---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:99231
- loss:CachedMultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: who ordered the charge of the light brigade
sentences:
- Charge of the Light Brigade The Charge of the Light Brigade was a charge of British
light cavalry led by Lord Cardigan against Russian forces during the Battle of
Balaclava on 25 October 1854 in the Crimean War. Lord Raglan, overall commander
of the British forces, had intended to send the Light Brigade to prevent the Russians
from removing captured guns from overrun Turkish positions, a task well-suited
to light cavalry.
- UNICEF The United Nations International Children's Emergency Fund was created
by the United Nations General Assembly on 11 December 1946, to provide emergency
food and healthcare to children in countries that had been devastated by World
War II. The Polish physician Ludwik Rajchman is widely regarded as the founder
of UNICEF and served as its first chairman from 1946. On Rajchman's suggestion,
the American Maurice Pate was appointed its first executive director, serving
from 1947 until his death in 1965.[5][6] In 1950, UNICEF's mandate was extended
to address the long-term needs of children and women in developing countries everywhere.
In 1953 it became a permanent part of the United Nations System, and the words
"international" and "emergency" were dropped from the organization's name, making
it simply the United Nations Children's Fund, retaining the original acronym,
"UNICEF".[3]
- Marcus Jordan Marcus James Jordan (born December 24, 1990) is an American former
college basketball player who played for the UCF Knights men's basketball team
of Conference USA.[1] He is the son of retired Hall of Fame basketball player
Michael Jordan.
- source_sentence: what part of the cow is the rib roast
sentences:
- Standing rib roast A standing rib roast, also known as prime rib, is a cut of
beef from the primal rib, one of the nine primal cuts of beef. While the entire
rib section comprises ribs six through 12, a standing rib roast may contain anywhere
from two to seven ribs.
- Blaine Anderson Kurt begins to mend their relationship in "Thanksgiving", just
before New Directions loses at Sectionals to the Warblers, and they spend Christmas
together in New York City.[29][30] Though he and Kurt continue to be on good terms,
Blaine finds himself developing a crush on his best friend, Sam, which he knows
will come to nothing as he knows Sam is not gay; the two of them team up to find
evidence that the Warblers cheated at Sectionals, which means New Directions will
be competing at Regionals. He ends up going to the Sadie Hawkins dance with Tina
Cohen-Chang (Jenna Ushkowitz), who has developed a crush on him, but as friends
only.[31] When Kurt comes to Lima for the wedding of glee club director Will (Matthew
Morrison) and Emma (Jayma Mays)—which Emma flees—he and Blaine make out beforehand,
and sleep together afterward, though they do not resume a permanent relationship.[32]
- 'Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky Soyúz,
IPA: [sɐˈvʲɛt͡skʲɪj sɐˈjus] ( listen)), officially the Union of Soviet Socialist
Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr. Soyúz Sovétskikh
Sotsialistícheskikh Respúblik, IPA: [sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx
rʲɪˈspublʲɪk] ( listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was
a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union
of multiple national Soviet republics,[a] its government and economy were highly
centralized. The country was a one-party state, governed by the Communist Party
with Moscow as its capital in its largest republic, the Russian Soviet Federative
Socialist Republic. The Russian nation had constitutionally equal status among
the many nations of the union but exerted de facto dominance in various respects.[7]
Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk.
The Soviet Union was one of the five recognized nuclear weapons states and possessed
the largest stockpile of weapons of mass destruction.[8] It was a founding permanent
member of the United Nations Security Council, as well as a member of the Organization
for Security and Co-operation in Europe (OSCE) and the leading member of the Council
for Mutual Economic Assistance (CMEA) and the Warsaw Pact.'
- source_sentence: what is the current big bang theory season
sentences:
- Byzantine army From the seventh to the 12th centuries, the Byzantine army was
among the most powerful and effective military forces in the world – neither
Middle Ages Europe nor (following its early successes) the fracturing Caliphate
could match the strategies and the efficiency of the Byzantine army. Restricted
to a largely defensive role in the 7th to mid-9th centuries, the Byzantines developed
the theme-system to counter the more powerful Caliphate. From the mid-9th century,
however, they gradually went on the offensive, culminating in the great conquests
of the 10th century under a series of soldier-emperors such as Nikephoros II Phokas,
John Tzimiskes and Basil II. The army they led was less reliant on the militia
of the themes; it was by now a largely professional force, with a strong and well-drilled
infantry at its core and augmented by a revived heavy cavalry arm. With one of
the most powerful economies in the world at the time, the Empire had the resources
to put to the field a powerful host when needed, in order to reclaim its long-lost
territories.
- The Big Bang Theory The Big Bang Theory is an American television sitcom created
by Chuck Lorre and Bill Prady, both of whom serve as executive producers on the
series, along with Steven Molaro. All three also serve as head writers. The show
premiered on CBS on September 24, 2007.[3] The series' tenth season premiered
on September 19, 2016.[4] In March 2017, the series was renewed for two additional
seasons, bringing its total to twelve, and running through the 2018–19 television
season. The eleventh season is set to premiere on September 25, 2017.[5]
- 2016 NCAA Division I Softball Tournament The 2016 NCAA Division I Softball Tournament
was held from May 20 through June 8, 2016 as the final part of the 2016 NCAA Division
I softball season. The 64 NCAA Division I college softball teams were to be selected
out of an eligible 293 teams on May 15, 2016. Thirty-two teams were awarded an
automatic bid as champions of their conference, and thirty-two teams were selected
at-large by the NCAA Division I softball selection committee. The tournament culminated
with eight teams playing in the 2016 Women's College World Series at ASA Hall
of Fame Stadium in Oklahoma City in which the Oklahoma Sooners were crowned the
champions.
- source_sentence: what happened to tates mom on days of our lives
sentences:
- 'Paige O''Hara Donna Paige Helmintoller, better known as Paige O''Hara (born May
10, 1956),[1] is an American actress, voice actress, singer and painter. O''Hara
began her career as a Broadway actress in 1983 when she portrayed Ellie May Chipley
in the musical Showboat. In 1991, she made her motion picture debut in Disney''s
Beauty and the Beast, in which she voiced the film''s heroine, Belle. Following
the critical and commercial success of Beauty and the Beast, O''Hara reprised
her role as Belle in the film''s two direct-to-video follow-ups, Beauty and the
Beast: The Enchanted Christmas and Belle''s Magical World.'
- M. Shadows Matthew Charles Sanders (born July 31, 1981), better known as M. Shadows,
is an American singer, songwriter, and musician. He is best known as the lead
vocalist, songwriter, and a founding member of the American heavy metal band Avenged
Sevenfold. In 2017, he was voted 3rd in the list of Top 25 Greatest Modern Frontmen
by Ultimate Guitar.[1]
- Theresa Donovan In July 2013, Jeannie returns to Salem, this time going by her
middle name, Theresa. Initially, she strikes up a connection with resident bad
boy JJ Deveraux (Casey Moss) while trying to secure some pot.[28] During a confrontation
with JJ and his mother Jennifer Horton (Melissa Reeves) in her office, her aunt
Kayla confirms that Theresa is in fact Jeannie and that Jen promised to hire her
as her assistant, a promise she reluctantly agrees to. Kayla reminds Theresa it
is her last chance at a fresh start.[29] Theresa also strikes up a bad first impression
with Jennifer's daughter Abigail Deveraux (Kate Mansi) when Abigail smells pot
on Theresa in her mother's office.[30] To continue to battle against Jennifer,
she teams up with Anne Milbauer (Meredith Scott Lynn) in hopes of exacting her
perfect revenge. In a ploy, Theresa reveals her intentions to hopefully woo Dr.
Daniel Jonas (Shawn Christian). After sleeping with JJ, Theresa overdoses on marijuana
and GHB. Upon hearing of their daughter's overdose and continuing problems, Shane
and Kimberly return to town in the hopes of handling their daughter's problem,
together. After believing that Theresa has a handle on her addictions, Shane and
Kimberly leave town together. Theresa then teams up with hospital co-worker Anne
Milbauer (Meredith Scott Lynn) to conspire against Jennifer, using Daniel as a
way to hurt their relationship. In early 2014, following a Narcotics Anonymous
(NA) meeting, she begins a sexual and drugged-fused relationship with Brady Black
(Eric Martsolf). In 2015, after it is found that Kristen DiMera (Eileen Davidson)
stole Theresa's embryo and carried it to term, Brady and Melanie Jonas return
her son, Christopher, to her and Brady, and the pair rename him Tate. When Theresa
moves into the Kiriakis mansion, tensions arise between her and Victor. She eventually
expresses her interest in purchasing Basic Black and running it as her own fashion
company, with financial backing from Maggie Horton (Suzanne Rogers). In the hopes
of finding the right partner, she teams up with Kate Roberts (Lauren Koslow) and
Nicole Walker (Arianne Zucker) to achieve the goal of purchasing Basic Black,
with Kate and Nicole's business background and her own interest in fashion design.
As she and Brady share several instances of rekindling their romance, she is kicked
out of the mansion by Victor; as a result, Brady quits Titan and moves in with
Theresa and Tate, in their own penthouse.
- source_sentence: where does the last name francisco come from
sentences:
- Francisco Francisco is the Spanish and Portuguese form of the masculine given
name Franciscus (corresponding to English Francis).
- 'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah),
is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the
Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls
(Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia,
born as Hadassah but known as Esther, who becomes queen of Persia and thwarts
a genocide of her people. The story forms the core of the Jewish festival of Purim,
during which it is read aloud twice: once in the evening and again the following
morning. The books of Esther and Song of Songs are the only books in the Hebrew
Bible that do not explicitly mention God.[2]'
- Times Square Times Square is a major commercial intersection, tourist destination,
entertainment center and neighborhood in the Midtown Manhattan section of New
York City at the junction of Broadway and Seventh Avenue. It stretches from West
42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements,
Times Square is sometimes referred to as "The Crossroads of the World",[2] "The
Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the
"heart of the world".[7] One of the world's busiest pedestrian areas,[8] it is
also the hub of the Broadway Theater District[9] and a major center of the world's
entertainment industry.[10] Times Square is one of the world's most visited tourist
attractions, drawing an estimated 50 million visitors annually.[11] Approximately
330,000 people pass through Times Square daily,[12] many of them tourists,[13]
while over 460,000 pedestrians walk through Times Square on its busiest days.[7]
datasets:
- sentence-transformers/natural-questions
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
co2_eq_emissions:
emissions: 119.22214303175399
energy_consumed: 0.30671862514665216
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.791
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: MPNet base trained on Natural Questions pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
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: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
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: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.28
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.56
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.72
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.76
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5257981082890367
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4493809523809524
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4607137051964638
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.74
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20666666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15600000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.36
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.58
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.71
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.73
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.569704410576423
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5262222222222221
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5211366938031639
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.33
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5800000000000001
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.73
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.33
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15000000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5700000000000001
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.715
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.745
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5477512594327298
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4878015873015873
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.49092519949981384
name: Cosine Map@100
---
# MPNet base trained on Natural Questions pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) 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:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'MPNetModel'})
(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:
```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("tomaarsen/mpnet-base-nq-prompts-repro")
# Run inference
queries = [
"where does the last name francisco come from",
]
documents = [
'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
'Times Square Times Square is a major commercial intersection, tourist destination, entertainment center and neighborhood in the Midtown Manhattan section of New York City at the junction of Broadway and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements, Times Square is sometimes referred to as "The Crossroads of the World",[2] "The Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the "heart of the world".[7] One of the world\'s busiest pedestrian areas,[8] it is also the hub of the Broadway Theater District[9] and a major center of the world\'s entertainment industry.[10] Times Square is one of the world\'s most visited tourist attractions, drawing an estimated 50 million visitors annually.[11] Approximately 330,000 people pass through Times Square daily,[12] many of them tourists,[13] while over 460,000 pedestrians walk through Times Square on its busiest days.[7]',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7532, -0.0007, 0.0170]])
```
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoMSMARCO` and `NanoNQ`
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
```json
{
"query_prompt": "query: ",
"corpus_prompt": "document: "
}
```
| Metric | NanoMSMARCO | NanoNQ |
|:--------------------|:------------|:-----------|
| cosine_accuracy@1 | 0.28 | 0.38 |
| cosine_accuracy@3 | 0.56 | 0.6 |
| cosine_accuracy@5 | 0.72 | 0.74 |
| cosine_accuracy@10 | 0.76 | 0.76 |
| cosine_precision@1 | 0.28 | 0.38 |
| cosine_precision@3 | 0.1867 | 0.2067 |
| cosine_precision@5 | 0.144 | 0.156 |
| cosine_precision@10 | 0.076 | 0.08 |
| cosine_recall@1 | 0.28 | 0.36 |
| cosine_recall@3 | 0.56 | 0.58 |
| cosine_recall@5 | 0.72 | 0.71 |
| cosine_recall@10 | 0.76 | 0.73 |
| **cosine_ndcg@10** | **0.5258** | **0.5697** |
| cosine_mrr@10 | 0.4494 | 0.5262 |
| cosine_map@100 | 0.4607 | 0.5211 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nq"
],
"query_prompts": {
"msmarco": "query: ",
"nq": "query: "
},
"corpus_prompts": {
"msmarco": "document: ",
"nq": "document: "
}
}
```
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.33 |
| cosine_accuracy@3 | 0.58 |
| cosine_accuracy@5 | 0.73 |
| cosine_accuracy@10 | 0.76 |
| cosine_precision@1 | 0.33 |
| cosine_precision@3 | 0.1967 |
| cosine_precision@5 | 0.15 |
| cosine_precision@10 | 0.078 |
| cosine_recall@1 | 0.32 |
| cosine_recall@3 | 0.57 |
| cosine_recall@5 | 0.715 |
| cosine_recall@10 | 0.745 |
| **cosine_ndcg@10** | **0.5478** |
| cosine_mrr@10 | 0.4878 |
| cosine_map@100 | 0.4909 |
## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,231 training samples
* Columns: query and answer
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 10 tokens
- mean: 11.74 tokens
- max: 24 tokens
| - min: 15 tokens
- mean: 137.2 tokens
- max: 508 tokens
|
* Samples:
| query | answer |
|:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| who is required to report according to the hmda | Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5] |
| what is the definition of endoplasmic reticulum in biology | Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 using elect... |
| what does the ski mean in polish names | Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today. |
* Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 16
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: query and answer
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 10 tokens
- mean: 11.78 tokens
- max: 22 tokens
| - min: 11 tokens
- mean: 135.64 tokens
- max: 512 tokens
|
* Samples:
| query | answer |
|:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| difference between russian blue and british blue cat | Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits. |
| who played the little girl on mrs doubtfire | Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing. |
| what year did the movie the sound of music come out | The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000. |
* Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"mini_batch_size": 16
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `prompts`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: 12
- `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`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `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`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}