carlfeynman/reproduce-static-retrieval-mrl-en-v1

[REPRODUCE] Static Embeddings with BERT uncased tokenizer finetuned on various datasets

This is a sentence-transformers model trained on the gooaq, msmarco, s2orc, allnli and paq datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: inf tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • gooaq
  • msmarco
  • s2orc
  • allnli
  • paq
• Language: en
• License: apache-2.0

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): StaticEmbedding(
    (embedding): EmbeddingBag(30522, 1024, mode='mean')
  )
)

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("carlfeynman/reproduce-static-retrieval-mrl-en-v1")
# Run inference
sentences = [
    'A boy with a basketballs glowers at the camera.',
    'The boy scowls',
    'The boy is smiling',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

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

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.32	0.64	0.54	0.22	0.66	0.16	0.4	0.2	0.8	0.28	0.12	0.52	0.5918
cosine_accuracy@3	0.54	0.88	0.82	0.44	0.82	0.44	0.54	0.46	0.92	0.52	0.48	0.64	0.9184
cosine_accuracy@5	0.64	0.92	0.84	0.5	0.86	0.54	0.6	0.58	0.96	0.62	0.6	0.68	0.9796
cosine_accuracy@10	0.82	0.94	0.94	0.64	0.94	0.66	0.7	0.68	0.98	0.72	0.68	0.74	1.0
cosine_precision@1	0.32	0.64	0.54	0.22	0.66	0.16	0.4	0.2	0.8	0.28	0.12	0.52	0.5918
cosine_precision@3	0.2	0.6067	0.2733	0.1867	0.36	0.1467	0.3467	0.1533	0.36	0.2267	0.16	0.22	0.585
cosine_precision@5	0.152	0.548	0.18	0.132	0.264	0.108	0.3	0.12	0.24	0.184	0.12	0.144	0.6
cosine_precision@10	0.112	0.454	0.1	0.098	0.148	0.066	0.244	0.074	0.128	0.14	0.068	0.08	0.5204
cosine_recall@1	0.1567	0.0582	0.5067	0.1269	0.33	0.16	0.0614	0.19	0.7107	0.0597	0.12	0.485	0.0406
cosine_recall@3	0.25	0.166	0.7567	0.2901	0.54	0.44	0.0938	0.44	0.8653	0.1417	0.48	0.61	0.1204
cosine_recall@5	0.3163	0.2233	0.8033	0.3347	0.66	0.54	0.1147	0.55	0.9227	0.1897	0.6	0.655	0.203
cosine_recall@10	0.4413	0.3264	0.9033	0.453	0.74	0.66	0.1376	0.67	0.9593	0.2887	0.68	0.72	0.3313
cosine_ndcg@10	0.3503	0.5612	0.7223	0.3321	0.6508	0.4069	0.3251	0.4285	0.8744	0.2658	0.4064	0.6054	0.5595
cosine_mrr@10	0.4538	0.7552	0.6857	0.3497	0.7467	0.3269	0.4908	0.3556	0.8667	0.4189	0.3179	0.5862	0.7495
cosine_map@100	0.2755	0.4216	0.6591	0.2723	0.5744	0.341	0.1535	0.3595	0.8354	0.2027	0.3345	0.5721	0.4415

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.4194
cosine_accuracy@3	0.6476
cosine_accuracy@5	0.7169
cosine_accuracy@10	0.8031
cosine_precision@1	0.4194
cosine_precision@3	0.2942
cosine_precision@5	0.2378
cosine_precision@10	0.1717
cosine_recall@1	0.2312
cosine_recall@3	0.3995
cosine_recall@5	0.4702
cosine_recall@10	0.5624
cosine_ndcg@10	0.4991
cosine_mrr@10	0.5464
cosine_map@100	0.4187

Training Details

Training Datasets

gooaq

• Dataset: gooaq at b089f72
• Size: 3,012,496 training samples
• Columns: question and answer
• Approximate statistics based on the first 1000 samples:

  	question	answer
  type	string	string
  details	min: 18 characters mean: 43.23 characters max: 96 characters	min: 55 characters mean: 253.36 characters max: 371 characters

• Samples:

  question	answer
  what is the difference between broilers and layers?	An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.
  what is the difference between chronological order and spatial order?	As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.
  is kamagra same as viagra?	Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

msmarco

• Dataset: msmarco at 84ed2d3
• Size: 502,939 training samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 11 characters mean: 33.26 characters max: 197 characters	min: 96 characters mean: 356.24 characters max: 1006 characters	min: 68 characters mean: 327.52 characters max: 995 characters

• Samples:

  query	positive	negative
  when was the sullivan acts	Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.	Sullivan Act Tim Sullivan, a major Irish criminal passed the Sullivan Act in 1911 to help his constituents rob strangers or to help them against Italian incomers. That is the crux of story that goes with a very early gun control law.
  can lavender grow indoors	Growing Lavender Indoors. People ALWAYS ask if you can grow lavender indoors. Well, you can, but most Lavender does best outside. Here is our winter experiment to show you what it would look like. This is one of our 4 Lavender Babies from Fall 2010. Our test specimen is L. x intermedia 'Grosso'.	Lavender can be grown indoors with a bit of effort to keep it in the conditions it loves to thrive. First off begin with choosing a variety that is better able to tolerate the conditions inside a home. To successfully grow Lavender indoors you need to create optimal growing conditions which is hard to do inside a house.
  what kind of barley do you malt	Barley is a wonderfully versatile cereal grain with a rich nutlike flavor and an appealing chewy, pasta-like consistency. Its appearance resembles wheat berries, although it is slightly lighter in color. Sprouted barley is naturally high in maltose, a sugar that serves as the basis for both malt syrup sweetener.	Specialty grains that can be used in this way are usually barley, malted or unmalted, that has been treated differently at the malting company. Crystal malt is one of the specialty grains. It is available in a whole range of colors, from 20 to 120 Lovibond. Crystal malt is malted barley that is heated while wet.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

s2orc

• Dataset: s2orc at 8cfc394
• Size: 90,000 training samples
• Columns: title and abstract
• Approximate statistics based on the first 1000 samples:

  	title	abstract
  type	string	string
  details	min: 31 characters mean: 80.02 characters max: 185 characters	min: 84 characters mean: 635.31 characters max: 1023 characters

• Samples:

  title	abstract
  Modeling Method of Flow Diversion of the Three Outlets in Jingjiang Reach Under Unsteady Flow Conditions	The Yangtze River Flood Protection Physical Model is built under the financial support of World Bank loan.Based on theoretical analysis and experimental study,a modeling method of flow diversion of the three outlets in Jingjiang Reach under unsteady flow conditions was established for the model.Validation tests under both steady and unsteady flow conditions manifested that with this modeling method,the experimental flow diversion proves to be consistent with that of the prototype and therefore meets the requirements for precision.Being validated,this modeling method has been applied to Yangtze River Flood Protection Physical Model to study the flood routing features in Jingjiang reach.
  Enlightening on medical administration by clinical governance in British	Medical quality and safety were the responsibilities of medical system in view of British clinical governance. Medical regulation institutes were considered to be built and be authorized regulation rights. British medical administration was introduced and its enlightening in China was mentioned.
  APPLICATION OF A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR SHIPPING COMPANY PERFORMANCE EVALUATION	Combining fuzzy set theory, Analytic Hierarchy Process (AHP) and concept of entropy, a fuzzy Multiple Criteria Decision-Making (MCDM) model for shipping company performance evaluation is proposed. First, the AHP is used to construct subjective weights for all criteria and sub-criteria. Then, linguistic values characterized by triangular fuzzy numbers and trapezoidal fuzzy numbers are used to denote the evaluation values of all alternatives with respect to various subjective and objective criteria. Finally, the aggregation fuzzy assessment of different shipping companies is ranked to determine the best selection. Utilizing this fuzzy MCDM model, the decision-maker's fuzzy assessment and the trade-off between various evaluations criteria can be taken into account in the aggregation process, thus ensuring more effective and accurate decision-making.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

allnli

• Dataset: allnli at d482672
• Size: 557,850 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 18 characters mean: 34.88 characters max: 193 characters	min: 15 characters mean: 46.49 characters max: 181 characters	min: 16 characters mean: 50.47 characters max: 204 characters

• Samples:

  anchor	positive	negative
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.	A person is at a diner, ordering an omelette.
  Children smiling and waving at camera	There are children present	The kids are frowning
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.	The boy skates down the sidewalk.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

paq

• Dataset: paq at 74601d8
• Size: 64,371,441 training samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 25 characters mean: 50.56 characters max: 104 characters	min: 509 characters mean: 620.96 characters max: 773 characters

• Samples:

  query	answer
  in veetla visheshanga ganesh is the husband of	Veetla Visheshanga a song which reminds Ganga's memory. She is actually not Ganga but Gowri and her lover is the groom named Ganesh. When both were about to marry they were stopped by some goons because of which Gowri fell from the mountain but survived with injuries. Gopal who found the truth brought Ganesh to unite them. Gopal insists Gowri to marry Ganesh as both of them are lovers to which Gowri unwillingly accepts. But while Ganesh tries to tie the Mangal Sutra, Gowri stops him and she goes to Gopal saying that he may not need her but she needs him
  when did simon property group became a publicly traded company	of the S&P 100. Simon Property Group has been the subject of several lawsuits and investigations regarding civil rights and discrimination. Simon Property Group was formed in 1993 when the majority of the shopping center interests of Melvin Simon & Associates became a publicly traded company. Melvin Simon & Associates, owned by brothers Melvin Simon and Herbert Simon, was founded in 1960 in Indianapolis, Indiana, and had long been one of the top shopping center developers in the United States. In 1996, Simon DeBartolo Group was created when Simon Property merged with former rival DeBartolo Realty Corp. This was shortly
  what was the nationality of antoine faivre	Theosophy (Boehmian) below. "Theosophy": The scholar of esotericism Wouter Hanegraaff described Christian theosophy as "one of the major currents in the history of Western esotericism". Christian theosophy is an under-researched area; a general history of it has never been written. The French scholar Antoine Faivre had a specific interest in the theosophers and illuminists of the eighteenth and nineteenth centuries. He wrote his doctoral thesis on Karl von Eckartshausen and Christian theosophy. Scholars of esotericism have argued that Faivre's definition of Western esotericism relies on his own specialist focus on Christian theosophy, Renaissance Hermeticism, and Romantic "Naturphilosophie" and therefore creates an "ideal"

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Evaluation Datasets

gooaq

• Dataset: gooaq at b089f72
• Size: 3,012,496 evaluation samples
• Columns: question and answer
• Approximate statistics based on the first 1000 samples:

  	question	answer
  type	string	string
  details	min: 18 characters mean: 43.17 characters max: 98 characters	min: 51 characters mean: 254.12 characters max: 360 characters

• Samples:

  question	answer
  how do i program my directv remote with my tv?	['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']
  are rodrigues fruit bats nocturnal?	Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.
  why does your heart rate increase during exercise bbc bitesize?	During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

msmarco

• Dataset: msmarco at 84ed2d3
• Size: 502,939 evaluation samples
• Columns: query, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	query	positive	negative
  type	string	string	string
  details	min: 10 characters mean: 33.36 characters max: 137 characters	min: 67 characters mean: 347.87 characters max: 906 characters	min: 57 characters mean: 318.18 characters max: 906 characters

• Samples:

  query	positive	negative
  is cabinet refacing worth the cost?	Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.	Most cabinet refacing projects cost about $4,000 to $10,000. The price varies based on the materials you select and the size and configuration of your kitchen. Wood veneer doors, for example, will cost less than solid wood doors.
  is the fovea ethmoidalis a bone	Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a “cock's comb,” that projects intracranially and attaches to the falx cerebri.	Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a “cock's comb,” that projects intracranially and attaches to the falx cerebri.
  average pitches per inning	The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.	The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

s2orc

• Dataset: s2orc at 8cfc394
• Size: 10,000 evaluation samples
• Columns: title and abstract
• Approximate statistics based on the first 1000 samples:

  	title	abstract
  type	string	string
  details	min: 31 characters mean: 80.04 characters max: 198 characters	min: 96 characters mean: 653.93 characters max: 1023 characters

• Samples:

  title	abstract
  Screen Printing Ink Film Thickness Analysis of the Passive RFID Tag Antenna	The relationship between the screen mesh and the theoretical and practical ink film thickness was analyzed based on the main influencing factors of the ink film thickness by screen printing.A calculation model for the ink thickness was established based on the screen under static and compressive deformation.The relation curve between the screen mesh and the ink film thickness was fitted and the suitable printing craft parameter was chosen to print two kinds of RFID tag antennas.The fluctuation of the antenna resistance was analyzed to demonstrate the reliability of the passive RFID tag antenna manufactured by screen printing technology.
  Subclinical organ damage and cardiovascular risk prediction	AbstractTraditional cardiovascular risk factors have poor prognostic value for individuals and screening for subclinical organ damage has been recommended in hypertension in recent guidelines. The aim of this review was to investigate the clinical impact of the additive prognostic information provided by measuring subclinical organ damage. We have (i) reviewed recent studies linking markers of subclinical organ damage in the heart, blood vessels and kidney to cardiovascular risk; (ii) discussed the evidence for improvement in cardiovascular risk prediction using markers of subclinical organ damage; (iii) investigated which and how many markers to measure and (iv) finally discussed whether measuring subclinical organ damage provided benefits beyond risk prediction. In conclusion, more studies and if possible randomized studies are needed to investigate (i) the importance of markers of subclinical organ damage for risk discrimination, calibration and reclassification; and (ii) the econom...
  A Novel Approach to Simulate Climate Change Impacts on Vascular Epiphytes: Case Study in Taiwan	In the wet tropics, epiphytes form a conspicuous layer in the forest canopy, support abundant coexisting biota, and are known to have a critical influence on forest hydrology and nutrient cycling. Since canopy-dwelling plants have no vascular connection to the ground or their host plants, they are likely more sensitive to environmental changes than their soil-rooted counterparts, subsequently regarded as one of the groups most vulnerable to global climate change. Epiphytes have adapted to life in highly dynamic forest canopies by producing many, mostly wind-dispersed, seeds or spores. Consequently, epiphytes should colonize trees rapidly, which, in addition to atmospheric sensitivity and short life cycles, make epiphytes suitable climate change indicators. In this study, we assess the impact of climate change on Taiwanese epiphytes using a modeling approach.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

allnli

• Dataset: allnli at d482672
• Size: 6,584 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 15 characters mean: 72.82 characters max: 300 characters	min: 12 characters mean: 34.11 characters max: 126 characters	min: 11 characters mean: 36.38 characters max: 121 characters

• Samples:

  anchor	positive	negative
  Two women are embracing while holding to go packages.	Two woman are holding packages.	The men are fighting outside a deli.
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	Two kids in numbered jerseys wash their hands.	Two kids in jackets walk to school.
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	A man selling donuts to a customer.	A woman drinks her coffee in a small cafe.

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

paq

• Dataset: paq at 74601d8
• Size: 64,371,441 evaluation samples
• Columns: query and answer
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	min: 25 characters mean: 51.3 characters max: 108 characters	min: 504 characters mean: 623.09 characters max: 835 characters

• Samples:

  query	answer
  when did season 3 of the voice brasil start	The Voice Brasil (season 3) The third season of "The Voice Brasil", premiered on Rede Globo on September 18, 2014 in the 10:30 p.m. (BRT/AMT) slot immediately following the primetime telenovela "Império". The 22- and 24-year-old sertanejo duo Danilo Reis e Rafael won the competition on December 25, 2014 with 43% of the votes cast. This marked Lulu Santos' first win as a coach, the first stolen artist to win a Brazilian season of "The Voice", and the first time in any "The Voice" franchise that a duo won the competition. Online applications for "The Voice Brasil" were open on
  when did the little ranger first come out	Gang" theme song was an instrumental medley of "London Bridge", "Here We Go Round the Mulberry Bush" and "The Farmer in the Dell". It remained in use until the series ended in 1944. The Little Ranger The Little Ranger is a 1938 "Our Gang" short comedy film directed by Gordon Douglas. It was the 169th short in the "Our Gang" series, and the first produced by Metro-Goldwyn-Mayer, who purchased the rights to the series from creator Hal Roach. Snubbed by his girlfriend Darla, Alfalfa accepts the invitation of tomboyish Muggsy to attend the local picture show. While watching the adventures
  what is the name of rachel's sister in ninjaaiden	her among ten female characters who have never been featured on their games' cover arts, Samir Torres of VentureBeat wrote that while "Team Ninja sexualy exploits all of their female characters, yet Rachel somehow got axed from every modern "Ninja Gaiden" box art." Rachel (Ninja Gaiden) In 2004's "Ninja Gaiden", Rachel is a fiend hunter whom the game's protagonist Ryu Hayabusa meets in the Holy Vigoor Empire, where she is on a mission to destroy the fiends, as well as find her missing sister, Alma, who has become a Greater Fiend. Soon after they first meet, she is captured but

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          1024,
          512,
          256,
          128,
          64,
          32
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16384
• per_device_eval_batch_size: 4096
• learning_rate: 0.2
• num_train_epochs: 1
• warmup_ratio: 0.1
• fp16: True
• 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: 16384
• per_device_eval_batch_size: 4096
• 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: 0.2
• 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: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• 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
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	gooaq loss	msmarco loss	s2orc loss	allnli loss	paq loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0.0002	1	43.5181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0597	250	17.804	2.1081	12.8291	10.8194	14.2895	5.3792	0.3202	0.5446	0.6721	0.3176	0.6222	0.3867	0.3022	0.3952	0.8741	0.2474	0.3986	0.5913	0.5463	0.4783
0.1195	500	9.6842	1.6991	12.2374	10.6084	13.9790	4.7183	0.3148	0.5759	0.7063	0.3640	0.6250	0.3846	0.2832	0.4168	0.8659	0.2537	0.3744	0.5732	0.5509	0.4837
0.1792	750	8.7691	1.6922	12.0631	10.3970	12.4485	4.4473	0.3496	0.5664	0.7157	0.3179	0.6585	0.3826	0.2934	0.4040	0.8782	0.2523	0.3845	0.5962	0.5502	0.4884
0.2389	1000	8.606	1.6685	11.7765	10.2828	12.4139	4.2823	0.3509	0.5636	0.7026	0.3249	0.6562	0.4049	0.3123	0.4174	0.8673	0.2657	0.3969	0.5582	0.5514	0.4902
0.2987	1250	8.4178	1.6072	11.7581	9.2590	12.8865	4.2231	0.3341	0.5587	0.7103	0.3354	0.6534	0.4033	0.3116	0.4294	0.8663	0.2718	0.4048	0.5891	0.5466	0.4934
0.3584	1500	8.1084	1.6751	11.8237	9.8291	11.5805	4.1559	0.3345	0.5668	0.7094	0.3287	0.6535	0.3948	0.3311	0.4098	0.8632	0.2649	0.4171	0.5913	0.5514	0.4936
0.4182	1750	7.9489	1.5858	11.8367	9.8385	13.0328	4.0980	0.3543	0.5464	0.6984	0.3158	0.6582	0.3862	0.3233	0.4201	0.8665	0.2743	0.3924	0.5909	0.5577	0.4911
0.4779	2000	8.2594	1.6123	11.8052	9.9075	11.3651	4.0788	0.3491	0.5551	0.7208	0.3235	0.6570	0.4058	0.3220	0.4215	0.8801	0.2629	0.4143	0.5998	0.5514	0.4972
0.5376	2250	8.299	1.6416	11.7180	9.9462	10.7895	4.0423	0.3636	0.5582	0.7071	0.3048	0.6649	0.3951	0.3248	0.4316	0.8804	0.2561	0.4252	0.6036	0.5484	0.4972
0.5974	2500	7.7807	1.6518	11.7898	9.9235	11.1670	4.0001	0.3639	0.5556	0.7288	0.3148	0.6525	0.3979	0.3178	0.4436	0.8860	0.2593	0.4208	0.5935	0.5581	0.4994
0.6571	2750	7.8997	1.5797	11.6813	9.5124	11.4893	3.9633	0.3465	0.5562	0.7084	0.3101	0.6631	0.4102	0.3194	0.4410	0.8805	0.2566	0.4261	0.5983	0.5552	0.4978
0.7168	3000	8.0204	1.5620	11.6746	9.6655	10.8783	3.9539	0.3439	0.5569	0.7295	0.3173	0.6606	0.4129	0.3180	0.4521	0.8888	0.2576	0.4012	0.6065	0.5560	0.5001
0.7766	3250	8.0225	1.4596	11.5664	9.6954	10.9838	3.9493	0.3496	0.5626	0.7239	0.3330	0.6551	0.4197	0.3129	0.4491	0.8893	0.2726	0.4061	0.6103	0.5555	0.5031
0.8363	3500	7.6933	1.5522	11.6974	9.1753	11.2026	3.9082	0.3581	0.5570	0.7170	0.3216	0.6492	0.4018	0.3204	0.4360	0.8841	0.2675	0.4031	0.6052	0.5553	0.4982
0.8961	3750	7.711	1.5267	11.6615	9.4673	11.3195	3.8847	0.3563	0.5613	0.7162	0.3265	0.6497	0.4109	0.3253	0.4384	0.8713	0.2657	0.4195	0.6058	0.5566	0.5003
0.9558	4000	7.8549	1.5300	11.6244	9.1383	11.0781	3.8785	0.3533	0.5609	0.7153	0.3285	0.6528	0.4069	0.3250	0.4382	0.8744	0.2642	0.4068	0.5961	0.5595	0.4986
1.0	4185	-	-	-	-	-	-	0.3503	0.5612	0.7223	0.3321	0.6508	0.4069	0.3251	0.4285	0.8744	0.2658	0.4064	0.6054	0.5595	0.4991

Framework Versions

• Python: 3.10.15
• Sentence Transformers: 3.3.1
• Transformers: 4.47.1
• PyTorch: 2.4.1
• Accelerate: 1.1.1
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}