README.md

---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:157
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: Why does the author recommend reading the first few pages of the
    69-page PDF document related to the lawsuit?
  sentences:
  - 'We don’t yet know how to build GPT-4

    Frustratingly, despite the enormous leaps ahead we’ve had this year, we are yet
    to see an alternative model that’s better than GPT-4.

    OpenAI released GPT-4 in March, though it later turned out we had a sneak peak
    of it in February when Microsoft used it as part of the new Bing.

    This may well change in the next few weeks: Google’s Gemini Ultra has big claims,
    but isn’t yet available for us to try out.

    The team behind Mistral are working to beat GPT-4 as well, and their track record
    is already extremely strong considering their first public model only came out
    in September, and they’ve released two significant improvements since then.'
  - 'Just this week, the New York Times launched a landmark lawsuit against OpenAI
    and Microsoft over this issue. The 69 page PDF is genuinely worth reading—especially
    the first few pages, which lay out the issues in a way that’s surprisingly easy
    to follow. The rest of the document includes some of the clearest explanations
    of what LLMs are, how they work and how they are built that I’ve read anywhere.

    The legal arguments here are complex. I’m not a lawyer, but I don’t think this
    one will be easily decided. Whichever way it goes, I expect this case to have
    a profound impact on how this technology develops in the future.'
  - 'Nothing yet from Anthropic or Meta but I would be very surprised if they don’t
    have their own inference-scaling models in the works. Meta published a relevant
    paper Training Large Language Models to Reason in a Continuous Latent Space in
    December.

    Was the best currently available LLM trained in China for less than $6m?

    Not quite, but almost! It does make for a great attention-grabbing headline.

    The big news to end the year was the release of DeepSeek v3—dropped on Hugging
    Face on Christmas Day without so much as a README file, then followed by documentation
    and a paper the day after that.'
- source_sentence: Why does the author find the term “agents” frustrating?
  sentences:
  - 'Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac talks about
    Qwen2.5-Coder-32B in November—an Apache 2.0 licensed model!


    I can now run a GPT-4 class model on my laptop talks about running Meta’s Llama
    3.3 70B (released in December)'
  - '“Agents” still haven’t really happened yet

    I find the term “agents” extremely frustrating. It lacks a single, clear and widely
    understood meaning... but the people who use the term never seem to acknowledge
    that.

    If you tell me that you are building “agents”, you’ve conveyed almost no information
    to me at all. Without reading your mind I have no way of telling which of the
    dozens of possible definitions you are talking about.'
  - 'Terminology aside, I remain skeptical as to their utility based, once again,
    on the challenge of gullibility. LLMs believe anything you tell them. Any systems
    that attempts to make meaningful decisions on your behalf will run into the same
    roadblock: how good is a travel agent, or a digital assistant, or even a research
    tool if it can’t distinguish truth from fiction?

    Just the other day Google Search was caught serving up an entirely fake description
    of the non-existant movie “Encanto 2”. It turned out to be summarizing an imagined
    movie listing from a fan fiction wiki.'
- source_sentence: Which company released the QwQ model under an Apache 20 license?
  sentences:
  - 'Embeddings: What they are and why they matter

    61.7k

    79.3k



    Catching up on the weird world of LLMs

    61.6k

    85.9k



    llamafile is the new best way to run an LLM on your own computer

    52k

    66k



    Prompt injection explained, with video, slides, and a transcript

    51k

    61.9k



    AI-enhanced development makes me more ambitious with my projects

    49.6k

    60.1k



    Understanding GPT tokenizers

    49.5k

    61.1k



    Exploring GPTs: ChatGPT in a trench coat?

    46.4k

    58.5k



    Could you train a ChatGPT-beating model for $85,000 and run it in a browser?

    40.5k

    49.2k



    How to implement Q&A against your documentation with GPT3, embeddings and Datasette

    37.3k

    44.9k



    Lawyer cites fake cases invented by ChatGPT, judge is not amused

    37.1k

    47.4k'
  - 'OpenAI are not the only game in town here. Google released their first entrant
    in the category, gemini-2.0-flash-thinking-exp, on December 19th.

    Alibaba’s Qwen team released their QwQ model on November 28th—under an Apache
    2.0 license, and that one I could run on my own machine. They followed that up
    with a vision reasoning model called QvQ on December 24th, which I also ran locally.

    DeepSeek made their DeepSeek-R1-Lite-Preview model available to try out through
    their chat interface on November 20th.

    To understand more about inference scaling I recommend Is AI progress slowing
    down? by Arvind Narayanan and Sayash Kapoor.'
  - 'Against this photo of butterflies at the California Academy of Sciences:



    A shallow dish, likely a hummingbird or butterfly feeder, is red.  Pieces of orange
    slices of fruit are visible inside the dish.

    Two butterflies are positioned in the feeder, one is a dark brown/black butterfly
    with white/cream-colored markings.  The other is a large, brown butterfly with
    patterns of lighter brown, beige, and black markings, including prominent eye
    spots. The larger brown butterfly appears to be feeding on the fruit.'
- source_sentence: How does the 2024 review of Large Language Models build upon the
    insights from the 2023 review?
  sentences:
  - 'Law is not ethics. Is it OK to train models on people’s content without their
    permission, when those models will then be used in ways that compete with those
    people?

    As the quality of results produced by AI models has increased over the year, these
    questions have become even more pressing.

    The impact on human society in terms of these models is already huge, if difficult
    to objectively measure.

    People have certainly lost work to them—anecdotally, I’ve seen this for copywriters,
    artists and translators.

    There are a great deal of untold stories here. I’m hoping 2024 sees significant
    amounts of dedicated journalism on this topic.

    My blog in 2023

    Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django
    SQL Dashboard):'
  - 'The GPT-4 barrier was comprehensively broken

    In my December 2023 review I wrote about how We don’t yet know how to build GPT-4—OpenAI’s
    best model was almost a year old at that point, yet no other AI lab had produced
    anything better. What did OpenAI know that the rest of us didn’t?

    I’m relieved that this has changed completely in the past twelve months. 18 organizations
    now have models on the Chatbot Arena Leaderboard that rank higher than the original
    GPT-4 from March 2023 (GPT-4-0314 on the board)—70 models in total.'
  - 'Things we learned about LLMs in 2024






















    Simon Willison’s Weblog

    Subscribe







    Things we learned about LLMs in 2024

    31st December 2024

    A lot has happened in the world of Large Language Models over the course of 2024.
    Here’s a review of things we figured out about the field in the past twelve months,
    plus my attempt at identifying key themes and pivotal moments.

    This is a sequel to my review of 2023.

    In this article:'
- source_sentence: What is the challenge in building AI personal assistants based
    on the gullibility of language models?
  sentences:
  - 'Language Models are gullible. They “believe” what we tell them—what’s in their
    training data, then what’s in the fine-tuning data, then what’s in the prompt.

    In order to be useful tools for us, we need them to believe what we feed them!

    But it turns out a lot of the things we want to build need them not to be gullible.

    Everyone wants an AI personal assistant. If you hired a real-world personal assistant
    who believed everything that anyone told them, you would quickly find that their
    ability to positively impact your life was severely limited.'
  - 'Large Language Models

    They’re actually quite easy to build

    You can run LLMs on your own devices

    Hobbyists can build their own fine-tuned models

    We don’t yet know how to build GPT-4

    Vibes Based Development

    LLMs are really smart, and also really, really dumb

    Gullibility is the biggest unsolved problem

    Code may be the best application

    The ethics of this space remain diabolically complex

    My blog in 2023'
  - 'These price drops are driven by two factors: increased competition and increased
    efficiency. The efficiency thing is really important for everyone who is concerned
    about the environmental impact of LLMs. These price drops tie directly to how
    much energy is being used for running prompts.

    There’s still plenty to worry about with respect to the environmental impact of
    the great AI datacenter buildout, but a lot of the concerns over the energy cost
    of individual prompts are no longer credible.

    Here’s a fun napkin calculation: how much would it cost to generate short descriptions
    of every one of the 68,000 photos in my personal photo library using Google’s
    Gemini 1.5 Flash 8B (released in October), their cheapest model?'
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: 0.9583333333333334
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 1.0
      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: 0.9583333333333334
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3333333333333333
      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: 0.9583333333333334
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 1.0
      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: 0.9846220730654774
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.9791666666666666
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.9791666666666666
      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("dwb2023/legal-ft-794455c7-1bee-466a-8110-133f086ed907")
# Run inference
sentences = [
    'What is the challenge in building AI personal assistants based on the gullibility of language models?',
    'Language Models are gullible. They “believe” what we tell them—what’s in their training data, then what’s in the fine-tuning data, then what’s in the prompt.\nIn order to be useful tools for us, we need them to believe what we feed them!\nBut it turns out a lot of the things we want to build need them not to be gullible.\nEveryone wants an AI personal assistant. If you hired a real-world personal assistant who believed everything that anyone told them, you would quickly find that their ability to positively impact your life was severely limited.',
    'These price drops are driven by two factors: increased competition and increased efficiency. The efficiency thing is really important for everyone who is concerned about the environmental impact of LLMs. These price drops tie directly to how much energy is being used for running prompts.\nThere’s still plenty to worry about with respect to the environmental impact of the great AI datacenter buildout, but a lot of the concerns over the energy cost of individual prompts are no longer credible.\nHere’s a fun napkin calculation: how much would it cost to generate short descriptions of every one of the 68,000 photos in my personal photo library using Google’s Gemini 1.5 Flash 8B (released in October), their cheapest model?',
]
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   | 0.9583     |
| cosine_accuracy@3   | 1.0        |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.9583     |
| cosine_precision@3  | 0.3333     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.9583     |
| cosine_recall@3     | 1.0        |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.9846** |
| cosine_mrr@10       | 0.9792     |
| cosine_map@100      | 0.9792     |

<!--
## 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: 157 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 157 samples:
  |         | sentence_0                                                                        | sentence_1                                                                           |
  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                               |
  | details | <ul><li>min: 2 tokens</li><li>mean: 20.94 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.72 tokens</li><li>max: 214 tokens</li></ul> |
* Samples:
  | sentence_0                                                                          | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
  |:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What was the typical context length accepted by most models last year?</code> | <code>Gemini 1.5 Pro also illustrated one of the key themes of 2024: increased context lengths. Last year most models accepted 4,096 or 8,192 tokens, with the notable exception of Claude 2.1 which accepted 200,000. Today every serious provider has a 100,000+ token model, and Google’s Gemini series accepts up to 2 million.</code>                                                                                                                                                     |
  | <code>How many tokens can Google’s Gemini series accept in 2024?</code>             | <code>Gemini 1.5 Pro also illustrated one of the key themes of 2024: increased context lengths. Last year most models accepted 4,096 or 8,192 tokens, with the notable exception of Claude 2.1 which accepted 200,000. Today every serious provider has a 100,000+ token model, and Google’s Gemini series accepts up to 2 million.</code>                                                                                                                                                     |
  | <code>What are the new capabilities introduced by Google’s Gemini 15 Pro?</code>    | <code>The earliest of those was Google’s Gemini 1.5 Pro, released in February. In addition to producing GPT-4 level outputs, it introduced several brand new capabilities to the field—most notably its 1 million (and then later 2 million) token input context length, and the ability to input video.<br>I wrote about this at the time in The killer app of Gemini Pro 1.5 is video, which earned me a short appearance as a talking head in the Google I/O opening keynote in May.</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}
- `tp_size`: 0
- `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
- `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   | 0.9638         |
| 2.0   | 32   | 0.9484         |
| 3.0   | 48   | 0.9484         |
| 3.125 | 50   | 0.9484         |
| 4.0   | 64   | 0.9539         |
| 5.0   | 80   | 0.9692         |
| 6.0   | 96   | 0.9692         |
| 6.25  | 100  | 0.9692         |
| 7.0   | 112  | 0.9692         |
| 8.0   | 128  | 0.9846         |
| 9.0   | 144  | 0.9846         |
| 9.375 | 150  | 0.9846         |
| 10.0  | 160  | 0.9846         |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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