0.9696
4.0676	8000	0.8724	0.3361	0.9703
4.5760	9000	0.8366	0.3336	0.9704

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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