| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:1788 |
| - loss:TripletLoss |
| base_model: Alibaba-NLP/gte-base-en-v1.5 |
| widget: |
| - source_sentence: $194.99 |
| sentences: |
| - Polaroid t1235 Point & Shoot Digital Camera - Blue |
| - $199.00 |
| - price |
| - model |
| - source_sentence: 'Recertified: OLYMPUS Stylus 1040 Black 10.0 MP 2.7" 230K LCD 3X |
| Optical Zoom Digital Camera' |
| sentences: |
| - Nikon COOLPIX S60 10 Megapixel Digital Camera w/ 5x Optical Zoom, Optical VR Image |
| Stabilization, 3.5" High Res Touch Panel LCD, HD Pictmotion Slide Shows, & Autofocus |
| and Auto Exposure - Red |
| - model |
| - manufacturer |
| - Olympus America Inc. |
| - source_sentence: Kodak |
| sentences: |
| - Fuji |
| - model |
| - manufacturer |
| - FUJIFILM Instax 210 Instant Photo Camera |
| - source_sentence: $95.99 |
| sentences: |
| - $299.00 |
| - model |
| - SONY Cyber-shot S2100 Silver 12.1 MP 3.0" 230K LCD 3X Optical Zoom Digital Camera |
| - price |
| - source_sentence: Nikon COOLPIX S640 Precious Pink 12.2 MP 2.7" 230K LCD 5X Optical |
| Zoom 28mm Wide Angle Digital Camera |
| sentences: |
| - $649.00 |
| - price |
| - model |
| - Pentax K-r 12.4 Megapixel Digital SLR Camera (Body with Lens Kit) - 18 mm-55 mm |
| (Lens 1), 55 mm-300 mm (Lens 2) - Black |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy |
| - silhouette_cosine |
| - silhouette_euclidean |
| model-index: |
| - name: SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 |
| results: |
| - task: |
| type: triplet |
| name: Triplet |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: cosine_accuracy |
| value: 1.0 |
| name: Cosine Accuracy |
| - type: cosine_accuracy |
| value: 1.0 |
| name: Cosine Accuracy |
| - task: |
| type: silhouette |
| name: Silhouette |
| dataset: |
| name: Unknown |
| type: unknown |
| metrics: |
| - type: silhouette_cosine |
| value: 0.983250617980957 |
| name: Silhouette Cosine |
| - type: silhouette_euclidean |
| value: 0.8853786587715149 |
| name: Silhouette Euclidean |
| - type: silhouette_cosine |
| value: 0.9841798543930054 |
| name: Silhouette Cosine |
| - type: silhouette_euclidean |
| value: 0.888205349445343 |
| name: Silhouette Euclidean |
| --- |
| |
| # SentenceTransformer based on Alibaba-NLP/gte-base-en-v1.5 |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/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](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) <!-- at revision a829fd0e060bb84554da0dfd354d0de0f7712b7f --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Output Dimensionality:** 768 dimensions |
| - **Similarity Function:** Cosine Similarity |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### 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_2_to_verify_8-fold-10") |
| # Run inference |
| sentences = [ |
| 'Nikon COOLPIX S640 Precious Pink 12.2 MP 2.7" 230K LCD 5X Optical Zoom 28mm Wide Angle Digital Camera', |
| 'Pentax K-r 12.4 Megapixel Digital SLR Camera (Body with Lens Kit) - 18 mm-55 mm (Lens 1), 55 mm-300 mm (Lens 2) - Black', |
| '$649.00', |
| ] |
| 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] |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Triplet |
|
|
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
|
|
| | Metric | Value | |
| |:--------------------|:--------| |
| | **cosine_accuracy** | **1.0** | |
| |
| #### Silhouette |
| |
| * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> |
| |
| | Metric | Value | |
| |:----------------------|:-----------| |
| | **silhouette_cosine** | **0.9833** | |
| | silhouette_euclidean | 0.8854 | |
| |
| #### Triplet |
| |
| * Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) |
| |
| | Metric | Value | |
| |:--------------------|:--------| |
| | **cosine_accuracy** | **1.0** | |
| |
| #### Silhouette |
| |
| * Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code> |
| |
| | Metric | Value | |
| |:----------------------|:-----------| |
| | **silhouette_cosine** | **0.9842** | |
| | silhouette_euclidean | 0.8882 | |
|
|
| <!-- |
| ## Bias, Risks and Limitations |
|
|
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
|
|
| <!-- |
| ### Recommendations |
|
|
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
|
|
| ## Training Details |
|
|
| ### Training Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 1,788 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, and <code>neg_attr_name</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | pos_attr_name | neg_attr_name | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| |
| | type | string | string | string | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 12.37 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.89 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.37 tokens</li><li>max: 45 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 | pos_attr_name | neg_attr_name | |
| |:---------------------------|:----------------------|:-----------------------------------------------------------------------------------------------|:--------------------------|:-------------------| |
| | <code>Sony</code> | <code>FUJIFILM</code> | <code>SONY Cyber-shot S2100 Silver 12.1 MP 3.0" 230K LCD 3X Optical Zoom Digital Camera</code> | <code>manufacturer</code> | <code>model</code> | |
| | <code>SAKAR-VIVITAR</code> | <code>Kodak</code> | <code>Vivitar ViviCam T328 Black 12.0 MP 3.0" LCD 3X Optical Zoom Digital Camera</code> | <code>manufacturer</code> | <code>model</code> | |
| | <code>Panasonic</code> | <code>Sony</code> | <code>Panasonic Lumix DMC-FX37 Point & Shoot Digital Camera - White</code> | <code>manufacturer</code> | <code>model</code> | |
| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: |
| ```json |
| { |
| "distance_metric": "TripletDistanceMetric.EUCLIDEAN", |
| "triplet_margin": 5 |
| } |
| ``` |
|
|
| ### Evaluation Dataset |
|
|
| #### Unnamed Dataset |
|
|
| * Size: 199 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, and <code>neg_attr_name</code> |
| * Approximate statistics based on the first 199 samples: |
| | | anchor | positive | negative | pos_attr_name | neg_attr_name | |
| |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------| |
| | type | string | string | string | string | string | |
| | details | <ul><li>min: 3 tokens</li><li>mean: 10.52 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.95 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.41 tokens</li><li>max: 51 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 | pos_attr_name | neg_attr_name | |
| |:----------------------------------|:---------------------|:--------------------------------------------------------------------|:--------------------------|:-------------------| |
| | <code>Pentax Imaging</code> | <code>Pentax</code> | <code>FinePix XP10 Compact Camera</code> | <code>manufacturer</code> | <code>model</code> | |
| | <code>Olympus America Inc.</code> | <code>Canon</code> | <code>$143.02</code> | <code>manufacturer</code> | <code>price</code> | |
| | <code>$403.41</code> | <code>$179.95</code> | <code>Nikon Coolpix S210 Point & Shoot Digital Camera - Plum</code> | <code>price</code> | <code>model</code> | |
| * Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) 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`: {'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 |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | Validation Loss | cosine_accuracy | silhouette_cosine | |
| |:-----:|:----:|:-------------:|:---------------:|:---------------:|:-----------------:| |
| | -1 | -1 | - | - | 0.8492 | 0.3908 | |
| | 1.0 | 14 | 0.5592 | 0.0 | 1.0 | 0.9717 | |
| | 2.0 | 28 | 0.0 | 0.0 | 1.0 | 0.9823 | |
| | 3.0 | 42 | 0.0 | 0.0 | 1.0 | 0.9831 | |
| | 4.0 | 56 | 0.0 | 0.0 | 1.0 | 0.9832 | |
| | 5.0 | 70 | 0.0 | 0.0 | 1.0 | 0.9833 | |
| | -1 | -1 | - | - | 1.0 | 0.9842 | |
|
|
|
|
| ### 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} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
|
| <!-- |
| ## Model Card Contact |
|
|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
