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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1600000
- loss:TripletLoss
datasets: []
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy@10
- cosine_precision@10
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@10
- dot_accuracy@10
- dot_precision@10
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@10
widget:
- source_sentence: 'search_query: pokemon card mewtwo'
sentences:
- 'search_document: Personal AM/FM Pocket Radio Portable VR-robot, Mini Digital
Tuning Walkman Radio, with Rechargeable Battery, Earphone, Lock Screen for Walk/Jogging/Gym/Camping,
VR-robot, Electronics'
- 'search_document: Pokemon Mewtwo & Pikachu XY Evolutions TCG Card Game Decks -
60 Cards Each, Pokemon, '
- 'search_document: Ultra Pro Pokemon: Charizard Album, 2", Ultra Pro, '
- source_sentence: 'search_query: table runners 108 inches'
sentences:
- 'search_document: Sambosk Fall Buffalo Pumpkin Table Runner, Autumn Farmhouse
Table Runners for Kitchen Dining Coffee or Indoor and Outdoor Home Parties Decor
13 x 72 Inches SK006, Sambosk, Black White'
- 'search_document: EYEGUARD Readers 4 Pack of Thin and Elegant Womens Reading Glasses
with Beautiful Patterns for Ladies 1.00, EYEGUARD, Mix'
- 'search_document: Sunfiy 4 Pack Red Satin Table Runner 12 x 108 Inch Long Table
Runners for Wedding Birthday Parties Banquets Graduations Engagements, Sunfiy,
Red'
- source_sentence: 'search_query: nursing shoes for women'
sentences:
- 'search_document: Hawkwell Women''s Lightweight Comfort Slip Resistant Nursing
Shoes,White PU,10 M US, Hawkwell, 1923/White'
- 'search_document: REESE''S Peanut Butter Milk Chocolate You''re Amazing Appreciation
Candy Bars for Christmas and Holiday Season, 4.2 oz Bars, 12 Count, Reese''s, '
- 'search_document: adidas womens Cloudfoam Pure Running Shoe, Black/Black, 7.5
US, adidas, Black/Black/White'
- source_sentence: 'search_query: mens socks black and white'
sentences:
- 'search_document: Fruit of the Loom Men''s Essential 6 Pack Casual Crew Socks
| Arch Support | Black & White, Black, Shoe Size: 6-12, Fruit of the Loom, Black'
- 'search_document: adidas Originals Men''s Trefoil Crew Socks (6-Pair), White/Black
Black/White, Large, (Shoe Size 6-12), adidas Originals, White/Black'
- 'search_document: Fifty Shades of Grey, , '
- source_sentence: 'search_query: karoke set 2 microphone for adults'
sentences:
- 'search_document: EARISE T26 Portable Karaoke Machine Bluetooth Speaker with Wireless
Microphone, Rechargeable PA System with FM Radio, Audio Recording, Remote Control,
Supports TF Card/USB, Perfect for Party, EARISE, '
- 'search_document: FunWorld Men''s Complete 3D Zombie Costume, Grey, One Size,
Fun World, Multi'
- 'search_document: Starion KS829-B Bluetooth Karaoke Machine l Pedestal Design
w/Light Show l Two Karaoke Microphones, Starion, Black'
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer
results:
- task:
type: triplet
name: Triplet
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy
value: 0.7298125
name: Cosine Accuracy
- type: dot_accuracy
value: 0.2831875
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.72825
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.729875
name: Euclidean Accuracy
- type: max_accuracy
value: 0.729875
name: Max Accuracy
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: Unknown
type: unknown
metrics:
- type: pearson_cosine
value: 0.4148003591706621
name: Pearson Cosine
- type: spearman_cosine
value: 0.39973675544358156
name: Spearman Cosine
- type: pearson_manhattan
value: 0.37708819507475255
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.36992167570513307
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.3777862291730549
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.3707889635811508
name: Spearman Euclidean
- type: pearson_dot
value: 0.3813644395159763
name: Pearson Dot
- type: spearman_dot
value: 0.3817136551173837
name: Spearman Dot
- type: pearson_max
value: 0.4148003591706621
name: Pearson Max
- type: spearman_max
value: 0.39973675544358156
name: Spearman Max
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@10
value: 0.967
name: Cosine Accuracy@10
- type: cosine_precision@10
value: 0.6951
name: Cosine Precision@10
- type: cosine_recall@10
value: 0.6216729831257005
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8300106033542061
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9111154761904765
name: Cosine Mrr@10
- type: cosine_map@10
value: 0.7758485833963215
name: Cosine Map@10
- type: dot_accuracy@10
value: 0.946
name: Dot Accuracy@10
- type: dot_precision@10
value: 0.6369
name: Dot Precision@10
- type: dot_recall@10
value: 0.5693415261440723
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7668657376718138
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8754059523809526
name: Dot Mrr@10
- type: dot_map@10
value: 0.6962231903502142
name: Dot Map@10
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the triplets dataset. 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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- triplets
<!-- - **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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("lv12/esci-nomic-embed-text-v1_5_4")
# Run inference
sentences = [
'search_query: karoke set 2 microphone for adults',
'search_document: Starion KS829-B Bluetooth Karaoke Machine l Pedestal Design w/Light Show l Two Karaoke Microphones, Starion, Black',
'search_document: EARISE T26 Portable Karaoke Machine Bluetooth Speaker with Wireless Microphone, Rechargeable PA System with FM Radio, Audio Recording, Remote Control, Supports TF Card/USB, Perfect for Party, EARISE, ',
]
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** | **0.7298** |
| dot_accuracy | 0.2832 |
| manhattan_accuracy | 0.7282 |
| euclidean_accuracy | 0.7299 |
| max_accuracy | 0.7299 |
#### Semantic Similarity
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.4148 |
| **spearman_cosine** | **0.3997** |
| pearson_manhattan | 0.3771 |
| spearman_manhattan | 0.3699 |
| pearson_euclidean | 0.3778 |
| spearman_euclidean | 0.3708 |
| pearson_dot | 0.3814 |
| spearman_dot | 0.3817 |
| pearson_max | 0.4148 |
| spearman_max | 0.3997 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@10 | 0.967 |
| cosine_precision@10 | 0.6951 |
| cosine_recall@10 | 0.6217 |
| cosine_ndcg@10 | 0.83 |
| cosine_mrr@10 | 0.9111 |
| **cosine_map@10** | **0.7758** |
| dot_accuracy@10 | 0.946 |
| dot_precision@10 | 0.6369 |
| dot_recall@10 | 0.5693 |
| dot_ndcg@10 | 0.7669 |
| dot_mrr@10 | 0.8754 |
| dot_map@10 | 0.6962 |
<!--
## 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
#### triplets
* Dataset: triplets
* Size: 1,600,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 11.03 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 39.86 tokens</li><li>max: 104 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 39.73 tokens</li><li>max: 159 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>search_query: udt hydraulic fluid</code> | <code>search_document: Triax Agra UTTO XL Synthetic Blend Tractor Transmission and Hydraulic Oil, 6,000 Hour Life, 50% Less wear, 36F Pour Point, Replaces All OEM Tractor Fluids (5 Gallon Pail), TRIAX, </code> | <code>search_document: Shell Rotella T5 Synthetic Blend 15W-40 Diesel Engine Oil (1-Gallon, Case of 3), Shell Rotella, </code> |
| <code>search_query: cheetah print iphone xs case</code> | <code>search_document: iPhone Xs Case, iPhone Xs Case,Doowear Leopard Cheetah Protective Cover Shell For Girls Women,Slim Fit Anti Scratch Shockproof Soft TPU Bumper Flexible Rubber Gel Silicone Case for iPhone Xs / X-1, Ebetterr, 1</code> | <code>search_document: iPhone Xs & iPhone X Case, J.west Luxury Sparkle Bling Translucent Leopard Print Soft Silicone Phone Case Cover for Girls Women Flex Slim Design Pattern Drop Protective Case for iPhone Xs/x 5.8 inch, J.west, Leopard</code> |
| <code>search_query: platform shoes</code> | <code>search_document: Teva Women's Flatform Universal Platform Sandal, Black, 5 M US, Teva, Black</code> | <code>search_document: Vans Women's Old Skool Platform Trainers, (Black/White Y28), 5 UK 38 EU, Vans, Black/White</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.8
}
```
### Evaluation Dataset
#### triplets
* Dataset: triplets
* Size: 16,000 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 11.02 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 38.78 tokens</li><li>max: 87 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 38.81 tokens</li><li>max: 91 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------|
| <code>search_query: hogknobz</code> | <code>search_document: Black 2014-2015 HDsmallPARTS/LocEzy Saddlebag Mounting Hardware Knobs are replacement/compatible for Saddlebag Quick Release Pins on Harley Davidson Touring Motorcycles Theft Deterrent, LocEzy, </code> | <code>search_document: HANSWD Saddlebag Support Bars Brackets For SUZUKI YAMAHA KAWASAKI (Black), HANSWD, Black</code> |
| <code>search_query: tile sticker key finder</code> | <code>search_document: Tile Sticker (2020) 2-pack - Small, Adhesive Bluetooth Tracker, Item Locator and Finder for Remotes, Headphones, Gadgets and More, Tile, </code> | <code>search_document: Tile Pro Combo (2017) - 2 Pack (1 x Sport, 1 x Style) - Discontinued by Manufacturer, Tile, Graphite/Gold</code> |
| <code>search_query: adobe incense burner</code> | <code>search_document: AM Incense Burner Frankincense Resin - Luxury Globe Charcoal Bakhoor Burners for Office & Home Decor (Brown), AM, Brown</code> | <code>search_document: semli Large Incense Burner Backflow Incense Burner Holder Incense Stick Holder Home Office Decor, Semli, </code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.COSINE",
"triplet_margin": 0.8
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 2
- `learning_rate`: 1e-07
- `num_train_epochs`: 5
- `lr_scheduler_type`: polynomial
- `lr_scheduler_kwargs`: {'lr_end': 1e-08, 'power': 2.0}
- `warmup_ratio`: 0.05
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: 4
- `load_best_model_at_end`: True
- `gradient_checkpointing`: True
- `auto_find_batch_size`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 1e-07
- `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`: polynomial
- `lr_scheduler_kwargs`: {'lr_end': 1e-08, 'power': 2.0}
- `warmup_ratio`: 0.05
- `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
- `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`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: 4
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `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}
- `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`: True
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: True
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | triplets loss | cosine_accuracy | cosine_map@10 | spearman_cosine |
|:------:|:----:|:-------------:|:-------------:|:---------------:|:-------------:|:---------------:|
| 0.0008 | 10 | 0.7505 | - | - | - | - |
| 0.0016 | 20 | 0.7499 | - | - | - | - |
| 0.0024 | 30 | 0.7524 | - | - | - | - |
| 0.0032 | 40 | 0.7486 | - | - | - | - |
| 0.004 | 50 | 0.7493 | - | - | - | - |
| 0.0048 | 60 | 0.7476 | - | - | - | - |
| 0.0056 | 70 | 0.7483 | - | - | - | - |
| 0.0064 | 80 | 0.7487 | - | - | - | - |
| 0.0072 | 90 | 0.7496 | - | - | - | - |
| 0.008 | 100 | 0.7515 | 0.7559 | 0.7263 | 0.7684 | 0.3941 |
| 0.0088 | 110 | 0.7523 | - | - | - | - |
| 0.0096 | 120 | 0.7517 | - | - | - | - |
| 0.0104 | 130 | 0.7534 | - | - | - | - |
| 0.0112 | 140 | 0.746 | - | - | - | - |
| 0.012 | 150 | 0.7528 | - | - | - | - |
| 0.0128 | 160 | 0.7511 | - | - | - | - |
| 0.0136 | 170 | 0.7491 | - | - | - | - |
| 0.0144 | 180 | 0.752 | - | - | - | - |
| 0.0152 | 190 | 0.7512 | - | - | - | - |
| 0.016 | 200 | 0.7513 | 0.7557 | 0.7259 | 0.7688 | 0.3942 |
| 0.0168 | 210 | 0.7505 | - | - | - | - |
| 0.0176 | 220 | 0.7481 | - | - | - | - |
| 0.0184 | 230 | 0.7516 | - | - | - | - |
| 0.0192 | 240 | 0.7504 | - | - | - | - |
| 0.02 | 250 | 0.7498 | - | - | - | - |
| 0.0208 | 260 | 0.7506 | - | - | - | - |
| 0.0216 | 270 | 0.7486 | - | - | - | - |
| 0.0224 | 280 | 0.7471 | - | - | - | - |
| 0.0232 | 290 | 0.7511 | - | - | - | - |
| 0.024 | 300 | 0.7506 | 0.7553 | 0.7258 | 0.7692 | 0.3943 |
| 0.0248 | 310 | 0.7485 | - | - | - | - |
| 0.0256 | 320 | 0.7504 | - | - | - | - |
| 0.0264 | 330 | 0.7456 | - | - | - | - |
| 0.0272 | 340 | 0.7461 | - | - | - | - |
| 0.028 | 350 | 0.7496 | - | - | - | - |
| 0.0288 | 360 | 0.7518 | - | - | - | - |
| 0.0296 | 370 | 0.7514 | - | - | - | - |
| 0.0304 | 380 | 0.7479 | - | - | - | - |
| 0.0312 | 390 | 0.7507 | - | - | - | - |
| 0.032 | 400 | 0.7511 | 0.7547 | 0.7258 | 0.7695 | 0.3945 |
| 0.0328 | 410 | 0.7491 | - | - | - | - |
| 0.0336 | 420 | 0.7487 | - | - | - | - |
| 0.0344 | 430 | 0.7496 | - | - | - | - |
| 0.0352 | 440 | 0.7464 | - | - | - | - |
| 0.036 | 450 | 0.7518 | - | - | - | - |
| 0.0368 | 460 | 0.7481 | - | - | - | - |
| 0.0376 | 470 | 0.7493 | - | - | - | - |
| 0.0384 | 480 | 0.753 | - | - | - | - |
| 0.0392 | 490 | 0.7475 | - | - | - | - |
| 0.04 | 500 | 0.7498 | 0.7540 | 0.7262 | 0.7700 | 0.3948 |
| 0.0408 | 510 | 0.7464 | - | - | - | - |
| 0.0416 | 520 | 0.7506 | - | - | - | - |
| 0.0424 | 530 | 0.747 | - | - | - | - |
| 0.0432 | 540 | 0.7462 | - | - | - | - |
| 0.044 | 550 | 0.75 | - | - | - | - |
| 0.0448 | 560 | 0.7522 | - | - | - | - |
| 0.0456 | 570 | 0.7452 | - | - | - | - |
| 0.0464 | 580 | 0.7475 | - | - | - | - |
| 0.0472 | 590 | 0.7507 | - | - | - | - |
| 0.048 | 600 | 0.7494 | 0.7531 | 0.7269 | 0.7707 | 0.3951 |
| 0.0488 | 610 | 0.7525 | - | - | - | - |
| 0.0496 | 620 | 0.7446 | - | - | - | - |
| 0.0504 | 630 | 0.7457 | - | - | - | - |
| 0.0512 | 640 | 0.7462 | - | - | - | - |
| 0.052 | 650 | 0.7478 | - | - | - | - |
| 0.0528 | 660 | 0.7459 | - | - | - | - |
| 0.0536 | 670 | 0.7465 | - | - | - | - |
| 0.0544 | 680 | 0.7495 | - | - | - | - |
| 0.0552 | 690 | 0.7513 | - | - | - | - |
| 0.056 | 700 | 0.7445 | 0.7520 | 0.7274 | 0.7705 | 0.3954 |
| 0.0568 | 710 | 0.7446 | - | - | - | - |
| 0.0576 | 720 | 0.746 | - | - | - | - |
| 0.0584 | 730 | 0.7452 | - | - | - | - |
| 0.0592 | 740 | 0.7459 | - | - | - | - |
| 0.06 | 750 | 0.7419 | - | - | - | - |
| 0.0608 | 760 | 0.7462 | - | - | - | - |
| 0.0616 | 770 | 0.7414 | - | - | - | - |
| 0.0624 | 780 | 0.7444 | - | - | - | - |
| 0.0632 | 790 | 0.7419 | - | - | - | - |
| 0.064 | 800 | 0.7438 | 0.7508 | 0.7273 | 0.7712 | 0.3957 |
| 0.0648 | 810 | 0.7503 | - | - | - | - |
| 0.0656 | 820 | 0.7402 | - | - | - | - |
| 0.0664 | 830 | 0.7435 | - | - | - | - |
| 0.0672 | 840 | 0.741 | - | - | - | - |
| 0.068 | 850 | 0.7386 | - | - | - | - |
| 0.0688 | 860 | 0.7416 | - | - | - | - |
| 0.0696 | 870 | 0.7473 | - | - | - | - |
| 0.0704 | 880 | 0.7438 | - | - | - | - |
| 0.0712 | 890 | 0.7458 | - | - | - | - |
| 0.072 | 900 | 0.7446 | 0.7494 | 0.7279 | 0.7718 | 0.3961 |
| 0.0728 | 910 | 0.7483 | - | - | - | - |
| 0.0736 | 920 | 0.7458 | - | - | - | - |
| 0.0744 | 930 | 0.7473 | - | - | - | - |
| 0.0752 | 940 | 0.7431 | - | - | - | - |
| 0.076 | 950 | 0.7428 | - | - | - | - |
| 0.0768 | 960 | 0.7385 | - | - | - | - |
| 0.0776 | 970 | 0.7438 | - | - | - | - |
| 0.0784 | 980 | 0.7406 | - | - | - | - |
| 0.0792 | 990 | 0.7426 | - | - | - | - |
| 0.08 | 1000 | 0.7372 | 0.7478 | 0.7282 | 0.7725 | 0.3965 |
| 0.0808 | 1010 | 0.7396 | - | - | - | - |
| 0.0816 | 1020 | 0.7398 | - | - | - | - |
| 0.0824 | 1030 | 0.7376 | - | - | - | - |
| 0.0832 | 1040 | 0.7417 | - | - | - | - |
| 0.084 | 1050 | 0.7408 | - | - | - | - |
| 0.0848 | 1060 | 0.7415 | - | - | - | - |
| 0.0856 | 1070 | 0.7468 | - | - | - | - |
| 0.0864 | 1080 | 0.7427 | - | - | - | - |
| 0.0872 | 1090 | 0.7371 | - | - | - | - |
| 0.088 | 1100 | 0.7375 | 0.7460 | 0.7279 | 0.7742 | 0.3970 |
| 0.0888 | 1110 | 0.7434 | - | - | - | - |
| 0.0896 | 1120 | 0.7441 | - | - | - | - |
| 0.0904 | 1130 | 0.7378 | - | - | - | - |
| 0.0912 | 1140 | 0.735 | - | - | - | - |
| 0.092 | 1150 | 0.739 | - | - | - | - |
| 0.0928 | 1160 | 0.7408 | - | - | - | - |
| 0.0936 | 1170 | 0.7346 | - | - | - | - |
| 0.0944 | 1180 | 0.7389 | - | - | - | - |
| 0.0952 | 1190 | 0.7367 | - | - | - | - |
| 0.096 | 1200 | 0.7358 | 0.7440 | 0.729 | 0.7747 | 0.3975 |
| 0.0968 | 1210 | 0.7381 | - | - | - | - |
| 0.0976 | 1220 | 0.7405 | - | - | - | - |
| 0.0984 | 1230 | 0.7348 | - | - | - | - |
| 0.0992 | 1240 | 0.737 | - | - | - | - |
| 0.1 | 1250 | 0.7393 | - | - | - | - |
| 0.1008 | 1260 | 0.7411 | - | - | - | - |
| 0.1016 | 1270 | 0.7359 | - | - | - | - |
| 0.1024 | 1280 | 0.7276 | - | - | - | - |
| 0.1032 | 1290 | 0.7364 | - | - | - | - |
| 0.104 | 1300 | 0.7333 | 0.7418 | 0.7293 | 0.7747 | 0.3979 |
| 0.1048 | 1310 | 0.7367 | - | - | - | - |
| 0.1056 | 1320 | 0.7352 | - | - | - | - |
| 0.1064 | 1330 | 0.7333 | - | - | - | - |
| 0.1072 | 1340 | 0.737 | - | - | - | - |
| 0.108 | 1350 | 0.7361 | - | - | - | - |
| 0.1088 | 1360 | 0.7299 | - | - | - | - |
| 0.1096 | 1370 | 0.7339 | - | - | - | - |
| 0.1104 | 1380 | 0.7349 | - | - | - | - |
| 0.1112 | 1390 | 0.7318 | - | - | - | - |
| 0.112 | 1400 | 0.7336 | 0.7394 | 0.7292 | 0.7749 | 0.3983 |
| 0.1128 | 1410 | 0.7326 | - | - | - | - |
| 0.1136 | 1420 | 0.7317 | - | - | - | - |
| 0.1144 | 1430 | 0.7315 | - | - | - | - |
| 0.1152 | 1440 | 0.7321 | - | - | - | - |
| 0.116 | 1450 | 0.7284 | - | - | - | - |
| 0.1168 | 1460 | 0.7308 | - | - | - | - |
| 0.1176 | 1470 | 0.7287 | - | - | - | - |
| 0.1184 | 1480 | 0.727 | - | - | - | - |
| 0.1192 | 1490 | 0.7298 | - | - | - | - |
| 0.12 | 1500 | 0.7306 | 0.7368 | 0.7301 | 0.7755 | 0.3988 |
| 0.1208 | 1510 | 0.7269 | - | - | - | - |
| 0.1216 | 1520 | 0.7299 | - | - | - | - |
| 0.1224 | 1530 | 0.7256 | - | - | - | - |
| 0.1232 | 1540 | 0.721 | - | - | - | - |
| 0.124 | 1550 | 0.7274 | - | - | - | - |
| 0.1248 | 1560 | 0.7251 | - | - | - | - |
| 0.1256 | 1570 | 0.7248 | - | - | - | - |
| 0.1264 | 1580 | 0.7244 | - | - | - | - |
| 0.1272 | 1590 | 0.7275 | - | - | - | - |
| 0.128 | 1600 | 0.7264 | 0.7339 | 0.7298 | 0.7756 | 0.3991 |
| 0.1288 | 1610 | 0.7252 | - | - | - | - |
| 0.1296 | 1620 | 0.7287 | - | - | - | - |
| 0.1304 | 1630 | 0.7263 | - | - | - | - |
| 0.1312 | 1640 | 0.7216 | - | - | - | - |
| 0.132 | 1650 | 0.7231 | - | - | - | - |
| 0.1328 | 1660 | 0.728 | - | - | - | - |
| 0.1336 | 1670 | 0.7309 | - | - | - | - |
| 0.1344 | 1680 | 0.7243 | - | - | - | - |
| 0.1352 | 1690 | 0.7239 | - | - | - | - |
| 0.136 | 1700 | 0.7219 | 0.7309 | 0.7302 | 0.7768 | 0.3994 |
| 0.1368 | 1710 | 0.7212 | - | - | - | - |
| 0.1376 | 1720 | 0.7217 | - | - | - | - |
| 0.1384 | 1730 | 0.7118 | - | - | - | - |
| 0.1392 | 1740 | 0.7226 | - | - | - | - |
| 0.14 | 1750 | 0.7185 | - | - | - | - |
| 0.1408 | 1760 | 0.7228 | - | - | - | - |
| 0.1416 | 1770 | 0.7257 | - | - | - | - |
| 0.1424 | 1780 | 0.7177 | - | - | - | - |
| 0.1432 | 1790 | 0.722 | - | - | - | - |
| 0.144 | 1800 | 0.712 | 0.7276 | 0.7307 | 0.7763 | 0.3997 |
| 0.1448 | 1810 | 0.7193 | - | - | - | - |
| 0.1456 | 1820 | 0.7138 | - | - | - | - |
| 0.1464 | 1830 | 0.7171 | - | - | - | - |
| 0.1472 | 1840 | 0.7191 | - | - | - | - |
| 0.148 | 1850 | 0.7172 | - | - | - | - |
| 0.1488 | 1860 | 0.7168 | - | - | - | - |
| 0.1496 | 1870 | 0.7111 | - | - | - | - |
| 0.1504 | 1880 | 0.7203 | - | - | - | - |
| 0.1512 | 1890 | 0.7095 | - | - | - | - |
| 0.152 | 1900 | 0.7064 | 0.7240 | 0.7301 | 0.7762 | 0.3998 |
| 0.1528 | 1910 | 0.7147 | - | - | - | - |
| 0.1536 | 1920 | 0.7098 | - | - | - | - |
| 0.1544 | 1930 | 0.7193 | - | - | - | - |
| 0.1552 | 1940 | 0.7096 | - | - | - | - |
| 0.156 | 1950 | 0.7107 | - | - | - | - |
| 0.1568 | 1960 | 0.7146 | - | - | - | - |
| 0.1576 | 1970 | 0.7106 | - | - | - | - |
| 0.1584 | 1980 | 0.7079 | - | - | - | - |
| 0.1592 | 1990 | 0.7097 | - | - | - | - |
| 0.16 | 2000 | 0.71 | 0.7202 | 0.7298 | 0.7758 | 0.3997 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.38.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.27.2
- Datasets: 2.19.1
- Tokenizers: 0.15.2
## 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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