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AlexWortega
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qwen_emb_600_best_21.11

Sentence Similarity
sentence-transformers
Safetensors
qwen2
feature-extraction
Generated from Trainer
dataset_size:1077240
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
qwen_emb_600_best_21.11 / README.md
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---
base_model: Qwen/Qwen2.5-0.5B-Instruct
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1077240
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: When was the frigate the USS Alliance built?
sentences:
- 'Middle Stone Age
The Middle Stone Age (or MSA) was a period of African prehistory between the Early
Stone Age and the Later Stone Age. It is generally considered to have begun around
280,000 years ago and ended around 50–25,000 years ago.[1] The beginnings of particular
MSA stone tools have their origins as far back as 550–500,000 years ago and as
such some researchers consider this to be the beginnings of the MSA.[2] The MSA
is often mistakenly understood to be synonymous with the Middle Paleolithic of
Europe, especially due to their roughly contemporaneous time span, however, the
Middle Paleolithic of Europe represents an entirely different hominin population,
Homo neanderthalensis, than the MSA of Africa, which did not have Neanderthal
populations. Additionally, current archaeological research in Africa has yielded
much evidence to suggest that modern human behavior and cognition was beginning
to develop much earlier in Africa during the MSA than it was in Europe during
the Middle Paleolithic.[3] The MSA is associated with both anatomically modern
humans (Homo sapiens) as well as archaic Homo sapiens, sometimes referred to as
Homo helmei. Early physical evidence comes from the Gademotta Formation in Ethiopia,
the Kapthurin Formation in Kenya and Kathu Pan in South Africa.[2]'
- 'USS Alliance (1778)
Originally named Hancock, she was laid down in 1777 on the Merrimack River at
Amesbury, Massachusetts, by the partners and cousins, William and James K. Hackett,
launched on 28 April 1778, and renamed Alliance on 29 May 1778 by resolution of
the Continental Congress. Her first commanding officer was Capt. Pierre Landais,
a former officer of the French Navy who had come to the New World hoping to become
a naval counterpart of Lafayette. The frigate''s first captain was widely accepted
as such in America. Massachusetts made him an honorary citizen and the Continental
Congress gave him command of Alliance, thought to be the finest warship built
to that date on the western side of the Atlantic.'
- 'USS Frigate Bird (AMS-191)
The second ship in the Navy to be named "Frigate Bird", she was laid down 20 July
1953, as AMS-191; launched 24 October 1953, by Quincy Adams Yacht Yard, Inc.,
Quincy, Massachusetts; sponsored by Mrs. Matthew Gushing; and commissioned 13
January 1955, Lieutenant (jg) George B. Shick, Jr., in command. She was reclassified
MSC-191 on 7 February 1955.'
- source_sentence: How many bananas do Americans consume each year?
sentences:
- 'Dust Bowl
The Dust Bowl was a period of severe dust storms that greatly damaged the ecology
and agriculture of the American and Canadian prairies during the 1930s; severe
drought and a failure to apply dryland farming methods to prevent the aeolian
processes (wind erosion) caused the phenomenon.[1][2] The drought came in three
waves, 1934, 1936, and 1939–1940, but some regions of the high plains experienced
drought conditions for as many as eight years.[3] With insufficient understanding
of the ecology of the plains, farmers had conducted extensive deep plowing of
the virgin topsoil of the Great Plains during the previous decade; this had displaced
the native, deep-rooted grasses that normally trapped soil and moisture even during
periods of drought and high winds. The rapid mechanization of farm equipment,
especially small gasoline tractors, and widespread use of the combine harvester
contributed to farmers'' decisions to convert arid grassland (much of which received
no more than 10inches (~250mm) of precipitation per year) to cultivated cropland.[4]'
- 'Banana production in the United States
Commercial banana production in the United States is relatively limited in scale
and economic impact. While Americans eat 26 pounds (12kg) of bananas per person
per year, the vast majority of the fruit is imported from other countries, chiefly
Central and South America, where the US has previously occupied areas containing
banana plantations, and controlled the importation of bananas via various fruit
companies, such as Dole and Chiquita. [1]'
- 'Spinach in the United States
Per capita spinach consumption is greatest in the Northeast and Western US. About
80% of fresh-market spinach is purchased at retail and consumed at home, while
91% of processed spinach is consumed at home. Per capita spinach use is strongest
among Asians, highest among women 40 and older, and weakest among teenage girls.'
- source_sentence: Which rapper has the most Grammy wins?
sentences:
- 'Rhineland
The Rhineland (German: Rheinland, French: Rhénanie, Latinised name: Rhenania)
is the name used for a loosely defined area of Western Germany along the Rhine,
chiefly its middle section.'
- 'Grammy Award for Best Rap Album
6 wins Eminem 4 wins Kanye West 2 wins Outkast Kendrick Lamar'
- 'Latin Grammy Award records
René Pérez Joglar "Residente" and Eduardo Cabra "Visitante" with 24 awards, have
won more than any other male artist.'
- source_sentence: Is Iodine radioactive?
sentences:
- 'Double-decker bus
With the exception of coaches, double-decker buses are uncommon in the United
States. Many private operators, such as Megabus, run by Coach USA, employs double-decker
buses on its busier intercity routes.'
- 'Iodine-123
I is the most suitable isotope of iodine for the diagnostic study of thyroid diseases.
The half-life of approximately 13.13 hours is ideal for the 24-hour iodine uptake
test and I has other advantages for diagnostic imaging thyroid tissue and thyroid
cancer metastasis. The energy of the photon, 159 keV, is ideal for the NaI (sodium
iodide) crystal detector of current gamma cameras and also for the pinhole collimators.
It has much greater photon flux than I. It gives approximately 20 times the counting
rate of I for the same administered dose. The radiation burden to the thyroid
is far less (1%) than that of I. Moreover, scanning a thyroid remnant or metastasis
with I does not cause "stunning" of the tissue (with loss of uptake), because
of the low radiation burden of this isotope. (For the same reasons, I is never
used for thyroid cancer or Graves disease "treatment", and this role is reserved
for I.)'
- 'Iodine-131
Iodine-131 (131I) is an important radioisotope of iodine discovered by Glenn Seaborg
and John Livingood in 1938 at the University of California, Berkeley.[1] It has
a radioactive decay half-life of about eight days. It is associated with nuclear
energy, medical diagnostic and treatment procedures, and natural gas production.
It also plays a major role as a radioactive isotope present in nuclear fission
products, and was a significant contributor to the health hazards from open-air
atomic bomb testing in the 1950s, and from the Chernobyl disaster, as well as
being a large fraction of the contamination hazard in the first weeks in the Fukushima
nuclear crisis. This is because I-131 is a major fission product of uranium and
plutonium, comprising nearly 3% of the total products of fission (by weight).
See fission product yield for a comparison with other radioactive fission products.
I-131 is also a major fission product of uranium-233, produced from thorium.'
- source_sentence: Is a birth certificate a form of ID?
sentences:
- 'Identity documents in the United States
The birth certificate is the initial identification document issued to parents
shortly after the birth of their child. The birth certificate is typically issued
by local governments, usually the city or county where a child is born. It is
an important record, often called a "feeder document," because it establishes
U.S. citizenship through birthright citizenship, which is then used to obtain,
or is the basis for, all other identity documents.[2] By itself, the birth certificate
is usually only considered proof of citizenship but not proof of identity, since
it is issued without a photograph at birth, containing no identifying features.
A birth certificate is normally produced along with proof of identity, such as
a driver''s license or the testimony of a third party (such as a parent), to establish
identity or entitlement to a service.'
- 'Identity document
In Canada, different forms of identification documentation are used, but there
is no de jure national identity card. The Canadian passport is issued by the federal
(national) government, and the provinces and territories issue various documents
which can be used for identification purposes. The most commonly used forms of
identification within Canada are the health card and driver''s licence issued
by provincial and territorial governments. The widespread usage of these two documents
for identification purposes has made them de facto identity cards.'
- 'International Ladies'' Garment Workers'' Union
The ILGWU was founded on June 3, 1900[2] in New York City by seven local unions,
with a few thousand members between them. The union grew rapidly in the next few
years but began to stagnate as the conservative leadership favored the interests
of skilled workers, such as cutters. This did not sit well with the majority of
immigrant workers, particularly Jewish workers with a background in Bundist activities
in Tsarist Russia, or with Polish and Italian workers, many of whom had strong
socialist and anarchist leanings.'
model-index:
- name: SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.4
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.052000000000000005
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12833333333333333
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.145
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.19333333333333333
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.16942887258019743
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.24757142857142853
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1355707100998398
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.6
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.72
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.88
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4159999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.34
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05876239296513622
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12033972099857915
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1722048755781874
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23956661282775613
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43285369033010385
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6865000000000001
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.26928358503732847
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.66
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18666666666666668
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08599999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.63
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.79
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5142272491063538
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4404126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4252817429020436
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.5
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.096
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.062
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0968888888888889
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1971111111111111
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.274968253968254
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3351904761904762
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.23653831229880148
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2642460317460318
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.19230802555548754
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.46
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.56
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23333333333333336
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14800000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08999999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.23
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.35
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.37
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.45
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4053306177663136
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.512126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.34910258985509013
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.34
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.34
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.44
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.068
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.044000000000000004
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.34
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.34
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.44
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3192297885891097
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2824126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2997920687164519
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.52
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.24
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.18
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.012493999137489852
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.03666681098754983
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.05620504095265686
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.06687360120479266
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.22113733731375582
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3847142857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08151129221772184
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10000000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.27
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.37
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.47
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.53
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.397781398000215
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3704682539682539
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.36349470659883915
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.84
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.92
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.92
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.98
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.84
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.236
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.128
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7406666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8686666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8859999999999999
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.956
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8890148621063009
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8828571428571428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8629278499278499
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.62
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14800000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.118
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.053000000000000005
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.09300000000000001
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.24066666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21606068741193518
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35269047619047617
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.15911734324501478
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13999999999999999
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.42
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.52
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.37083936956670244
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2719126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.28287447040897273
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10400000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.066
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.235
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.355
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.465
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.58
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.39672106927390305
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35040476190476183
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3458399624509464
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.4489795918367347
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7142857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7959183673469388
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4489795918367347
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3945578231292517
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.3673469387755102
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.3346938775510204
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.0346506610646935
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.09241187721057745
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.13780926782570618
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.23921204048524203
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.37383535385863614
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6103984450923227
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3108247074769811
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.33761381475667196
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.49032967032967034
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5627629513343799
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6676609105180534
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.33761381475667196
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2139403453689168
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17194976452119312
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.12513029827315542
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18011250836329812
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.29934842463906286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3551682644865234
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.441603286977559
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3802306621694099
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.43513199272382946
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3136868503455821
name: Cosine Map@100
---
# SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It maps sentences & paragraphs to a 896-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:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) <!-- at revision 7ae557604adf67be50417f59c2c2f167def9a775 -->
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 896 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': 1024, 'do_lower_case': False}) with Transformer model: Qwen2Model
(1): Pooling({'word_embedding_dimension': 896, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("AlexWortega/qwen_emb_600_best_21.11")
# Run inference
sentences = [
'Is a birth certificate a form of ID?',
'Identity documents in the United States\nThe birth certificate is the initial identification document issued to parents shortly after the birth of their child. The birth certificate is typically issued by local governments, usually the city or county where a child is born. It is an important record, often called a "feeder document," because it establishes U.S. citizenship through birthright citizenship, which is then used to obtain, or is the basis for, all other identity documents.[2] By itself, the birth certificate is usually only considered proof of citizenship but not proof of identity, since it is issued without a photograph at birth, containing no identifying features. A birth certificate is normally produced along with proof of identity, such as a driver\'s license or the testimony of a third party (such as a parent), to establish identity or entitlement to a service.',
"Identity document\nIn Canada, different forms of identification documentation are used, but there is no de jure national identity card. The Canadian passport is issued by the federal (national) government, and the provinces and territories issue various documents which can be used for identification purposes. The most commonly used forms of identification within Canada are the health card and driver's licence issued by provincial and territorial governments. The widespread usage of these two documents for identification purposes has made them de facto identity cards.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]
# 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
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1 | 0.18 | 0.6 | 0.28 | 0.18 | 0.46 | 0.22 | 0.32 | 0.28 | 0.84 | 0.26 | 0.06 | 0.26 | 0.449 |
| cosine_accuracy@3 | 0.28 | 0.72 | 0.56 | 0.28 | 0.56 | 0.34 | 0.42 | 0.4 | 0.92 | 0.38 | 0.42 | 0.38 | 0.7143 |
| cosine_accuracy@5 | 0.32 | 0.82 | 0.66 | 0.4 | 0.6 | 0.34 | 0.46 | 0.5 | 0.92 | 0.48 | 0.52 | 0.5 | 0.7959 |
| cosine_accuracy@10 | 0.4 | 0.88 | 0.82 | 0.5 | 0.68 | 0.44 | 0.52 | 0.58 | 0.98 | 0.62 | 0.68 | 0.6 | 0.9796 |
| cosine_precision@1 | 0.18 | 0.6 | 0.28 | 0.18 | 0.46 | 0.22 | 0.32 | 0.28 | 0.84 | 0.26 | 0.06 | 0.26 | 0.449 |
| cosine_precision@3 | 0.1 | 0.4333 | 0.1867 | 0.1133 | 0.2333 | 0.1133 | 0.28 | 0.1333 | 0.3667 | 0.1533 | 0.14 | 0.1333 | 0.3946 |
| cosine_precision@5 | 0.072 | 0.416 | 0.136 | 0.096 | 0.148 | 0.068 | 0.24 | 0.1 | 0.236 | 0.148 | 0.104 | 0.104 | 0.3673 |
| cosine_precision@10 | 0.052 | 0.34 | 0.086 | 0.062 | 0.09 | 0.044 | 0.18 | 0.058 | 0.128 | 0.118 | 0.068 | 0.066 | 0.3347 |
| cosine_recall@1 | 0.07 | 0.0588 | 0.26 | 0.0969 | 0.23 | 0.22 | 0.0125 | 0.27 | 0.7407 | 0.053 | 0.06 | 0.235 | 0.0347 |
| cosine_recall@3 | 0.1283 | 0.1203 | 0.52 | 0.1971 | 0.35 | 0.34 | 0.0367 | 0.37 | 0.8687 | 0.093 | 0.42 | 0.355 | 0.0924 |
| cosine_recall@5 | 0.145 | 0.1722 | 0.63 | 0.275 | 0.37 | 0.34 | 0.0562 | 0.47 | 0.886 | 0.15 | 0.52 | 0.465 | 0.1378 |
| cosine_recall@10 | 0.1933 | 0.2396 | 0.79 | 0.3352 | 0.45 | 0.44 | 0.0669 | 0.53 | 0.956 | 0.2407 | 0.68 | 0.58 | 0.2392 |
| **cosine_ndcg@10** | **0.1694** | **0.4329** | **0.5142** | **0.2365** | **0.4053** | **0.3192** | **0.2211** | **0.3978** | **0.889** | **0.2161** | **0.3708** | **0.3967** | **0.3738** |
| cosine_mrr@10 | 0.2476 | 0.6865 | 0.4404 | 0.2642 | 0.5121 | 0.2824 | 0.3847 | 0.3705 | 0.8829 | 0.3527 | 0.2719 | 0.3504 | 0.6104 |
| cosine_map@100 | 0.1356 | 0.2693 | 0.4253 | 0.1923 | 0.3491 | 0.2998 | 0.0815 | 0.3635 | 0.8629 | 0.1591 | 0.2829 | 0.3458 | 0.3108 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3376 |
| cosine_accuracy@3 | 0.4903 |
| cosine_accuracy@5 | 0.5628 |
| cosine_accuracy@10 | 0.6677 |
| cosine_precision@1 | 0.3376 |
| cosine_precision@3 | 0.2139 |
| cosine_precision@5 | 0.1719 |
| cosine_precision@10 | 0.1251 |
| cosine_recall@1 | 0.1801 |
| cosine_recall@3 | 0.2993 |
| cosine_recall@5 | 0.3552 |
| cosine_recall@10 | 0.4416 |
| **cosine_ndcg@10** | **0.3802** |
| cosine_mrr@10 | 0.4351 |
| cosine_map@100 | 0.3137 |
<!--
## 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: 1,077,240 training samples
* Columns: <code>query</code>, <code>response</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | response | negative |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.76 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 141.88 tokens</li><li>max: 532 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 134.02 tokens</li><li>max: 472 tokens</li></ul> |
* Samples:
| query | response | negative |
|:--------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Was there a year 0?</code> | <code>Year zero<br>Year zero does not exist in the anno Domini system usually used to number years in the Gregorian calendar and in its predecessor, the Julian calendar. In this system, the year 1 BC is followed by AD 1. However, there is a year zero in astronomical year numbering (where it coincides with the Julian year 1 BC) and in ISO 8601:2004 (where it coincides with the Gregorian year 1 BC) as well as in all Buddhist and Hindu calendars.</code> | <code>504<br>Year 504 (DIV) was a leap year starting on Thursday (link will display the full calendar) of the Julian calendar. At the time, it was known as the Year of the Consulship of Nicomachus without colleague (or, less frequently, year 1257 "Ab urbe condita"). The denomination 504 for this year has been used since the early medieval period, when the Anno Domini calendar era became the prevalent method in Europe for naming years.</code> |
| <code>When is the dialectical method used?</code> | <code>Dialectic<br>Dialectic or dialectics (Greek: διαλεκτική, dialektikḗ; related to dialogue), also known as the dialectical method, is at base a discourse between two or more people holding different points of view about a subject but wishing to establish the truth through reasoned arguments. Dialectic resembles debate, but the concept excludes subjective elements such as emotional appeal and the modern pejorative sense of rhetoric.[1][2] Dialectic may be contrasted with the didactic method, wherein one side of the conversation teaches the other. Dialectic is alternatively known as minor logic, as opposed to major logic or critique.</code> | <code>Derek Bentley case<br>Another factor in the posthumous defence was that a "confession" recorded by Bentley, which was claimed by the prosecution to be a "verbatim record of dictated monologue", was shown by forensic linguistics methods to have been largely edited by policemen. Linguist Malcolm Coulthard showed that certain patterns, such as the frequency of the word "then" and the grammatical use of "then" after the grammatical subject ("I then" rather than "then I"), were not consistent with Bentley's use of language (his idiolect), as evidenced in court testimony. These patterns fit better the recorded testimony of the policemen involved. This is one of the earliest uses of forensic linguistics on record.</code> |
| <code>What do Grasshoppers eat?</code> | <code>Grasshopper<br>Grasshoppers are plant-eaters, with a few species at times becoming serious pests of cereals, vegetables and pasture, especially when they swarm in their millions as locusts and destroy crops over wide areas. They protect themselves from predators by camouflage; when detected, many species attempt to startle the predator with a brilliantly-coloured wing-flash while jumping and (if adult) launching themselves into the air, usually flying for only a short distance. Other species such as the rainbow grasshopper have warning coloration which deters predators. Grasshoppers are affected by parasites and various diseases, and many predatory creatures feed on both nymphs and adults. The eggs are the subject of attack by parasitoids and predators.</code> | <code>Groundhog<br>Very often the dens of groundhogs provide homes for other animals including skunks, red foxes, and cottontail rabbits. The fox and skunk feed upon field mice, grasshoppers, beetles and other creatures that destroy farm crops. In aiding these animals, the groundhog indirectly helps the farmer. In addition to providing homes for itself and other animals, the groundhog aids in soil improvement by bringing subsoil to the surface. The groundhog is also a valuable game animal and is considered a difficult sport when hunted in a fair manner. In some parts of Appalachia, they are eaten.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `gradient_accumulation_steps`: 8
- `learning_rate`: 0.0001
- `max_grad_norm`: 0.01
- `num_train_epochs`: 2
- `warmup_ratio`: 0.4
- `bf16`: True
- `dataloader_num_workers`: 8
- `batch_sampler`: no_duplicates
#### 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`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 0.01
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.4
- `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`: True
- `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`: True
- `dataloader_num_workers`: 8
- `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`: False
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training 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 |
|:------:|:----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0002 | 2 | 1.8808 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0005 | 4 | 1.9239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0007 | 6 | 2.0324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0010 | 8 | 2.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0012 | 10 | 2.0336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0014 | 12 | 1.9943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0017 | 14 | 1.971 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0019 | 16 | 1.9206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0021 | 18 | 1.8157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0024 | 20 | 1.8605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0026 | 22 | 1.862 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0029 | 24 | 1.9313 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0031 | 26 | 1.8326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0033 | 28 | 1.9208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0036 | 30 | 2.4718 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0038 | 32 | 2.4819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0040 | 34 | 2.4956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0043 | 36 | 2.4335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0045 | 38 | 2.4694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0048 | 40 | 2.5719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0050 | 42 | 2.4666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0052 | 44 | 2.4919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0055 | 46 | 2.4179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0057 | 48 | 2.4022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0059 | 50 | 2.39 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0062 | 52 | 2.4682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0064 | 54 | 2.3442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0067 | 56 | 2.3157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0069 | 58 | 2.2665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0071 | 60 | 2.2969 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0074 | 62 | 2.1652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0076 | 64 | 2.1243 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0078 | 66 | 2.0499 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0081 | 68 | 2.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0083 | 70 | 1.8372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0086 | 72 | 1.6257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0088 | 74 | 1.6398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0090 | 76 | 1.4927 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0093 | 78 | 1.3491 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0095 | 80 | 1.3303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0097 | 82 | 1.3846 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0100 | 84 | 1.2647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0102 | 86 | 1.1579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0105 | 88 | 1.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0107 | 90 | 0.9201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0109 | 92 | 0.8631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0112 | 94 | 0.7801 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0114 | 96 | 0.7813 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0116 | 98 | 0.7898 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0119 | 100 | 0.722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0121 | 102 | 0.7595 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0124 | 104 | 0.6245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0126 | 106 | 0.6036 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0128 | 108 | 0.7248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0131 | 110 | 0.637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0133 | 112 | 0.6205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0135 | 114 | 0.5956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0138 | 116 | 0.6126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0140 | 118 | 0.547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0143 | 120 | 0.5414 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0145 | 122 | 0.4896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0147 | 124 | 0.5351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0150 | 126 | 0.5404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0152 | 128 | 0.4479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0154 | 130 | 1.1779 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0157 | 132 | 1.4533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0159 | 134 | 1.5042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0162 | 136 | 1.2167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0164 | 138 | 1.2484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0166 | 140 | 1.1236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0169 | 142 | 1.1729 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0171 | 144 | 1.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0173 | 146 | 1.0314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0176 | 148 | 0.9106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0178 | 150 | 0.8994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0181 | 152 | 0.9679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0183 | 154 | 0.8576 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0185 | 156 | 0.777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0188 | 158 | 0.8527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0190 | 160 | 0.864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0192 | 162 | 0.807 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0195 | 164 | 0.9083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0197 | 166 | 0.7705 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0200 | 168 | 0.7179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0202 | 170 | 0.7485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0204 | 172 | 0.7198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0207 | 174 | 0.7712 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0209 | 176 | 0.82 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0212 | 178 | 0.7744 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0214 | 180 | 0.7668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0216 | 182 | 0.6501 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0219 | 184 | 0.6327 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0221 | 186 | 0.752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0223 | 188 | 0.6204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0226 | 190 | 0.6258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0228 | 192 | 0.607 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0231 | 194 | 0.5688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0233 | 196 | 0.6831 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0235 | 198 | 0.5653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0238 | 200 | 0.5966 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0240 | 202 | 0.5798 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0242 | 204 | 0.5991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0245 | 206 | 0.5856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0247 | 208 | 0.5935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0250 | 210 | 0.5624 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0252 | 212 | 0.6188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0254 | 214 | 0.5497 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0257 | 216 | 0.582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0259 | 218 | 0.5912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0261 | 220 | 0.4818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0264 | 222 | 0.5686 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0266 | 224 | 0.5174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0269 | 226 | 0.523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0271 | 228 | 0.5337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0273 | 230 | 0.5253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0276 | 232 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0278 | 234 | 0.5351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0280 | 236 | 0.5202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0283 | 238 | 0.4611 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0285 | 240 | 0.4509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0288 | 242 | 0.5217 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0290 | 244 | 0.5256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0292 | 246 | 0.5529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0295 | 248 | 0.4944 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0297 | 250 | 0.568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0299 | 252 | 0.5024 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0302 | 254 | 0.5094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0304 | 256 | 0.5057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0307 | 258 | 0.5424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0309 | 260 | 0.6485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0311 | 262 | 0.4823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0314 | 264 | 0.475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0316 | 266 | 0.3753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0318 | 268 | 0.5117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0321 | 270 | 0.4067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0323 | 272 | 0.4706 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0326 | 274 | 0.4099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0328 | 276 | 0.4251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0330 | 278 | 0.4392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0333 | 280 | 0.5373 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0335 | 282 | 0.4259 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0337 | 284 | 0.4227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0340 | 286 | 0.4774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0342 | 288 | 0.4878 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0345 | 290 | 0.5619 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0347 | 292 | 0.5061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0349 | 294 | 0.5434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0352 | 296 | 0.5115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0354 | 298 | 0.4281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0356 | 300 | 0.4287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0359 | 302 | 0.4864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0361 | 304 | 0.4724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0364 | 306 | 0.4607 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0366 | 308 | 0.3978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0368 | 310 | 0.4851 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0371 | 312 | 0.3466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0373 | 314 | 0.565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0375 | 316 | 0.4122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0378 | 318 | 0.3757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0380 | 320 | 0.4673 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0383 | 322 | 0.4358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0385 | 324 | 0.4423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0387 | 326 | 0.3754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0390 | 328 | 0.4358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0392 | 330 | 0.408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0394 | 332 | 0.3901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0397 | 334 | 0.4155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0399 | 336 | 0.379 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0402 | 338 | 0.373 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0404 | 340 | 0.2917 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0406 | 342 | 0.3755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0409 | 344 | 0.3262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0411 | 346 | 0.4975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0414 | 348 | 0.3469 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0416 | 350 | 0.3895 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0418 | 352 | 0.4424 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0421 | 354 | 0.3609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0423 | 356 | 0.434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0425 | 358 | 0.4474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0428 | 360 | 0.3514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0430 | 362 | 0.4029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0433 | 364 | 0.4438 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0435 | 366 | 0.4271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0437 | 368 | 0.3825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0440 | 370 | 0.3848 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0442 | 372 | 0.4088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0444 | 374 | 0.4188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0447 | 376 | 0.4333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0449 | 378 | 0.3784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0452 | 380 | 0.4509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0454 | 382 | 0.4084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0456 | 384 | 0.371 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0459 | 386 | 0.3965 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0461 | 388 | 0.375 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0463 | 390 | 0.4098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0466 | 392 | 0.4198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0468 | 394 | 0.3854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0471 | 396 | 0.3146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0473 | 398 | 0.3892 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0475 | 400 | 0.3295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0478 | 402 | 0.4124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0480 | 404 | 0.3039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0482 | 406 | 0.3353 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0485 | 408 | 0.4382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0487 | 410 | 0.4013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0490 | 412 | 0.3283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0492 | 414 | 0.4264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0494 | 416 | 0.4295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0497 | 418 | 0.3451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0499 | 420 | 0.2973 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0501 | 422 | 0.3734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0504 | 424 | 0.3992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0506 | 426 | 0.3234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0509 | 428 | 0.4007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0511 | 430 | 0.4446 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0513 | 432 | 0.282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0516 | 434 | 0.3922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0518 | 436 | 0.4224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0520 | 438 | 0.3362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0523 | 440 | 0.3461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0525 | 442 | 0.344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0528 | 444 | 0.4355 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0530 | 446 | 0.3443 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0532 | 448 | 0.4363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0535 | 450 | 0.3282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0537 | 452 | 0.3761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0539 | 454 | 0.3279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0542 | 456 | 0.3774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0544 | 458 | 0.3888 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0547 | 460 | 0.5149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0549 | 462 | 0.343 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0551 | 464 | 0.3943 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0554 | 466 | 0.366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0556 | 468 | 0.344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0558 | 470 | 0.3681 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0561 | 472 | 0.3041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0563 | 474 | 0.3857 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0566 | 476 | 0.3665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0568 | 478 | 0.3871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0570 | 480 | 0.4707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0573 | 482 | 0.4031 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0575 | 484 | 0.385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0577 | 486 | 0.2868 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0580 | 488 | 0.3637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0582 | 490 | 0.4484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0585 | 492 | 0.4984 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0587 | 494 | 0.3725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0589 | 496 | 0.3102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0592 | 498 | 0.3529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0594 | 500 | 0.3929 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0596 | 502 | 0.3012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0599 | 504 | 0.4137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0601 | 506 | 0.3987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0604 | 508 | 0.3724 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0606 | 510 | 0.3761 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0608 | 512 | 0.389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0611 | 514 | 0.3775 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0613 | 516 | 0.3429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0616 | 518 | 0.348 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0618 | 520 | 0.3706 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0620 | 522 | 0.3563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0623 | 524 | 0.3029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0625 | 526 | 0.4227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0627 | 528 | 0.3457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0630 | 530 | 0.3666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0632 | 532 | 0.3331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0635 | 534 | 0.3362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0637 | 536 | 0.3974 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0639 | 538 | 0.3841 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0642 | 540 | 0.3318 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0644 | 542 | 0.3349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0646 | 544 | 0.461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0649 | 546 | 0.3271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0651 | 548 | 0.3901 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0654 | 550 | 0.3292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0656 | 552 | 0.3291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0658 | 554 | 0.374 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0661 | 556 | 0.3432 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0663 | 558 | 0.2994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0665 | 560 | 0.3391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0668 | 562 | 0.3764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0670 | 564 | 0.2555 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0673 | 566 | 0.3553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0675 | 568 | 0.3436 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0677 | 570 | 0.4347 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0680 | 572 | 0.3271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0682 | 574 | 0.2988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0684 | 576 | 0.3698 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0687 | 578 | 0.3309 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0689 | 580 | 0.3529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0692 | 582 | 0.3685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0694 | 584 | 0.333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0696 | 586 | 0.3344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0699 | 588 | 0.3496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0701 | 590 | 0.3616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0703 | 592 | 0.3637 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0706 | 594 | 0.3745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0708 | 596 | 0.3465 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0711 | 598 | 0.4128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0713 | 600 | 0.3674 | 0.1694 | 0.4329 | 0.5142 | 0.2365 | 0.4053 | 0.3192 | 0.2211 | 0.3978 | 0.8890 | 0.2161 | 0.3708 | 0.3967 | 0.3738 | 0.3802 |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.0
- Transformers: 4.46.2
- PyTorch: 2.1.1+cu121
- Accelerate: 0.34.2
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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{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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