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---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:100K<n<1M
- loss:CachedMultipleNegativesRankingLoss
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: shark'
sentences:
- 'search_query: skull'
- 'search_query: car picture frame'
- 'search_query: cartera de guchi'
- source_sentence: 'search_query: aolvo'
sentences:
- 'search_query: laço homem'
- 'search_query: vdi to hdmi cable'
- 'search_query: beads without holes'
- source_sentence: 'search_query: 赤色のカバン'
sentences:
- 'search_query: 結婚式 ガーランド'
- 'search_query: remaches zapatero'
- 'search_query: small feaux potted plants'
- source_sentence: 'search_query: vipkid'
sentences:
- 'search_query: ceiling lamps for kids'
- 'search_query: apple あいふぉんケース 12'
- 'search_query: zapatos zaragoza mujer'
- source_sentence: 'search_query: お布団バッグ'
sentences:
- 'search_query: 足なしソファー'
- 'search_query: all color handbag'
- 'search_query: tundra black out emblems'
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: 0.787
name: Cosine Accuracy
- type: dot_accuracy
value: 0.22
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.762
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.768
name: Euclidean Accuracy
- type: max_accuracy
value: 0.787
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: お布団バッグ',
'search_query: 足なしソファー',
'search_query: all color handbag',
]
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** | **0.787** |
| dot_accuracy | 0.22 |
| manhattan_accuracy | 0.762 |
| euclidean_accuracy | 0.768 |
| max_accuracy | 0.787 |
<!--
## 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: 12.11 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 49.91 tokens</li><li>max: 166 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 50.64 tokens</li><li>max: 152 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>search_query: blー5c</code> | <code>search_document: [EnergyPower] TECSUN PL-368 電池2個セット SSB・同期検波・長波 [交換用バッテリーBL-5C付] デジタルDSPポケット短波ラジオ 超小型 長・中波用外付アンテナ 10キー ポータブルBCL受信機 FMステレオ/LW/MW/SW ワールドバンドレシーバー 850局プリセットメモリー シグナルメーター USB充電 スリープタイマー アラー, TECSUN, PL-368 電池＋セット [ブラック]</code> | <code>search_document: RADIWOWで作る SIHUADON R108 ポータブル BCL短波ラジオAM FM LW SW 航空無線 DSPレシーバー LCD 良好屋内および屋外アクティビティの両親への贈り物, RADIWOW, グレー</code> |
| <code>search_query: かわいいロングtシャツ</code> | <code>search_document: レディース ロンt 半袖 tシャツ オーバーサイズ コットン スリット 大きいサイズ 白 シャツ ビッグシルエット ワンピース シャツワンピ ロングｔシャツ おおきいサイズ 夏 ピンク カジュアル カップ付き カーディガン キラキラ キャミソール キャミ サテン シンプル シニア シフォン シースルー シ, Sleeping Sheep(スリーピング シープ), ホワイト</code> | <code>search_document: Perkisboby スポーツウェア レディース ヨガウェア 4点セット 上下セット 5点セットウェア フィットネス 2点セット ジャージ スポーツブラ パンツ パーカー 半袖 ハーフパンツ, Perkisboby, 2点セット-グレー</code> |
| <code>search_query: iphone xr otterbox symmetry case</code> | <code>search_document: Symmetry Clear Series Case for iPhone XR (ONLY) Symmetry Case for iPhone XR Symmetry Case - Clear, VTSOU, Clear</code> | <code>search_document: OtterBox Symmetry Series Case for Apple iPhone XS Max - Tonic Violet / Purple, OtterBox, Tonic Violet / Purple</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### 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: 12.13 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 50.76 tokens</li><li>max: 173 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 54.25 tokens</li><li>max: 161 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>search_query: snack vending machine</code> | <code>search_document: Red All Metal Triple Compartment Commercial Vending Machine for 1 inch Gumballs, 1 inch Toy Capsules, Bouncy Balls, Candy, Nuts with Stand by American Gumball Company, American Gumball Company, CANDY RED</code> | <code>search_document: Vending Machine Halloween Costume - Funny Snack Food Adult Men & Women Outfits, Hauntlook, Multicolored</code> |
| <code>search_query: slim credit card holder without id window</code> | <code>search_document: Banuce Top Grain Leather Card Holder for Women Men Unisex ID Credit Card Case Slim Card Wallet Black, Banuce, 1 ID + 5 Card Slots: Black</code> | <code>search_document: Mens Wallet RFID Genuine Leather Bifold Wallets For Men, ID Window 16 Card Holders Gift Box, Swallowmall, Black Stripe</code> |
| <code>search_query: gucci belts for women</code> | <code>search_document: Gucci Women's Gg0027o 50Mm Optical Glasses, Gucci, Havana</code> | <code>search_document: Gucci G-Gucci Gold PVD Women's Watch(Model:YA125511), Gucci, PVD/Brown</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 4
- `gradient_accumulation_steps`: 2
- `learning_rate`: 1e-06
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: 2
- `load_best_model_at_end`: 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`: 4
- `per_device_eval_batch_size`: 4
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 1e-06
- `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
- `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`: 2
- `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`: 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 | 100 | 0.7191 | - | - |
| 0.016 | 200 | 0.6917 | - | - |
| 0.024 | 300 | 0.7129 | - | - |
| 0.032 | 400 | 0.6826 | - | - |
| 0.04 | 500 | 0.7317 | - | - |
| 0.048 | 600 | 0.7237 | - | - |
| 0.056 | 700 | 0.6904 | - | - |
| 0.064 | 800 | 0.6815 | - | - |
| 0.072 | 900 | 0.6428 | - | - |
| 0.08 | 1000 | 0.6561 | 0.6741 | 0.74 |
| 0.088 | 1100 | 0.6097 | - | - |
| 0.096 | 1200 | 0.6426 | - | - |
| 0.104 | 1300 | 0.618 | - | - |
| 0.112 | 1400 | 0.6346 | - | - |
| 0.12 | 1500 | 0.611 | - | - |
| 0.128 | 1600 | 0.6092 | - | - |
| 0.136 | 1700 | 0.6512 | - | - |
| 0.144 | 1800 | 0.646 | - | - |
| 0.152 | 1900 | 0.6584 | - | - |
| 0.16 | 2000 | 0.6403 | 0.6411 | 0.747 |
| 0.168 | 2100 | 0.5882 | - | - |
| 0.176 | 2200 | 0.6361 | - | - |
| 0.184 | 2300 | 0.5641 | - | - |
| 0.192 | 2400 | 0.5734 | - | - |
| 0.2 | 2500 | 0.6156 | - | - |
| 0.208 | 2600 | 0.6252 | - | - |
| 0.216 | 2700 | 0.634 | - | - |
| 0.224 | 2800 | 0.5743 | - | - |
| 0.232 | 2900 | 0.5222 | - | - |
| 0.24 | 3000 | 0.5604 | 0.6180 | 0.765 |
| 0.248 | 3100 | 0.5864 | - | - |
| 0.256 | 3200 | 0.5541 | - | - |
| 0.264 | 3300 | 0.5661 | - | - |
| 0.272 | 3400 | 0.5493 | - | - |
| 0.28 | 3500 | 0.556 | - | - |
| 0.288 | 3600 | 0.56 | - | - |
| 0.296 | 3700 | 0.5552 | - | - |
| 0.304 | 3800 | 0.5833 | - | - |
| 0.312 | 3900 | 0.5578 | - | - |
| 0.32 | 4000 | 0.5495 | 0.6009 | 0.769 |
| 0.328 | 4100 | 0.5245 | - | - |
| 0.336 | 4200 | 0.477 | - | - |
| 0.344 | 4300 | 0.5536 | - | - |
| 0.352 | 4400 | 0.5493 | - | - |
| 0.36 | 4500 | 0.532 | - | - |
| 0.368 | 4600 | 0.5341 | - | - |
| 0.376 | 4700 | 0.528 | - | - |
| 0.384 | 4800 | 0.5574 | - | - |
| 0.392 | 4900 | 0.4953 | - | - |
| 0.4 | 5000 | 0.5365 | 0.5969 | 0.779 |
| 0.408 | 5100 | 0.4835 | - | - |
| 0.416 | 5200 | 0.4573 | - | - |
| 0.424 | 5300 | 0.5554 | - | - |
| 0.432 | 5400 | 0.5623 | - | - |
| 0.44 | 5500 | 0.5955 | - | - |
| 0.448 | 5600 | 0.5086 | - | - |
| 0.456 | 5700 | 0.5081 | - | - |
| 0.464 | 5800 | 0.4829 | - | - |
| 0.472 | 5900 | 0.5066 | - | - |
| 0.48 | 6000 | 0.4997 | 0.5920 | 0.776 |
| 0.488 | 6100 | 0.5075 | - | - |
| 0.496 | 6200 | 0.5051 | - | - |
| 0.504 | 6300 | 0.5019 | - | - |
| 0.512 | 6400 | 0.4774 | - | - |
| 0.52 | 6500 | 0.4975 | - | - |
| 0.528 | 6600 | 0.4756 | - | - |
| 0.536 | 6700 | 0.4656 | - | - |
| 0.544 | 6800 | 0.4671 | - | - |
| 0.552 | 6900 | 0.4646 | - | - |
| 0.56 | 7000 | 0.5595 | 0.5853 | 0.777 |
| 0.568 | 7100 | 0.4812 | - | - |
| 0.576 | 7200 | 0.506 | - | - |
| 0.584 | 7300 | 0.49 | - | - |
| 0.592 | 7400 | 0.464 | - | - |
| 0.6 | 7500 | 0.441 | - | - |
| 0.608 | 7600 | 0.4492 | - | - |
| 0.616 | 7700 | 0.457 | - | - |
| 0.624 | 7800 | 0.493 | - | - |
| 0.632 | 7900 | 0.4174 | - | - |
| 0.64 | 8000 | 0.4686 | 0.5809 | 0.785 |
| 0.648 | 8100 | 0.4529 | - | - |
| 0.656 | 8200 | 0.4784 | - | - |
| 0.664 | 8300 | 0.4697 | - | - |
| 0.672 | 8400 | 0.4489 | - | - |
| 0.68 | 8500 | 0.4439 | - | - |
| 0.688 | 8600 | 0.4063 | - | - |
| 0.696 | 8700 | 0.4634 | - | - |
| 0.704 | 8800 | 0.4446 | - | - |
| 0.712 | 8900 | 0.4725 | - | - |
| 0.72 | 9000 | 0.3954 | 0.5769 | 0.781 |
| 0.728 | 9100 | 0.4536 | - | - |
| 0.736 | 9200 | 0.4583 | - | - |
| 0.744 | 9300 | 0.4415 | - | - |
| 0.752 | 9400 | 0.4716 | - | - |
| 0.76 | 9500 | 0.4393 | - | - |
| 0.768 | 9600 | 0.4332 | - | - |
| 0.776 | 9700 | 0.4236 | - | - |
| 0.784 | 9800 | 0.4021 | - | - |
| 0.792 | 9900 | 0.4324 | - | - |
| 0.8 | 10000 | 0.4197 | 0.5796 | 0.78 |
| 0.808 | 10100 | 0.4576 | - | - |
| 0.816 | 10200 | 0.4238 | - | - |
| 0.824 | 10300 | 0.4468 | - | - |
| 0.832 | 10400 | 0.4301 | - | - |
| 0.84 | 10500 | 0.414 | - | - |
| 0.848 | 10600 | 0.4563 | - | - |
| 0.856 | 10700 | 0.4212 | - | - |
| 0.864 | 10800 | 0.3905 | - | - |
| 0.872 | 10900 | 0.4384 | - | - |
| 0.88 | 11000 | 0.3474 | 0.5709 | 0.788 |
| 0.888 | 11100 | 0.4396 | - | - |
| 0.896 | 11200 | 0.3819 | - | - |
| 0.904 | 11300 | 0.3748 | - | - |
| 0.912 | 11400 | 0.4217 | - | - |
| 0.92 | 11500 | 0.3893 | - | - |
| 0.928 | 11600 | 0.3835 | - | - |
| 0.936 | 11700 | 0.4303 | - | - |
| 0.944 | 11800 | 0.4274 | - | - |
| 0.952 | 11900 | 0.4089 | - | - |
| 0.96 | 12000 | 0.4009 | 0.5710 | 0.786 |
| 0.968 | 12100 | 0.3832 | - | - |
| 0.976 | 12200 | 0.3543 | - | - |
| 0.984 | 12300 | 0.4866 | - | - |
| 0.992 | 12400 | 0.4531 | - | - |
| 1.0 | 12500 | 0.3728 | - | - |
| 1.008 | 12600 | 0.386 | - | - |
| 1.016 | 12700 | 0.3622 | - | - |
| 1.024 | 12800 | 0.4013 | - | - |
| 1.032 | 12900 | 0.3543 | - | - |
| 1.04 | 13000 | 0.3918 | 0.5712 | 0.792 |
| 1.048 | 13100 | 0.3961 | - | - |
| 1.056 | 13200 | 0.3804 | - | - |
| 1.064 | 13300 | 0.4049 | - | - |
| 1.072 | 13400 | 0.3374 | - | - |
| 1.08 | 13500 | 0.3746 | - | - |
| 1.088 | 13600 | 0.3162 | - | - |
| 1.096 | 13700 | 0.3536 | - | - |
| 1.104 | 13800 | 0.3101 | - | - |
| 1.112 | 13900 | 0.3704 | - | - |
| 1.12 | 14000 | 0.3412 | 0.5758 | 0.788 |
| 1.1280 | 14100 | 0.342 | - | - |
| 1.1360 | 14200 | 0.383 | - | - |
| 1.144 | 14300 | 0.3554 | - | - |
| 1.152 | 14400 | 0.4013 | - | - |
| 1.16 | 14500 | 0.3486 | - | - |
| 1.168 | 14600 | 0.3367 | - | - |
| 1.176 | 14700 | 0.3737 | - | - |
| 1.184 | 14800 | 0.319 | - | - |
| 1.192 | 14900 | 0.3211 | - | - |
| 1.2 | 15000 | 0.3284 | 0.5804 | 0.787 |
</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",
}
```
#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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