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

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the ๐Ÿค— Hub
model = SentenceTransformer("albertus-sussex/veriscrape-sbert-camera-reference_9_to_verify_1-fold-7")
# Run inference
sentences = [
    '$299.99',
    '$128.27',
    'SONY',
]
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]

Evaluation

Metrics

Triplet

Metric Value
cosine_accuracy 1.0

Silhouette

  • Evaluated with veriscrape.training.SilhouetteEvaluator
Metric Value
silhouette_cosine 0.9816
silhouette_euclidean 0.877

Triplet

Metric Value
cosine_accuracy 1.0

Silhouette

  • Evaluated with veriscrape.training.SilhouetteEvaluator
Metric Value
silhouette_cosine 0.9816
silhouette_euclidean 0.8769

Training Details

Training Dataset

Unnamed Dataset

  • Size: 8,937 training samples
  • Columns: anchor, positive, negative, pos_attr_name, and neg_attr_name
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative pos_attr_name neg_attr_name
    type string string string string string
    details
    • min: 3 tokens
    • mean: 12.23 tokens
    • max: 79 tokens
    • min: 3 tokens
    • mean: 11.96 tokens
    • max: 63 tokens
    • min: 3 tokens
    • mean: 10.86 tokens
    • max: 59 tokens
    • min: 3 tokens
    • mean: 3.0 tokens
    • max: 3 tokens
    • min: 3 tokens
    • mean: 3.0 tokens
    • max: 3 tokens
  • Samples:
    anchor positive negative pos_attr_name neg_attr_name
    : VuPoint Solutions Casio $501.99 manufacturer price
    $184.95 $299.99 SONY price manufacturer
    Casio Computer Co., Ltd : Sony Casio Exilim Ex-s8 Point & Shoot Digital Camera - 12.1 Megapixel - 2.70"... manufacturer model
  • Loss: TripletLoss with these parameters:
    {
        "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
        "triplet_margin": 5
    }
    

Evaluation Dataset

Unnamed Dataset

  • Size: 993 evaluation samples
  • Columns: anchor, positive, negative, pos_attr_name, and neg_attr_name
  • Approximate statistics based on the first 993 samples:
    anchor positive negative pos_attr_name neg_attr_name
    type string string string string string
    details
    • min: 3 tokens
    • mean: 11.86 tokens
    • max: 79 tokens
    • min: 3 tokens
    • mean: 11.63 tokens
    • max: 79 tokens
    • min: 3 tokens
    • mean: 10.63 tokens
    • max: 44 tokens
    • min: 3 tokens
    • mean: 3.0 tokens
    • max: 3 tokens
    • min: 3 tokens
    • mean: 3.0 tokens
    • max: 3 tokens
  • Samples:
    anchor positive negative pos_attr_name neg_attr_name
    FinePix Z70 Point & Shoot Digital Camera FUJIFILM FINEPIX HS10 Black 10.0 MP 3.0" 230K Tilting LCD 30X Optical Zoom 24mm Wide Angle Digital Camera $72.95 model price
    Fujifilm Panasonic Fujifilm FinePix J50 Point & Shoot Digital Camera manufacturer model
    Panasonic Lumix DMC-ZR3 Point & Shoot Digital Camera - 14.1 Megapixel - 2.70" Active Matrix TFT Color LCD - Black Sony Cyber-shot DSC-W370 14.1 Megapixel Compact Camera - 6 mm-42 mm - Silver SONY model manufacturer
  • Loss: TripletLoss with these parameters:
    {
        "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

Click to expand
  • 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: {'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: 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

Training Logs

Epoch Step Training Loss Validation Loss cosine_accuracy silhouette_cosine
-1 -1 - - 0.7986 0.3618
1.0 70 0.2669 0.0 1.0 0.9814
2.0 140 0.0 0.0 1.0 0.9816
3.0 210 0.0 0.0 1.0 0.9816
4.0 280 0.0 0.0 1.0 0.9816
5.0 350 0.0 0.0 1.0 0.9816
-1 -1 - - 1.0 0.9816

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

@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

@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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