metadata
base_model: Snowflake/snowflake-arctic-embed-l
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:400
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
What does it mean for a decision to not be considered arbitrary and
capricious according to the provided context?
sentences:
- >-
Aravind Srinivas, Founding Story and Journey of Perplexity, YOUTUBE, at
17:57 (Jan. 18, 2024),
https://www.youtube.com/watch?v=ygRVDIwheB4.
17 See, e.g., Avoiding Plagiarism Guide, APA Style 7th Edition (last
visited Aug. 30, 2024),
https://apastyle.apa.org/instructional-aids/avoiding-plagiarism.pdf.
18 See What is Perplexity?, supra note 1 (promoting Perplexity’s
“Reliable sources” with an
explanation that “[e]very answer is backed by citations from trusted
news outlets, academic papers,
and established blogs”).
19 Madhumita Murgia & Cristina Criddle, Perplexity’s popularity surges
as AI search start-up takes
on Google, THE FINANCIAL TIMES (Aug. 9, 2024),
https://www.ft.com/content/87af3340-2611-
4650-9ae3-036927e9f65c.
- >-
30
Serv. Comm'n, 43 Mass. App. Ct. 300, 303 (1997). A decision is not
arbitrary and capricious if
"reasonable minds could differ" on the proper outcome. See Kinchla v.
Board of Appeals of
Falmouth, 11 Mass. App. Ct. 927, 927 (1981).
In determining the appropriate definition of general words used in a
statute, the courts may
look to sources outside the statute such as "their use in other legal
contexts: and dictionary
definitions." See Commonwealth v. Correia, 17 Mass.App.Ct. 233, 235
(1983) “Arbitrary” is
defined as subject to individual will or judgment without restriction;
contingent solely upon one's
discretion… having unlimited power; uncontrolled or unrestricted by law;
despotic; tyrannical;
- |-
purpose of providing a substitute product.
Case 1:24-cv-07984 Document 1 Filed 10/21/24 Page 3 of 42
- source_sentence: What percentage of applicants were admitted to Stanford last year?
sentences:
- >-
to which RNH is currently applying are extremely competitive and the
admissions process for
admission into such schools is rigorous. These schools command an
extensive applicant pool of
high academic achievers with high test scores, grade point averages,
including grades of A’s and
B’s only. Stanford is one of the most competitive schools in the
country. Last year, 4% of the
applicant pool were admitted. Thousands of extremely well qualified,
who elsewhere would be
highly admissible, were denied. It is essential that any applicant have
the most competitive
transcript possible. A C+ is a red flag that will be noticed far more
quickly and glaringly than the
Case 1:24-cv-12437-WGY Document 8 Filed 10/08/24 Page 6 of 42
- >-
18
upon in affirming the decision through an appeal to exclude RNH and his
classmate from the NHS.
Id. at ¶145. At that time, Defendant Swanson and other Defendants knew
or should have known
that the District inducted at least seven students into NHS, who had
academic infractions on their
record, one of which was because of the prior use of AI. Id. at
¶146.
The “committee” that adjudicated selection for NHS this year did not
include teachers who
know and are familiar with RNH and his classmate. Id. at ¶147. This is
due to the then escalating
contract conflict with the Hingham Educators Association (“HEA”) where
HEA engaged in an
- >-
42
CERTIFICATE OF SERVICE
I, Peter S. Farrell, hereby certify that I served a copy of the
foregoing on all counsel of
record pursuant to Local Rule 5.4(c) by causing a copy of the same to be
electronically filed and
served through the CM/ECF filing system to:
Gareth W. Notis, Esquire
Morrison Mahoney LLP
250 Summer Street
Boston, MA 02210
[email protected]
______________________________
Peter S. Farrell
Case 1:24-cv-12437-WGY Document 8 Filed 10/08/24 Page 42 of 42
- source_sentence: What is the case number for the document filed on 10/08/24?
sentences:
- Case 1:24-cv-07984 Document 1 Filed 10/21/24 Page 19 of 42
- Case 1:24-cv-12437-WGY Document 8 Filed 10/08/24 Page 33 of 42
- >-
11 See, e.g., Elizabeth Lopatto, Perplexity’s Grand Theft AI, THE VERGE
(June 27, 2024),
https://www.theverge.com/2024/6/27/24187405/perplexity-ai-twitter-lie-plagiarism
(describing
Perplexity as a “rent-seeking middleman on high-quality sources” that
“starve[s] the primary
source of ad revenue”); Dhruv Mehrotra & Tim Marchman, Perplexity Is a
Bullsh*t Machine,
WIRED
(June
19,
2024),
https://www.wired.com/story/perplexity-is-a-bullshit-machine
(discussing Perplexity’s reliance on recent news articles for its
content as well as its tendency to
falsely attribute information) (asterisk added); Casey Newton, How to
Stop Perplexity and save
the web from bad AI, PLATFORMER (June 20, 2024),
https://www.platformer.news/how-to-stop-
- source_sentence: >-
How does Perplexity gather and compile information from authoritative
sources?
sentences:
- >-
utilize have been trained. To employ a RAG system, AI applications
typically utilize indexed
databases that house all the content from which the AI application will
retrieve specific information
to generate outputs for its users. The larger the index, the more
“answers” the AI application can
provide.
51.
In Perplexity’s words, it “scours the internet, gathering information
from
authoritative sources like articles, websites, and journals.”6 It then,
“compiles the most relevant
insights into a coherent, easy-to-understand answer” automatically
generated from those original
sources.7
52.
The assembling of authoritative sources for a RAG index is a distinct
process from
- >-
9
26.
Perplexity processes subscription purchases from customers in this State
and
District, transmits Plaintiffs’ copyrighted content to users in this
State and District, and has a
significant number of customers in this State and District.
27.
As a direct and proximate result of Perplexity’s unauthorized use
and/or
dissemination of Plaintiffs’ copyrighted works and trademarks in New
York and elsewhere,
Plaintiffs have lost and will continue to lose revenue and profits from
the market for content
licensing, subscribers, visitors, and users.
FACTUAL ALLEGATIONS
I.
Plaintiffs’ Robust Businesses and Copyrighted Works
28.
Dow Jones began in 1882 as a niche news agency in a Wall Street
basement,
- >-
1
UNITED STATES DISTRICT COURT
SOUTHERN DISTRICT OF NEW YORK
DOW JONES & COMPANY, INC.
and NYP HOLDINGS, INC.,
Plaintiffs,
v.
PERPLEXITY AI, INC.,
Defendant.
Civil Action No. 24-cv-7984
COMPLAINT
JURY TRIAL DEMANDED
Plaintiffs Dow Jones & Company, Inc. (“Dow Jones”) and NYP Holdings,
Inc. (“NYP
Holdings”) (collectively, “Plaintiffs”), by and through their attorneys,
Torridon Law PLLC, for
their Complaint, hereby allege against Defendant Perplexity AI, Inc.
(“Perplexity” or
“Defendant”), as follows:
NATURE OF THE ACTION
1.
Perplexity is a generative artificial intelligence company that claims
to provide its
- source_sentence: >-
What recent partnership did News Corp enter into regarding licensing
content for OpenAI's applications?
sentences:
- >-
integrity infractions. Plain and simple. It should not take the
Plaintiffs engaging counsel,
demanding information and forcing Hingham to investigate this matter to
reveal that selection for
NHS was a manipulated sham conducted by the Defendants, who at all times
relevant were state
actors.
C. The Student Will Suffer Irreparable Harm If The Injunction is Not
Granted
In order for the Plaintiffs to obtain injunctive relief, they must show
that they are "likely to
suffer irreparable injury before a decision is rendered on the merits."
See Philips Elecs. N. Am.
Corp. v. Halperin, 2000 Mass. Super LEXIS 574 citing Sierra Club v.
Larson, 769 F. Supp. 420,
- >-
licensing initiatives abound.”3 For example, News Corp recently
partnered with OpenAI to license
its content for certain uses in OpenAI’s applications. OpenAI users will
have the benefit of
accessing Plaintiffs’ content, whether quoted or summarized by OpenAI.
This cooperative
relationship will allow OpenAI and Plaintiffs to experiment with new
product experiences and
revenue models.
15.
Generative AI technology can be developed in two ways. It can be
developed
legally by recognizing the legitimate rights of copyright holders and by
including in the AI business
model the legitimate costs and benefits of licensing the copyrighted
material, or it can be developed
- >-
ban or prohibition on the use of AI by students. The Defendants were not
trained on any policies
or procedures for use of AI alone, never mind what they were “able to
do” to students who used
it. The entire purpose behind having such policies and procedures in
place is to ensure notice,
equity, fairness and to be sure: a level playing field for all.
Making matters worse, there exists
no adequate procedures and policies for the induction of an applicant
into NHS when compared to
other members who are inducted despite the same or similar infractions.
This is a denial of student
rights of the highest order.
In the case here, RNH was disciplined on an ad hoc and on-going basis
over more than six
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: 0.6875
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8541666666666666
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9583333333333334
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9791666666666666
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6875
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28472222222222215
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19166666666666665
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09791666666666665
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6875
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8541666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9583333333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9791666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8280840444145441
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7793650793650793
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7812590187590187
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.6875
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8541666666666666
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9583333333333334
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9791666666666666
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6875
name: Dot Precision@1
- type: dot_precision@3
value: 0.28472222222222215
name: Dot Precision@3
- type: dot_precision@5
value: 0.19166666666666665
name: Dot Precision@5
- type: dot_precision@10
value: 0.09791666666666665
name: Dot Precision@10
- type: dot_recall@1
value: 0.6875
name: Dot Recall@1
- type: dot_recall@3
value: 0.8541666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.9583333333333334
name: Dot Recall@5
- type: dot_recall@10
value: 0.9791666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8280840444145441
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7793650793650793
name: Dot Mrr@10
- type: dot_map@100
value: 0.7812590187590187
name: Dot Map@100
SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a sentence-transformers model finetuned from 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
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 1024 tokens
- Similarity Function: Cosine Similarity
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
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:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("llm-wizard/legal-ft-arctic-l")
# Run inference
sentences = [
"What recent partnership did News Corp enter into regarding licensing content for OpenAI's applications?",
'licensing initiatives abound.”3 For example, News Corp recently partnered with OpenAI to license \nits content for certain uses in OpenAI’s applications. OpenAI users will have the benefit of \naccessing Plaintiffs’ content, whether quoted or summarized by OpenAI. This cooperative \nrelationship will allow OpenAI and Plaintiffs to experiment with new product experiences and \nrevenue models. \n15. \nGenerative AI technology can be developed in two ways. It can be developed \nlegally by recognizing the legitimate rights of copyright holders and by including in the AI business \nmodel the legitimate costs and benefits of licensing the copyrighted material, or it can be developed',
'integrity infractions. Plain and simple. It should not take the Plaintiffs engaging counsel, \ndemanding information and forcing Hingham to investigate this matter to reveal that selection for \nNHS was a manipulated sham conducted by the Defendants, who at all times relevant were state \nactors. \nC. The Student Will Suffer Irreparable Harm If The Injunction is Not Granted \nIn order for the Plaintiffs to obtain injunctive relief, they must show that they are "likely to \nsuffer irreparable injury before a decision is rendered on the merits." See Philips Elecs. N. Am. \nCorp. v. Halperin, 2000 Mass. Super LEXIS 574 citing Sierra Club v. Larson, 769 F. Supp. 420,',
]
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]
Evaluation
Metrics
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
Metric | Value |
---|---|
cosine_accuracy@1 | 0.6875 |
cosine_accuracy@3 | 0.8542 |
cosine_accuracy@5 | 0.9583 |
cosine_accuracy@10 | 0.9792 |
cosine_precision@1 | 0.6875 |
cosine_precision@3 | 0.2847 |
cosine_precision@5 | 0.1917 |
cosine_precision@10 | 0.0979 |
cosine_recall@1 | 0.6875 |
cosine_recall@3 | 0.8542 |
cosine_recall@5 | 0.9583 |
cosine_recall@10 | 0.9792 |
cosine_ndcg@10 | 0.8281 |
cosine_mrr@10 | 0.7794 |
cosine_map@100 | 0.7813 |
dot_accuracy@1 | 0.6875 |
dot_accuracy@3 | 0.8542 |
dot_accuracy@5 | 0.9583 |
dot_accuracy@10 | 0.9792 |
dot_precision@1 | 0.6875 |
dot_precision@3 | 0.2847 |
dot_precision@5 | 0.1917 |
dot_precision@10 | 0.0979 |
dot_recall@1 | 0.6875 |
dot_recall@3 | 0.8542 |
dot_recall@5 | 0.9583 |
dot_recall@10 | 0.9792 |
dot_ndcg@10 | 0.8281 |
dot_mrr@10 | 0.7794 |
dot_map@100 | 0.7813 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 400 training samples
- Columns:
sentence_0
andsentence_1
- Approximate statistics based on the first 400 samples:
sentence_0 sentence_1 type string string details - min: 10 tokens
- mean: 20.73 tokens
- max: 34 tokens
- min: 25 tokens
- mean: 140.37 tokens
- max: 260 tokens
- Samples:
sentence_0 sentence_1 How does Perplexity's business model differ from that of traditional search engines?
11.
Perplexity’s business is fundamentally distinct from that of traditional search
engines that also copy a vast amount of content into their indices but do so merely to provide links
to the originating sites. In its traditional form, a search engine is a tool for discovery, pointing
searchers to websites such as the pages of The Wall Street Journal or the New York Post, where the
users can click to find the information and answers they seek. Those clicks in turn provide revenue
for content producers. In part because traditional search engines that simply provide hyperlinks
promote merely the discovery of copyrighted content, and not its substitution (and commercialWhat role do clicks on traditional search engines play in the revenue generation for content producers?
11.
Perplexity’s business is fundamentally distinct from that of traditional search
engines that also copy a vast amount of content into their indices but do so merely to provide links
to the originating sites. In its traditional form, a search engine is a tool for discovery, pointing
searchers to websites such as the pages of The Wall Street Journal or the New York Post, where the
users can click to find the information and answers they seek. Those clicks in turn provide revenue
for content producers. In part because traditional search engines that simply provide hyperlinks
promote merely the discovery of copyrighted content, and not its substitution (and commercialWho were the founders of Dow Jones?
founded by reporters Charles Dow, Edward Jones, and Charles Bergstresser. Publishing the first
edition of The Wall Street Journal in July 1889, Dow Jones has now expanded into a worldwide
news powerhouse. It creates and distributes some of the most widely recognized and reputable
publications in the news industry, including, in addition to The Wall Street Journal, Dow Jones
Newswires, MarketWatch, Financial News, and Barron’s.
29.
Dow Jones is a trusted source of accurate, original news stories, data and analytics,
and financial and business insight for millions of customers across the country and around the
world.
30.
A recipient of 39 Pulitzer Prizes, the award-winning newsroom at The Wall Street - Loss:
MatryoshkaLoss
with these parameters:{ "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
: stepsper_device_train_batch_size
: 10per_device_eval_batch_size
: 10num_train_epochs
: 10multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 10per_device_eval_batch_size
: 10per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 10max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseeval_use_gather_object
: Falsebatch_sampler
: batch_samplermulti_dataset_batch_sampler
: round_robin
Training Logs
Epoch | Step | cosine_map@100 |
---|---|---|
1.0 | 40 | 0.7519 |
1.25 | 50 | 0.8072 |
2.0 | 80 | 0.7892 |
2.5 | 100 | 0.7949 |
3.0 | 120 | 0.7850 |
3.75 | 150 | 0.7537 |
4.0 | 160 | 0.7905 |
5.0 | 200 | 0.7650 |
6.0 | 240 | 0.7860 |
6.25 | 250 | 0.7806 |
7.0 | 280 | 0.7819 |
7.5 | 300 | 0.7820 |
8.0 | 320 | 0.7820 |
8.75 | 350 | 0.7821 |
9.0 | 360 | 0.7823 |
10.0 | 400 | 0.7813 |
Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.2.1
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.2
- Tokenizers: 0.19.1
Citation
BibTeX
Sentence Transformers
@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
@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
@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}
}