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albertus-sussex/veriscrape-sbert-camera-reference_8_to_verify_2-fold-2

sourceHugging Faceupdated 1y agoView on Hugging Face

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Model Card

SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-base-en-v1.5. It maps sentences & paragraphs to a 768-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: Alibaba-NLP/gte-base-en-v1.5
—Maximum Sequence Length: 512 tokens
—Output Dimensionality: 768 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': 512, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("albertus-sussex/veriscrape-sbert-camera-reference_8_to_verify_2-fold-2")
# Run inference
sentences = [
    'CANON',
    'Sakar International, Inc',
    '$599.00',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Triplet

—Evaluated with <code>TripletEvaluator</code>

Metric	Value
cosine_accuracy	1.0

Silhouette

—Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

Metric	Value
silhouette_cosine	0.9839
silhouette_euclidean	0.8829

Triplet

—Evaluated with <code>TripletEvaluator</code>

Metric	Value
cosine_accuracy	1.0

Silhouette

—Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>

Metric	Value
silhouette_cosine	0.9838
silhouette_euclidean	0.8831

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Training Details

Training Dataset

Unnamed Dataset

—Size: 8,208 training samples
—Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>posattrname</code>, and <code>negattrname</code>
—Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | posattrname | negattrname | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| | type | string | string | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 12.21 tokens</li><li>max: 70 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.22 tokens</li><li>max: 79 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.61 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> |
—Samples: | anchor | positive | negative | posattrname | negattrname | |:-----------------------|:---------------------|:------------------------------------------------------------------------------------------|:--------------------------|:-------------------| | <code>Casio</code> | <code>Olympus</code> | <code>CASIO EXILIM EX-Z35 Black 12 MP 2.5" 230K LCD 3X Optical Zoom Digital Camera</code> | <code>manufacturer</code> | <code>model</code> | | <code>$137.00</code> | <code>$146.99</code> | <code>Ge J1455 Point & Shoot Digital Camera - 14.1 Megapixel - 3" Active Matrix...</code> | <code>price</code> | <code>model</code> | | <code>Panasonic</code> | <code>Nikon</code> | <code>$369.99</code> | <code>manufacturer</code> | <code>price</code> |
—Loss: <code>TripletLoss</code> with these parameters:

json

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }

Evaluation Dataset

Unnamed Dataset

—Size: 912 evaluation samples
—Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>posattrname</code>, and <code>negattrname</code>
—Approximate statistics based on the first 912 samples: | | anchor | positive | negative | posattrname | negattrname | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| | type | string | string | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 12.14 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.91 tokens</li><li>max: 77 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.78 tokens</li><li>max: 59 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 3.0 tokens</li><li>max: 3 tokens</li></ul> |
—Samples: | anchor | positive | negative | posattrname | negattrname | |:---------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------|:--------------------------|:-------------------| | <code>Kodak C143 12 Megapixel Digital Camera 3x Optical Zoom - Blue</code> | <code>Stylus Tough 8010 - Digital camera - compact - 14.0 Mpix - optical zoom: 5 x - supported memory: SD, SDHC - silver (227660)</code> | <code>$169.00</code> | <code>model</code> | <code>price</code> | | <code>Olympus</code> | <code>OLYMPUS</code> | <code>Olympus FE45 10 Megapixel Digital Camera - Titanium</code> | <code>manufacturer</code> | <code>model</code> | | <code>Sony</code> | <code>CASIO</code> | <code>Sony DSC-TX9 12.2MP Digital Camera - Red</code> | <code>manufacturer</code> | <code>model</code> |
—Loss: <code>TripletLoss</code> with these parameters:

json

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }

Training Hyperparameters

Non-Default Hyperparameters

—eval_strategy: epoch
—per_device_train_batch_size: 128
—per_device_eval_batch_size: 128
—num_train_epochs: 5
—warmup_ratio: 0.1

All Hyperparameters

<details><summary>Click to expand</summary>

—overwrite_output_dir: False
—do_predict: False
—eval_strategy: epoch
—prediction_loss_only: True
—per_device_train_batch_size: 128
—per_device_eval_batch_size: 128
—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: 1.0
—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: 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: {'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: 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: False
—hub_always_push: False
—gradient_checkpointing: False
—gradient_checkpointing_kwargs: None
—include_inputs_for_metrics: False
—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
—prompts: None
—batch_sampler: batch_sampler
—multi_dataset_batch_sampler: proportional

</details>

Training Logs

Epoch	Step	Training Loss	Validation Loss	cosine_accuracy	silhouette_cosine
-1	-1	-	-	0.8015	0.3632
1.0	65	0.266	0.0	1.0	0.9837
2.0	130	0.0	0.0	1.0	0.9839
3.0	195	0.0	0.0	1.0	0.9839
4.0	260	0.0	0.0	1.0	0.9839
5.0	325	0.0	0.0	1.0	0.9839
-1	-1	-	-	1.0	0.9838

Framework Versions

—Python: 3.10.16
—Sentence Transformers: 4.0.1
—Transformers: 4.45.2
—PyTorch: 2.5.1+cu124
—Accelerate: 1.5.2
—Datasets: 3.1.0
—Tokenizers: 0.20.3

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",
}

TripletLoss

bibtex

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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