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esci-nomic-embed-text-v1_5

Sentence Similarity
sentence-transformers
Safetensors
nomic_bert
feature-extraction
dataset_size:100K<n<1M
loss:CachedMultipleNegativesRankingLoss
custom_code
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
esci-nomic-embed-text-v1_5 / README.md
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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:TripletLoss
base_model: nomic-ai/nomic-embed-text-v1.5
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
widget:
- source_sentence: 'search_query: floral'
sentences:
- 'search_query: hair dryer'
- 'search_query: leporad tumbler'
- 'search_query: cerrojo sin cerradura'
- source_sentence: 'search_query: 赤ワイシャツ'
sentences:
- 'search_query: sワークス ロードシューズ'
- 'search_query: ropa astronauta'
- 'search_query: rosa azul preservada'
- source_sentence: 'search_query: ギター カポ'
sentences:
- 'search_query: カゴバック'
- 'search_query: midi flowy dress'
- 'search_query: pesticide sprayer'
- source_sentence: 'search_query: note 9'
sentences:
- 'search_query: samsung s9'
- 'search_query: wallflower jeans'
- 'search_query: 12 pomos sin tornillos'
- source_sentence: 'search_query: 傘 鬼滅の刃'
sentences:
- 'search_query: ノースフェイス リュック'
- 'search_query: お札 を 折ら ない ミニ 財布'
- 'search_query: buffalo plaid earrings'
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
results:
- task:
type: triplet
name: Triplet
dataset:
name: triplet esci
type: triplet-esci
metrics:
- type: cosine_accuracy
value: 0.655
name: Cosine Accuracy
- type: dot_accuracy
value: 0.343
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.657
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.656
name: Euclidean Accuracy
- type: max_accuracy
value: 0.657
name: Max Accuracy
---
# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-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:** [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision 91d2d6bfdddf0b0da840f901b533e99bae30d757 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 tokens
- **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': 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("sentence_transformers_model_id")
# Run inference
sentences = [
'search_query: 傘 鬼滅の刃',
'search_query: ノースフェイス リュック',
'search_query: お札 を 折ら ない ミニ 財布',
]
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
* Dataset: `triplet-esci`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.655** |
| dot_accuracy | 0.343 |
| manhattan_accuracy | 0.657 |
| euclidean_accuracy | 0.656 |
| max_accuracy | 0.657 |
<!--
## 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: 100,000 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: 12.21 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 51.18 tokens</li><li>max: 209 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 52.69 tokens</li><li>max: 175 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>search_query: college cactus backpack</code> | <code>search_document: Teecho Waterproof Cute Backpack for Girl Casual Print School Bag Women Laptop Backpack Cactus, Teecho, Cactus</code> | <code>search_document: JanSport Huntington Backpack - Lightweight Laptop Bag | Edo Floral, JanSport, Edo Floral</code> |
| <code>search_query: yellow laces for sneakers</code> | <code>search_document: DELELE Solid Flat Shoelaces Hollow Thick Athletic Shoe Laces Strings Light Yellow 2 Pair 63", DELELE, 05 Light Yellow</code> | <code>search_document: Marrywindix 29 Pairs 47" Flat Colourful Athletic Shoe Laces for Sneakers Skate Shoes Boots Sport Shoes (29 Colors), Marrywindix, Colorful</code> |
| <code>search_query: home sign grey</code> | <code>search_document: Bigtime Signs Home Sweet Home Sign - 11.75 inch x 9 inch Rigid PVC Signs Decor - Printed Rustic Wood LOOK - Predrilled Hole for Easy Hanging - Family Decoration for Home, Door, Mantle, Porch, Bigtime Signs, Home Sweet Home</code> | <code>search_document: Yankario Funny Bathroom Wall Decor Sign, Farmhouse Rustic Bathroom Decorations Wall Art , 12" by 6" Best Seat Wood Plaque, Yankario, grey 1</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: 1,000 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: 12.24 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 53.16 tokens</li><li>max: 173 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 53.72 tokens</li><li>max: 175 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>search_query: black vinyl placemat</code> | <code>search_document: Red-A Dining Table Placemats Set of 4 Heat-Resistant Wipeable Table Mats for Kitchen Table Decoration Waterproof Vinyl Placemats Easy to Clean,Black w/Brown, Red-A, Black</code> | <code>search_document: Winknowl Placemats, Set of 8 Heat Resistant Stain Resistant Non-Slip Woven Vinyl Insulation Placemats, Washable Durable Elegant Table Mats for Dining (Black), Winknowl, Black</code> |
| <code>search_query: 1 1/2 leather belts without buckle</code> | <code>search_document: Vatee's Women's/Men's Real Leather Replacement Belt Strap No Buckle 1 1/2"(38mm) Wide 45" Long Black, Vatee's, 154: Black</code> | <code>search_document: Women Skinny Leather Belt Thin Waist Jeans Belt for Pants in Pin Buckle Belt by WHIPPY, Black/Brown, Suit Pants 24-29 Inches, WHIPPY, 2-black+brown</code> |
| <code>search_query: 1x cat 7a conector de red rj45 sin herramientas</code> | <code>search_document: deleyCON 3,0m RJ45 Cable Plano Cable de Red de Categoría CAT7 Cable Ethernet U/FTP con Revestimiento Interior de Cobre - Negro, deleyCON, Negro</code> | <code>search_document: Conector de Odedo®, 2 unidades, categoría 6, UTP RJ45, para cable de conexión, prolongación de 6,3 mm, AWG 23-26 montaje sin herramientas, contactos dorados, odedo, Weiß</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
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `learning_rate`: 1e-05
- `lr_scheduler_type`: cosine_with_restarts
- `warmup_ratio`: 0.1
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `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`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 1e-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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_restarts
- `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
- `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`: 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}
- `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
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | triplet-esci_cosine_accuracy |
|:------:|:-----:|:-------------:|:------:|:----------------------------:|
| 0.008 | 200 | 3.9805 | - | - |
| 0.016 | 400 | 4.0739 | - | - |
| 0.024 | 600 | 4.0571 | - | - |
| 0.032 | 800 | 3.8848 | - | - |
| 0.04 | 1000 | 3.8249 | 3.9825 | 0.664 |
| 0.048 | 1200 | 3.7097 | - | - |
| 0.056 | 1400 | 3.6869 | - | - |
| 0.064 | 1600 | 3.4327 | - | - |
| 0.072 | 1800 | 3.64 | - | - |
| 0.08 | 2000 | 3.3813 | 3.8244 | 0.657 |
| 0.088 | 2200 | 3.4011 | - | - |
| 0.096 | 2400 | 3.34 | - | - |
| 0.104 | 2600 | 3.2488 | - | - |
| 0.112 | 2800 | 3.5031 | - | - |
| 0.12 | 3000 | 3.3615 | 3.7263 | 0.674 |
| 0.128 | 3200 | 3.1028 | - | - |
| 0.136 | 3400 | 3.2969 | - | - |
| 0.144 | 3600 | 3.0463 | - | - |
| 0.152 | 3800 | 3.1194 | - | - |
| 0.16 | 4000 | 3.2372 | 3.6599 | 0.673 |
| 0.168 | 4200 | 3.2954 | - | - |
| 0.176 | 4400 | 3.2753 | - | - |
| 0.184 | 4600 | 3.179 | - | - |
| 0.192 | 4800 | 3.2646 | - | - |
| 0.2 | 5000 | 3.1295 | 3.6405 | 0.677 |
| 0.208 | 5200 | 3.2211 | - | - |
| 0.216 | 5400 | 3.2222 | - | - |
| 0.224 | 5600 | 2.9471 | - | - |
| 0.232 | 5800 | 3.1564 | - | - |
| 0.24 | 6000 | 3.1099 | 3.6138 | 0.684 |
| 0.248 | 6200 | 2.9399 | - | - |
| 0.256 | 6400 | 3.1087 | - | - |
| 0.264 | 6600 | 3.2675 | - | - |
| 0.272 | 6800 | 3.2149 | - | - |
| 0.28 | 7000 | 2.9484 | 3.6086 | 0.673 |
| 0.288 | 7200 | 3.0829 | - | - |
| 0.296 | 7400 | 3.1864 | - | - |
| 0.304 | 7600 | 3.1201 | - | - |
| 0.312 | 7800 | 3.0698 | - | - |
| 0.32 | 8000 | 2.9968 | 3.5750 | 0.668 |
| 0.328 | 8200 | 3.0636 | - | - |
| 0.336 | 8400 | 3.1293 | - | - |
| 0.344 | 8600 | 3.1282 | - | - |
| 0.352 | 8800 | 3.1415 | - | - |
| 0.36 | 9000 | 2.7868 | 3.5211 | 0.693 |
| 0.368 | 9200 | 2.9714 | - | - |
| 0.376 | 9400 | 2.9409 | - | - |
| 0.384 | 9600 | 2.9071 | - | - |
| 0.392 | 9800 | 2.9154 | - | - |
| 0.4 | 10000 | 2.9709 | 3.5510 | 0.683 |
| 0.408 | 10200 | 2.741 | - | - |
| 0.416 | 10400 | 2.678 | - | - |
| 0.424 | 10600 | 2.8429 | - | - |
| 0.432 | 10800 | 2.9782 | - | - |
| 0.44 | 11000 | 2.9548 | 3.5369 | 0.681 |
| 0.448 | 11200 | 2.8452 | - | - |
| 0.456 | 11400 | 2.8578 | - | - |
| 0.464 | 11600 | 2.9211 | - | - |
| 0.472 | 11800 | 2.897 | - | - |
| 0.48 | 12000 | 2.7651 | 3.6031 | 0.687 |
| 0.488 | 12200 | 2.9472 | - | - |
| 0.496 | 12400 | 2.6198 | - | - |
| 0.504 | 12600 | 2.8444 | - | - |
| 0.512 | 12800 | 2.7384 | - | - |
| 0.52 | 13000 | 2.7827 | 3.6082 | 0.68 |
| 0.528 | 13200 | 2.6882 | - | - |
| 0.536 | 13400 | 2.6722 | - | - |
| 0.544 | 13600 | 2.7218 | - | - |
| 0.552 | 13800 | 2.7278 | - | - |
| 0.56 | 14000 | 2.7156 | 3.5606 | 0.677 |
| 0.568 | 14200 | 2.5748 | - | - |
| 0.576 | 14400 | 2.5414 | - | - |
| 0.584 | 14600 | 2.6918 | - | - |
| 0.592 | 14800 | 2.5429 | - | - |
| 0.6 | 15000 | 2.5724 | 3.5178 | 0.694 |
| 0.608 | 15200 | 2.7594 | - | - |
| 0.616 | 15400 | 2.4841 | - | - |
| 0.624 | 15600 | 2.4667 | - | - |
| 0.632 | 15800 | 2.6253 | - | - |
| 0.64 | 16000 | 2.5001 | 3.5428 | 0.683 |
| 0.648 | 16200 | 2.5707 | - | - |
| 0.656 | 16400 | 2.4924 | - | - |
| 0.664 | 16600 | 2.5419 | - | - |
| 0.672 | 16800 | 2.487 | - | - |
| 0.68 | 17000 | 2.4747 | 3.5825 | 0.681 |
| 0.688 | 17200 | 2.4194 | - | - |
| 0.696 | 17400 | 2.5714 | - | - |
| 0.704 | 17600 | 2.4069 | - | - |
| 0.712 | 17800 | 2.5767 | - | - |
| 0.72 | 18000 | 2.5952 | 3.6268 | 0.684 |
| 0.728 | 18200 | 2.4023 | - | - |
| 0.736 | 18400 | 2.3989 | - | - |
| 0.744 | 18600 | 2.379 | - | - |
| 0.752 | 18800 | 2.4943 | - | - |
| 0.76 | 19000 | 2.3762 | 3.5686 | 0.701 |
| 0.768 | 19200 | 2.4825 | - | - |
| 0.776 | 19400 | 2.4451 | - | - |
| 0.784 | 19600 | 2.5374 | - | - |
| 0.792 | 19800 | 2.4569 | - | - |
| 0.8 | 20000 | 2.2353 | 3.6429 | 0.681 |
| 0.808 | 20200 | 2.3447 | - | - |
| 0.816 | 20400 | 2.3083 | - | - |
| 0.824 | 20600 | 2.2126 | - | - |
| 0.832 | 20800 | 2.3935 | - | - |
| 0.84 | 21000 | 2.5115 | 3.6387 | 0.68 |
| 0.848 | 21200 | 2.1469 | - | - |
| 0.856 | 21400 | 2.2717 | - | - |
| 0.864 | 21600 | 2.2993 | - | - |
| 0.872 | 21800 | 2.3519 | - | - |
| 0.88 | 22000 | 2.2947 | 3.5908 | 0.671 |
| 0.888 | 22200 | 2.3249 | - | - |
| 0.896 | 22400 | 2.2452 | - | - |
| 0.904 | 22600 | 2.114 | - | - |
| 0.912 | 22800 | 2.208 | - | - |
| 0.92 | 23000 | 2.4168 | 3.6659 | 0.671 |
| 0.928 | 23200 | 2.2098 | - | - |
| 0.936 | 23400 | 2.1805 | - | - |
| 0.944 | 23600 | 2.122 | - | - |
| 0.952 | 23800 | 2.1364 | - | - |
| 0.96 | 24000 | 2.1464 | 3.6284 | 0.671 |
| 0.968 | 24200 | 2.1298 | - | - |
| 0.976 | 24400 | 2.2657 | - | - |
| 0.984 | 24600 | 2.304 | - | - |
| 0.992 | 24800 | 2.175 | - | - |
| 1.0 | 25000 | 2.1349 | 3.6532 | 0.681 |
| 1.008 | 25200 | 2.0151 | - | - |
| 1.016 | 25400 | 2.0881 | - | - |
| 1.024 | 25600 | 1.9897 | - | - |
| 1.032 | 25800 | 2.1987 | - | - |
| 1.04 | 26000 | 1.9913 | 3.6511 | 0.672 |
| 1.048 | 26200 | 1.9088 | - | - |
| 1.056 | 26400 | 1.9555 | - | - |
| 1.064 | 26600 | 1.6892 | - | - |
| 1.072 | 26800 | 2.0404 | - | - |
| 1.08 | 27000 | 1.6976 | 3.6465 | 0.671 |
| 1.088 | 27200 | 1.894 | - | - |
| 1.096 | 27400 | 1.8056 | - | - |
| 1.104 | 27600 | 1.6426 | - | - |
| 1.112 | 27800 | 2.0203 | - | - |
| 1.12 | 28000 | 1.697 | 3.6182 | 0.681 |
| 1.1280 | 28200 | 1.5562 | - | - |
| 1.1360 | 28400 | 1.6061 | - | - |
| 1.144 | 28600 | 1.5201 | - | - |
| 1.152 | 28800 | 1.4388 | - | - |
| 1.16 | 29000 | 1.5198 | 3.5924 | 0.676 |
| 1.168 | 29200 | 1.6404 | - | - |
| 1.176 | 29400 | 1.6058 | - | - |
| 1.184 | 29600 | 1.6063 | - | - |
| 1.192 | 29800 | 1.4398 | - | - |
| 1.2 | 30000 | 1.4952 | 3.6587 | 0.665 |
| 1.208 | 30200 | 1.5077 | - | - |
| 1.216 | 30400 | 1.3177 | - | - |
| 1.224 | 30600 | 1.095 | - | - |
| 1.232 | 30800 | 1.2841 | - | - |
| 1.24 | 31000 | 1.3544 | 3.6066 | 0.684 |
| 1.248 | 31200 | 1.2188 | - | - |
| 1.256 | 31400 | 1.1761 | - | - |
| 1.264 | 31600 | 1.2601 | - | - |
| 1.272 | 31800 | 1.2057 | - | - |
| 1.28 | 32000 | 1.0478 | 3.6371 | 0.681 |
| 1.288 | 32200 | 1.0888 | - | - |
| 1.296 | 32400 | 1.1335 | - | - |
| 1.304 | 32600 | 1.1297 | - | - |
| 1.312 | 32800 | 1.0302 | - | - |
| 1.32 | 33000 | 1.0583 | 3.6186 | 0.685 |
| 1.328 | 33200 | 1.0623 | - | - |
| 1.336 | 33400 | 0.9047 | - | - |
| 1.3440 | 33600 | 1.0706 | - | - |
| 1.3520 | 33800 | 1.0877 | - | - |
| 1.3600 | 34000 | 0.8205 | 3.6613 | 0.653 |
| 1.3680 | 34200 | 0.9596 | - | - |
| 1.376 | 34400 | 0.8855 | - | - |
| 1.384 | 34600 | 0.9186 | - | - |
| 1.392 | 34800 | 0.8087 | - | - |
| 1.4 | 35000 | 0.9732 | 3.6558 | 0.662 |
| 1.408 | 35200 | 0.8753 | - | - |
| 1.416 | 35400 | 0.8257 | - | - |
| 1.424 | 35600 | 0.8689 | - | - |
| 1.432 | 35800 | 0.8596 | - | - |
| 1.44 | 36000 | 0.9202 | 3.6872 | 0.66 |
| 1.448 | 36200 | 0.8993 | - | - |
| 1.456 | 36400 | 0.8889 | - | - |
| 1.464 | 36600 | 0.9138 | - | - |
| 1.472 | 36800 | 0.8212 | - | - |
| 1.48 | 37000 | 0.7591 | 3.6708 | 0.666 |
| 1.488 | 37200 | 0.769 | - | - |
| 1.496 | 37400 | 0.8656 | - | - |
| 1.504 | 37600 | 0.9134 | - | - |
| 1.512 | 37800 | 0.7212 | - | - |
| 1.52 | 38000 | 0.8118 | 3.6249 | 0.672 |
| 1.528 | 38200 | 0.7454 | - | - |
| 1.536 | 38400 | 0.7491 | - | - |
| 1.544 | 38600 | 0.8148 | - | - |
| 1.552 | 38800 | 0.6845 | - | - |
| 1.56 | 39000 | 0.6169 | 3.6868 | 0.679 |
| 1.568 | 39200 | 0.7377 | - | - |
| 1.576 | 39400 | 0.7296 | - | - |
| 1.584 | 39600 | 0.7204 | - | - |
| 1.592 | 39800 | 0.6748 | - | - |
| 1.6 | 40000 | 0.6494 | 3.7054 | 0.673 |
| 1.608 | 40200 | 0.7435 | - | - |
| 1.616 | 40400 | 0.6196 | - | - |
| 1.624 | 40600 | 0.6977 | - | - |
| 1.6320 | 40800 | 0.7442 | - | - |
| 1.6400 | 41000 | 0.5824 | 3.7500 | 0.66 |
| 1.6480 | 41200 | 0.6144 | - | - |
| 1.6560 | 41400 | 0.5909 | - | - |
| 1.6640 | 41600 | 0.6717 | - | - |
| 1.6720 | 41800 | 0.6436 | - | - |
| 1.6800 | 42000 | 0.6161 | 3.6769 | 0.676 |
| 1.688 | 42200 | 0.5282 | - | - |
| 1.696 | 42400 | 0.6647 | - | - |
| 1.704 | 42600 | 0.649 | - | - |
| 1.712 | 42800 | 0.6284 | - | - |
| 1.72 | 43000 | 0.7055 | 3.6992 | 0.671 |
| 1.728 | 43200 | 0.6249 | - | - |
| 1.736 | 43400 | 0.5722 | - | - |
| 1.744 | 43600 | 0.621 | - | - |
| 1.752 | 43800 | 0.6129 | - | - |
| 1.76 | 44000 | 0.501 | 3.7589 | 0.662 |
| 1.768 | 44200 | 0.5566 | - | - |
| 1.776 | 44400 | 0.576 | - | - |
| 1.784 | 44600 | 0.6428 | - | - |
| 1.792 | 44800 | 0.5629 | - | - |
| 1.8 | 45000 | 0.5134 | 3.7530 | 0.659 |
| 1.808 | 45200 | 0.4681 | - | - |
| 1.8160 | 45400 | 0.6268 | - | - |
| 1.8240 | 45600 | 0.5877 | - | - |
| 1.8320 | 45800 | 0.5219 | - | - |
| 1.8400 | 46000 | 0.545 | 3.7755 | 0.658 |
| 1.8480 | 46200 | 0.4539 | - | - |
| 1.8560 | 46400 | 0.5255 | - | - |
| 1.8640 | 46600 | 0.5573 | - | - |
| 1.8720 | 46800 | 0.5508 | - | - |
| 1.88 | 47000 | 0.5391 | 3.7489 | 0.653 |
| 1.888 | 47200 | 0.4276 | - | - |
| 1.896 | 47400 | 0.4906 | - | - |
| 1.904 | 47600 | 0.3771 | - | - |
| 1.912 | 47800 | 0.4959 | - | - |
| 1.92 | 48000 | 0.5377 | 3.7770 | 0.658 |
| 1.928 | 48200 | 0.4807 | - | - |
| 1.936 | 48400 | 0.5239 | - | - |
| 1.944 | 48600 | 0.4441 | - | - |
| 1.952 | 48800 | 0.4536 | - | - |
| 1.96 | 49000 | 0.5265 | 3.7507 | 0.669 |
| 1.968 | 49200 | 0.3817 | - | - |
| 1.976 | 49400 | 0.4468 | - | - |
| 1.984 | 49600 | 0.5766 | - | - |
| 1.992 | 49800 | 0.4789 | - | - |
| 2.0 | 50000 | 0.4853 | 3.7328 | 0.663 |
| 2.008 | 50200 | 0.3744 | - | - |
| 2.016 | 50400 | 0.4662 | - | - |
| 2.024 | 50600 | 0.394 | - | - |
| 2.032 | 50800 | 0.3938 | - | - |
| 2.04 | 51000 | 0.3586 | 3.8004 | 0.656 |
| 2.048 | 51200 | 0.3522 | - | - |
| 2.056 | 51400 | 0.4173 | - | - |
| 2.064 | 51600 | 0.3177 | - | - |
| 2.072 | 51800 | 0.4113 | - | - |
| 2.08 | 52000 | 0.3027 | 3.7366 | 0.665 |
| 2.088 | 52200 | 0.3693 | - | - |
| 2.096 | 52400 | 0.4268 | - | - |
| 2.104 | 52600 | 0.3678 | - | - |
| 2.112 | 52800 | 0.4192 | - | - |
| 2.12 | 53000 | 0.3105 | 3.7831 | 0.661 |
| 2.128 | 53200 | 0.3228 | - | - |
| 2.136 | 53400 | 0.2408 | - | - |
| 2.144 | 53600 | 0.2804 | - | - |
| 2.152 | 53800 | 0.3034 | - | - |
| 2.16 | 54000 | 0.3562 | 3.7866 | 0.656 |
| 2.168 | 54200 | 0.3526 | - | - |
| 2.176 | 54400 | 0.414 | - | - |
| 2.184 | 54600 | 0.3678 | - | - |
| 2.192 | 54800 | 0.2965 | - | - |
| 2.2 | 55000 | 0.3691 | 3.8108 | 0.655 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.0
- 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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