metadata
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:221599363
- loss:MultipleNegativesRankingLoss
base_model: thebajajra/RexBERT-base
widget:
- source_sentence: I found out that this novel was based on real ...
sentences:
- >-
I found out that this novel was based on real people only by reading the
afterword. This is a tremendously important piece of information about
the book.
- >-
I recently got a mbp 16 and although I’m very impressed by the speakers
I still wanted to purchase a set of external speakers for the desk
setup. The thing is since these are so good I don’t even know at which
price point I should be shopping to get something better.
The other day a youtuber I watch said that he has been using the mbp 16
speakers instead of his $200 speakers because he doesn’t feel the need
to anymore.
So, is a pair of $60 speakers going to be better or do I need to go
higher in price to really hear a difference?
- >-
Larry A Winters is a real good story teller. His use and knowledge of
Jessie Black as the heroine indicates a familiarity that makes the
reader wonder if Larry and Jessie are one and the same. A real page
turner but not quite in the cant put it down stage.
- source_sentence: Excellent seller but product did not work and returned for ...
sentences:
- >-
Summary The K-II continuous-sample-flow-with-banding-9 zonal centrifuge
hasbeen developed for large-scale virus isolation. The cylindrical
aluminum rotor (capacity 3600 ml) contains a 3 liter gradient and has a
700 ml stream volume. Fluid line seals are located on opposite ends of
the rotor, eliminating the possibility of cross-leakage. An air turbine
drive is used to accelerate the rotor to 35,000 rpm (83,440 g ,
maximum). The development of safe armor and control systems is
described.
- >-
I have a 2015 model and both front turn signals are busted after a
wheelie mistake.
The left one doesn't light up and the right one is missing.
Where can I get the original stock turn signals?
- >-
Excellent seller but product did not work and returned for a full
refund. Did not pair up with my ATT box. Was with a tech from ATT but
we both could not get it to pair up.
- source_sentence: where will the men's rings be held in 2016 olympics
sentences:
- >-
the event of there being more than two gymnasts from same NOC, only the
first two ranked among them would qualify for the final, with the next
best ranked gymnast qualifying instead. Gymnastics at the 2016 Summer
Olympics – Men's rings The Men's rings competition at the 2016 Summer
Olympics was held at the HSBC Arena on 15 August. The medals were
presented by Bernard Rajzman IOC member, Brazil and Wolfgang Willam, FIG
Executive Committee Member. The top 8 qualifiers in the qualification
phase (limit two per NOC), based on combined score of each apparatus,
advanced to the individual all-around
- >-
Russians marked the anniversary of the 1917 Bolshevik Revolution on
Sunday with marches, Communist rallies and protests against a
parliamentary proposal to scrap what was once the most sacred Soviet
holiday.
- >-
The only reason I could think of is the system has to log each
individual painting as it’s own but since the same asset doesn’t require
any new space and all the strokes are memorized anyway as you can move
them individually, is there any real difference aside from perhaps the
mild tedium of one way or the other?
Just one of the small questions that keep me up at night.
- source_sentence: So back to a semi-normal profile.
sentences:
- >-
but suffers from uneven pacing. It's dark in tone, but not as arresting
as, say, 3 AM or Damiano's Devil in Miss Jones. I'm glad I saw it, but
there's little chance I'll ever revisit (except, maybe, to check out
that Graham/Colt scene...)." The Story of Joanna The Story of Joanna is
a 1975 pornographic film directed by Gerard Damiano and starring Jamie
Gillis and Terri Hall. The film has a sado-masochism theme influenced by
"Story of O" (1954). It is considered one of the classics of the Golden
Age of Porn (1969–1984). It has been inducted into the XRCO
- >
Introduction
Most medical schools across the globe use academic achievement as the
primary selection criteria for admission into medical school. 1 This
also applies to all medical schools in Nepal where entrance examinations
conducted by the universities or Academies are based on general science
subjects. 2,3 However, academic achievement alone as the predictor of
someone becoming a 'good' physician has been questioned by many.
[3][4][5][6][7][8] Certainly, personal qualities play an immense role in
medical practice, which in itself is a complex phenomenon. [9][10] Patan
Academy of Health Sciences (PAHS) is an autonomous, health sciences
institute established in 2008 in Nepal with a mandate to improve the
health of people in rural Nepal by producing health professionals who
were competent, compassionate, and willing to serve in rural Nepal. It
was clear from the outset that academic attainment alone among the
aspirants for medical school was not going to be enough as admission
criteria for the School of Medicine of PAHS. Further, PAHS also
determined the desired characteristics/attributes of its graduates
involving all the stakeholders a priori. 11 In this respect, a
psychometric test battery (Personal Qualities Assessment, PQA 9-10 ) and
an Admission OSCE [12][13] procedures were explored to see if they could
be used to select medical students for PAHS. This paper reports the
validation of the PQA test battery using science and health sciences
students as they represented the majority of prospective applicants for
the PAHS undergraduate medical education program commonly known as
Bachelor in Medicine and Bachelor in Surgery (MBBS) in Nepal/South Asia,
as well as non-science students of public/community schools. 14
Method
The Personal Qualities Assessment (PQA) test battery is used
commercially to select health science students in many countries around
the world (http://www.pqa.net.au/research.html) and found to be valid,
reliable and predictive across different population. [9][10] The PQA
test battery tests the cognitive ability through PQA Test A1 or Mental
Agility Test (MAT) and a range of non-cognitive qualities though PQA
Test A2 (Moral Orientation for Justice and Care: MOJAC) and PQA Test A3
(Empathy, Confidence, Aloofness and Narcissism: ECAN). 15 PAHS, Nepal
and PQA Innovation, Australia collaborated to locally validate and use
PQA test for selecting medical students of School of Medicine, PAHS in
early 2008. PAHS conducted the pilot tests of PQA test batteries using
Optical Mark Reader (OMR) sheets, scanned them, created raw file and,
sent it as secured spreadsheet file to PQA team after the test. The PQA
team then scored the tests using pre-defined keys and rules and, send
them back as secured spreadsheet and report files to PAHS for further
processing.
In order to validate the PQA tools in local context, they were
forward-translated to Nepali by a professional bilingual person and was
back-translated to English by another bilingual professional under the
aegis of PAHS Admission team formed in 2008. The original PQA and
back-translated PQA tools were then discussed iteratively among PAHS
Admission and PQA teams before finalizing the Nepali version with
consensus.
The Personal Qualities Assessment tests in Nepali language was pretested
with volunteer 10+2 non-science students of public/community schools
located inside (n=75) and outside of Kathmandu valley (n=95) and
volunteer 10+2 science students of a public school located outskirts of
Kathmandu (n=35). As per PQA norm, only the volunteer students
completing 80% and above items on Test A2 and Test A3 were included in
the final analysis, whereas data of all the volunteer students on Test
A1 was included in the final analysis. These tests were scored using the
pre-defined keys and rules in Australia by the PQA team. Ethical
approval was obtained from the Institutional Review Committee of PAHS
(Ref: phs2204081608).
Journal of Patan Academy of Health Sciences. 2022Aug;9(2):95-101.
The Personal Qualities Assessment Test A1 (48 items with complex verbal,
numerical, spatial, and abstract reasoning) and PQA Test A4 (90 items
with simple verbal, numerical, spatial, and abstract reasoning) in
English and Nepali languages were trailed again with the larger pool of
volunteer 10+2 science students (n=131) of two community colleges and
10+3 health science students (n=56) of a Government College, both
located outside of Kathmandu valley. Descriptive statistics were used to
describe the test scores whereas the ttest was used to compare the test
scores between groups. A p-value less than 0.05 was taken as a
statistically significant result.
Result
The Personal Qualities Assessment tests in Nepali were pre-tested first
with 205 (110 males and 95 females) 10+2 non-science students in 2008,
and Test A1 and Test A4 in English and Nepali were pre-tested again with
187(141 males and 46 females) 10+2 science and 10+3 health science
students in 2009. The mean±standard deviation (range) of students' age
(in years) in the first and second samples were 20.3±1.3 (17-24) and
18.5±3.4 .
The cognitive ability test (PQA Test A1/MAT) had lower mean±SD scores
15.3±3.7 than the norm 27.6±5.6 (multinational pool of 1187 applicants
to medical schools). The range revealed the minimum and maximum scores
as 7 and 25 with a median of 15. The internal consistency reliability
coefficient (Cronbach's Alpha) was very low (0.27) for the first
pre-test samples.
On the other hand, the non-cognitive personality tests had comparable
mean±SD scores of 109.4±13.9 for Test A2 and 259.5± 20.1 for Test A3
with the multinational norms. Most importantly, the Coefficient alpha or
the internal consistency reliability of Test A2 and Test A3 were greater
than 0.80 (higher than the minimum value of 0.70) for both the tests.
Further, a significant and low degree of negative correlation (r=-0.153,
p=0.028) was found between Test A1 and Test A3 whereas a non-significant
low negative correlation was observed between Test A1 and Test A2
(r=-0.125, p=0.074). As Personal Qualities Assessment Test A1/MAT score
in Pre-Test I followed a normal distribution (Shapiro-Wilk=0.987,
p-value=0.068) and both science and non-science groups had equal
variance (Levene's F=0.421, p-value=0.517), independent samples t-test
was used to compare Test A1 score between science and non-science
students, Table 2. Test A1 scores were found to be higher for science
students and the result was highly significant statistically
(t-test=-3.963, p-value<0.0001). The scatterplot of the standardized
scores (zscores) of Test A2/MOJAC and Test A3/ECAN in Nepal language
from non-science students shows that most of these students' LibCom
(total of MOJAC) and ECAN z-scores lie between -2 and +2 SD and few
students' scores were outside of this range, Figure 1. The Test A1
(English language) scores for 10+3 health science students and 10+2
science students were not significantly different but Test A4 (English
language) scores for 10+3 health science students and 10+2 science
students were statistically different in the second pre-test, Table
4.
Discussion
The MAT (Test A1) score was found to be lower than the international
norms for both 10+2 science as well as non-science students, which
suggests unfamiliarity with this form of test, differences in schooling,
general cultural differences in approach to tests, etc. among these
public/community school students. As the Test A1 questions were based on
complex verbal, numerical, spatial, and abstract reasoning dimensions,
it suggests that higher school students of Nepal require more exposure
and practice on these types of aptitude tests as they are used widely to
select students, screen recruits for military/police forces, and test
job applicants. 16,17 The MAT (Test A1) in Nepali language scores were
found to be significantly higher among higher secondary level science
students compared to non-science students, possibly due to mathematical
intuition leading to plausible numerical and abstract reasoning as part
of their courses rather than higher verbal and spatial reasoning
abilities. The MAT in the Nepali language had a low internal consistency
reliability coefficient in the first pre-test, indicating that the
different types of items i.e., verbal, numerical, spatial, and abstract
reasoning items included in the test were of differing difficulties for
this group, who may have guessed many of their answers. Also, possibly
the volunteers felt that since their future was not at stake, they did
not feel the need to fully exercise their intellectual ability in
answering the questions, as most of them (170 out of 205) were
non-science students. It may also be true that Test A1 in the Nepali
language is not a suitable cognitive ability test for the 10+2 science
as well as 10+2 nonscience students.
On the other hand, MOJAC (Test A2) and NACE (Test A3) scores in Pre-Test
I were similar to the international norm and had very high internal
construct reliability (>0.80) suggesting they are satisfactory tests for
Nepali applicants at 10+2 level or equivalent in both science and
nonscience streams. These tests could be used to deselect outliers,
i.e., students with potential behavior problems where outliers were
Journal of Patan Academy of Health Sciences. 2022Aug;9(2):95-101.
defined statistically as below -2 SD and above +2 SD for the
standardized total MOJAC (LibCom) and ECAN scores. 15 Tests A2 and Test
A3 had small negative but statistically insignificant correlations with
Test A1, showing that they measured different traits (Test A1 measuring
cognitive abilities and Test A2 and A3 measure non-cognitive traits) and
thus are both potentially useful for selecting students for all the
undergraduate level health-related programs in Nepal.
Ironically, the small negative correlation further indicates that there
is a slight tendency for those who are stronger in cognitive skills to
be weaker in interpersonal skills, but there were still a substantial
proportion of applicants who are strong in both.
During the second pre-test done to check the consistency of the first
pre-test results, Test A1 in Nepali was again found to have a slightly
low internal reliability coefficient (Alpha < 0.50) for a larger pool of
n=89 of volunteer science and health science students whereas the
original MAT/Test A1 in the English language had a slightly more
acceptable internal construct reliability (Alpha>0.60) for n=98
volunteer science and health science students. This result is similar to
2003 Scottish medical school applicants 10 though it is lower than the
PQA international norm student sample average of 0.73. 15 Coefficient
alpha of 0.60 and above is considered good and 0.70 and above is
considered very good for tests with complex items i.e., MAT (Test A1) in
the English language used in Nepal. 18 Further, Test A4 in the English
language showed statistically different and higher results for science
and health sciences students compared to Test A4 in the Nepali language
whereas Test A1 in the English language showed higher but statistically
insignificant results compared to Test A1 in the Nepali language. So,
Test A4 containing simple verbal, numerical, spatial, and abstract
reasoning items is found to be easy whereas Test A1 containing complex
verbal, numerical, spatial, and abstract reasoning items is found to be
difficult for both groups of students, Figure 1.
When the Test A1 and Test A4 test scores in the English language were
analyzed separately for the science and health science students, Test A1
scores were not found to be statistically different indicating a fair
test to select undergraduate medical students compared to Test A4 which
produced a statistically different score. Thus, MAT (Test A1) in the
English version was chosen to select MBBS students of the School of
Medicine, Patan Academy of Health Sciences as it had sufficient internal
consistency reliability and was fair to both science and health science
students, despite being a bit difficult test of verbal, numerical,
spatial and abstract reasoning aptitude required for the course. Recent
studies confirmed the predictive validity of PQA tests among medical
students in the UK, which remains to be done at PAHS. [18][19][20]
Conclusion
The MAT (PQA Test A1) in English was found to be a reliable test to
select medical students for PAHS and similar institutions in Nepal as it
was also found to be fair among 10+2 science/10+3 health science
students. Also, PQA Test A2 and Test A3 in Nepali were found to be fair
and reliable tests to identify unusual personality traits and to
deselect such candidates for all the undergraduate level health science
programs in Nepal and beyond.
PAHS Admission committee conducted the second Pre-Test of Test A1 and
Test A4 in Nepali and English languages with the large (187) volunteer
10+2 science students and 10+3 health science students in public
school/college outside of Kathmandu valley as Nepali Test A1 results
were not promising with the non-science and the science students. The
Mental Agility Test (Test A1) had lower mean±SD scores for English
18.4±5.0 language and Nepali 16.8±4.3 language than the multinational
norm of 27.6±5.6. Yet, Test A1 in the English language's mean score of
18.4 was different from Test A1 in Nepali's mean score of 16.8, which
was also statistically significant (t=2.3348, p-value=0.0206). Although
the mean of Test A1 in the English language (18.4) done in 2009 was
found to be higher than Test A1 in the Nepali language (17.51) for the
10+2 Test A1 in the English language and 0.49 for the Nepali language
for the second pre-test samples. On the other hand, the mean percentage
score of the General Ability Test (Test A4) in English language and
Nepali Journal of Patan Academy of Health Sciences. 2022Aug;9(2):95-101.
language were 50.0 and 47.6 respectively, which was higher than the mean
percentage score of Test A1 in the English language (38.3) and Nepali
language (35.0). The mean percentage scores were statistically different
for English and Nepali versions of Test A4 (t=2.310, p-value=0.022) but
it was not statistically different for English and Nepali versions of
Test A1 (t=1.367, p-value=0.171). Further analysis of Test A1 and Test
A4 scores in English and Nepali languages with the sex of the applicants
was not found to be statistically different. However, Test A1 and Test
A4 showed statistically significant negativescience students in 2008,
they were not
significantly
different
(t=0.9769,
p-
value=0.3304), Table 2 & 3. The internal
consistency reliability (coefficient alpha), a
proxy for construct validity, was found to be
0.63 for correlations with the age of the students for
English (r=-0.226, p-value=0.025; r=-0.395, p-
value<0.001) and Nepali (r=-0.261, p-
value=0.011;
r=-0.211,
p-value=0.047)
languages.
Table 1 .
1Personal qualities assessment pre-test I with non-science major
students, 2008, NepalTable 2. Test A1 (Nepali) score of 10+2 science and
non-science students, 2008Test (Language)
Nepali candidates
International candidates
N Mean
SD
Median
Range Alpha
N Mean
SD
Test A1-NEPALI
205
15.3
3.7
15.0
7 -25
0.27
1811
27.6
5.6
Test A2-NEPALI
205 109.4
13.9
108.0
79 -147
0.82
9762 116.0
15.3
Test A3-NEPALI
205 259.6
20.1
259.0
216 -320
0.81
7032 283.0
22.8
Stream
N Mean
SD Median
Min
Max
t
p-value
Non-sciences -NEPALI (48 items)
170 14.87 3.643
15.00
7
24
3.963
<0.0001
Science -NEPALI (48 items)
35 17.51 3.338
17.00
12
25
Total
205 15.32 3.721
15.00
7
25
Table 3 .
3Personal qualities assessment pre-test II with science and health
science students, 2009, NepalTest (Language)
Nepali candidates
International candidates
N
Mean
SD
Median
Range Alpha
N Mean
SD
Test A1-ENGLISH
(48 items)
98
18.4
5.0
18.0
8 -30
0.63
1811
27.6
5.6
Test A1-NEPALI
(48 items)
89
16.8
4.3
17.0
8 -25
0.49
NA
Test A4-ENGLISH
(90 items)
98
45.0
11.3
44.5
18 -77
0.88
NA
Test A4-NEPALI
(90 items)
89
42.8
10.1
43.0
12 -60
NA
NA
NA=Not Available
Table 4 .
4Comparison of mental agility test (test A1) and general ability test
(test A4) (English version) scores
among science and health sciences students, pre-test II, 2009, Nepal
Test and Stream
(Test: Discipline)
Nepal Candidates
N
Mean
SD
SEM
Median
Range
T
p-value
Test A1-ENGLISH:
10+3 Health Sciences
56
18.3
4.5
0.61
17.0
12 -29
0.342
0.772
Test A1-ENGLISH:
10+2 Sciences
42
18.6
5.6
0.87
18.5
8 -30
Test A4-ENGLISH:
10+3 Health Sciences
56
41.77
9.3
1.25
42.5
18 -60
3.292
0.002
Test A4-ENGLISH:
10+2 Sciences
42
49.24
12.3
1.90
51.5
24 -77
SEM = Standard error of the measurement
AcknowledgmentWe are grateful to the officials of PAHS and the two
Admission Teams for their help during this study. We are thankful to all
the faculty and fellows at PSG FAIMER Regional Institute, Coimbatore,
India for their inputs and feedback as this study was conducted as part
of the educational innovation project of the first author to fulfill the
partial requirement for the FAIMER fellowship in medical education in
2008-2010.Conflict of Interest None
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|
- >-
Profile
After the winking / rating / favoriting experiment, I figured it was
time to go back to my normally scheduled profile with a few of the
undertones left over from the old one. Old Profile
What do you guys think?
Also it's amazing what a picture of a pet can do. :|
- source_sentence: Corners are still lifting.
sentences:
- >-
Great product. I purchased this item becuase my wrists would ache after
triceps day at the gym. I would never be able to straighten my wrist and
this helped in fixing that issue.
- >-
These are awesome quart jars. They have a beautiful color, and I use
them for storing soups, nuts and homemade nut milk. I would purchase
them again.
- >-
Hello, I got my ender 3 a little over a year ago and have gotten many
successful prints off of my machine.
I have always had a problem with the corners of my prints lifting. I
originally used a glass plate. That by itself was horrible, but then I
added hairspray, and that worked. The problem was that on long prints,
corners still lifted.
After doing this for around 5 months I switched to a PEI sheet.
This worked comparably as well as the glass/hairspray combo, except the
corners STILL LIFT on long prints.
Now I have a PEI sheet on boro glass with an EZABL attached and the
corners of my prints are STILL LIFTING.
I don't know what i could possibly be doing wrong. The bed must be
level. I get beautiful first layers, which I have tried to "smudge"
around during printing and I can confirm that the plastic is being layed
down solidly.
If anyone could enlighten me as to what is going on I would be thrilled.
I do have my first layer printing at 30% speed with 150% layer width
with the print cooling fan off as well. Printing PLA at 200C tool temp,
60C bed.
datasets:
- nomic-ai/nomic-embed-unsupervised-data
pipeline_tag: sentence-similarity
library_name: sentence-transformers
SentenceTransformer based on thebajajra/RexBERT-base
This is a sentence-transformers model finetuned from thebajajra/RexBERT-base on the nomic-embed-unsupervised-data dataset. It maps sentences & paragraphs to a 768-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: thebajajra/RexBERT-base
- Maximum Sequence Length: 1024 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: en
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': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Pooling({'word_embedding_dimension': 768, '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:
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("sentence_transformers_model_id")
# Run inference
queries = [
"Corners are still lifting.",
]
documents = [
'Hello, I got my ender 3 a little over a year ago and have gotten many successful prints off of my machine. \n\nI have always had a problem with the corners of my prints lifting. I originally used a glass plate. That by itself was horrible, but then I added hairspray, and that worked. The problem was that on long prints, corners still lifted.\n\nAfter doing this for around 5 months I switched to a PEI sheet.\n\nThis worked comparably as well as the glass/hairspray combo, except the corners STILL LIFT on long prints.\n\nNow I have a PEI sheet on boro glass with an EZABL attached and the corners of my prints are STILL LIFTING.\n\nI don\'t know what i could possibly be doing wrong. The bed must be level. I get beautiful first layers, which I have tried to "smudge" around during printing and I can confirm that the plastic is being layed down solidly.\n\nIf anyone could enlighten me as to what is going on I would be thrilled.\n\nI do have my first layer printing at 30% speed with 150% layer width with the print cooling fan off as well. Printing PLA at 200C tool temp, 60C bed.',
'These are awesome quart jars. They have a beautiful color, and I use them for storing soups, nuts and homemade nut milk. I would purchase them again.',
'Great product. I purchased this item becuase my wrists would ache after triceps day at the gym. I would never be able to straighten my wrist and this helped in fixing that issue.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.5487, 0.0457, 0.1961]])
Training Details
Training Dataset
nomic-embed-unsupervised-data
- Dataset: nomic-embed-unsupervised-data at 917bae6
- Size: 221,599,363 training samples
- Columns:
queryanddocument - Approximate statistics based on the first 1000 samples:
query document type string string details - min: 6 tokens
- mean: 34.17 tokens
- max: 1024 tokens
- min: 8 tokens
- mean: 166.07 tokens
- max: 1024 tokens
- Samples:
query document Effect of steam reforming on methane-fueled chemical looping combustion with Cu-based oxygen carrierAbstract The reduction characteristics of Cu-based oxygen carrier with H 2 , CO and CH 4 were investigated using a fixed bed reactor, TPR and TGA. Results showed that temperatures for the complete reduction of Cu-based oxygen carrier with H 2 and CO are 300 °C and 225 °C, respectively, while the corresponding temperature with CH 4 is 650 °C. The carbon deposition from CH 4 occurred at over 550 °C. CO-chemisorption experiments were also conducted on the oxygen carrier, and it was indicated that Cu-based oxygen carrier sinter seriously at 700 °C. In order to lower the required reduction temperature of oxygen carriers, a new chemical looping combustion (CLC) process with CH 4 steam reforming has been presented in this paper. The basic feasibility of the process was illustrated using CuO–SiO 2 . The new CLC process has the potential to replace the conventional gas-fired middle- and low-pressure steam and hot water boilers.who appointed onesicritus as chief pilot of the fleetby the king to hold a conference with the Indian philosophers or Gymnosophists, the details of which have been transmitted to us from his own account of the interview. It was Onesicritus, whom Alexander first sent to summon Dandamis to his court. When later Onesicritus returned empty-handed with the reply of Dandamis, the King went to forest to visit Dandamis. When Alexander constructed his fleet on the Hydaspes, he appointed Onesicritus to the important position of pilot of the king's ship, or chief pilot of the fleet (). Onesicritus held this position not only during the descent of the Indus,when did the madonna of foligno go to parisMadonna of Foligno hence the name. In 1799 it was carried to Paris, France by Napoleon. There, in 1802, the painting was transferred from panel to canvas by Hacquin and restored by Roser of Heidelberg. A note was made by the restorer: "Rapporto dei cittadini Guijon Vincent Tannay e Berthollet sul ristauro dei quadri di Raffaello conosciuto sotto il nome di Madonna di Foligno." In 1815, after the Battle of Waterloo, it was returned to Italy, where it was placed in the room with the Transfiguration in the Pinacoteca Vaticana of the Vatican Museum in the Vatican City. The painting is a "sacra - Loss:
MultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false }
Evaluation Dataset
nomic-embed-unsupervised-data
- Dataset: nomic-embed-unsupervised-data at 917bae6
- Size: 1,113,579 evaluation samples
- Columns:
queryanddocument - Approximate statistics based on the first 1000 samples:
query document type string string details - min: 5 tokens
- mean: 31.98 tokens
- max: 1024 tokens
- min: 6 tokens
- mean: 161.48 tokens
- max: 1024 tokens
- Samples:
query document Concise methods for the synthesis of chiral polyoxazolines and their application in asymmetric hydrosilylationSeven polyoxazoline ligands were synthesized in high yield in a one-pot reaction by heating polycarboxylic acids or their esters and chiral β-amino alcohols under reflux with concomitant removal of water or the alcohol produced in the reaction. The method is much simpler and more efficient in comparison to those methods reported in the literature.The compounds were used as chiral ligands in the rhodium-catalyzed asymmetric hydrosilylation of aromatic ketones, and the effects of the linkers and the substituents present on the oxazoline rings on the yield and enantioselectivity investigated. Compound 2 was identified as the best ligand of this family for the hydrosilylation of aromatic ketones.On the road to a stronger public health workforce: visual tools to address complex challenges.The Public Health Workforce Taxonomy: Revisions and Recommendations for Implementation140mm Jetflo fan availability?I recently purchased a Nepton 280L, and would like to install an additional pair of 140mm Jetflo fans. Unfortunately they don't seem to be currently available, will they be in the future?
Thank you so much!
PS - I'm loving the cooling system! - Loss:
MultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim", "gather_across_devices": false }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 384per_device_eval_batch_size: 128learning_rate: 1e-05num_train_epochs: 4warmup_steps: 1000bf16: Truedataloader_num_workers: 20dataloader_prefetch_factor: 4ddp_find_unused_parameters: False
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 384per_device_eval_batch_size: 128per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 1e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 4max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 1000log_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: Falsebf16: Truefp16: 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: Truedataloader_num_workers: 20dataloader_prefetch_factor: 4past_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}parallelism_config: Nonedeepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torch_fusedoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthproject: huggingfacetrackio_space_id: trackioddp_find_unused_parameters: Falseddp_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: Nonehub_always_push: Falsehub_revision: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_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: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: noneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Trueprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}
Training Logs
Click to expand
| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0014 | 100 | 4.5665 | - |
| 0.0028 | 200 | 2.223 | - |
| 0.0042 | 300 | 0.3767 | - |
| 0.0055 | 400 | 0.1622 | - |
| 0.0069 | 500 | 0.1154 | - |
| 0.0083 | 600 | 0.0934 | - |
| 0.0097 | 700 | 0.0797 | - |
| 0.0111 | 800 | 0.0704 | - |
| 0.0125 | 900 | 0.0625 | - |
| 0.0139 | 1000 | 0.0582 | - |
| 0.0152 | 1100 | 0.0535 | - |
| 0.0166 | 1200 | 0.0492 | - |
| 0.0180 | 1300 | 0.0463 | - |
| 0.0194 | 1400 | 0.044 | - |
| 0.0208 | 1500 | 0.0416 | - |
| 0.0222 | 1600 | 0.0395 | - |
| 0.0236 | 1700 | 0.0381 | - |
| 0.0250 | 1800 | 0.0367 | - |
| 0.0263 | 1900 | 0.0358 | - |
| 0.0277 | 2000 | 0.0345 | - |
| 0.0291 | 2100 | 0.0335 | - |
| 0.0305 | 2200 | 0.0319 | - |
| 0.0319 | 2300 | 0.0318 | - |
| 0.0333 | 2400 | 0.0304 | - |
| 0.0347 | 2500 | 0.0301 | - |
| 0.0360 | 2600 | 0.0291 | - |
| 0.0374 | 2700 | 0.0293 | - |
| 0.0388 | 2800 | 0.0281 | - |
| 0.0402 | 2900 | 0.0277 | - |
| 0.0416 | 3000 | 0.0266 | - |
| 0.0430 | 3100 | 0.0265 | - |
| 0.0444 | 3200 | 0.0261 | - |
| 0.0457 | 3300 | 0.0253 | - |
| 0.0471 | 3400 | 0.0256 | - |
| 0.0485 | 3500 | 0.0247 | - |
| 0.0499 | 3600 | 0.0239 | - |
| 0.0513 | 3700 | 0.0239 | - |
| 0.0527 | 3800 | 0.0235 | - |
| 0.0541 | 3900 | 0.0233 | - |
| 0.0555 | 4000 | 0.0229 | - |
| 0.0568 | 4100 | 0.0227 | - |
| 0.0582 | 4200 | 0.0226 | - |
| 0.0596 | 4300 | 0.0221 | - |
| 0.0610 | 4400 | 0.0219 | - |
| 0.0624 | 4500 | 0.0211 | - |
| 0.0638 | 4600 | 0.0212 | - |
| 0.0652 | 4700 | 0.021 | - |
| 0.0665 | 4800 | 0.0205 | - |
| 0.0679 | 4900 | 0.0202 | - |
| 0.0693 | 5000 | 0.0206 | - |
| 0.0707 | 5100 | 0.0199 | - |
| 0.0721 | 5200 | 0.0202 | - |
| 0.0735 | 5300 | 0.0194 | - |
| 0.0749 | 5400 | 0.0195 | - |
| 0.0762 | 5500 | 0.0189 | - |
| 0.0776 | 5600 | 0.0194 | - |
| 0.0790 | 5700 | 0.0189 | - |
| 0.0804 | 5800 | 0.0183 | - |
| 0.0818 | 5900 | 0.0184 | - |
| 0.0832 | 6000 | 0.0183 | - |
| 0.0846 | 6100 | 0.018 | - |
| 0.0859 | 6200 | 0.0178 | - |
| 0.0873 | 6300 | 0.018 | - |
| 0.0887 | 6400 | 0.0174 | - |
| 0.0901 | 6500 | 0.0175 | - |
| 0.0915 | 6600 | 0.0176 | - |
| 0.0929 | 6700 | 0.0171 | - |
| 0.0943 | 6800 | 0.0168 | - |
| 0.0957 | 6900 | 0.0174 | - |
| 0.0970 | 7000 | 0.0171 | - |
| 0.0984 | 7100 | 0.0169 | - |
| 0.0998 | 7200 | 0.0167 | - |
| 0.1012 | 7300 | 0.0165 | - |
| 0.1026 | 7400 | 0.0166 | - |
| 0.1040 | 7500 | 0.0162 | - |
| 0.1054 | 7600 | 0.0164 | - |
| 0.1067 | 7700 | 0.0159 | - |
| 0.1081 | 7800 | 0.0159 | - |
| 0.1095 | 7900 | 0.0162 | - |
| 0.1109 | 8000 | 0.0157 | - |
| 0.1123 | 8100 | 0.0157 | - |
| 0.1137 | 8200 | 0.0155 | - |
| 0.1151 | 8300 | 0.0154 | - |
| 0.1164 | 8400 | 0.0155 | - |
| 0.1178 | 8500 | 0.0154 | - |
| 0.1192 | 8600 | 0.015 | - |
| 0.1206 | 8700 | 0.0151 | - |
| 0.1220 | 8800 | 0.0149 | - |
| 0.1234 | 8900 | 0.015 | - |
| 0.1248 | 9000 | 0.0146 | - |
| 0.1262 | 9100 | 0.015 | - |
| 0.1275 | 9200 | 0.0148 | - |
| 0.1289 | 9300 | 0.0145 | - |
| 0.1303 | 9400 | 0.0146 | - |
| 0.1317 | 9500 | 0.0148 | - |
| 0.1331 | 9600 | 0.0143 | - |
| 0.1345 | 9700 | 0.0144 | - |
| 0.1359 | 9800 | 0.0142 | - |
| 0.1372 | 9900 | 0.0142 | - |
| 0.1386 | 10000 | 0.0141 | - |
| 0.1400 | 10100 | 0.0139 | - |
| 0.1414 | 10200 | 0.0141 | - |
| 0.1428 | 10300 | 0.0139 | - |
| 0.1442 | 10400 | 0.0136 | - |
| 0.1456 | 10500 | 0.0135 | - |
| 0.1469 | 10600 | 0.0135 | - |
| 0.1483 | 10700 | 0.0134 | - |
| 0.1497 | 10800 | 0.0136 | - |
| 0.1511 | 10900 | 0.0133 | - |
| 0.1525 | 11000 | 0.0135 | - |
| 0.1539 | 11100 | 0.0133 | - |
| 0.1553 | 11200 | 0.0134 | - |
| 0.1567 | 11300 | 0.0133 | - |
| 0.1580 | 11400 | 0.0134 | - |
| 0.1594 | 11500 | 0.013 | - |
| 0.1608 | 11600 | 0.0131 | - |
| 0.1622 | 11700 | 0.0129 | - |
| 0.1636 | 11800 | 0.0127 | - |
| 0.1650 | 11900 | 0.0129 | - |
| 0.1664 | 12000 | 0.0125 | - |
| 0.1677 | 12100 | 0.0129 | - |
| 0.1691 | 12200 | 0.013 | - |
| 0.1705 | 12300 | 0.013 | - |
| 0.1719 | 12400 | 0.013 | - |
| 0.1733 | 12500 | 0.0125 | - |
| 0.1747 | 12600 | 0.0125 | - |
| 0.1761 | 12700 | 0.0122 | - |
| 0.1774 | 12800 | 0.0124 | - |
| 0.1788 | 12900 | 0.0124 | - |
| 0.1802 | 13000 | 0.0121 | - |
| 0.1816 | 13100 | 0.0124 | - |
| 0.1830 | 13200 | 0.0122 | - |
| 0.1844 | 13300 | 0.0123 | - |
| 0.1858 | 13400 | 0.0121 | - |
| 0.1871 | 13500 | 0.012 | - |
| 0.1885 | 13600 | 0.0118 | - |
| 0.1899 | 13700 | 0.0119 | - |
| 0.1913 | 13800 | 0.0117 | - |
| 0.1927 | 13900 | 0.0119 | - |
| 0.1941 | 14000 | 0.0119 | - |
| 0.1955 | 14100 | 0.0117 | - |
| 0.1969 | 14200 | 0.0119 | - |
| 0.1982 | 14300 | 0.0116 | - |
| 0.1996 | 14400 | 0.0116 | - |
| 0.2 | 14427 | - | 0.0044 |
| 0.2010 | 14500 | 0.012 | - |
| 0.2024 | 14600 | 0.0116 | - |
| 0.2038 | 14700 | 0.0118 | - |
| 0.2052 | 14800 | 0.0116 | - |
| 0.2066 | 14900 | 0.0118 | - |
| 0.2079 | 15000 | 0.0118 | - |
| 0.2093 | 15100 | 0.0113 | - |
| 0.2107 | 15200 | 0.0114 | - |
| 0.2121 | 15300 | 0.0115 | - |
| 0.2135 | 15400 | 0.0116 | - |
| 0.2149 | 15500 | 0.0113 | - |
| 0.2163 | 15600 | 0.0115 | - |
| 0.2176 | 15700 | 0.0112 | - |
| 0.2190 | 15800 | 0.0112 | - |
| 0.2204 | 15900 | 0.0114 | - |
| 0.2218 | 16000 | 0.0111 | - |
| 0.2232 | 16100 | 0.0112 | - |
| 0.2246 | 16200 | 0.0111 | - |
| 0.2260 | 16300 | 0.011 | - |
| 0.2274 | 16400 | 0.011 | - |
| 0.2287 | 16500 | 0.0109 | - |
| 0.2301 | 16600 | 0.0106 | - |
| 0.2315 | 16700 | 0.011 | - |
| 0.2329 | 16800 | 0.011 | - |
| 0.2343 | 16900 | 0.0108 | - |
| 0.2357 | 17000 | 0.0106 | - |
| 0.2371 | 17100 | 0.0108 | - |
| 0.2384 | 17200 | 0.0107 | - |
| 0.2398 | 17300 | 0.0105 | - |
| 0.2412 | 17400 | 0.0107 | - |
| 0.2426 | 17500 | 0.011 | - |
| 0.2440 | 17600 | 0.0105 | - |
| 0.2454 | 17700 | 0.0107 | - |
| 0.2468 | 17800 | 0.0106 | - |
| 0.2481 | 17900 | 0.0108 | - |
| 0.2495 | 18000 | 0.0106 | - |
| 0.2509 | 18100 | 0.0105 | - |
| 0.2523 | 18200 | 0.0103 | - |
| 0.2537 | 18300 | 0.0104 | - |
| 0.2551 | 18400 | 0.0105 | - |
| 0.2565 | 18500 | 0.0103 | - |
| 0.2578 | 18600 | 0.0104 | - |
| 0.2592 | 18700 | 0.0103 | - |
| 0.2606 | 18800 | 0.0102 | - |
| 0.2620 | 18900 | 0.0101 | - |
| 0.2634 | 19000 | 0.0102 | - |
| 0.2648 | 19100 | 0.0103 | - |
| 0.2662 | 19200 | 0.01 | - |
| 0.2676 | 19300 | 0.0103 | - |
| 0.2689 | 19400 | 0.0101 | - |
| 0.2703 | 19500 | 0.0103 | - |
| 0.2717 | 19600 | 0.0101 | - |
| 0.2731 | 19700 | 0.0103 | - |
| 0.2745 | 19800 | 0.0102 | - |
| 0.2759 | 19900 | 0.0102 | - |
| 0.2773 | 20000 | 0.0103 | - |
| 0.2786 | 20100 | 0.0101 | - |
| 0.2800 | 20200 | 0.0102 | - |
| 0.2814 | 20300 | 0.0099 | - |
| 0.2828 | 20400 | 0.0099 | - |
| 0.2842 | 20500 | 0.0099 | - |
| 0.2856 | 20600 | 0.0098 | - |
| 0.2870 | 20700 | 0.0099 | - |
| 0.2883 | 20800 | 0.0097 | - |
| 0.2897 | 20900 | 0.0101 | - |
| 0.2911 | 21000 | 0.0098 | - |
| 0.2925 | 21100 | 0.0099 | - |
| 0.2939 | 21200 | 0.0099 | - |
| 0.2953 | 21300 | 0.0098 | - |
| 0.2967 | 21400 | 0.0096 | - |
| 0.2981 | 21500 | 0.0097 | - |
| 0.2994 | 21600 | 0.0097 | - |
| 0.3008 | 21700 | 0.0099 | - |
| 0.3022 | 21800 | 0.0098 | - |
| 0.3036 | 21900 | 0.0096 | - |
| 0.3050 | 22000 | 0.0097 | - |
| 0.3064 | 22100 | 0.0098 | - |
| 0.3078 | 22200 | 0.0094 | - |
| 0.3091 | 22300 | 0.0096 | - |
| 0.3105 | 22400 | 0.0095 | - |
| 0.3119 | 22500 | 0.0098 | - |
| 0.3133 | 22600 | 0.0096 | - |
| 0.3147 | 22700 | 0.0094 | - |
| 0.3161 | 22800 | 0.0095 | - |
| 0.3175 | 22900 | 0.0093 | - |
| 0.3188 | 23000 | 0.0093 | - |
| 0.3202 | 23100 | 0.0093 | - |
| 0.3216 | 23200 | 0.0094 | - |
| 0.3230 | 23300 | 0.0094 | - |
| 0.3244 | 23400 | 0.0093 | - |
| 0.3258 | 23500 | 0.0091 | - |
| 0.3272 | 23600 | 0.0093 | - |
| 0.3286 | 23700 | 0.0093 | - |
| 0.3299 | 23800 | 0.0093 | - |
| 0.3313 | 23900 | 0.0093 | - |
| 0.3327 | 24000 | 0.0093 | - |
| 0.3341 | 24100 | 0.009 | - |
| 0.3355 | 24200 | 0.0093 | - |
| 0.3369 | 24300 | 0.0089 | - |
| 0.3383 | 24400 | 0.0089 | - |
| 0.3396 | 24500 | 0.0092 | - |
| 0.3410 | 24600 | 0.009 | - |
| 0.3424 | 24700 | 0.0092 | - |
| 0.3438 | 24800 | 0.009 | - |
| 0.3452 | 24900 | 0.0091 | - |
| 0.3466 | 25000 | 0.0088 | - |
| 0.3480 | 25100 | 0.009 | - |
| 0.3493 | 25200 | 0.0089 | - |
| 0.3507 | 25300 | 0.0088 | - |
| 0.3521 | 25400 | 0.0089 | - |
| 0.3535 | 25500 | 0.0089 | - |
| 0.3549 | 25600 | 0.009 | - |
| 0.3563 | 25700 | 0.0092 | - |
| 0.3577 | 25800 | 0.0089 | - |
| 0.3590 | 25900 | 0.0089 | - |
| 0.3604 | 26000 | 0.009 | - |
| 0.3618 | 26100 | 0.0088 | - |
| 0.3632 | 26200 | 0.0088 | - |
| 0.3646 | 26300 | 0.0091 | - |
| 0.3660 | 26400 | 0.0088 | - |
| 0.3674 | 26500 | 0.0089 | - |
| 0.3688 | 26600 | 0.0087 | - |
| 0.3701 | 26700 | 0.0089 | - |
| 0.3715 | 26800 | 0.0087 | - |
| 0.3729 | 26900 | 0.0088 | - |
| 0.3743 | 27000 | 0.0086 | - |
| 0.3757 | 27100 | 0.0088 | - |
| 0.3771 | 27200 | 0.0087 | - |
| 0.3785 | 27300 | 0.0085 | - |
| 0.3798 | 27400 | 0.0085 | - |
| 0.3812 | 27500 | 0.0086 | - |
| 0.3826 | 27600 | 0.0088 | - |
| 0.3840 | 27700 | 0.0084 | - |
| 0.3854 | 27800 | 0.0086 | - |
| 0.3868 | 27900 | 0.0085 | - |
| 0.3882 | 28000 | 0.0085 | - |
| 0.3895 | 28100 | 0.0086 | - |
| 0.3909 | 28200 | 0.0085 | - |
| 0.3923 | 28300 | 0.0086 | - |
| 0.3937 | 28400 | 0.0088 | - |
| 0.3951 | 28500 | 0.0086 | - |
| 0.3965 | 28600 | 0.0085 | - |
| 0.3979 | 28700 | 0.0086 | - |
| 0.3993 | 28800 | 0.0085 | - |
| 0.4 | 28854 | - | 0.0031 |
| 0.4006 | 28900 | 0.0084 | - |
| 0.4020 | 29000 | 0.0084 | - |
| 0.4034 | 29100 | 0.0085 | - |
| 0.4048 | 29200 | 0.0083 | - |
| 0.4062 | 29300 | 0.0084 | - |
| 0.4076 | 29400 | 0.0084 | - |
| 0.4090 | 29500 | 0.0084 | - |
| 0.4103 | 29600 | 0.0082 | - |
| 0.4117 | 29700 | 0.0085 | - |
| 0.4131 | 29800 | 0.0083 | - |
| 0.4145 | 29900 | 0.0081 | - |
| 0.4159 | 30000 | 0.0084 | - |
| 0.4173 | 30100 | 0.0085 | - |
| 0.4187 | 30200 | 0.0081 | - |
| 0.4200 | 30300 | 0.0084 | - |
| 0.4214 | 30400 | 0.0084 | - |
| 0.4228 | 30500 | 0.0082 | - |
| 0.4242 | 30600 | 0.0084 | - |
| 0.4256 | 30700 | 0.0084 | - |
| 0.4270 | 30800 | 0.0082 | - |
| 0.4284 | 30900 | 0.0081 | - |
| 0.4297 | 31000 | 0.0081 | - |
| 0.4311 | 31100 | 0.0079 | - |
| 0.4325 | 31200 | 0.0082 | - |
| 0.4339 | 31300 | 0.0082 | - |
| 0.4353 | 31400 | 0.0082 | - |
| 0.4367 | 31500 | 0.0079 | - |
| 0.4381 | 31600 | 0.0079 | - |
| 0.4395 | 31700 | 0.0081 | - |
| 0.4408 | 31800 | 0.008 | - |
| 0.4422 | 31900 | 0.0081 | - |
| 0.4436 | 32000 | 0.0081 | - |
| 0.4450 | 32100 | 0.0081 | - |
| 0.4464 | 32200 | 0.0078 | - |
| 0.4478 | 32300 | 0.0079 | - |
| 0.4492 | 32400 | 0.0081 | - |
| 0.4505 | 32500 | 0.0081 | - |
| 0.4519 | 32600 | 0.0081 | - |
| 0.4533 | 32700 | 0.0079 | - |
| 0.4547 | 32800 | 0.0079 | - |
| 0.4561 | 32900 | 0.0079 | - |
| 0.4575 | 33000 | 0.0079 | - |
| 0.4589 | 33100 | 0.0079 | - |
| 0.4602 | 33200 | 0.0078 | - |
| 0.4616 | 33300 | 0.0077 | - |
| 0.4630 | 33400 | 0.008 | - |
| 0.4644 | 33500 | 0.0079 | - |
| 0.4658 | 33600 | 0.008 | - |
| 0.4672 | 33700 | 0.0079 | - |
| 0.4686 | 33800 | 0.0078 | - |
| 0.4700 | 33900 | 0.008 | - |
| 0.4713 | 34000 | 0.0077 | - |
| 0.4727 | 34100 | 0.0077 | - |
| 0.4741 | 34200 | 0.0078 | - |
| 0.4755 | 34300 | 0.0076 | - |
| 0.4769 | 34400 | 0.0078 | - |
| 0.4783 | 34500 | 0.0078 | - |
| 0.4797 | 34600 | 0.0078 | - |
| 0.4810 | 34700 | 0.0079 | - |
| 0.4824 | 34800 | 0.0078 | - |
| 0.4838 | 34900 | 0.0077 | - |
| 0.4852 | 35000 | 0.0075 | - |
| 0.4866 | 35100 | 0.0076 | - |
| 0.4880 | 35200 | 0.0078 | - |
| 0.4894 | 35300 | 0.0076 | - |
| 0.4907 | 35400 | 0.0078 | - |
| 0.4921 | 35500 | 0.0077 | - |
| 0.4935 | 35600 | 0.0076 | - |
| 0.4949 | 35700 | 0.0076 | - |
| 0.4963 | 35800 | 0.0077 | - |
| 0.4977 | 35900 | 0.0076 | - |
| 0.4991 | 36000 | 0.0077 | - |
| 0.5005 | 36100 | 0.0077 | - |
| 0.5018 | 36200 | 0.0077 | - |
| 0.5032 | 36300 | 0.0077 | - |
| 0.5046 | 36400 | 0.0076 | - |
| 0.5060 | 36500 | 0.0076 | - |
| 0.5074 | 36600 | 0.0077 | - |
| 0.5088 | 36700 | 0.0076 | - |
| 0.5102 | 36800 | 0.0075 | - |
| 0.5115 | 36900 | 0.0077 | - |
| 0.5129 | 37000 | 0.0076 | - |
| 0.5143 | 37100 | 0.0075 | - |
| 0.5157 | 37200 | 0.0074 | - |
| 0.5171 | 37300 | 0.0074 | - |
| 0.5185 | 37400 | 0.0075 | - |
| 0.5199 | 37500 | 0.0075 | - |
| 0.5212 | 37600 | 0.0074 | - |
| 0.5226 | 37700 | 0.0074 | - |
| 0.5240 | 37800 | 0.0072 | - |
| 0.5254 | 37900 | 0.0076 | - |
| 0.5268 | 38000 | 0.0075 | - |
| 0.5282 | 38100 | 0.0072 | - |
| 0.5296 | 38200 | 0.0074 | - |
| 0.5309 | 38300 | 0.0073 | - |
| 0.5323 | 38400 | 0.0073 | - |
| 0.5337 | 38500 | 0.0074 | - |
| 0.5351 | 38600 | 0.0073 | - |
| 0.5365 | 38700 | 0.0073 | - |
| 0.5379 | 38800 | 0.0074 | - |
| 0.5393 | 38900 | 0.0072 | - |
| 0.5407 | 39000 | 0.0076 | - |
| 0.5420 | 39100 | 0.0072 | - |
| 0.5434 | 39200 | 0.0073 | - |
| 0.5448 | 39300 | 0.0071 | - |
| 0.5462 | 39400 | 0.0072 | - |
| 0.5476 | 39500 | 0.0073 | - |
| 0.5490 | 39600 | 0.0074 | - |
| 0.5504 | 39700 | 0.0072 | - |
| 0.5517 | 39800 | 0.0072 | - |
| 0.5531 | 39900 | 0.0073 | - |
| 0.5545 | 40000 | 0.0071 | - |
| 0.5559 | 40100 | 0.0072 | - |
| 0.5573 | 40200 | 0.0072 | - |
| 0.5587 | 40300 | 0.0071 | - |
| 0.5601 | 40400 | 0.0072 | - |
| 0.5614 | 40500 | 0.0071 | - |
| 0.5628 | 40600 | 0.0073 | - |
| 0.5642 | 40700 | 0.0073 | - |
| 0.5656 | 40800 | 0.0072 | - |
| 0.5670 | 40900 | 0.0071 | - |
| 0.5684 | 41000 | 0.0073 | - |
| 0.5698 | 41100 | 0.0072 | - |
| 0.5712 | 41200 | 0.0071 | - |
| 0.5725 | 41300 | 0.0074 | - |
| 0.5739 | 41400 | 0.0072 | - |
| 0.5753 | 41500 | 0.0071 | - |
| 0.5767 | 41600 | 0.0071 | - |
| 0.5781 | 41700 | 0.007 | - |
| 0.5795 | 41800 | 0.0071 | - |
| 0.5809 | 41900 | 0.0071 | - |
| 0.5822 | 42000 | 0.0073 | - |
| 0.5836 | 42100 | 0.0071 | - |
| 0.5850 | 42200 | 0.0069 | - |
| 0.5864 | 42300 | 0.0071 | - |
| 0.5878 | 42400 | 0.0072 | - |
| 0.5892 | 42500 | 0.0073 | - |
| 0.5906 | 42600 | 0.0071 | - |
| 0.5919 | 42700 | 0.0071 | - |
| 0.5933 | 42800 | 0.0072 | - |
| 0.5947 | 42900 | 0.0071 | - |
| 0.5961 | 43000 | 0.0072 | - |
| 0.5975 | 43100 | 0.007 | - |
| 0.5989 | 43200 | 0.0072 | - |
| 0.6 | 43281 | - | 0.0026 |
| 0.6003 | 43300 | 0.0071 | - |
| 0.6016 | 43400 | 0.0069 | - |
| 0.6030 | 43500 | 0.007 | - |
| 0.6044 | 43600 | 0.0069 | - |
| 0.6058 | 43700 | 0.007 | - |
| 0.6072 | 43800 | 0.0068 | - |
| 0.6086 | 43900 | 0.0071 | - |
| 0.6100 | 44000 | 0.0069 | - |
| 0.6114 | 44100 | 0.0069 | - |
| 0.6127 | 44200 | 0.0069 | - |
| 0.6141 | 44300 | 0.0071 | - |
| 0.6155 | 44400 | 0.0071 | - |
| 0.6169 | 44500 | 0.007 | - |
| 0.6183 | 44600 | 0.0069 | - |
| 0.6197 | 44700 | 0.0069 | - |
| 0.6211 | 44800 | 0.007 | - |
| 0.6224 | 44900 | 0.0068 | - |
| 0.6238 | 45000 | 0.0069 | - |
| 0.6252 | 45100 | 0.0069 | - |
| 0.6266 | 45200 | 0.0069 | - |
| 0.6280 | 45300 | 0.0068 | - |
| 0.6294 | 45400 | 0.0069 | - |
| 0.6308 | 45500 | 0.007 | - |
| 0.6321 | 45600 | 0.0068 | - |
| 0.6335 | 45700 | 0.0068 | - |
| 0.6349 | 45800 | 0.0068 | - |
| 0.6363 | 45900 | 0.0069 | - |
| 0.6377 | 46000 | 0.007 | - |
| 0.6391 | 46100 | 0.0067 | - |
| 0.6405 | 46200 | 0.0066 | - |
| 0.6419 | 46300 | 0.0069 | - |
| 0.6432 | 46400 | 0.0068 | - |
| 0.6446 | 46500 | 0.007 | - |
| 0.6460 | 46600 | 0.0069 | - |
| 0.6474 | 46700 | 0.0069 | - |
| 0.6488 | 46800 | 0.0068 | - |
| 0.6502 | 46900 | 0.007 | - |
| 0.6516 | 47000 | 0.0069 | - |
| 0.6529 | 47100 | 0.0067 | - |
| 0.6543 | 47200 | 0.0068 | - |
| 0.6557 | 47300 | 0.0065 | - |
| 0.6571 | 47400 | 0.0067 | - |
| 0.6585 | 47500 | 0.007 | - |
| 0.6599 | 47600 | 0.0067 | - |
| 0.6613 | 47700 | 0.0067 | - |
| 0.6626 | 47800 | 0.0068 | - |
| 0.6640 | 47900 | 0.0067 | - |
| 0.6654 | 48000 | 0.0066 | - |
| 0.6668 | 48100 | 0.0069 | - |
| 0.6682 | 48200 | 0.0067 | - |
| 0.6696 | 48300 | 0.0067 | - |
| 0.6710 | 48400 | 0.0067 | - |
| 0.6724 | 48500 | 0.0069 | - |
| 0.6737 | 48600 | 0.0066 | - |
| 0.6751 | 48700 | 0.0066 | - |
| 0.6765 | 48800 | 0.0068 | - |
| 0.6779 | 48900 | 0.0067 | - |
| 0.6793 | 49000 | 0.0067 | - |
| 0.6807 | 49100 | 0.0068 | - |
| 0.6821 | 49200 | 0.0066 | - |
| 0.6834 | 49300 | 0.0067 | - |
| 0.6848 | 49400 | 0.0065 | - |
| 0.6862 | 49500 | 0.0067 | - |
| 0.6876 | 49600 | 0.0066 | - |
| 0.6890 | 49700 | 0.0065 | - |
| 0.6904 | 49800 | 0.0067 | - |
| 0.6918 | 49900 | 0.0066 | - |
| 0.6931 | 50000 | 0.0066 | - |
| 0.6945 | 50100 | 0.0066 | - |
| 0.6959 | 50200 | 0.0065 | - |
| 0.6973 | 50300 | 0.0068 | - |
| 0.6987 | 50400 | 0.0068 | - |
| 0.7001 | 50500 | 0.0066 | - |
| 0.7015 | 50600 | 0.0067 | - |
| 0.7028 | 50700 | 0.0068 | - |
| 0.7042 | 50800 | 0.0066 | - |
| 0.7056 | 50900 | 0.0065 | - |
| 0.7070 | 51000 | 0.0065 | - |
| 0.7084 | 51100 | 0.0065 | - |
| 0.7098 | 51200 | 0.0066 | - |
| 0.7112 | 51300 | 0.0065 | - |
| 0.7126 | 51400 | 0.0064 | - |
| 0.7139 | 51500 | 0.0063 | - |
| 0.7153 | 51600 | 0.0064 | - |
| 0.7167 | 51700 | 0.0063 | - |
| 0.7181 | 51800 | 0.0064 | - |
| 0.7195 | 51900 | 0.0065 | - |
| 0.7209 | 52000 | 0.0065 | - |
| 0.7223 | 52100 | 0.0065 | - |
| 0.7236 | 52200 | 0.0065 | - |
| 0.7250 | 52300 | 0.0065 | - |
| 0.7264 | 52400 | 0.0065 | - |
| 0.7278 | 52500 | 0.0065 | - |
| 0.7292 | 52600 | 0.0064 | - |
| 0.7306 | 52700 | 0.0065 | - |
| 0.7320 | 52800 | 0.0064 | - |
| 0.7333 | 52900 | 0.0064 | - |
| 0.7347 | 53000 | 0.0065 | - |
| 0.7361 | 53100 | 0.0063 | - |
| 0.7375 | 53200 | 0.0063 | - |
| 0.7389 | 53300 | 0.0064 | - |
| 0.7403 | 53400 | 0.0064 | - |
| 0.7417 | 53500 | 0.0064 | - |
| 0.7431 | 53600 | 0.0066 | - |
| 0.7444 | 53700 | 0.0064 | - |
| 0.7458 | 53800 | 0.0063 | - |
| 0.7472 | 53900 | 0.0064 | - |
| 0.7486 | 54000 | 0.0063 | - |
| 0.7500 | 54100 | 0.0063 | - |
| 0.7514 | 54200 | 0.0062 | - |
| 0.7528 | 54300 | 0.0064 | - |
| 0.7541 | 54400 | 0.0063 | - |
| 0.7555 | 54500 | 0.0063 | - |
| 0.7569 | 54600 | 0.0062 | - |
| 0.7583 | 54700 | 0.0063 | - |
| 0.7597 | 54800 | 0.0062 | - |
| 0.7611 | 54900 | 0.0062 | - |
| 0.7625 | 55000 | 0.0063 | - |
| 0.7638 | 55100 | 0.0065 | - |
| 0.7652 | 55200 | 0.0064 | - |
| 0.7666 | 55300 | 0.0062 | - |
| 0.7680 | 55400 | 0.0064 | - |
| 0.7694 | 55500 | 0.0063 | - |
| 0.7708 | 55600 | 0.0063 | - |
| 0.7722 | 55700 | 0.0062 | - |
| 0.7735 | 55800 | 0.0063 | - |
| 0.7749 | 55900 | 0.0062 | - |
| 0.7763 | 56000 | 0.0063 | - |
| 0.7777 | 56100 | 0.0064 | - |
| 0.7791 | 56200 | 0.0062 | - |
| 0.7805 | 56300 | 0.0065 | - |
| 0.7819 | 56400 | 0.006 | - |
| 0.7833 | 56500 | 0.0065 | - |
| 0.7846 | 56600 | 0.006 | - |
| 0.7860 | 56700 | 0.0062 | - |
| 0.7874 | 56800 | 0.0064 | - |
| 0.7888 | 56900 | 0.0061 | - |
| 0.7902 | 57000 | 0.0063 | - |
| 0.7916 | 57100 | 0.0062 | - |
| 0.7930 | 57200 | 0.0062 | - |
| 0.7943 | 57300 | 0.0062 | - |
| 0.7957 | 57400 | 0.0062 | - |
| 0.7971 | 57500 | 0.0062 | - |
| 0.7985 | 57600 | 0.0061 | - |
| 0.7999 | 57700 | 0.0061 | - |
| 0.8 | 57708 | - | 0.0022 |
| 0.8013 | 57800 | 0.0064 | - |
| 0.8027 | 57900 | 0.0062 | - |
| 0.8040 | 58000 | 0.0063 | - |
| 0.8054 | 58100 | 0.0061 | - |
| 0.8068 | 58200 | 0.0061 | - |
| 0.8082 | 58300 | 0.0063 | - |
| 0.8096 | 58400 | 0.0062 | - |
| 0.8110 | 58500 | 0.0062 | - |
| 0.8124 | 58600 | 0.0061 | - |
| 0.8138 | 58700 | 0.0062 | - |
| 0.8151 | 58800 | 0.0061 | - |
| 0.8165 | 58900 | 0.0061 | - |
| 0.8179 | 59000 | 0.0062 | - |
| 0.8193 | 59100 | 0.0062 | - |
| 0.8207 | 59200 | 0.0061 | - |
| 0.8221 | 59300 | 0.006 | - |
| 0.8235 | 59400 | 0.0061 | - |
| 0.8248 | 59500 | 0.006 | - |
| 0.8262 | 59600 | 0.006 | - |
| 0.8276 | 59700 | 0.0061 | - |
| 0.8290 | 59800 | 0.0062 | - |
| 0.8304 | 59900 | 0.0059 | - |
| 0.8318 | 60000 | 0.006 | - |
| 0.8332 | 60100 | 0.006 | - |
| 0.8345 | 60200 | 0.0061 | - |
| 0.8359 | 60300 | 0.006 | - |
| 0.8373 | 60400 | 0.0059 | - |
| 0.8387 | 60500 | 0.0061 | - |
| 0.8401 | 60600 | 0.006 | - |
| 0.8415 | 60700 | 0.0059 | - |
| 0.8429 | 60800 | 0.006 | - |
| 0.8443 | 60900 | 0.0061 | - |
| 0.8456 | 61000 | 0.0062 | - |
| 0.8470 | 61100 | 0.006 | - |
| 0.8484 | 61200 | 0.006 | - |
| 0.8498 | 61300 | 0.0059 | - |
| 0.8512 | 61400 | 0.0059 | - |
| 0.8526 | 61500 | 0.006 | - |
| 0.8540 | 61600 | 0.006 | - |
| 0.8553 | 61700 | 0.0059 | - |
| 0.8567 | 61800 | 0.006 | - |
| 0.8581 | 61900 | 0.0059 | - |
| 0.8595 | 62000 | 0.0059 | - |
| 0.8609 | 62100 | 0.0059 | - |
| 0.8623 | 62200 | 0.0059 | - |
| 0.8637 | 62300 | 0.0062 | - |
| 0.8650 | 62400 | 0.0061 | - |
| 0.8664 | 62500 | 0.0059 | - |
| 0.8678 | 62600 | 0.006 | - |
| 0.8692 | 62700 | 0.0061 | - |
| 0.8706 | 62800 | 0.0059 | - |
| 0.8720 | 62900 | 0.0061 | - |
| 0.8734 | 63000 | 0.006 | - |
| 0.8747 | 63100 | 0.0059 | - |
| 0.8761 | 63200 | 0.0059 | - |
| 0.8775 | 63300 | 0.0057 | - |
| 0.8789 | 63400 | 0.006 | - |
| 0.8803 | 63500 | 0.0058 | - |
| 0.8817 | 63600 | 0.0059 | - |
| 0.8831 | 63700 | 0.0058 | - |
| 0.8845 | 63800 | 0.0058 | - |
| 0.8858 | 63900 | 0.0059 | - |
| 0.8872 | 64000 | 0.0059 | - |
| 0.8886 | 64100 | 0.0059 | - |
| 0.8900 | 64200 | 0.0058 | - |
| 0.8914 | 64300 | 0.0058 | - |
| 0.8928 | 64400 | 0.006 | - |
| 0.8942 | 64500 | 0.0059 | - |
| 0.8955 | 64600 | 0.0059 | - |
| 0.8969 | 64700 | 0.0059 | - |
| 0.8983 | 64800 | 0.0058 | - |
| 0.8997 | 64900 | 0.0059 | - |
| 0.9011 | 65000 | 0.0059 | - |
| 0.9025 | 65100 | 0.0058 | - |
| 0.9039 | 65200 | 0.0058 | - |
| 0.9052 | 65300 | 0.0058 | - |
| 0.9066 | 65400 | 0.0059 | - |
| 0.9080 | 65500 | 0.0057 | - |
| 0.9094 | 65600 | 0.0057 | - |
| 0.9108 | 65700 | 0.0059 | - |
| 0.9122 | 65800 | 0.0059 | - |
| 0.9136 | 65900 | 0.0058 | - |
| 0.9150 | 66000 | 0.0058 | - |
| 0.9163 | 66100 | 0.0058 | - |
| 0.9177 | 66200 | 0.0057 | - |
| 0.9191 | 66300 | 0.0057 | - |
| 0.9205 | 66400 | 0.0059 | - |
| 0.9219 | 66500 | 0.0056 | - |
| 0.9233 | 66600 | 0.0058 | - |
| 0.9247 | 66700 | 0.0057 | - |
| 0.9260 | 66800 | 0.0058 | - |
| 0.9274 | 66900 | 0.0056 | - |
| 0.9288 | 67000 | 0.0057 | - |
| 0.9302 | 67100 | 0.0057 | - |
| 0.9316 | 67200 | 0.0055 | - |
| 0.9330 | 67300 | 0.0058 | - |
| 0.9344 | 67400 | 0.0058 | - |
| 0.9357 | 67500 | 0.0058 | - |
| 0.9371 | 67600 | 0.0057 | - |
| 0.9385 | 67700 | 0.0058 | - |
| 0.9399 | 67800 | 0.0056 | - |
| 0.9413 | 67900 | 0.0057 | - |
| 0.9427 | 68000 | 0.0058 | - |
| 0.9441 | 68100 | 0.0058 | - |
| 0.9454 | 68200 | 0.0057 | - |
| 0.9468 | 68300 | 0.0057 | - |
| 0.9482 | 68400 | 0.0057 | - |
| 0.9496 | 68500 | 0.0057 | - |
| 0.9510 | 68600 | 0.0057 | - |
| 0.9524 | 68700 | 0.0057 | - |
| 0.9538 | 68800 | 0.0059 | - |
| 0.9552 | 68900 | 0.0058 | - |
| 0.9565 | 69000 | 0.0058 | - |
| 0.9579 | 69100 | 0.0056 | - |
| 0.9593 | 69200 | 0.0057 | - |
| 0.9607 | 69300 | 0.0057 | - |
| 0.9621 | 69400 | 0.0057 | - |
| 0.9635 | 69500 | 0.0058 | - |
| 0.9649 | 69600 | 0.0056 | - |
| 0.9662 | 69700 | 0.0059 | - |
| 0.9676 | 69800 | 0.0055 | - |
| 0.9690 | 69900 | 0.0057 | - |
| 0.9704 | 70000 | 0.0054 | - |
| 0.9718 | 70100 | 0.0055 | - |
| 0.9732 | 70200 | 0.0055 | - |
| 0.9746 | 70300 | 0.0057 | - |
| 0.9759 | 70400 | 0.0057 | - |
| 0.9773 | 70500 | 0.0057 | - |
| 0.9787 | 70600 | 0.0056 | - |
| 0.9801 | 70700 | 0.0058 | - |
| 0.9815 | 70800 | 0.0054 | - |
| 0.9829 | 70900 | 0.0057 | - |
| 0.9843 | 71000 | 0.0056 | - |
| 0.9857 | 71100 | 0.0057 | - |
| 0.9870 | 71200 | 0.0057 | - |
| 0.9884 | 71300 | 0.0056 | - |
| 0.9898 | 71400 | 0.0057 | - |
| 0.9912 | 71500 | 0.0055 | - |
| 0.9926 | 71600 | 0.0055 | - |
| 0.9940 | 71700 | 0.0057 | - |
| 0.9954 | 71800 | 0.0057 | - |
| 0.9967 | 71900 | 0.0056 | - |
| 0.9981 | 72000 | 0.0058 | - |
| 0.9995 | 72100 | 0.0056 | - |
| 1.0 | 72135 | - | 0.0020 |
Framework Versions
- Python: 3.11.13
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.8.0+cu129
- Accelerate: 1.11.0
- Datasets: 4.3.0
- Tokenizers: 0.22.1
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",
}
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}
}