Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:6661966
loss:MultipleNegativesRankingLoss
loss:CachedMultipleNegativesRankingLoss
loss:SoftmaxLoss
loss:AnglELoss
loss:CoSENTLoss
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use tasksource/ModernBERT-base-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tasksource/ModernBERT-base-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tasksource/ModernBERT-base-embed") sentences = [ "Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra dropped the apple. Sandra went to the garden. Sandra went back to the bedroom. Sandra went back to the office. Mary went back to the office. Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to the garden. Sandra put down the apple there. Mary went back to the bathroom. Daniel travelled to the garden. Mary took the milk. Sandra grabbed the apple. Mary left the milk there. Sandra journeyed to the bedroom. John travelled to the office. John went back to the garden. Sandra journeyed to the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the milk. Mary went to the hallway. John moved to the hallway. Mary picked up the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary discarded the milk. John journeyed to the garden. Mary dropped the football. Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary travelled to the bathroom. Daniel went to the bedroom. Mary went to the hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk. Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden. Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk. Sandra put down the milk. John journeyed to the garden. Sandra went back to the hallway. Sandra picked up the apple. Sandra got the football. Sandra moved to the garden. Daniel moved to the bathroom. Daniel travelled to the garden. Sandra went back to the bathroom. Sandra discarded the football.", "In the adulthood stage, it can jump, walk, run", "The chocolate is bigger than the container.", "The football before the bathroom was in the garden." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- loss:CachedMultipleNegativesRankingLoss
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- loss:SoftmaxLoss
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base_model:
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widget:
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Sandra went
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the garden.
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football.
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to the
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to the
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football.
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sentences:
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- In the adulthood stage, it can jump, walk, run
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- The chocolate is bigger than the container.
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- The football before the bathroom was in the garden.
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- source_sentence:
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Speaker 1: I am very devastated these days.
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Speaker 2: That seems bad and I am sorry to hear that. What happened?
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Speaker 1: My father day 3 weeks ago.I still can'
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Speaker 2: I am truly sorry to hear that. Please accept my apologies for
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loss. May he rest in peace
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sentences:
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- 'The main emotion of this example dialogue is: content'
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- 'This text is about: genealogy'
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- My opinion is to wait until the child itself expresses a desire for this.
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- source_sentence: Francis I of France was a king.
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sentences:
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Prototype
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datasets:
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- tomaarsen/natural-questions-hard-negatives
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- loss:CachedMultipleNegativesRankingLoss
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- loss:SoftmaxLoss
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- loss:CosineSimilarityLoss
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base_model:
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- tasksource/ModernBERT-base-nli
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- answerdotai/ModernBERT-base
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widget:
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- source_sentence: >-
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Daniel went to the kitchen. Sandra went back to the kitchen. Daniel moved to
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the garden. Sandra grabbed the apple. Sandra went back to the office. Sandra
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dropped the apple. Sandra went to the garden. Sandra went back to the
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bedroom. Sandra went back to the office. Mary went back to the office.
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Daniel moved to the bathroom. Sandra grabbed the apple. Sandra travelled to
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the garden. Sandra put down the apple there. Mary went back to the bathroom.
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Daniel travelled to the garden. Mary took the milk. Sandra grabbed the
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apple. Mary left the milk there. Sandra journeyed to the bedroom. John
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travelled to the office. John went back to the garden. Sandra journeyed to
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the garden. Mary grabbed the milk. Mary left the milk. Mary grabbed the
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milk. Mary went to the hallway. John moved to the hallway. Mary picked up
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the football. Sandra journeyed to the kitchen. Sandra left the apple. Mary
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discarded the milk. John journeyed to the garden. Mary dropped the football.
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Daniel moved to the bathroom. Daniel journeyed to the kitchen. Mary
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travelled to the bathroom. Daniel went to the bedroom. Mary went to the
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hallway. Sandra got the apple. Sandra went back to the hallway. Mary moved
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to the kitchen. Sandra dropped the apple there. Sandra grabbed the milk.
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Sandra journeyed to the bathroom. John went back to the kitchen. Sandra went
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to the kitchen. Sandra travelled to the bathroom. Daniel went to the garden.
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Daniel moved to the kitchen. Sandra dropped the milk. Sandra got the milk.
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Sandra put down the milk. John journeyed to the garden. Sandra went back to
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the hallway. Sandra picked up the apple. Sandra got the football. Sandra
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moved to the garden. Daniel moved to the bathroom. Daniel travelled to the
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garden. Sandra went back to the bathroom. Sandra discarded the football.
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sentences:
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- In the adulthood stage, it can jump, walk, run
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- The chocolate is bigger than the container.
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- The football before the bathroom was in the garden.
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- source_sentence: >-
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Context: I am devasted.
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Speaker 1: I am very devastated these days.
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Speaker 2: That seems bad and I am sorry to hear that. What happened?
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Speaker 1: My father day 3 weeks ago.I still can't believe.
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Speaker 2: I am truly sorry to hear that. Please accept my apologies for
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your loss. May he rest in peace
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sentences:
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- 'The main emotion of this example dialogue is: content'
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- 'This text is about: genealogy'
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- My opinion is to wait until the child itself expresses a desire for this.
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- source_sentence: Francis I of France was a king.
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sentences:
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- >-
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The Apple QuickTake -LRB- codenamed Venus , Mars , Neptune -RRB- is one of
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the first consumer digital camera lines .. digital camera. digital camera.
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It was launched in 1994 by Apple Computer and was marketed for three years
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before being discontinued in 1997 .. Apple Computer. Apple Computer. Three
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models of the product were built including the 100 and 150 , both built by
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Kodak ; and the 200 , built by Fujifilm .. Kodak. Kodak. Fujifilm. Fujifilm.
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The QuickTake cameras had a resolution of 640 x 480 pixels maximum -LRB- 0.3
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Mpx -RRB- .. resolution. Display resolution. The 200 model is only
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officially compatible with the Apple Macintosh for direct connections ,
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while the 100 and 150 model are compatible with both the Apple Macintosh and
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Microsoft Windows .. Apple Macintosh. Apple Macintosh. Microsoft Windows.
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Microsoft Windows. Because the QuickTake 200 is almost identical to the Fuji
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DS-7 or to Samsung 's Kenox SSC-350N , Fuji 's software for that camera can
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be used to gain Windows compatibility for the QuickTake 200 .. Some other
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software replacements also exist as well as using an external reader for the
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removable media of the QuickTake 200 .. Time Magazine profiled QuickTake as
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`` the first consumer digital camera '' and ranked it among its `` 100
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greatest and most influential gadgets from 1923 to the present '' list ..
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digital camera. digital camera. Time Magazine. Time Magazine. While the
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QuickTake was probably the first digicam to have wide success , technically
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this is not true as the greyscale Dycam Model 1 -LRB- also marketed as the
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Logitech FotoMan -RRB- was the first consumer digital camera to be sold in
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the US in November 1990 .. digital camera. digital camera. greyscale.
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greyscale. At least one other camera , the Fuji DS-X , was sold in Japan
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even earlier , in late 1989 .
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- >-
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retina that consists of retinal ganglion cells and displaced amacrine cells
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.. retina. retina. In the macula lutea , the layer forms several strata ..
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macula lutea. macula lutea. The cells are somewhat flask-shaped ; the
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rounded internal surface of each resting on the stratum opticum , and
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sending off an axon which is prolonged into it .. flask. Laboratory flask.
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stratum opticum. stratum opticum. axon. axon. From the opposite end numerous
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dendrites extend into the inner plexiform layer , where they branch and form
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flattened arborizations at different levels .. inner plexiform layer. inner
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plexiform layer. arborizations. arborizations. dendrites. dendrites. The
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ganglion cells vary much in size , and the dendrites of the smaller ones as
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a rule arborize in the inner plexiform layer as soon as they enter it ;
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while those of the larger cells ramify close to the inner nuclear layer ..
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inner plexiform layer. inner plexiform layer. dendrites. dendrites. inner
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nuclear layer. inner nuclear layer
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- >-
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Foyt 's race team in USAC Championship car racing including the Indianapolis
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500 .. A. J. Foyt. A. J. Foyt. USAC. United States Auto Club. Championship
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car. American Championship car racing. Indianapolis 500. Indianapolis 500.
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It was used from 1966 to 1983 with Foyt himself making 141 starts in the car
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, winning 25 times .. George Snider had the second most starts with 24 ..
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George Snider. George Snider. Jim McElreath has the only other win with a
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Coyote chassis .. Jim McElreath. Jim McElreath. Foyt drove a Coyote to
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victory in the Indy 500 in 1967 and 1977 .. With Foyt 's permission , fellow
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Indy 500 champion Eddie Cheever 's Cheever Racing began using the Coyote
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name for his new Daytona Prototype chassis , derived from the Fabcar chassis
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design that he had purchased the rights to in 2007 .. Eddie Cheever. Eddie
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Cheever. Cheever Racing. Cheever Racing. Daytona Prototype. Daytona
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Prototype
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datasets:
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- tomaarsen/natural-questions-hard-negatives
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