README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:156
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: How does Google Gemini's recent feature compare to ChatGPT's live
    video option?
  sentences:
  - 'The environmental impact got much, much worse

    The much bigger problem here is the enormous competitive buildout of the infrastructure
    that is imagined to be necessary for these models in the future.

    Companies like Google, Meta, Microsoft and Amazon are all spending billions of
    dollars rolling out new datacenters, with a very material impact on the electricity
    grid and the environment. There’s even talk of spinning up new nuclear power stations,
    but those can take decades.

    Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued
    crash in LLM prices might hint that it’s not. But would you want to be the big
    tech executive that argued NOT to build out this infrastructure only to be proven
    wrong in a few years’ time?'
  - The most recent twist, again from December (December was a lot) is live video.
    ChatGPT voice mode now provides the option to share your camera feed with the
    model and talk about what you can see in real time. Google Gemini have a preview
    of the same feature, which they managed to ship the day before ChatGPT did.
  - 'So far, I think they’re a net positive. I’ve used them on a personal level to
    improve my productivity (and entertain myself) in all sorts of different ways.
    I think people who learn how to use them effectively can gain a significant boost
    to their quality of life.

    A lot of people are yet to be sold on their value! Some think their negatives
    outweigh their positives, some think they are all hot air, and some even think
    they represent an existential threat to humanity.

    They’re actually quite easy to build

    The most surprising thing we’ve learned about LLMs this year is that they’re actually
    quite easy to build.'
- source_sentence: What are the potential environmental impacts of the competitive
    buildout of infrastructure by major tech companies?
  sentences:
  - 'An interesting point of comparison here could be the way railways rolled out
    around the world in the 1800s. Constructing these required enormous investments
    and had a massive environmental impact, and many of the lines that were built
    turned out to be unnecessary—sometimes multiple lines from different companies
    serving the exact same routes!

    The resulting bubbles contributed to several financial crashes, see Wikipedia
    for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They
    left us with a lot of useful infrastructure and a great deal of bankruptcies and
    environmental damage.

    The year of slop'
  - 'The environmental impact got much, much worse

    The much bigger problem here is the enormous competitive buildout of the infrastructure
    that is imagined to be necessary for these models in the future.

    Companies like Google, Meta, Microsoft and Amazon are all spending billions of
    dollars rolling out new datacenters, with a very material impact on the electricity
    grid and the environment. There’s even talk of spinning up new nuclear power stations,
    but those can take decades.

    Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued
    crash in LLM prices might hint that it’s not. But would you want to be the big
    tech executive that argued NOT to build out this infrastructure only to be proven
    wrong in a few years’ time?'
  - '7th: Prompts.js


    9th: I can now run a GPT-4 class model on my laptop


    10th: ChatGPT Canvas can make API requests now, but it’s complicated


    11th: Gemini 2.0 Flash: An outstanding multi-modal LLM with a sci-fi streaming
    mode


    19th: Building Python tools with a one-shot prompt using uv run and Claude Projects


    19th: Gemini 2.0 Flash “Thinking mode”


    20th: December in LLMs has been a lot


    20th: Live blog: the 12th day of OpenAI—“Early evals for OpenAI o3”


    24th: Trying out QvQ—Qwen’s new visual reasoning model


    31st: Things we learned about LLMs in 2024





    (This list generated using Django SQL Dashboard with a SQL query written for me
    by Claude.)'
- source_sentence: What are some of the capabilities of Large Language Models (LLMs)
    mentioned in the context?
  sentences:
  - 'My personal laptop is a 64GB M2 MacBook Pro from 2023. It’s a powerful machine,
    but it’s also nearly two years old now—and crucially it’s the same laptop I’ve
    been using ever since I first ran an LLM on my computer back in March 2023 (see
    Large language models are having their Stable Diffusion moment).

    That same laptop that could just about run a GPT-3-class model in March last year
    has now run multiple GPT-4 class models! Some of my notes on that:'
  - 'Here’s the sequel to this post: Things we learned about LLMs in 2024.

    Large Language Models

    In the past 24-36 months, our species has discovered that you can take a GIANT
    corpus of text, run it through a pile of GPUs, and use it to create a fascinating
    new kind of software.

    LLMs can do a lot of things. They can answer questions, summarize documents, translate
    from one language to another, extract information and even write surprisingly
    competent code.

    They can also help you cheat at your homework, generate unlimited streams of fake
    content and be used for all manner of nefarious purposes.'
  - '24th: Notes on the new Claude analysis JavaScript code execution tool


    27th: Run a prompt to generate and execute jq programs using llm-jq


    29th: You can now run prompts against images, audio and video in your terminal
    using LLM


    30th: W̶e̶e̶k̶n̶o̶t̶e̶s̶  Monthnotes for October




    November


    4th: Claude 3.5 Haiku


    7th: Project: VERDAD—tracking misinformation in radio broadcasts using Gemini
    1.5


    12th: Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac


    19th: Notes from Bing Chat—Our First Encounter With Manipulative AI


    25th: Ask questions of SQLite databases and CSV/JSON files in your terminal




    December


    4th: First impressions of the new Amazon Nova LLMs (via a new llm-bedrock plugin)


    7th: Prompts.js'
- source_sentence: What significant event occurred in 2024 related to the term "slop"?
  sentences:
  - 'Then there’s the rest. If you browse the Chatbot Arena leaderboard today—still
    the most useful single place to get a vibes-based evaluation of models—you’ll
    see that GPT-4-0314 has fallen to around 70th place. The 18 organizations with
    higher scoring models are Google, OpenAI, Alibaba, Anthropic, Meta, Reka AI, 01
    AI, Amazon, Cohere, DeepSeek, Nvidia, Mistral, NexusFlow, Zhipu AI, xAI, AI21
    Labs, Princeton and Tencent.

    Training a GPT-4 beating model was a huge deal in 2023. In 2024 it’s an achievement
    that isn’t even particularly notable, though I personally still celebrate any
    time a new organization joins that list.

    Some of those GPT-4 models run on my laptop'
  - 'The year of slop

    Synthetic training data works great

    LLMs somehow got even harder to use

    Knowledge is incredibly unevenly distributed

    LLMs need better criticism

    Everything tagged “llms” on my blog in 2024'
  - 'The year of slop

    2024 was the year that the word "slop" became a term of art. I wrote about this
    in May, expanding on this tweet by @deepfates:'
- source_sentence: How does the user experience with the default LLM chat UI compare
    to using a more familiar interface?
  sentences:
  - 'The models may have got more capable, but most of the limitations remained the
    same. OpenAI’s o1 may finally be able to (mostly) count the Rs in strawberry,
    but its abilities are still limited by its nature as an LLM and the constraints
    placed on it by the harness it’s running in. o1 can’t run web searches or use
    Code Interpreter, but GPT-4o can—both in that same ChatGPT UI. (o1 will pretend
    to do those things if you ask it to, a regression to the URL hallucinations bug
    from early 2023).

    What are we doing about this? Not much. Most users are thrown in at the deep end.
    The default LLM chat UI is like taking brand new computer users, dropping them
    into a Linux terminal and expecting them to figure it all out.'
  - 'I’ve found myself using this a lot. I noticed how much I was relying on it in
    October and wrote Everything I built with Claude Artifacts this week, describing
    14 little tools I had put together in a seven day period.

    Since then, a whole bunch of other teams have built similar systems. GitHub announced
    their version of this—GitHub Spark—in October. Mistral Chat added it as a feature
    called Canvas in November.

    Steve Krouse from Val Town built a version of it against Cerebras, showcasing
    how a 2,000 token/second LLM can iterate on an application with changes visible
    in less than a second.'
  - 'I think people who complain that LLM improvement has slowed are often missing
    the enormous advances in these multi-modal models. Being able to run prompts against
    images (and audio and video) is a fascinating new way to apply these models.

    Voice and live camera mode are science fiction come to life

    The audio and live video modes that have started to emerge deserve a special mention.

    The ability to talk to ChatGPT first arrived in September 2023, but it was mostly
    an illusion: OpenAI used their excellent Whisper speech-to-text model and a new
    text-to-speech model (creatively named tts-1) to enable conversations with the
    ChatGPT mobile apps, but the actual model just saw text.'
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: 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: 0.8333333333333334
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9583333333333334
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8333333333333334
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3194444444444444
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20000000000000004
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10000000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.8333333333333334
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9583333333333334
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.9228630130990606
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8972222222222221
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8972222222222221
      name: Cosine 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 d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **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("melghorab/legal-ft-v0")
# Run inference
sentences = [
    'How does the user experience with the default LLM chat UI compare to using a more familiar interface?',
    'The models may have got more capable, but most of the limitations remained the same. OpenAI’s o1 may finally be able to (mostly) count the Rs in strawberry, but its abilities are still limited by its nature as an LLM and the constraints placed on it by the harness it’s running in. o1 can’t run web searches or use Code Interpreter, but GPT-4o can—both in that same ChatGPT UI. (o1 will pretend to do those things if you ask it to, a regression to the URL hallucinations bug from early 2023).\nWhat are we doing about this? Not much. Most users are thrown in at the deep end. The default LLM chat UI is like taking brand new computer users, dropping them into a Linux terminal and expecting them to figure it all out.',
    'I think people who complain that LLM improvement has slowed are often missing the enormous advances in these multi-modal models. Being able to run prompts against images (and audio and video) is a fascinating new way to apply these models.\nVoice and live camera mode are science fiction come to life\nThe audio and live video modes that have started to emerge deserve a special mention.\nThe ability to talk to ChatGPT first arrived in September 2023, but it was mostly an illusion: OpenAI used their excellent Whisper speech-to-text model and a new text-to-speech model (creatively named tts-1) to enable conversations with the ChatGPT mobile apps, but the actual model just saw text.',
]
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   | 0.8333     |
| cosine_accuracy@3   | 0.9583     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.8333     |
| cosine_precision@3  | 0.3194     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.8333     |
| cosine_recall@3     | 0.9583     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9229** |
| cosine_mrr@10       | 0.8972     |
| cosine_map@100      | 0.8972     |

<!--
## 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: 156 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 156 samples:
  |         | sentence_0                                                                        | sentence_1                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 14 tokens</li><li>mean: 20.3 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 130.45 tokens</li><li>max: 204 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                    | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
  |:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What role does synthetic data play in the pretraining of models, particularly in the Phi series?</code> | <code>Synthetic data as a substantial component of pretraining is becoming increasingly common, and the Phi series of models has consistently emphasized the importance of synthetic data. Rather than serving as a cheap substitute for organic data, synthetic data has several direct advantages over organic data.</code>                                                                                                                                                                                                                                                                                                                                                   |
  | <code>How does synthetic data compare to organic data in terms of advantages?</code>                          | <code>Synthetic data as a substantial component of pretraining is becoming increasingly common, and the Phi series of models has consistently emphasized the importance of synthetic data. Rather than serving as a cheap substitute for organic data, synthetic data has several direct advantages over organic data.</code>                                                                                                                                                                                                                                                                                                                                                   |
  | <code>What analogy is used to describe LLMs in the context provided?</code>                                   | <code>A drum I’ve been banging for a while is that LLMs are power-user tools—they’re chainsaws disguised as kitchen knives. They look deceptively simple to use—how hard can it be to type messages to a chatbot?—but in reality you need a huge depth of both understanding and experience to make the most of them and avoid their many pitfalls.<br>If anything, this problem got worse in 2024.<br>We’ve built computer systems you can talk to in human language, that will answer your questions and usually get them right! ... depending on the question, and how you ask it, and whether it’s accurately reflected in the undocumented and secret training set.</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`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `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`: 10
- `per_device_eval_batch_size`: 10
- `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`: 10
- `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`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0   | 16   | 0.9163         |
| 2.0   | 32   | 0.9330         |
| 3.0   | 48   | 0.9330         |
| 3.125 | 50   | 0.9330         |
| 4.0   | 64   | 0.9067         |
| 5.0   | 80   | 0.9067         |
| 6.0   | 96   | 0.9247         |
| 6.25  | 100  | 0.9247         |
| 7.0   | 112  | 0.9247         |
| 8.0   | 128  | 0.9229         |
| 9.0   | 144  | 0.9229         |
| 9.375 | 150  | 0.9229         |
| 10.0  | 160  | 0.9229         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.0
- 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}
}
```

<!--
## 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.*
-->