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: Why does the author find the term "agents" extremely frustrating?
  sentences:
  - 'We already knew LLMs were spookily good at writing code. If you prompt them right,
    it turns out they can build you a full interactive application using HTML, CSS
    and JavaScript (and tools like React if you wire up some extra supporting build
    mechanisms)—often in a single prompt.

    Anthropic kicked this idea into high gear when they released Claude Artifacts,
    a groundbreaking new feature that was initially slightly lost in the noise due
    to being described half way through their announcement of the incredible Claude
    3.5 Sonnet.

    With Artifacts, Claude can write you an on-demand interactive application and
    then let you use it directly inside the Claude interface.

    Here’s my Extract URLs app, entirely generated by Claude:'
  - '“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.'
  - 'I love the term “slop” because it so succinctly captures one of the ways we should
    not be using generative AI!

    Slop was even in the running for Oxford Word of the Year 2024, but it lost to
    brain rot.

    Synthetic training data works great

    An idea that surprisingly seems to have stuck in the public consciousness is that
    of “model collapse”. This was first described in the paper The Curse of Recursion:
    Training on Generated Data Makes Models Forget in May 2023, and repeated in Nature
    in July 2024 with the more eye-catching headline AI models collapse when trained
    on recursively generated data.'
- source_sentence: What paper did Meta publish in December that is relevant to inference-scaling
    models?
  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:'
  - '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.'
  - 'The GPT-4 barrier was comprehensively broken

    Some of those GPT-4 models run on my laptop

    LLM prices crashed, thanks to competition and increased efficiency

    Multimodal vision is common, audio and video are starting to emerge

    Voice and live camera mode are science fiction come to life

    Prompt driven app generation is a commodity already

    Universal access to the best models lasted for just a few short months

    “Agents” still haven’t really happened yet

    Evals really matter

    Apple Intelligence is bad, Apple’s MLX library is excellent

    The rise of inference-scaling “reasoning” models

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

    The environmental impact got better

    The environmental impact got much, much worse'
- source_sentence: How does the performance of the Llama 32 3B model compare to GPT-4
    according to the context?
  sentences:
  - 'I think this means that, as individual users, we don’t need to feel any guilt
    at all for the energy consumed by the vast majority of our prompts. The impact
    is likely neglible compared to driving a car down the street or maybe even watching
    a video on YouTube.

    Likewise, training. DeepSeek v3 training for less than $6m is a fantastic sign
    that training costs can and should continue to drop.

    For less efficient models I find it useful to compare their energy usage to commercial
    flights. The largest Llama 3 model cost about the same as a single digit number
    of fully loaded passenger flights from New York to London. That’s certainly not
    nothing, but once trained that model can be used by millions of people at no extra
    training cost.'
  - 'Meta’s Llama 3.2 models deserve a special mention. They may not be GPT-4 class,
    but at 1B and 3B sizes they punch massively above their weight. I run Llama 3.2
    3B on my iPhone using the free MLC Chat iOS app and it’s a shockingly capable
    model for its tiny (<2GB) size. Try firing it up and asking it for “a plot outline
    of a Netflix Christmas movie where a data journalist falls in love with a local
    ceramacist”. Here’s what I got, at a respectable 20 tokens per second:'
  - 'Prince Canuma’s excellent, fast moving mlx-vlm project brings vision LLMs to
    Apple Silicon as well. I used that recently to run Qwen’s QvQ.

    While MLX is a game changer, Apple’s own “Apple Intelligence” features have mostly
    been a disappointment. I wrote about their initial announcement in June, and I
    was optimistic that Apple had focused hard on the subset of LLM applications that
    preserve user privacy and minimize the chance of users getting mislead by confusing
    features.'
- source_sentence: What was introduced by the Chatbot Arena team in December regarding
    user interaction with models?
  sentences:
  - '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:'
  - 'The two main categories I see are people who think AI agents are obviously things
    that go and act on your behalf—the travel agent model—and people who think in
    terms of LLMs that have been given access to tools which they can run in a loop
    as part of solving a problem. The term “autonomy” is often thrown into the mix
    too, again without including a clear definition.

    (I also collected 211 definitions on Twitter a few months ago—here they are in
    Datasette Lite—and had gemini-exp-1206 attempt to summarize them.)

    Whatever the term may mean, agents still have that feeling of perpetually “coming
    soon”.'
  - 'Then in December, the Chatbot Arena team introduced a whole new leaderboard for
    this feature, driven by users building the same interactive app twice with two
    different models and voting on the answer. Hard to come up with a more convincing
    argument that this feature is now a commodity that can be effectively implemented
    against all of the leading models.

    I’ve been tinkering with a version of this myself for my Datasette project, with
    the goal of letting users use prompts to build and iterate on custom widgets and
    data visualizations against their own data. I also figured out a similar pattern
    for writing one-shot Python programs, enabled by uv.'
- source_sentence: What does the cost of training the DeepSeek v3 model suggest about
    the future of training costs for AI models?
  sentences:
  - 'I think this means that, as individual users, we don’t need to feel any guilt
    at all for the energy consumed by the vast majority of our prompts. The impact
    is likely neglible compared to driving a car down the street or maybe even watching
    a video on YouTube.

    Likewise, training. DeepSeek v3 training for less than $6m is a fantastic sign
    that training costs can and should continue to drop.

    For less efficient models I find it useful to compare their energy usage to commercial
    flights. The largest Llama 3 model cost about the same as a single digit number
    of fully loaded passenger flights from New York to London. That’s certainly not
    nothing, but once trained that model can be used by millions of people at no extra
    training cost.'
  - '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?

    Each photo would need 260 input tokens and around 100 output tokens.

    260 * 68,000 = 17,680,000 input tokens

    17,680,000 * $0.0375/million = $0.66

    100 * 68,000 = 6,800,000 output tokens

    6,800,000 * $0.15/million = $1.02'
  - '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'
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: 0.75
      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: 0.75
      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: 0.75
      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: 0.8884777424494903
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8506944444444445
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8506944444444443
      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("Gonalb/legal-ft-v0")
# Run inference
sentences = [
    'What does the cost of training the DeepSeek v3 model suggest about the future of training costs for AI models?',
    'I think this means that, as individual users, we don’t need to feel any guilt at all for the energy consumed by the vast majority of our prompts. The impact is likely neglible compared to driving a car down the street or maybe even watching a video on YouTube.\nLikewise, training. DeepSeek v3 training for less than $6m is a fantastic sign that training costs can and should continue to drop.\nFor less efficient models I find it useful to compare their energy usage to commercial flights. The largest Llama 3 model cost about the same as a single digit number of fully loaded passenger flights from New York to London. That’s certainly not nothing, but once trained that model can be used by millions of people at no extra training cost.',
    'Large Language Models\nThey’re actually quite easy to build\nYou can run LLMs on your own devices\nHobbyists can build their own fine-tuned models\nWe don’t yet know how to build GPT-4\nVibes Based Development\nLLMs are really smart, and also really, really dumb\nGullibility is the biggest unsolved problem\nCode may be the best application\nThe ethics of this space remain diabolically complex\nMy blog in 2023',
]
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   | 0.75       |
| cosine_accuracy@3   | 0.9583     |
| cosine_accuracy@5   | 1.0        |
| cosine_accuracy@10  | 1.0        |
| cosine_precision@1  | 0.75       |
| cosine_precision@3  | 0.3194     |
| cosine_precision@5  | 0.2        |
| cosine_precision@10 | 0.1        |
| cosine_recall@1     | 0.75       |
| cosine_recall@3     | 0.9583     |
| cosine_recall@5     | 1.0        |
| cosine_recall@10    | 1.0        |
| **cosine_ndcg@10**  | **0.8885** |
| cosine_mrr@10       | 0.8507     |
| cosine_map@100      | 0.8507     |

<!--
## 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: 13 tokens</li><li>mean: 20.15 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 130.44 tokens</li><li>max: 204 tokens</li></ul> |
* Samples:
  | sentence_0                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:-----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What tools did the author describe in their writing about Claude Artifacts?</code> | <code>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.<br>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.<br>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.</code> |
  | <code>What is the name of the feature added by Mistral Chat in November?</code>          | <code>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.<br>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.<br>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.</code> |
  | <code>Why does the author find the term "agents" extremely frustrating?</code>           | <code>“Agents” still haven’t really happened yet<br>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.<br>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.</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   | 0.8968         |
| 2.0   | 32   | 0.8939         |
| 3.0   | 48   | 0.8786         |
| 3.125 | 50   | 0.8731         |
| 4.0   | 64   | 0.8702         |
| 5.0   | 80   | 0.8684         |
| 6.0   | 96   | 0.8713         |
| 6.25  | 100  | 0.8731         |
| 7.0   | 112  | 0.8885         |
| 8.0   | 128  | 0.8856         |
| 9.0   | 144  | 0.8885         |
| 9.375 | 150  | 0.8885         |
| 10.0  | 160  | 0.8885         |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

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

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

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

<!--
## 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.*
-->