Hugging Face's logo

tomerRest
/
line_item_embeddings

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:97524
loss:CosineSimilarityLoss
text-embeddings-inference
Model card Files
xet
 Community
line_item_embeddings / README.md
tomerRest's picture
tomerRest
Add new SentenceTransformer model
b668bd3 verified
preview code
|
Raw
History Blame Contribute Delete
11.7 kB
metadata 
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:97524
  - loss:CosineSimilarityLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: Lasagna Sheets Koch 2kg
    sentences:
      - PASTADURA
      - Salt Chicken 3kg Gluten Free Saxa
      - 2022 Jerzu Antichi Podero Vermentino di Sardegna D.O.C.
  - source_sentence: Dare Triple Espresso 500mL BTL (6)
    sentences:
      - '[D.GOODS] JX Si Chuan Pepper (500g/pkt)'
      - Herb, Chives(bunch)
      - Smirnoff Red Vodka
  - source_sentence: Eristoff Vodka 700ml
    sentences:
      - BTL BELVEDERE VODKA PURE ORGANIC 7
      - 'OCEAN SPRAY PET CLASSIC CRANBERRY : 1500'
      - PECAN NUTS IKG [A]
  - source_sentence: 4200 - 10KG ~ PANKO JAPANESE BREADCRUMBS ~~ 10KG BAG
    sentences:
      - URBUN SLICED MI LK BUN 30' S (1)
      - GCB BRD HALF WHITE SLICED 400G
      - 5L BURNT CARAMEL CTN4
  - source_sentence: Espresso Supply Black Coffee Jug 1.9L
    sentences:
      - B1504, LAMB MINCE 2KG
      - Toby’s Estate Woolloomooloo Blend Beans 500g
      - Rocket 1.5KG Box
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: 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("tomerRest/line_item_embeddings")
# Run inference
sentences = [
    'Espresso Supply Black Coffee Jug 1.9L',
    'B1504, LAMB MINCE 2KG',
    'Rocket 1.5KG Box',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

  • Size: 97,524 training samples
  • Columns: sentence_0, sentence_1, and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string float
    details
    • min: 4 tokens
    • mean: 11.76 tokens
    • max: 34 tokens
    • min: 3 tokens
    • mean: 11.71 tokens
    • max: 49 tokens
    • min: 0.0
    • mean: 0.49
    • max: 1.0
  • Samples:
    sentence_0 sentence_1 label
    LYRE'S GIN & TONIC 24PACK CANS: 250 ml LUPI OIL OLIVE EXTRA 0.0
    Chorizo (Bulk Pack) Mild CHILLILONG RED (MILD) KG 0.0
    Cauca Cold Brew Concentrate 10L Market Lane Kenya Kii AA Washed 250g 1.0
  • Loss: CosineSimilarityLoss with these parameters:
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • num_train_epochs: 2
  • multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: no
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 32
  • 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: 5e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1
  • num_train_epochs: 2
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • 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: False
  • 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: 0
  • dataloader_prefetch_factor: None
  • 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: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • 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
  • dispatch_batches: None
  • split_batches: 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
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: round_robin

Training Logs

Epoch Step Training Loss
0.1640 500 0.1608
0.3281 1000 0.1106
0.4921 1500 0.0924
0.6562 2000 0.0822
0.8202 2500 0.0719
0.9843 3000 0.0664
1.1483 3500 0.0581
1.3123 4000 0.0557
1.4764 4500 0.053
1.6404 5000 0.0505
1.8045 5500 0.0512
1.9685 6000 0.0477

Framework Versions

  • Python: 3.12.7
  • Sentence Transformers: 3.3.1
  • Transformers: 4.49.0
  • PyTorch: 2.6.0
  • Accelerate: 1.4.0
  • Datasets: 3.4.1
  • Tokenizers: 0.21.1

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",
}