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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7134
- loss:TripletLoss
base_model: Alibaba-NLP/gte-base-en-v1.5
widget:
- source_sentence: NIKON - PHOTO
sentences:
- from
- manufacturer
- price
- Pentax Imaging
- source_sentence: $108.99
sentences:
- model
- $87.99
- price
- Coolpix S80 Compact Camera
- source_sentence: ': Casio'
sentences:
- Casio Exlim EX-Z1200 12MP Digtial Camera with 3x Anti Shake Optical Zoom
- model
- ': Fuji'
- manufacturer
- source_sentence: Panasonic Dmc-fx37s 10mp Digital Camera 5x Optical Zoom 2.5" Lcd
25mm Leica Lens (dmcfx37s)
sentences:
- model
- $84.62
- price
- Ge C1033 Point & Shoot Digital Camera - 10.1 Megapixel - 2.40" Active Matrix Tft
Color Lcd - Black 3x Optical Zoom - 5.7x - Ge C1033-bk (c1033bk)
- source_sentence: $108.99
sentences:
- price
- Panasonic
- manufacturer
- $123.99
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: 0.9974779486656189
name: Cosine Accuracy
- type: cosine_accuracy
value: 0.9988649487495422
name: Cosine Accuracy
- task:
type: silhouette
name: Silhouette
dataset:
name: Unknown
type: unknown
metrics:
- type: silhouette_cosine
value: 0.9446604251861572
name: Silhouette Cosine
- type: silhouette_euclidean
value: 0.838313102722168
name: Silhouette Euclidean
- type: silhouette_cosine
value: 0.9412728548049927
name: Silhouette Cosine
- type: silhouette_euclidean
value: 0.832588791847229
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:** 64 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': 64, '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-wo-ref-deepseek-chat-0324")
# Run inference
sentences = [
'$108.99',
'$123.99',
'Panasonic',
]
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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## 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** | **0.9975** |
#### Silhouette
* Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>
| Metric | Value |
|:----------------------|:-----------|
| **silhouette_cosine** | **0.9447** |
| silhouette_euclidean | 0.8383 |
#### Triplet
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.9989** |
#### Silhouette
* Evaluated with <code>veriscrape.training.SilhouetteEvaluator</code>
| Metric | Value |
|:----------------------|:-----------|
| **silhouette_cosine** | **0.9413** |
| silhouette_euclidean | 0.8326 |
<!--
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 7,134 training samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | string | string | string | string | int |
| details | <ul><li>min: 3 tokens</li><li>mean: 12.12 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.28 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.66 tokens</li><li>max: 64 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> | <ul><li>0: ~8.80%</li><li>1: ~9.40%</li><li>2: ~12.30%</li><li>3: ~9.60%</li><li>4: ~11.00%</li><li>5: ~6.50%</li><li>6: ~10.00%</li><li>7: ~11.00%</li><li>8: ~10.70%</li><li>9: ~10.70%</li></ul> |
* Samples:
| anchor | positive | negative | pos_attr_name | neg_attr_name | website_id |
|:--------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:--------------------------|:--------------------------|:---------------|
| <code>Sakar International, Inc</code> | <code>Fuji Photo Film Co. Ltd</code> | <code>Coolpix S1100pj Compact Camera</code> | <code>manufacturer</code> | <code>model</code> | <code>9</code> |
| <code>Olympus Stylus Tough 3000 Point & Shoot Digital Camera - 12 Megapixel - 2.70" Lcd - Pink 3.6x Optical Zoom - 5x (227625)</code> | <code>Canon Powershot A470 Digital Camera With Selphy Cp760 Compact Photo Printer - Blue - 7.1 Megapixel - 16:9 - 3.4x Optical Zoom - 4x Digital Zoom - 2.5" Active Matrix Tft Color Lcd - 32mb Secure Digital</code> | <code>Olympus Corporation</code> | <code>model</code> | <code>manufacturer</code> | <code>1</code> |
| <code>$204.95</code> | <code>$89.00</code> | <code>Fujifilm Z800EXR 12 MP Digital Point and Shoot Camera (Red) BigVALUEInc 8PC Saver Bundle!</code> | <code>price</code> | <code>model</code> | <code>0</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: 793 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, <code>negative</code>, <code>pos_attr_name</code>, <code>neg_attr_name</code>, and <code>website_id</code>
* Approximate statistics based on the first 793 samples:
| | anchor | positive | negative | pos_attr_name | neg_attr_name | website_id |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | string | string | string | string | int |
| details | <ul><li>min: 3 tokens</li><li>mean: 12.85 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.51 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.06 tokens</li><li>max: 64 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> | <ul><li>0: ~9.33%</li><li>1: ~10.72%</li><li>2: ~12.86%</li><li>3: ~10.84%</li><li>4: ~8.95%</li><li>5: ~5.93%</li><li>6: ~13.11%</li><li>7: ~10.34%</li><li>8: ~8.70%</li><li>9: ~9.21%</li></ul> |
* Samples:
| anchor | positive | negative | pos_attr_name | neg_attr_name | website_id |
|:----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|:-------------------------------------------|:--------------------------|:--------------------------|:---------------|
| <code>VistaQuest Corporation</code> | <code>General Electric Company</code> | <code>EasyShare C142 Compact Camera</code> | <code>manufacturer</code> | <code>model</code> | <code>3</code> |
| <code>Kodak EasyShare Z1485 IS Point & Shoot Digital Camera - Pink</code> | <code>Nikon Coolpix S1100pj 14.1 Megapixel Compact Camera - 5 mm-25 mm - Violet</code> | <code>Kodak</code> | <code>model</code> | <code>manufacturer</code> | <code>2</code> |
| <code>Panasonic Lumix DMC-ZS7 Point & Shoot Digital Camera - 12.1 Megapixel - 3" Active Matrix TFT Color LCD - Black</code> | <code>Memorex Flash Micro Point & Shoot Digital Camera</code> | <code>Pentax Imaging</code> | <code>model</code> | <code>manufacturer</code> | <code>2</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 | - | - | 0.9180 | 0.3826 |
| 1.0 | 56 | 0.2179 | 0.0492 | 0.9975 | 0.9327 |
| 2.0 | 112 | 0.0169 | 0.0601 | 0.9975 | 0.9443 |
| 3.0 | 168 | 0.0191 | 0.0394 | 0.9962 | 0.9398 |
| 4.0 | 224 | 0.0126 | 0.0457 | 0.9975 | 0.9419 |
| 5.0 | 280 | 0.0135 | 0.0444 | 0.9975 | 0.9447 |
| -1 | -1 | - | - | 0.9989 | 0.9413 |
### Framework Versions
- Python: 3.10.16
- Sentence Transformers: 4.1.0
- Transformers: 4.45.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.6.0
- 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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