This is a sentence-transformers model finetuned from jhu-clsp/ettin-encoder-32m on the natural-questions dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: jhu-clsp/ettin-encoder-32m
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Dataset:
- natural-questions
Language: en
License: mit

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ModernBertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
)

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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/ettin-encoder-32m-nq")
# Run inference
queries = [
    'where does the last name francisco come from',
]
documents = [
    'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
    'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
    'Times Square Times Square is a major commercial intersection, tourist destination, entertainment center and neighborhood in the Midtown Manhattan section of New York City at the junction of Broadway and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements, Times Square is sometimes referred to as "The Crossroads of the World",[2] "The Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the "heart of the world".[7] One of the world\'s busiest pedestrian areas,[8] it is also the hub of the Broadway Theater District[9] and a major center of the world\'s entertainment industry.[10] Times Square is one of the world\'s most visited tourist attractions, drawing an estimated 50 million visitors annually.[11] Approximately 330,000 people pass through Times Square daily,[12] many of them tourists,[13] while over 460,000 pedestrians walk through Times Square on its busiest days.[7]',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 384] [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7318, -0.1705,  0.1582]])

Evaluation

Metrics

Information Retrieval

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.26	0.46	0.38	0.38	0.56	0.2	0.24	0.3	0.74	0.26	0.22	0.38	0.3673
cosine_accuracy@3	0.38	0.7	0.64	0.5	0.7	0.4	0.3	0.54	0.86	0.48	0.56	0.5	0.8367
cosine_accuracy@5	0.62	0.74	0.72	0.52	0.7	0.62	0.4	0.56	0.98	0.54	0.68	0.58	0.9592
cosine_accuracy@10	0.68	0.8	0.78	0.6	0.72	0.72	0.52	0.66	0.98	0.62	0.8	0.62	0.9796
cosine_precision@1	0.26	0.46	0.38	0.38	0.56	0.2	0.24	0.3	0.74	0.26	0.22	0.38	0.3673
cosine_precision@3	0.1467	0.4267	0.2133	0.2	0.2867	0.1333	0.1933	0.18	0.3467	0.2133	0.1867	0.1733	0.4626
cosine_precision@5	0.14	0.36	0.148	0.144	0.176	0.124	0.168	0.112	0.244	0.168	0.136	0.124	0.449
cosine_precision@10	0.086	0.326	0.08	0.084	0.102	0.072	0.136	0.066	0.132	0.124	0.08	0.068	0.3551
cosine_recall@1	0.139	0.0531	0.38	0.1559	0.28	0.2	0.0202	0.29	0.654	0.0547	0.22	0.355	0.028
cosine_recall@3	0.1873	0.1082	0.62	0.2846	0.43	0.4	0.0515	0.51	0.822	0.1317	0.56	0.48	0.1006
cosine_recall@5	0.2923	0.1298	0.71	0.3144	0.44	0.62	0.0569	0.53	0.9427	0.1727	0.68	0.555	0.1566
cosine_recall@10	0.3523	0.2053	0.77	0.3961	0.51	0.72	0.0733	0.62	0.97	0.2537	0.8	0.59	0.2313
cosine_ndcg@10	0.2915	0.3964	0.5804	0.3343	0.4837	0.4397	0.1727	0.4598	0.8547	0.2409	0.508	0.4805	0.392
cosine_mrr@10	0.3802	0.5869	0.5219	0.4549	0.6262	0.3513	0.3	0.4222	0.8223	0.3863	0.4142	0.4571	0.601
cosine_map@100	0.2351	0.2684	0.5208	0.2725	0.4172	0.3624	0.0655	0.4152	0.8136	0.1808	0.4202	0.4484	0.2906

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator with these parameters:
json
1{ 2 "dataset_names": [ 3 "climatefever", 4 "dbpedia", 5 "fever", 6 "fiqa2018", 7 "hotpotqa", 8 "msmarco", 9 "nfcorpus", 10 "nq", 11 "quoraretrieval", 12 "scidocs", 13 "arguana", 14 "scifact", 15 "touche2020" 16 ], 17 "dataset_id": "sentence-transformers/NanoBEIR-en" 18}

Metric	Value
cosine_accuracy@1	0.3652
cosine_accuracy@3	0.569
cosine_accuracy@5	0.663
cosine_accuracy@10	0.7292
cosine_precision@1	0.3652
cosine_precision@3	0.2433
cosine_precision@5	0.1918
cosine_precision@10	0.1316
cosine_recall@1	0.2177
cosine_recall@3	0.3604
cosine_recall@5	0.4308
cosine_recall@10	0.4994
cosine_ndcg@10	0.4334
cosine_mrr@10	0.4865
cosine_map@100	0.3624

Training Details

Training Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 99,231 training samples
Columns: query and answer
Approximate statistics based on the first 100 samples:
query answer
type string string
modality text text
details
min: 10 tokens
mean: 12.59 tokens
max: 20 tokens
min: 25 tokens
mean: 137.11 tokens
max: 256 tokens

Samples:

query	answer
`who is required to report according to the hmda`	`Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5]`
`what is the definition of endoplasmic reticulum in biology`	Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 using elect...
`what does the ski mean in polish names`	`Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Evaluation Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 1,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 100 samples:
query answer
type string string
modality text text
details
min: 10 tokens
mean: 12.38 tokens
max: 21 tokens
min: 28 tokens
mean: 135.85 tokens
max: 256 tokens

Samples:

query	answer
`difference between russian blue and british blue cat`	Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.
`who played the little girl on mrs doubtfire`	`Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.`
`what year did the movie the sound of music come out`	The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false, 5 "directions": [ 6 "query_to_doc" 7 ], 8 "partition_mode": "joint", 9 "hardness_mode": null, 10 "hardness_strength": 0.0 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 128
num_train_epochs: 1
learning_rate: 2e-05
warmup_steps: 0.1
weight_decay: 0.01
bf16: True
per_device_eval_batch_size: 128
load_best_model_at_end: True
seed: 12
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 128
num_train_epochs: 1
max_steps: -1
learning_rate: 2e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 128
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 12
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
-1	-1	-	-	0.0363	0.1037	0.0575	0.0165	0.1019	0.0377	0.0254	0.0390	0.5263	0.0481	0.0714	0.1174	0.0370	0.0937
0.0013	1	4.5760	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0206	16	4.5077	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0412	32	4.1129	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0619	48	2.8395	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0825	64	1.6542	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1005	78	-	0.7129	0.2108	0.3464	0.5367	0.2633	0.4452	0.3854	0.1445	0.3854	0.8079	0.2103	0.4435	0.4621	0.3719	0.3857
0.1031	80	0.8147	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1237	96	0.5783	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1443	112	0.4297	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1649	128	0.4041	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1856	144	0.3973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2010	156	-	0.4034	0.2789	0.3912	0.5562	0.3016	0.4592	0.4622	0.1696	0.4216	0.8294	0.2304	0.4971	0.4584	0.3838	0.4184
0.2062	160	0.3813	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2268	176	0.3556	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2474	192	0.3225	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2680	208	0.2939	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2887	224	0.2833	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3015	234	-	0.3301	0.2745	0.3738	0.5697	0.3042	0.4675	0.4293	0.1746	0.4460	0.8375	0.2532	0.4993	0.4666	0.3941	0.4223
0.3093	240	0.2903	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3299	256	0.2575	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3505	272	0.2571	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3711	288	0.2450	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3918	304	0.2263	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4021	312	-	0.2777	0.2995	0.3741	0.5586	0.3064	0.4529	0.4529	0.1800	0.4351	0.8500	0.2498	0.5063	0.4669	0.3918	0.4249
0.4124	320	0.2897	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4330	336	0.2230	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4536	352	0.2332	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4742	368	0.2267	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4948	384	0.2091	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5026	390	-	0.2610	0.3008	0.3967	0.5717	0.3267	0.4791	0.4578	0.1892	0.4588	0.8435	0.2376	0.5088	0.4652	0.4012	0.4336
0.5155	400	0.2167	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5361	416	0.2310	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5567	432	0.1923	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5773	448	0.2155	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5979	464	0.2278	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6031	468	-	0.2452	0.2988	0.3916	0.5681	0.3208	0.4819	0.4346	0.1804	0.4500	0.8633	0.2461	0.5167	0.4704	0.4080	0.4331
0.6186	480	0.2180	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6392	496	0.2251	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6598	512	0.2084	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6804	528	0.2151	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7010	544	0.2226	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7036	546	-	0.2400	0.2811	0.3822	0.5647	0.3245	0.4670	0.4322	0.1787	0.4481	0.8507	0.2456	0.5080	0.4877	0.4004	0.4285
0.7216	560	0.1917	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7423	576	0.2255	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7629	592	0.2359	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7835	608	0.2250	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8041	624	0.2228	0.2283	0.2992	0.3855	0.5851	0.3303	0.4716	0.4443	0.1761	0.4624	0.8551	0.2446	0.5107	0.4838	0.3949	0.4341
0.8247	640	0.2062	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8454	656	0.2009	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8660	672	0.1844	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8866	688	0.1930	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9046	702	-	0.2240	0.2976	0.3969	0.5733	0.3284	0.4660	0.4496	0.1762	0.4605	0.8567	0.2404	0.5115	0.4783	0.3999	0.4335
0.9072	704	0.2017	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9278	720	0.1989	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9485	736	0.2157	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9691	752	0.1923	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9897	768	0.1778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0	776	-	0.2224	0.2931	0.3918	0.5757	0.335	0.4821	0.4468	0.1733	0.4607	0.8574	0.2414	0.5113	0.4831	0.3975	0.4346
-1	-1	-	-	0.2915	0.3964	0.5804	0.3343	0.4837	0.4397	0.1727	0.4598	0.8547	0.2409	0.5080	0.4805	0.3920	0.4334

The bold row denotes the saved checkpoint.

Training Time

Training: 3.9 minutes
Evaluation: 7.5 minutes
Total: 11.4 minutes

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.5.0.dev0
Transformers: 5.6.2
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0.dev0
Datasets: 4.8.4
Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

	query	answer
type	string	string
modality	text	text
details	min: 10 tokens mean: 12.59 tokens max: 20 tokens	min: 25 tokens mean: 137.11 tokens max: 256 tokens