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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: What was the total amount of current assets reported by The Hershey
Company for the year 2023?
sentences:
- The total AUS for all categories, including alternative investments, equity, fixed
income, and liquidity products, summed up to $2,812 billion in 2023.
- The Hershey Company reported a total of current assets amounting to $2,912,103
for the year 2023.
- Information on legal proceedings is included in Note 15 to the Consolidated Financial
Statements.
- source_sentence: What is listed under Item 8 in the document?
sentences:
- Chubb Limited further advanced their goal of greater product, customer, and geographical
diversification with incremental purchases that led to a controlling majority
interest in Huatai Insurance Group Co. Ltd, owning about 76.5 percent as of July
1, 2023.
- Item 8 includes Financial Statements and Supplementary Data.
- Further, state attorneys general may bring civil actions seeking either injunction
or an unspecified amount in damages in response to violations of the HIPAA privacy
and security regulations.
- source_sentence: What were the main factors contributing to the change in net sales
for fiscal 2022?
sentences:
- The decrease in consolidated net sales in fiscal 2022 compared to fiscal 2021
was primarily attributable to the translation impact of a stronger U.S. dollar,
a decline in sales from new software releases and video game accessories, partially
offset by an increase in sales of new gaming hardware and toys and collectibles.
- We receive payment from the delivery partner subsequent to the transfer of food
and the payment terms are short-term in nature.
- Net cash used in investing activities was $30.0 million in the year ended December
31, 2022, and increased to $73.3 million in the year ended December 31, 2023.
- source_sentence: What informs the ESG disclosures mentioned in the text?
sentences:
- Common Equity Tier 1 (CET1) Capital refers to the total of common stock and related
surplus minus treasury stock, retained earnings, AOCI, and qualifying minority
interests after factoring in the necessary regulatory adjustments and deductions.
- Constant currency revenue percentage change is calculated by determining the change
in current period revenues over prior period revenues where current period foreign
currency revenues are translated using prior year exchange outstanding rates and
hedging effects are excluded from revenues of both periods.
- Our ESG disclosures are also informed by relevant topics identified through third-party
ESG reporting organizations, frameworks and standards, such as the TCFD.
- source_sentence: How many new aircraft did Delta Air Lines take delivery of in 2023?
sentences:
- In 2023, Delta took delivery of 43 aircraft.
- The listing of our common stock on the NYSE could potentially create a conflict
between the exchange’s regulatory responsibilities to vigorously oversee the listing
and trading of securities, on the one hand, and our commercial and economic interest,
on the other hand.
- 'The Company''s enterprise DEI Strategy is aligned to the DEI Vision and Mission
and rests on four core pillars: •Build a workforce of individuals with diverse
backgrounds, cultures, abilities and perspectives •Foster a culture of inclusion
where every individual belongs •Transform talent and business processes to achieve
equitable opportunities for all •Drive innovation and growth with our business
to serve diverse markets around the world.'
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: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8614285714285714
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9171428571428571
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17228571428571426
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09171428571428569
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8614285714285714
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9171428571428571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8082439242024833
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7734971655328796
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7770743874539329
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.6914285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8685714285714285
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9185714285714286
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6914285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1737142857142857
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09185714285714283
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6914285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8685714285714285
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9185714285714286
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8056533729911755
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7695113378684802
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7731633620598676
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.6928571428571428
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8328571428571429
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: 0.6928571428571428
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2776190476190476
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: 0.6928571428571428
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8328571428571429
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: 0.8031697277454632
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7687063492063488
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.772758974076829
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.67
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8028571428571428
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8628571428571429
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9057142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.67
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2676190476190476
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17257142857142854
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09057142857142855
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.67
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8028571428571428
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8628571428571429
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9057142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7882417708737697
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7505816326530609
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7545140112362249
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6557142857142857
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7871428571428571
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8171428571428572
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8742857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6557142857142857
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2623809523809524
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16342857142857142
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08742857142857141
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6557142857142857
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7871428571428571
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8171428571428572
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8742857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7637005971170125
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7285300453514736
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7336775414052045
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("ChristianBernhard/bge-base-financial-matryoshka")
# Run inference
sentences = [
'How many new aircraft did Delta Air Lines take delivery of in 2023?',
'In 2023, Delta took delivery of 43 aircraft.',
'The listing of our common stock on the NYSE could potentially create a conflict between the exchange’s regulatory responsibilities to vigorously oversee the listing and trading of securities, on the one hand, and our commercial and economic interest, on the other hand.',
]
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 | 0.7 | 0.6914 | 0.6929 | 0.67 | 0.6557 |
| cosine_accuracy@3 | 0.8329 | 0.8329 | 0.8329 | 0.8029 | 0.7871 |
| cosine_accuracy@5 | 0.8614 | 0.8686 | 0.87 | 0.8629 | 0.8171 |
| cosine_accuracy@10 | 0.9171 | 0.9186 | 0.91 | 0.9057 | 0.8743 |
| cosine_precision@1 | 0.7 | 0.6914 | 0.6929 | 0.67 | 0.6557 |
| cosine_precision@3 | 0.2776 | 0.2776 | 0.2776 | 0.2676 | 0.2624 |
| cosine_precision@5 | 0.1723 | 0.1737 | 0.174 | 0.1726 | 0.1634 |
| cosine_precision@10 | 0.0917 | 0.0919 | 0.091 | 0.0906 | 0.0874 |
| cosine_recall@1 | 0.7 | 0.6914 | 0.6929 | 0.67 | 0.6557 |
| cosine_recall@3 | 0.8329 | 0.8329 | 0.8329 | 0.8029 | 0.7871 |
| cosine_recall@5 | 0.8614 | 0.8686 | 0.87 | 0.8629 | 0.8171 |
| cosine_recall@10 | 0.9171 | 0.9186 | 0.91 | 0.9057 | 0.8743 |
| **cosine_ndcg@10** | **0.8082** | **0.8057** | **0.8032** | **0.7882** | **0.7637** |
| cosine_mrr@10 | 0.7735 | 0.7695 | 0.7687 | 0.7506 | 0.7285 |
| cosine_map@100 | 0.7771 | 0.7732 | 0.7728 | 0.7545 | 0.7337 |
<!--
## 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>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 20.82 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 47.65 tokens</li><li>max: 371 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What challenges did the company face in its supply chain during fiscal 2021?</code> | <code>During fiscal 2021, we experienced significant disruptions in our supply chain which impacted our ability to ship products from overseas on a timely basis.</code> |
| <code>Is the information on Legal proceedings in the report straightforward or referenced to another section?</code> | <code>The information on Legal proceedings called for by Item 3 is incorporated by reference to Note 19 of the Notes to Consolidated Financial Statements in Item 8 of the report.</code> |
| <code>What factors particularly influence sales comparisons and comparable sales growth according to the annual report?</code> | <code>Sales comparisons can also be particularly influenced by certain factors that are beyond our control: fluctuations in currency exchange rates (with respect to our international operations); inflation or deflation and changes in the cost of gasoline and associated competitive conditions.</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
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `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
- `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 | 1.5819 | - | - | - | - | - |
| 0.9746 | 12 | - | 0.7909 | 0.7912 | 0.7907 | 0.7723 | 0.7444 |
| 1.6244 | 20 | 0.6676 | - | - | - | - | - |
| 1.9492 | 24 | - | 0.7991 | 0.7994 | 0.7983 | 0.7849 | 0.7571 |
| 2.4365 | 30 | 0.4321 | - | - | - | - | - |
| 2.9239 | 36 | - | 0.8089 | 0.8048 | 0.8016 | 0.7879 | 0.7637 |
| 3.2487 | 40 | 0.3958 | - | - | - | - | - |
| **3.8985** | **48** | **-** | **0.8082** | **0.8057** | **0.8032** | **0.7882** | **0.7637** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.2.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## 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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