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---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: During the year ended December 31, 2023, cash flows used in investing
activities also included proceeds from the sale of our investment in Saudi Cinema
Company, LLC of $30.0 million.
sentences:
- What are some of the risks associated with the company's ability to maintain its
concession in Macao and gaming license in Singapore?
- What were the proceeds from the sale of investment in Saudi Cinema Company, LLC
during 2023?
- What financial impact did the change in accounting estimate regarding server and
network equipment have on Microsoft in fiscal year 2023?
- source_sentence: During 2023, U.S. sales of natural gas averaged 4.7 billion cubic
feet per day.
sentences:
- What constitutes a material weakness in internal control over financial reporting,
according to the criteria set by COSO?
- What was Chevron's total average daily sales of natural gas in the U.S. in 2023?
- What total amount of assets were measured at fair value as of January 31, 2022,
and how is this divided across the fair value hierarchy levels?
- source_sentence: The net cash provided by operating activities during fiscal 2023
was related to net income of $208 million, adjusted for non-cash items including
$3.8 billion of depreciation and amortization and $3.3 billion related to stock-based
compensation expense.
sentences:
- What was the net cash provided by operating activities for fiscal 2023?
- How does Nike protect its intellectual property rights against infringement?
- What specific feature does the Peloton Bike+ offer regarding workout experience?
- source_sentence: Year-over-Year Changes in Operating Results for 2023 compared to
2022 showed a decrease of $1,858 million for FedEx Express, an increase of $498
million for FedEx Ground, and an increase of $262 million for FedEx Freight.
sentences:
- How did comparable sales growth, including fuel, contribute to net sales for Sam's
Club in fiscal 2023?
- What is the role of Level 1, Level 2, and Level 3 inputs in the fair value hierarchy
according to ASC 820?
- What were the operating results changes year-over-year for the FedEx Express,
Ground, and Freight segments in 2023 compared to 2022?
- source_sentence: Caterpillar Insurance Co. Ltd. is registered as a Class 2 (General
Business) and Class B (Long-Term) insurer with the Bermuda Monetary Authority.
sentences:
- What types of insurance licenses does Caterpillar Insurance Co. Ltd. hold in Bermuda?
- What is indicated by 'Item 8' in a financial document?
- What does Gross Merchandise Volume (GMV) represent in financial terms?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6985714285714286
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8314285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8728571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9171428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6985714285714286
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27714285714285714
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17457142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09171428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6985714285714286
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8314285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8728571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9171428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8091312862711041
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7744716553287979
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.778107400978576
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6771428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8171428571428572
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8642857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9171428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6771428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2723809523809524
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17285714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09171428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6771428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8171428571428572
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8642857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9171428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7978178514618532
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7596043083900226
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7625576612954725
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.66
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8014285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8542857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9028571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.66
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2671428571428571
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17085714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09028571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.66
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8014285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8542857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9028571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7797125058125993
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7404512471655325
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7439184556821083
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 384-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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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})
(2): Normalize()
)
```
## 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("MistyDragon/bge-small-financial-matryoshka")
# Run inference
sentences = [
'Caterpillar Insurance Co. Ltd. is registered as a Class 2 (General Business) and Class B (Long-Term) insurer with the Bermuda Monetary Authority.',
'What types of insurance licenses does Caterpillar Insurance Co. Ltd. hold in Bermuda?',
"What is indicated by 'Item 8' in a financial document?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# 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
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 256
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.6986 |
| cosine_accuracy@3 | 0.8314 |
| cosine_accuracy@5 | 0.8729 |
| cosine_accuracy@10 | 0.9171 |
| cosine_precision@1 | 0.6986 |
| cosine_precision@3 | 0.2771 |
| cosine_precision@5 | 0.1746 |
| cosine_precision@10 | 0.0917 |
| cosine_recall@1 | 0.6986 |
| cosine_recall@3 | 0.8314 |
| cosine_recall@5 | 0.8729 |
| cosine_recall@10 | 0.9171 |
| **cosine_ndcg@10** | **0.8091** |
| cosine_mrr@10 | 0.7745 |
| cosine_map@100 | 0.7781 |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 128
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.6771 |
| cosine_accuracy@3 | 0.8171 |
| cosine_accuracy@5 | 0.8643 |
| cosine_accuracy@10 | 0.9171 |
| cosine_precision@1 | 0.6771 |
| cosine_precision@3 | 0.2724 |
| cosine_precision@5 | 0.1729 |
| cosine_precision@10 | 0.0917 |
| cosine_recall@1 | 0.6771 |
| cosine_recall@3 | 0.8171 |
| cosine_recall@5 | 0.8643 |
| cosine_recall@10 | 0.9171 |
| **cosine_ndcg@10** | **0.7978** |
| cosine_mrr@10 | 0.7596 |
| cosine_map@100 | 0.7626 |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"truncate_dim": 64
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.66 |
| cosine_accuracy@3 | 0.8014 |
| cosine_accuracy@5 | 0.8543 |
| cosine_accuracy@10 | 0.9029 |
| cosine_precision@1 | 0.66 |
| cosine_precision@3 | 0.2671 |
| cosine_precision@5 | 0.1709 |
| cosine_precision@10 | 0.0903 |
| cosine_recall@1 | 0.66 |
| cosine_recall@3 | 0.8014 |
| cosine_recall@5 | 0.8543 |
| cosine_recall@10 | 0.9029 |
| **cosine_ndcg@10** | **0.7797** |
| cosine_mrr@10 | 0.7405 |
| cosine_map@100 | 0.7439 |
<!--
## 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
#### json
* Dataset: json
* Size: 6,300 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 47.77 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 20.48 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Return on investment (ROI) | 12.7 | % | | 14.9 | %</code> | <code>What was the return on investment (ROI) for the average invested capital in the latest period and how did this compare to the prior period?</code> |
| <code>According to the terms of the Senior Credit Facilities, cash amounts exceeding $175 million can be deducted from the total debt in the leverage ratio calculation, though this is subject to certain restrictions.</code> | <code>How does the Senior Credit Facilities' treatment of cash affect the calculation of the leverage ratio?</code> |
| <code>In 2023, approximately 67% of the total U.S. dialysis patient service revenues were generated from government-based programs.</code> | <code>What percentage of the total U.S. dialysis patient service revenues were generated from government-based programs in 2023?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### 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`: 8
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-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`: 4
- `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
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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`: 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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.1015 | 10 | 4.9287 | - | - | - |
| 0.2030 | 20 | 3.7753 | - | - | - |
| 0.3046 | 30 | 2.7807 | - | - | - |
| 0.4061 | 40 | 2.6642 | - | - | - |
| 0.5076 | 50 | 1.8158 | - | - | - |
| 0.6091 | 60 | 1.2895 | - | - | - |
| 0.7107 | 70 | 1.356 | - | - | - |
| 0.8122 | 80 | 1.2217 | - | - | - |
| 0.9137 | 90 | 1.2548 | - | - | - |
| 1.0 | 99 | - | 0.7949 | 0.7853 | 0.7609 |
| 1.0102 | 100 | 1.1693 | - | - | - |
| 1.1117 | 110 | 1.0828 | - | - | - |
| 1.2132 | 120 | 0.9545 | - | - | - |
| 1.3147 | 130 | 1.1774 | - | - | - |
| 1.4162 | 140 | 0.55 | - | - | - |
| 1.5178 | 150 | 0.891 | - | - | - |
| 1.6193 | 160 | 0.9661 | - | - | - |
| 1.7208 | 170 | 0.9355 | - | - | - |
| 1.8223 | 180 | 0.9888 | - | - | - |
| 1.9239 | 190 | 1.0157 | - | - | - |
| 2.0 | 198 | - | 0.8067 | 0.7945 | 0.7742 |
| 2.0203 | 200 | 0.7944 | - | - | - |
| 2.1218 | 210 | 0.5637 | - | - | - |
| 2.2234 | 220 | 0.3895 | - | - | - |
| 2.3249 | 230 | 1.0888 | - | - | - |
| 2.4264 | 240 | 0.8784 | - | - | - |
| 2.5279 | 250 | 0.5746 | - | - | - |
| 2.6294 | 260 | 1.064 | - | - | - |
| 2.7310 | 270 | 0.8036 | - | - | - |
| 2.8325 | 280 | 0.6005 | - | - | - |
| 2.9340 | 290 | 0.7571 | - | - | - |
| **3.0** | **297** | **-** | **0.81** | **0.7982** | **0.7785** |
| 3.0305 | 300 | 0.6178 | - | - | - |
| 3.1320 | 310 | 0.5013 | - | - | - |
| 3.2335 | 320 | 0.7171 | - | - | - |
| 3.3350 | 330 | 0.5717 | - | - | - |
| 3.4365 | 340 | 0.7031 | - | - | - |
| 3.5381 | 350 | 0.8601 | - | - | - |
| 3.6396 | 360 | 0.597 | - | - | - |
| 3.7411 | 370 | 0.4611 | - | - | - |
| 3.8426 | 380 | 0.6503 | - | - | - |
| 3.9442 | 390 | 0.3176 | - | - | - |
| 4.0 | 396 | - | 0.8091 | 0.7978 | 0.7797 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.8.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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