--- language: - en library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:156483 - loss:AdaptiveLayerLoss - loss:CoSENTLoss - loss:GISTEmbedLoss - loss:OnlineContrastiveLoss - loss:MultipleNegativesSymmetricRankingLoss base_model: microsoft/deberta-v3-small datasets: - sentence-transformers/all-nli - sentence-transformers/stsb - tals/vitaminc - nyu-mll/glue - allenai/scitail - sentence-transformers/xsum - sentence-transformers/sentence-compression - allenai/sciq - allenai/qasc - allenai/openbookqa - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/quora-duplicates - sentence-transformers/gooaq widget: - source_sentence: Centrosome-independent mitotic spindle formation in vertebrates. sentences: - Birds pair up with the same bird in mating season. - We use voltage to keep track of electric potential energy. - A mitotic spindle forms from the centrosomes. - source_sentence: Three women in festive dresses are dancing in a large city street while a large crowd is behind them holding up a banner. sentences: - Three women dance in front of a large crowd - A person is taking a little girl with a toy from an Airman. - A man is sitting. - source_sentence: A young child is washing his hands with soap in the kitchen. sentences: - A surfer catches a wave. - Male in brown leather jacket and black slacks, looking down at his phone - The child uses soap to wash his hands. - source_sentence: A group of children enjoy their time on the beach. sentences: - People watch another person do a trick. - Children are at the beach. - A person is kicking. - source_sentence: Are juiveniles capable of reproduction? sentences: - In at least some species, juveniles are capable of reproduction before reaching the adult size and shape. - The Alpine Rhine begins in the most western part of the Swiss canton of Graubünden, and later forms the border between Switzerland to the West and Liechtenstein and later Austria to the East. - Private school numbers have been in decline since the mid-1970s as a result of many private schools opting to become state-integrated schools, mostly due of financial difficulties stemming from changes in student numbers and/or the economy. pipeline_tag: sentence-similarity --- # SentenceTransformer based on microsoft/deberta-v3-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa), [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) datasets. 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/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) - [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) - [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - **Language:** en ### 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}) with Transformer model: DebertaV2Model (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("bobox/DeBERTaV3-TR-AllSoft-LT-n") # Run inference sentences = [ 'Are juiveniles capable of reproduction?', 'In at least some species, juveniles are capable of reproduction before reaching the adult size and shape.', 'Private school numbers have been in decline since the mid-1970s as a result of many private schools opting to become state-integrated schools, mostly due of financial difficulties stemming from changes in student numbers and/or the economy.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Training Details ### Training Datasets #### nli-pairs * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 12,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------|:-------------------------------------------------| | A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | | Children smiling and waving at camera | There are children present | | A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### sts-label * Dataset: [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 5,749 training samples * Columns: sentence1, sentence2, and score * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | score | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------| | type | string | string | float | | details | | | | * Samples: | sentence1 | sentence2 | score | |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------| | A plane is taking off. | An air plane is taking off. | 1.0 | | A man is playing a large flute. | A man is playing a flute. | 0.76 | | A man is spreading shreded cheese on a pizza. | A man is spreading shredded cheese on an uncooked pizza. | 0.76 | * Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "pairwise_cos_sim" } ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 15,594 training samples * Columns: label, sentence1, and sentence2 * Approximate statistics based on the first 1000 samples: | | label | sentence1 | sentence2 | |:--------|:-----------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | int | string | string | | details | | | | * Samples: | label | sentence1 | sentence2 | |:---------------|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1 | August Burns Red is a Christian band . | August Burns Red is a Christian band . | | 1 | The 2018 UEFA Super Cup will be played at the A . Le Coq Arena after 14 August , 2018 . | It will be played at the A . Le Coq Arena in Tallinn , Estonia , on 15 August 2018 , and it will be the first European club final held in Estonia. | | 1 | Father of the Bride album was preceded by two double singles . | `` It will be released on May 3 , 2019 by Columbia Records , and was preceded by two double singles : `` '' Harmony Hall '' '' / `` '' 2021 '' '' and `` '' Sunflower '' '' / `` '' Big Blue '' '' . '' | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 8,000 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:---------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | Who was awarded first prize in the all-Arab oud contest in Egypt in 2003? | Israeli Arab musicians have achieved fame beyond Israel's borders: Elias and Murkus frequently play to audiences in Europe and America, and oud player Darwish Darwish (Prof. Elias's student) was awarded first prize in the all-Arab oud contest in Egypt in 2003. | 0 | | What are the three principles every plan should consider? | According to Vitruvius, a good building should satisfy the three principles of firmitas, utilitas, venustas, commonly known by the original translation – firmness, commodity and delight. | 0 | | More importantly than their language aspect how do the Seto and Võro dialects set themselves apart from each other? | These are sometimes considered either variants of a South Estonian language, or separate languages altogether. | 0 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "OnlineContrastiveLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 14,987 training samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:--------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | We call the major artery carrying recently oxygenated blood away from the heart the aorta. | What do we call the major artery carrying recently oxygenated blood away from the heart? | | The understory of the rainforest commonly has ferns and other ground plants. | What part of the rainforest commonly has ferns and other ground plants? | | Dimensional analysis is used to carry out metric unit conversions. | What is used to carry out metric unit conversions? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 8,600 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------| | Composition Of Blood Blood consists of red and white cells, blood platelets and watery plasma (serum). | Blood is made up of red blood cells, white blood cells, platelets, and plasma. | | The diversity among organisms has arisen through mutations , which are changes in the structure of DNA molecules. | Mutations are changes to an organism’s dna and are an important driver of diversity in populations. | | As America's most abundant energy source, coal is currently used to generate nearly 60 percent of our electricity. | Coal is the source of most electricity. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 2,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------| | Kate and Pete Shippey campaigned for all grounds to have them because their eldest son struggled to watch matches at Sunderland's Stadium of Light.
The club was the first to open a room, with a view of the pitch, last year.
Mr Shippey said it was "tremendous" Middlesbrough Football Club had also now followed suit.
"I'm hoping over the coming years that many fans, kids with the challenges, and their parents and carers, will come and enjoy the game, open a door that previously was shut to them," he said.
The space at Middlesbrough has no view of the pitch but will live stream matches on a big screen.
There are therapeutic lights, autism-appropriate toys and a quiet feed of crowd noise.
The Shippeys have been involved in similar "sensory sanctuaries" at Watford, Airdrie and Rangers football clubs.
Mrs Shippey said a football match was a "very unpredictable environment".
"The over-stimulation, the speakers are noisy, people around you are noisy, they might be shouting out to a friend before the match starts and then obviously, when the game kicks off, the roar of the crowds, people shouting at players," she said.
Her son "can't handle it, it's just far too much", she said.
Middlesbrough FC's room opens on Sunday for its match against Manchester United. | A couple with three autistic sons have persuaded a fifth football club to open a sensory room for fans with the condition. | | The animated programmes ask youngsters to choose between two options at several points in their plots.
The firm says the nature of its online streaming platform has allowed it to experiment with "branching narrative" tech in a way that would not be possible for traditional broadcasters.
But it acknowledges that such shows are more costly to make than normal.
"It was actually a little bit more than twice as much animation as a typical episode," explained Doug Langdale, executive producer of the Puss in Book series, which was made in conjunction with Dreamworks Studios.
"It was about 50 minutes [of footage] where it would normally be 22. Especially with computer animation, that's tremendously more expensive.
"It's not easy or cheap. But it's the next thing, and we've got to try it."
The programmes can be watched and controlled via smart TVs, games consoles and iOS devices - but cannot be downloaded and viewed offline.
In addition to the special episode of Puss in Book - subtitled Trapped in an Epic Tale - Netflix is making an interactive episode of the stop-motion series Buddy Thunderstruck available.
The former has two possible endings and the latter, four, but in both cases there are several ways that viewers can steer the stories to their conclusions.
It has taken two years to bring the shows to screen, with part of the challenge being trying to ensure their plots remain logical and compelling whatever choices are made.
A third child's show, based on the superhero Stretch Armstrong, is planned for 2018. But at present Netflix has no plans for adult-themed choice-based shows, nor has it committed itself to making further examples for children.
"The main priority right now is starting to learn how our members are going to engage with this [and] learning how we can tell these stories," Carla Fisher, Netflix's director of product innovation, told the BBC.
"Then we will go from there."
Netflix is far from being the first to develop interactive programming.
Beyond the many video games that have adopted the format, there have been:
However, one media analyst said scripted entertainment risked being made "gimmicky" by being forced into an interactive format.
"When it comes to linear entertainment there is an attraction to being presented with a fixed story, and having that crafted narrative presented to you by the director and writers," explained Tom Harrington of Enders Analysis.
"I doubt Netflix is going to be filled with these kind of interactive shows in 10 years.
"But it does know the value of great press, and it will get lots of publicity out of this." | Netflix is launching two interactive children's TV shows that let audiences determine the on-screen action. | | Media playback is unsupported on your device
26 January 2015 Last updated at 15:05 GMT
The number of paramedics leaving the profession in London has almost trebled in the last three years, and Jason Killens of London Ambulance Service says he cannot employ paramedics quickly enough from the UK's universities.
There will be another recruitment drive in Australia in September.
Sarah Harris meets the recruits marking their national day in their new home. | It is the first day of work for some of the 175 paramedics recruited in Australia to join the London Ambulance Service. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### compression-pairs * Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 8,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| | The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints. | USHL completes expansion draft | | Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month. | Bud Selig to speak at St. Norbert College | | It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit. | It's cherry time | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 11,679 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What type of organism is commonly used in preparation of foods such as cheese and yogurt? | Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine. | | What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere? | Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere. | | Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what? | Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 8,134 training samples * Columns: id, sentence1, and sentence2 * Approximate statistics based on the first 1000 samples: | | id | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | id | sentence1 | sentence2 | |:--------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K | What type of water formation is formed by clouds? | beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds. | | 3LS2AMNW5FPNJK3C3PZLZCPX562OQO | Where do beads of water come from? | beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water | | 3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3 | What forms beads of water? | beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### openbookqa_pairs * Dataset: [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) at [388097e](https://huggingface.co/datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454) * Size: 2,740 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------|:--------------------------------------------------------------------------| | The sun is responsible for | the sun is the source of energy for physical cycles on Earth | | When food is reduced in the stomach | digestion is when stomach acid breaks down food | | Stars are | a star is made of gases | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 12,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what are the liberal arts? | liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects. | | what is the mechanism of action of fibrinolytic or thrombolytic drugs? | Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure. | | what is normal plat count | 78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL). | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 12,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | when did richmond last play in a preliminary final | Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next. | | who sang what in the world's come over you | Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel. | | who produces the most wool in the world | Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 12,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which American-born Sinclair won the Nobel Prize for Literature in 1930? | The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. | | Where in England was Dame Judi Dench born? | Judi Dench - IMDb IMDb Actress | Music Department | Soundtrack Judi Dench was born in York, England, to Eleanora Olive (Jones), who was from Dublin, Ireland, and Reginald Arthur Dench, a doctor from Dorset, England. She attended Mount School in York, and studied at the Central School of Speech and Drama. She has performed with Royal Shakespeare Company, the National Theatre, and at Old Vic Theatre. She is a ... See full bio » Born: a list of 35 people created 02 Jul 2011 a list of 35 people created 19 Apr 2012 a list of 35 people created 28 May 2014 a list of 25 people created 05 Aug 2014 a list of 26 people created 18 May 2015 Do you have a demo reel? Add it to your IMDbPage How much of Judi Dench's work have you seen? User Polls Won 1 Oscar. Another 59 wins & 163 nominations. See more awards  » Known For  2016 The Hollow Crown (TV Series) Cecily, Duchess of York  2015 The Vote (TV Movie) Christine Metcalfe - Total War (1996) ... Narrator (voice) - Stalemate (1996) ... Narrator (voice)  1992 The Torch (TV Mini-Series) Aba  1990 Screen One (TV Series) Anne  1989 Behaving Badly (TV Mini-Series) Bridget  1981 BBC2 Playhouse (TV Series) Sister Scarli  1976 Arena (TV Series documentary) Sweetie Simpkins  1973 Ooh La La! (TV Series) Amélie  1966 Court Martial (TV Series) Marthe  1963 Z Cars (TV Series) Elena Collins  1963 Love Story (TV Series) Pat McKendrick  1960 The Terrible Choice (TV Series) Good Angel Music department (1 credit)   A Fine Romance (TV Series) (theme sung by - 14 episodes, 1981 - 1983) (theme song sung by - 12 episodes, 1983 - 1984) - A Romantic Meal (1984) ... (theme song sung by) - Problems (1984) ... (theme song sung by)  2013 Fifty Years on Stage (TV Movie) (performer: "Send in the Clowns")  2009 Nine (performer: "Folies Bergère") - What's Wrong with Mrs Bale? (1997) ... (performer: "Raindrops Keep Fallin' On My Head" - uncredited) - Misunderstandings (1993) ... (performer: "Walkin' My Baby Back Home" - uncredited)  1982-1984 A Fine Romance (TV Series) (performer - 2 episodes) - The Telephone Call (1984) ... (performer: "Boogie Woogie Bugle Boy" - uncredited) - Furniture (1982) ... (performer: "Rule, Britannia!" - uncredited) Hide   2009 Waiting in Rhyme (Video short) (special thanks)  2007 Expresso (Short) (special thanks)  1999 Shakespeare in Love and on Film (TV Movie documentary) (thanks - as Dame Judi Dench) Hide   2016 Rio Olympics (TV Mini-Series) Herself  2015 In Conversation (TV Series documentary) Herself  2015 Entertainment Tonight (TV Series) Herself  2015 CBS This Morning (TV Series) Herself - Guest  2015 The Insider (TV Series) Herself  1999-2014 Cinema 3 (TV Series) Herself  2013 Good Day L.A. (TV Series) Herself - Guest  2013 Arena (TV Series documentary) Herself  2013 At the Movies (TV Series) Herself  2013 Shooting Bond (Video documentary) Herself  2013 Bond's Greatest Moments (TV Movie documentary) Herself  2012 Made in Hollywood (TV Series) Herself  1999-2012 Charlie Rose (TV Series) Herself - Guest  2008-2012 This Morning (TV Series) Herself - Guest  2012 The Secrets of Skyfall (TV Short documentary) Herself  2012 Anderson Live (TV Series) Herself  2012 J. Edgar: A Complicated Man (Video documentary short) Herself  2011 The Many Faces of... (TV Series documentary) Herself / Various Characters  2011 Na plovárne (TV Series) Herself  2010 BBC Proms (TV Series) Herself  2010 The South Bank Show Revisited (TV Series documentary) Herself - Episode #6.68 (2009) ... Herself - Guest (as Dame Judi Dench)  2007-2009 Breakfast (TV Series)  2009 Larry King Live (TV Series) Herself - Guest  2009 The One Show (TV Series) Herself  2009 Cranford in Detail (Video documentary short) Herself / Miss Matty Jenkins (as Dame Judi Dench)  2005-2008 The South Bank Show (TV Series documentary) Herself  2008 Tavis Smiley (TV Series) Herself - Guest  2007 ITV News (TV Series) Herself - BAFTA Nominee  2007 The Making of Cranford (Video documentary short) Herself / Miss Matty Jenkyns (as Dame Judi Dench)  2006 Becoming Bond (TV Movie documentary) Herself  2006 Corazón de... (TV Series) Hers | | In which decade did Billboard magazine first publish and American hit chart? | The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day. he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### quora_pairs * Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 8,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| | Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me? | I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me? | | How can I be a good geologist? | What should I do to be a great geologist? | | How do I read and find my YouTube comments? | How can I see all my Youtube comments? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 12,500 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | is toprol xl the same as metoprolol? | Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure. | | are you experienced cd steve hoffman? | The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design. | | how are babushka dolls made? | Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` ### Evaluation Datasets #### nli-pairs * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 1,500 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | | 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. | | 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. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 1,304 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| | An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions. | Replace another in a molecule happens to atoms during a substitution reaction. | 0 | | Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase; | Wavelength is the distance between two corresponding points of adjacent waves called. | 1 | | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | 1 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 1,500 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | What came into force after the new constitution was herald? | As of that day, the new constitution heralding the Second Republic came into force. | 0 | | What is the first major city in the stream of the Rhine? | The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz. | 0 | | What is the minimum required if you want to teach in Canada? | In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher. | 0 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "OnlineContrastiveLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 0.5, "kl_div_weight": 1.25, "kl_temperature": 0.5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 28 - `per_device_eval_batch_size`: 24 - `learning_rate`: 2e-05 - `weight_decay`: 7.5e-07 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 5} - `warmup_ratio`: 0.2 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTaV3-TR-AllSoft-LT-n - `hub_strategy`: checkpoint - `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`: 28 - `per_device_eval_batch_size`: 24 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 7.5e-07 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 5} - `warmup_ratio`: 0.2 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `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`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTaV3-TR-AllSoft-LT-n - `hub_strategy`: checkpoint - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `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 - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | qnli-contrastive loss | nli-pairs loss | scitail-pairs-pos loss | |:------:|:-----:|:-------------:|:---------------------:|:--------------:|:----------------------:| | 0.0250 | 140 | 17.3783 | - | - | - | | 0.0500 | 280 | 10.3883 | - | - | - | | 0.0750 | 420 | 6.239 | - | - | - | | 0.1001 | 560 | 5.6986 | - | - | - | | 0.1251 | 700 | 5.4609 | 5.3307 | 5.2213 | 3.9345 | | 0.1501 | 840 | 5.2036 | - | - | - | | 0.1751 | 980 | 4.9986 | - | - | - | | 0.2001 | 1120 | 4.4918 | - | - | - | | 0.2251 | 1260 | 4.2202 | - | - | - | | 0.2501 | 1400 | 4.0922 | 4.3783 | 4.1164 | 2.4736 | | 0.2751 | 1540 | 3.3935 | - | - | - | | 0.3002 | 1680 | 3.5528 | - | - | - | | 0.3252 | 1820 | 3.6013 | - | - | - | | 0.3502 | 1960 | 3.275 | - | - | - | | 0.3752 | 2100 | 3.2832 | 3.4020 | 3.1333 | 1.8786 | | 0.4002 | 2240 | 3.0276 | - | - | - | | 0.4252 | 2380 | 2.8936 | - | - | - | | 0.4502 | 2520 | 2.7427 | - | - | - | | 0.4753 | 2660 | 3.0206 | - | - | - | | 0.5003 | 2800 | 2.575 | 2.8864 | 2.6768 | 1.5112 | | 0.5253 | 2940 | 2.5411 | - | - | - | | 0.5503 | 3080 | 2.5173 | - | - | - | | 0.5753 | 3220 | 2.5333 | - | - | - | | 0.6003 | 3360 | 2.6302 | - | - | - | | 0.6253 | 3500 | 2.5134 | 2.4320 | 2.3164 | 1.3295 | | 0.6503 | 3640 | 2.4442 | - | - | - | | 0.6754 | 3780 | 2.2516 | - | - | - | | 0.7004 | 3920 | 2.3152 | - | - | - | | 0.7254 | 4060 | 2.0466 | - | - | - | | 0.7504 | 4200 | 2.2551 | 2.4112 | 2.0880 | 1.1470 | | 0.7754 | 4340 | 2.1891 | - | - | - | | 0.8004 | 4480 | 2.0993 | - | - | - | | 0.8254 | 4620 | 1.7904 | - | - | - | | 0.8505 | 4760 | 2.0401 | - | - | - | | 0.8755 | 4900 | 1.8895 | 1.9137 | 1.8439 | 0.9787 | | 0.9005 | 5040 | 2.0058 | - | - | - | | 0.9255 | 5180 | 2.0126 | - | - | - | | 0.9505 | 5320 | 2.067 | - | - | - | | 0.9755 | 5460 | 2.1337 | - | - | - | | 1.0005 | 5600 | 1.8408 | 1.8655 | 1.7102 | 0.9210 | | 1.0255 | 5740 | 1.7539 | - | - | - | | 1.0506 | 5880 | 1.9194 | - | - | - | | 1.0756 | 6020 | 1.7071 | - | - | - | | 1.1006 | 6160 | 1.7053 | - | - | - | | 1.1256 | 6300 | 1.8023 | 1.5953 | 1.5203 | 0.7954 | | 1.1506 | 6440 | 1.7448 | - | - | - | | 1.1756 | 6580 | 1.7874 | - | - | - | | 1.2006 | 6720 | 1.7441 | - | - | - | | 1.2257 | 6860 | 1.4535 | - | - | - | | 1.2507 | 7000 | 1.7103 | 1.7566 | 1.4382 | 0.7307 | | 1.2757 | 7140 | 1.4028 | - | - | - | | 1.3007 | 7280 | 1.5999 | - | - | - | | 1.3257 | 7420 | 1.7167 | - | - | - | | 1.3507 | 7560 | 1.4903 | - | - | - | | 1.3757 | 7700 | 1.5666 | 1.5942 | 1.3535 | 0.6812 | | 1.4008 | 7840 | 1.4289 | - | - | - | | 1.4258 | 7980 | 1.4531 | - | - | - | | 1.4508 | 8120 | 1.2831 | - | - | - | | 1.4758 | 8260 | 1.5913 | - | - | - | | 1.5008 | 8400 | 1.2771 | 1.6052 | 1.2932 | 0.6548 | | 1.5258 | 8540 | 1.2718 | - | - | - | | 1.5508 | 8680 | 1.374 | - | - | - | | 1.5758 | 8820 | 1.4011 | - | - | - | | 1.6009 | 8960 | 1.571 | - | - | - | | 1.6259 | 9100 | 1.54 | 1.5300 | 1.2734 | 0.6386 | | 1.6509 | 9240 | 1.3765 | - | - | - | | 1.6759 | 9380 | 1.3131 | - | - | - | | 1.7009 | 9520 | 1.3459 | - | - | - | | 1.7259 | 9660 | 1.1947 | - | - | - | | 1.7509 | 9800 | 1.4422 | 1.5141 | 1.2626 | 0.6337 | | 1.7760 | 9940 | 1.3539 | - | - | - | | 1.8010 | 10080 | 1.3033 | - | - | - | | 1.8260 | 10220 | 1.1106 | - | - | - | | 1.8510 | 10360 | 1.3733 | - | - | - | | 1.8760 | 10500 | 1.2665 | 1.4497 | 1.2357 | 0.6096 | | 1.9010 | 10640 | 1.351 | - | - | - | | 1.9260 | 10780 | 1.3464 | - | - | - | | 1.9510 | 10920 | 1.1087 | - | - | - | | 1.9761 | 11060 | 1.1635 | - | - | - | | 2.0011 | 11200 | 1.1354 | 1.4097 | 1.1946 | 0.6180 | | 2.0261 | 11340 | 1.1174 | - | - | - | | 2.0511 | 11480 | 1.3446 | - | - | - | | 2.0761 | 11620 | 1.1409 | - | - | - | | 2.1011 | 11760 | 1.1902 | - | - | - | | 2.1261 | 11900 | 1.2951 | 1.2629 | 1.1403 | 0.5851 | | 2.1512 | 12040 | 1.2077 | - | - | - | | 2.1762 | 12180 | 1.2858 | - | - | - | | 2.2012 | 12320 | 1.267 | - | - | - | | 2.2262 | 12460 | 1.0026 | - | - | - | | 2.2512 | 12600 | 1.2523 | 1.3819 | 1.1073 | 0.5534 | | 2.2762 | 12740 | 1.0009 | - | - | - | | 2.3012 | 12880 | 1.1822 | - | - | - | | 2.3262 | 13020 | 1.3998 | - | - | - | | 2.3513 | 13160 | 1.0833 | - | - | - | | 2.3763 | 13300 | 1.1998 | 1.2879 | 1.0725 | 0.5350 | | 2.4013 | 13440 | 1.0898 | - | - | - | | 2.4263 | 13580 | 1.1658 | - | - | - | | 2.4513 | 13720 | 1.0127 | - | - | - | | 2.4763 | 13860 | 1.2826 | - | - | - | | 2.5013 | 14000 | 1.0243 | 1.3113 | 1.0619 | 0.5269 | | 2.5264 | 14140 | 1.0344 | - | - | - | | 2.5514 | 14280 | 1.1904 | - | - | - | | 2.5764 | 14420 | 1.2133 | - | - | - | | 2.6014 | 14560 | 1.3826 | - | - | - | | 2.6264 | 14700 | 1.3857 | 1.3533 | 1.0945 | 0.5431 | | 2.6514 | 14840 | 1.2257 | - | - | - | | 2.6764 | 14980 | 1.2372 | - | - | - | | 2.7014 | 15120 | 1.1853 | - | - | - | | 2.7265 | 15260 | 1.0465 | - | - | - | | 2.7515 | 15400 | 1.3469 | 1.5210 | 1.0670 | 0.5464 | | 2.7765 | 15540 | 1.1843 | - | - | - | | 2.8015 | 15680 | 1.113 | - | - | - | | 2.8265 | 15820 | 0.8078 | - | - | - | | 2.8515 | 15960 | 1.0437 | - | - | - | | 2.8765 | 16100 | 0.9093 | 1.1596 | 0.9908 | 0.4938 | | 2.9016 | 16240 | 1.044 | - | - | - | | 2.9266 | 16380 | 1.0227 | - | - | - | | 2.9516 | 16520 | 0.8159 | - | - | - | | 2.9766 | 16660 | 0.8426 | - | - | - | | 3.0016 | 16800 | 0.7955 | 1.1895 | 0.9681 | 0.4912 | | 3.0266 | 16940 | 0.856 | - | - | - | | 3.0516 | 17080 | 1.0754 | - | - | - | | 3.0766 | 17220 | 0.9151 | - | - | - | | 3.1017 | 17360 | 1.0051 | - | - | - | | 3.1267 | 17500 | 1.0075 | 1.0658 | 0.9583 | 0.4853 | | 3.1517 | 17640 | 0.9909 | - | - | - | | 3.1767 | 17780 | 1.029 | - | - | - | | 3.2017 | 17920 | 1.0292 | - | - | - | | 3.2267 | 18060 | 0.8334 | - | - | - | | 3.2517 | 18200 | 1.0119 | 1.1172 | 0.9485 | 0.4674 | | 3.2768 | 18340 | 0.8582 | - | - | - | | 3.3018 | 18480 | 1.0397 | - | - | - | | 3.3268 | 18620 | 1.1988 | - | - | - | | 3.3518 | 18760 | 0.9432 | - | - | - | | 3.3768 | 18900 | 1.0573 | 1.0815 | 0.9437 | 0.4679 | | 3.4018 | 19040 | 0.9829 | - | - | - | | 3.4268 | 19180 | 1.0573 | - | - | - | | 3.4518 | 19320 | 0.9449 | - | - | - | | 3.4769 | 19460 | 1.2005 | - | - | - | | 3.5019 | 19600 | 0.9171 | 1.2315 | 0.9503 | 0.4799 | | 3.5269 | 19740 | 0.9425 | - | - | - | | 3.5519 | 19880 | 1.1213 | - | - | - | | 3.5769 | 20020 | 1.1128 | - | - | - | | 3.6019 | 20160 | 1.331 | - | - | - | | 3.6269 | 20300 | 1.0495 | 1.1413 | 0.9468 | 0.4434 | | 3.6520 | 20440 | 0.9698 | - | - | - | | 3.6770 | 20580 | 0.9148 | - | - | - | | 3.7020 | 20720 | 0.9042 | - | - | - | | 3.7270 | 20860 | 0.8232 | - | - | - | | 3.7520 | 21000 | 1.0163 | 1.2883 | 0.9020 | 0.4574 | | 3.7770 | 21140 | 0.9735 | - | - | - | | 3.8020 | 21280 | 0.8371 | - | - | - | | 3.8271 | 21420 | 0.6344 | - | - | - | | 3.8521 | 21560 | 0.87 | - | - | - | | 3.8771 | 21700 | 0.7404 | 1.0644 | 0.8661 | 0.4353 | | 3.9021 | 21840 | 0.8486 | - | - | - | | 3.9271 | 21980 | 0.8895 | - | - | - | | 3.9521 | 22120 | 0.7476 | - | - | - | | 3.9771 | 22260 | 0.6761 | - | - | - |
### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.41.2 - PyTorch: 2.1.2 - Accelerate: 0.30.1 - Datasets: 2.19.2 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### AdaptiveLayerLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### CoSENTLoss ```bibtex @online{kexuefm-8847, title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, author={Su Jianlin}, year={2022}, month={Jan}, url={https://kexue.fm/archives/8847}, } ``` #### GISTEmbedLoss ```bibtex @misc{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, year={2024}, eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```