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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-base-en-v1.5
widget:
- source_sentence: Balance as of December 31, 2023 for Medicaid and Medicare Rebates
was $5,297 million, for Managed Care Rebates was $7,020 million, and for Wholesaler
Chargebacks was $1,172 million.
sentences:
- What can Membership Rewards points be redeemed for?
- What were the ending balances for Medicaid and Medicare Rebates, Managed Care
Rebates, and Wholesaler Chargebacks as of December 31, 2023?
- What was the percentage increase in the general and administrative expenses from
the fiscal year ending on October 2, 2022, to the fiscal year ending on October
1, 2023?
- source_sentence: In analyzing goodwill for potential impairment in the quantitative
impairment test, the company uses the market approach, when available and appropriate,
or a combination of the income and market approaches to estimate the reporting
unit’s fair value.
sentences:
- What is the purpose of Visa according to the overview provided?
- What approaches does the company use to analyze goodwill for potential impairment
in the quantitative impairment test?
- What method is used to record amortization and costs for owned content that is
predominantly monetized on an individual basis?
- source_sentence: This report includes forward-looking statements within the meaning
of the Private Securities Litigation Reform Act of 1995, which are subject to
risks and uncertainties.
sentences:
- What are forward-looking statements in financial reports?
- What percentage of the Pharmacy & Consumer Wellness segment's revenues did the
pharmacy category constitute in 2023?
- What are the depreciation methods and useful life estimates for buildings, furniture,
and computer equipment as mentioned in the company's accounting policies?
- source_sentence: We would use the net proceeds from the sale of any securities offered
pursuant to the shelf registration statement for general corporate purposes, which
may include funding for working capital, financing capital expenditures, research
and development, and potential acquisitions or strategic alliances.
sentences:
- What measures does Goldman Sachs employ to handle their cyber incident response?
- What awards did the company receive in 2022 for environmental and safety achievements?
- How are the proceeds from the shelf registration statement planned to be used?
- source_sentence: We use a variety of practices to measure and support progress against
these growth behaviors and to ensure that our employees are engaged and fulfilled
at work.
sentences:
- How does the company measure and support employee engagement and cultural growth?
- How does the company's membership format affect its profitability?
- What is the maximum additional exclusivity period granted by the FDA for approved
drugs that undergo pediatric testing?
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 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7071428571428572
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: 0.9228571428571428
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7071428571428572
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: 0.09228571428571428
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7071428571428572
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: 0.9228571428571428
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8152573597721203
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7808815192743759
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7835857411528796
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6971428571428572
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8742857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9157142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6971428571428572
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17485714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09157142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6971428571428572
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8742857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9157142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8089182108201057
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7743531746031744
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.777472809187461
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6957142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.83
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.87
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.91
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6957142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.174
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09099999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6957142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.83
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.87
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.91
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8052344976922489
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7713877551020404
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7749003964653882
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.6828571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8257142857142857
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8528571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9071428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6828571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2752380952380953
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17057142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09071428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6828571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8257142857142857
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8528571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9071428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7972100056891113
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7619444444444445
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7654665230481205
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6371428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8042857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8428571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8814285714285715
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6371428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2680952380952381
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16857142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08814285714285712
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6371428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8042857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8428571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8814285714285715
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7645594630559873
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7265028344671197
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7306525198080603
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(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("mogmix/bge-base-financial-matryoshka")
# Run inference
sentences = [
'We use a variety of practices to measure and support progress against these growth behaviors and to ensure that our employees are engaged and fulfilled at work.',
'How does the company measure and support employee engagement and cultural growth?',
"How does the company's membership format affect its profitability?",
]
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
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.7071 | 0.6971 | 0.6957 | 0.6829 | 0.6371 |
| cosine_accuracy@3 | 0.8314 | 0.8329 | 0.83 | 0.8257 | 0.8043 |
| cosine_accuracy@5 | 0.8729 | 0.8743 | 0.87 | 0.8529 | 0.8429 |
| cosine_accuracy@10 | 0.9229 | 0.9157 | 0.91 | 0.9071 | 0.8814 |
| cosine_precision@1 | 0.7071 | 0.6971 | 0.6957 | 0.6829 | 0.6371 |
| cosine_precision@3 | 0.2771 | 0.2776 | 0.2767 | 0.2752 | 0.2681 |
| cosine_precision@5 | 0.1746 | 0.1749 | 0.174 | 0.1706 | 0.1686 |
| cosine_precision@10 | 0.0923 | 0.0916 | 0.091 | 0.0907 | 0.0881 |
| cosine_recall@1 | 0.7071 | 0.6971 | 0.6957 | 0.6829 | 0.6371 |
| cosine_recall@3 | 0.8314 | 0.8329 | 0.83 | 0.8257 | 0.8043 |
| cosine_recall@5 | 0.8729 | 0.8743 | 0.87 | 0.8529 | 0.8429 |
| cosine_recall@10 | 0.9229 | 0.9157 | 0.91 | 0.9071 | 0.8814 |
| **cosine_ndcg@10** | **0.8153** | **0.8089** | **0.8052** | **0.7972** | **0.7646** |
| cosine_mrr@10 | 0.7809 | 0.7744 | 0.7714 | 0.7619 | 0.7265 |
| cosine_map@100 | 0.7836 | 0.7775 | 0.7749 | 0.7655 | 0.7307 |
<!--
## 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: 4 tokens</li><li>mean: 45.46 tokens</li><li>max: 439 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 20.55 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
| <code>We believe our residential connectivity revenue will increase as a result of growth in average domestic broadband revenue per customer, as well as increases in domestic wireless and international connectivity revenue.</code> | <code>What are the projected trends for Comcast's residential connectivity revenue in 2023?</code> |
| <code>The company's Artificial Intelligence Platform (AIP) leverages machine learning technologies and LLMs within the Gotham and Foundry platforms to connect AI with enterprise data, aiding in decision-making processes.</code> | <code>How does the company integrate large language models with its software platforms?</code> |
| <code>The impairment charges for Depop and Elo7 were influenced by factors such as macroeconomic conditions including reopening and inflation, as well as management changes and revised projected cash flows affecting their fair values.</code> | <code>What factors contributed to the impairment charges for Depop and Elo7 in 2022?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `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`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `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`: True
- `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
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.8122 | 10 | 1.5675 | - | - | - | - | - |
| 1.0 | 13 | - | 0.8000 | 0.7975 | 0.7897 | 0.7811 | 0.7419 |
| 1.5685 | 20 | 0.6203 | - | - | - | - | - |
| 2.0 | 26 | - | 0.8114 | 0.8063 | 0.8044 | 0.7928 | 0.7599 |
| 2.3249 | 30 | 0.4678 | - | - | - | - | - |
| 3.0 | 39 | - | 0.8152 | 0.8092 | 0.8046 | 0.7967 | 0.7660 |
| 3.0812 | 40 | 0.4106 | - | - | - | - | - |
| **3.731** | **48** | **-** | **0.8153** | **0.8089** | **0.8052** | **0.7972** | **0.7646** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.12.7
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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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