Hugging Face's logo

CoExperiences
/
snowflake-l-marketing-tuned

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:600
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
snowflake-l-marketing-tuned / README.md
pattonma's picture
pattonma
Add new SentenceTransformer model
b7bb480 verified
preview code
|
raw
history blame contribute delete
28.7 kB
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:600
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: What is the date of the Gallup report regarding employer care for
employee wellbeing?
sentences:
- sense of purpose Defining work wellbeing
- What constitutes meaningful conversations between managers and employees? Gallup
found they include recognition and discussion about collaboration, goals, and
priorities, and the employee’s strengths. These conversations prevent employees
from feeling disconnected from the organization because managers stay in touch
with what each employee contributes and can then articulate how that work affects
the larger organization. The conversations ensure that expectations can be adjusted
as the business needs change and in what ways those changing expectations interact
with coworker roles.
- March 18, 2022 Gallup https://www.gallup.com/workplace/390776/percent-feel-employer-cares-wellbeing-plummets.aspx
Gallup World Headquarters, 901 F Street, Washington, D.C., 20001, U.S.A +1 202.715.3030
- source_sentence: What services does Evernorth Health Services provide?
sentences:
- 'Focusing on employee wellbeing and acknowledging the whole person. Since work
and life are blended for many, consider the demands of life inside and out of
the workplace. Consider career, social, financial, physical, and community wellbeing
impacts and resources.
Tailoring communication to reach their team where they are. Transparent and creative
omnichannel communication to employees and customers is more likely to reach and
resonate with a wide variety of people in many different work-life situations.'
- 'Investor Relations
Careers
Bottom FB - column 3
COVID Resource Center
Health and Wellness
Member Resources
Bottom FB - column 4
The Cigna Group
Cigna Healthcare
Evernorth Health Services
International'
- 1. The evolution of the disease burden. While McKinsey & Company employs many
medical experts and scientists, we are not a disease forecasting firm. We rely
on disease-burden forecasts globally and for the United States provided by IHME,
which maintains the most comprehensive database of the global disease burden and
for the United States as whole. Forecasts of the global and US disease burden
are inherently uncertain and health shocks such as the COVID-19 pandemic may affect
forecasts.
- source_sentence: How does the theme of "Wellbeing" relate to employees' perceptions
of their work-life balance?
sentences:
- "engagement as an extremely important priority—are effectively using metrics and\
\ shared some best practices for tying engagement to business performance. \n\
Copyright © 2013 Harvard Business School Publishing. All rights reserved.The Impact\
\ of \nEmployee Engagement on Performance\nhighlights\n71%\nof respondents rank\
\ \nemployee engagement as \nvery important to achieving \noverall organizational\
\ success.\n72%\nof respondents rank recognition \ngiven for high performers\
\ as \nhaving a significant impact on \nemployee engagement.\n24% \nof respondents\
\ say employees \nin their organization are \nhighly engaged."
- 'figure 10
Senior managers were far more likely to be optimistic than their middle-management
colleagues were in their perceptions of engagement levels. Since middle managers
are tasked with handling more day-to-day employee issues, their assessment is
likely the more accurate. This implies that in many firms senior man-agers may
need to take off the rose-colored glasses and take a closer look at the barriers
to engagement that may be present, and then find more effective ways of overcoming
them.'
- 'Gallup analysts identified individuals in its database who have declined in clarity
of expectations from 2020 to 2023. Among this group, across job types and work
locations, the largest areas of decline fit into five themes:
Feedback and Performance Focus
Received meaningful feedback in the last week
Performance managed to motivate outstanding performance
Manager keeps me informed on what is going on
Pride in quality of products/services
Freedom to make decisions needed to do my job well
Goals/Priorities
Manager includes me in goal setting
Feel prepared to do my job
Wellbeing
Organization cares about my wellbeing
Able to maintain a healthy balance between work and personal life
Team
Feel like part of the team'
- source_sentence: What impact does having one meaningful conversation per week with
each team member have on high-performance relationships according to Gallup?
sentences:
- 'Fewer than one in four U.S. employees feel strongly that their organization cares
about their wellbeing -- the lowest percentage in nearly a decade.
This finding has significant implications, as work and life have never been more
blended and employee wellbeing matters more than ever-- to employees and the resiliency
of organizations. The discovery is based on a random sample of 15,001 full and
part-time U.S. employees who were surveyed in February 2022.'
- has developed an open-access dashboard for more than 80 measures at the county,
state, and national levels. This data has highlighted, for example, the disproportionate
impact of COVID-19 on communities of color as well as physical health and behavioral
health vulnerability to COVID-19.
- Gallup finds that a manager having one meaningful conversation per week with each
team member develops high-performance relationships more than any other leadership
activity. Gallup analytics have found managers can be quickly upskilled to have
these ongoing strengths-based conversations that bring purpose and clear expectations
to work, which is now deteriorating in U.S. organizations.
- source_sentence: How does Alexis Krivkovich's perspective as a mother influence
her optimism about the future of women in the workplace?
sentences:
- 'Author(s)
Jim Harter, Ph.D., is Chief Scientist, Workplace for Gallup and bestselling author
of Culture Shock, Wellbeing at Work, It''s the Manager, 12: The Elements of Great
Managing and Wellbeing: The Five Essential Elements. His research is also featured
in the groundbreaking New York Times bestseller, First, Break All the Rules. Dr.
Harter has led more than 1,000 studies of workplace effectiveness, including the
largest ongoing meta-analysis of human potential and business-unit performance.
His work has also appeared in many publications, including Harvard Business Review,
The New York Times and The Wall Street Journal, and in many prominent academic
journals.
Sangeeta Agrawal contributed analysis to this article.
Survey Methods'
- "Learn more about the \nWork Happiness Score at: \ngo.indeed.com/happiness"
- 'Lucia Rahilly: Sometimes, I feel that we’ve been talking about these issues since
I was in college, and that can feel discouraging. What are you most optimistic
about going into 2022, coming out of this Women in the Workplace report?
Alexis Krivkovich: I’m most optimistic about the fact that we’re having an honest
conversation, and now with a real fact base. We’re not talking about these things
as perception but as real and measured experiences that companies can’t hide from—and
they don’t want to.
As a mother of three young daughters, it gives me real hope because I’ve been
thinking about this question for 20 years. But in 20 years, when they’re fully
in the workplace, maybe we’ll have a totally different paradigm.'
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
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.81
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.93
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.97
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.98
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.81
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30999999999999994
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19399999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09799999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.81
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.93
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.97
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.98
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9036533710134148
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8780952380952383
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8798376623376624
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.81
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.93
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.97
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.81
name: Dot Precision@1
- type: dot_precision@3
value: 0.30999999999999994
name: Dot Precision@3
- type: dot_precision@5
value: 0.19399999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.09799999999999998
name: Dot Precision@10
- type: dot_recall@1
value: 0.81
name: Dot Recall@1
- type: dot_recall@3
value: 0.93
name: Dot Recall@3
- type: dot_recall@5
value: 0.97
name: Dot Recall@5
- type: dot_recall@10
value: 0.98
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9036533710134148
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8780952380952383
name: Dot Mrr@10
- type: dot_map@100
value: 0.8798376623376624
name: Dot Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-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:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision 9a9e5834d2e89cdd8bb72b64111dde496e4fe78c -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 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': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, '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("CoExperiences/snowflake-l-marketing-tuned")
# Run inference
sentences = [
"How does Alexis Krivkovich's perspective as a mother influence her optimism about the future of women in the workplace?",
'Lucia Rahilly: Sometimes, I feel that we’ve been talking about these issues since I was in college, and that can feel discouraging. What are you most optimistic about going into 2022, coming out of this Women in the Workplace report?\n\nAlexis Krivkovich: I’m most optimistic about the fact that we’re having an honest conversation, and now with a real fact base. We’re not talking about these things as perception but as real and measured experiences that companies can’t hide from—and they don’t want to.\n\nAs a mother of three young daughters, it gives me real hope because I’ve been thinking about this question for 20 years. But in 20 years, when they’re fully in the workplace, maybe we’ll have a totally different paradigm.',
'Learn more about the \nWork Happiness Score at: \ngo.indeed.com/happiness',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 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
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.81 |
| cosine_accuracy@3 | 0.93 |
| cosine_accuracy@5 | 0.97 |
| cosine_accuracy@10 | 0.98 |
| cosine_precision@1 | 0.81 |
| cosine_precision@3 | 0.31 |
| cosine_precision@5 | 0.194 |
| cosine_precision@10 | 0.098 |
| cosine_recall@1 | 0.81 |
| cosine_recall@3 | 0.93 |
| cosine_recall@5 | 0.97 |
| cosine_recall@10 | 0.98 |
| cosine_ndcg@10 | 0.9037 |
| cosine_mrr@10 | 0.8781 |
| **cosine_map@100** | **0.8798** |
| dot_accuracy@1 | 0.81 |
| dot_accuracy@3 | 0.93 |
| dot_accuracy@5 | 0.97 |
| dot_accuracy@10 | 0.98 |
| dot_precision@1 | 0.81 |
| dot_precision@3 | 0.31 |
| dot_precision@5 | 0.194 |
| dot_precision@10 | 0.098 |
| dot_recall@1 | 0.81 |
| dot_recall@3 | 0.93 |
| dot_recall@5 | 0.97 |
| dot_recall@10 | 0.98 |
| dot_ndcg@10 | 0.9037 |
| dot_mrr@10 | 0.8781 |
| dot_map@100 | 0.8798 |
<!--
## 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: 600 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 600 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 20.08 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 110.85 tokens</li><li>max: 187 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What significant change occurred in employees' perceptions of their employer's care for their wellbeing during the pandemic?</code> | <code>Workplace<br><br>Percent Who Feel Employer Cares About Their Wellbeing Plummets<br><br>Share on LinkedIn<br><br>Share on Twitter<br><br>Share on Facebook<br><br>Share via Email<br><br>Print<br><br>Share on LinkedIn<br><br>Share on Twitter<br><br>Share on Facebook<br><br>Share via Email<br><br>Print<br><br>Workplace<br><br>March 18, 2022<br><br>Percent Who Feel Employer Cares About Their Wellbeing Plummets<br><br>by Jim Harter<br><br>Story Highlights<br><br>Employees' perceptions of their organization caring about their wellbeing drops<br><br>During the onset of the pandemic, employees felt employers had more care and concern<br><br>Employees who feel their employer cares about their wellbeing are 69% less likely to actively search for a job</code> |
| <code>How does feeling cared for by an employer impact employees' job search behavior?</code> | <code>Workplace<br><br>Percent Who Feel Employer Cares About Their Wellbeing Plummets<br><br>Share on LinkedIn<br><br>Share on Twitter<br><br>Share on Facebook<br><br>Share via Email<br><br>Print<br><br>Share on LinkedIn<br><br>Share on Twitter<br><br>Share on Facebook<br><br>Share via Email<br><br>Print<br><br>Workplace<br><br>March 18, 2022<br><br>Percent Who Feel Employer Cares About Their Wellbeing Plummets<br><br>by Jim Harter<br><br>Story Highlights<br><br>Employees' perceptions of their organization caring about their wellbeing drops<br><br>During the onset of the pandemic, employees felt employers had more care and concern<br><br>Employees who feel their employer cares about their wellbeing are 69% less likely to actively search for a job</code> |
| <code>What percentage of U.S. employees feel strongly that their organization cares about their wellbeing?</code> | <code>Fewer than one in four U.S. employees feel strongly that their organization cares about their wellbeing -- the lowest percentage in nearly a decade.<br><br>This finding has significant implications, as work and life have never been more blended and employee wellbeing matters more than ever-- to employees and the resiliency of organizations. The discovery is based on a random sample of 15,001 full and part-time U.S. employees who were surveyed in February 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": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 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`: 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`: 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, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `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
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 1.0 | 30 | 0.8782 |
| 1.6667 | 50 | 0.8878 |
| 2.0 | 60 | 0.8854 |
| 3.0 | 90 | 0.8853 |
| 3.3333 | 100 | 0.8845 |
| 4.0 | 120 | 0.8793 |
| 5.0 | 150 | 0.8798 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.5.0+cu124
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->