--- base_model: bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp 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 - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/quora-duplicates - sentence-transformers/gooaq language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:203905 - loss:AdaptiveLayerLoss - loss:CoSENTLoss - loss:GISTEmbedLoss - loss:OnlineContrastiveLoss - loss:MultipleNegativesSymmetricRankingLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: Do you think Donald Trump or Hillary Clinton will be the next president of the US? sentences: - Why was Hillary Clinton replaced as Secretary of State? Why did she resign? - How's the outlook for Donald Trump looking at becoming our next president? - How can I shoot a video on a Canon T3i? - source_sentence: Which is the best coaching center for SSC and Banking in Chandigarh? sentences: - Which are the best SSC and banking coaching centers in Chandigarh? - Is the nursery web spider bite dangerous? - Which is the best antivirus ever? - source_sentence: The Texas Rangers have sought bankruptcy protection in hopes of getting their $575-million US sale to a team of investors cleared by midsummer. sentences: - Texas Rangers seek bankruptcy protection - Schatz endorsed by three big labor unions - Steelers running back Moore out with MCL sprain - source_sentence: the united states established the open door policy toward china as a way to sentences: - Open Door Policy Open Door policy was rooted in the desire of U.S. businesses to trade with Chinese markets, though it also tapped the deep-seated sympathies of those who opposed imperialism, with the policy pledging to protect China's sovereignty and territorial integrity from partition. It had little legal standing, and was mainly used to mediate competing interests of the colonial powers without much meaningful input from the Chinese, creating lingering resentment and causing it to later be seen as a symbol of national humiliation by Chinese historians. - Renal sodium reabsorption Most of the reabsorption (65%) occurs in the proximal tubule. In the latter part it is favoured by an electrochemical driving force, but initially it needs the cotransporter SGLT and the Na-H antiporter. Sodium passes along an electrochemical gradient (passive transport) from the lumen into the tubular cell, together with water and chloride which also diffuse passively. Water is reabsorbed to the same degree, resulting in the concentration in the end of the proximal tubule being the same as in the beginning. In other words, the reabsorption in the proximal tubule is isosmotic. - List of peerages inherited by women In the peerages of the British Isles, most titles have traditionally been created for men and with remainder to male heirs. However, some titles are created with special remainders to allow women to inherit them. Some of the oldest English baronies were created by writ and pass to female heirs when a peer dies with daughters and no sons, while some titles are created with a man's family in mind, if he is without sons and unlikely to produce any. The following is a list of women who have inherited titles with the British peerages. - source_sentence: Hydrogen is in gas form at room temperature. sentences: - Ectotherms undergo a variety of changes at the cellular level to acclimatize to shifts in what? - How hot is it on the surface of the Sun? - In what form of matter is hydrogen at room temperature? model-index: - name: SentenceTransformer based on bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.7886090121261621 name: Pearson Cosine - type: spearman_cosine value: 0.8082711320565998 name: Spearman Cosine - type: pearson_manhattan value: 0.7124505022408925 name: Pearson Manhattan - type: spearman_manhattan value: 0.706282541172725 name: Spearman Manhattan - type: pearson_euclidean value: 0.7191399216750348 name: Pearson Euclidean - type: spearman_euclidean value: 0.7111267572078126 name: Spearman Euclidean - type: pearson_dot value: 0.5098599737801768 name: Pearson Dot - type: spearman_dot value: 0.49111574200700003 name: Spearman Dot - type: pearson_max value: 0.7886090121261621 name: Pearson Max - type: spearman_max value: 0.8082711320565998 name: Spearman Max --- # SentenceTransformer based on bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp](https://huggingface.co/bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp) 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), [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [compression-pairs3](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, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [nq_pairs2](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), [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) and [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) 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:** [bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp](https://huggingface.co/bobox/DeBERTa-ST-AllLayers-v3-checkpoints-tmp) - **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) - [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [compression-pairs3](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 - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [nq_pairs2](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) - [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) - [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) - **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/DeBERTa-ST-AllLayers-v3.1") # Run inference sentences = [ 'Hydrogen is in gas form at room temperature.', 'In what form of matter is hydrogen at room temperature?', 'How hot is it on the surface of the Sun?', ] 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] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.7886 | | **spearman_cosine** | **0.8083** | | pearson_manhattan | 0.7125 | | spearman_manhattan | 0.7063 | | pearson_euclidean | 0.7191 | | spearman_euclidean | 0.7111 | | pearson_dot | 0.5099 | | spearman_dot | 0.4911 | | pearson_max | 0.7886 | | spearman_max | 0.8083 | ## 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: 18,000 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 18,000 training samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:-------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Manchester had a population of more than 540,000 in 2017 and was the 5th most populous English district . | Manchester ( ) is a major city and metropolitan borough in Greater Manchester , England , with a population of 545,500 as of 2017 ( 5th most populous English district ) . | | Manchester had a population of less than 540,000 in 2018 and was the 4th most populous English district . | Manchester ( ) is a major city and metropolitan borough in Greater Manchester , England , with a population of 534,982 as of 2018 ( 4th most populous English district ) . | | Traditional Chinese medicine is founded on more than 4000 years of ancient Chinese medical science and practice . | Traditional Chinese medicine ( TCM ; ) is an ancient system of medical diagnosis and treatment of illnesses with a holistic focus on disease prevention through diet , healthy lifestyle changes , exercise and is built on a patient centered clinically oriented foundation of more than 6,500 years of ancient Chinese medical science and practice that includes various forms of herbal medicine , acupuncture , massage ( tui na ) , exercise ( qigong ) , and dietary therapy , but recently also influenced by modern Western medicine . | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 17,000 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 | |:--------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | What kind of sound did Kanye abandon a rap and hiphop one for with his fourth album? | West's fourth studio album, 808s & Heartbreak (2008), marked an even more radical departure from his previous releases, largely abandoning rap and hip hop stylings in favor of a stark electropop sound composed of virtual synthesis, the Roland TR-808 drum machine, and explicitly auto-tuned vocal tracks. | 0 | | When did hostilities in the Persian Gulf War begin? | The lead up to the war began with the Iraqi invasion of Kuwait in August 1990 which was met with immediate economic sanctions by the United Nations against Iraq. | 0 | | What were two of Marvel's comic heroes in fantasy, swords and magic settings? | Once again, Marvel attempted to diversify, and with the updating of the Comics Code achieved moderate to strong success with titles themed to horror (The Tomb of Dracula), martial arts, (Shang-Chi: Master of Kung Fu), sword-and-sorcery (Conan the Barbarian, Red Sonja), satire (Howard the Duck) and science fiction (2001: | 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 14,312 training samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| | Thermal energy constitutes the total kinetic energy of all the atoms that make up an object. | What kind of energy constitutes the total kinetic energy of all the atoms that make up an object? | | Overharvesting is a serious threat particularly to aquatic species. | Overharvesting is a serious threat particularly to which species? | | Cellulose is created by the polymerization of glucose. | What is created by the polymerization of glucose? | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------| | Most of the ozone in the Earth's atmosphere lies in the stratosphere, the layer above the troposphere. | The stratosphere is the layer above the troposphere. | | Exocytosis, fertilization of an egg by sperm and transport of waste products to the lysozome are a few of the many eukaryotic processes that rely on some form of fusion. | The cell expels waste and other particles through a process called exocytosis. | | Mercury, the smallest planet in the Solar System, has the most eccentric orbit. | Mercury is the smallest planet in our solar system. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 4,000 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------| | Mitchell Starc took 4-36 after captain Steve Smith hit 165 as Australia declared their first innings on 624-8.
Australia had 68 overs to force a win. They reduced Pakistan to 91-5 at tea before sealing victory in 53.2 overs to take an unassailable 2-0 series lead.
Pakistan captain Misbah-ul-Haq was fined 40% of his match fee and the rest of his team 20% for a slow over rate.
"It finished a lot better than it started," Starc said. "It's fantastic for us to get that result. The belief was there and it was a fantastic way to finish.
"We knew we only had two sessions to get those 10 wickets and together as a bowling unit we've done really well."
The third Test starts in Sydney on 3 January but Misbah has not committed to playing in the match after a poor series so far.
The 42-year-old, who was dismissed for a two-ball duck after managing just 11 in the first innings, only scored nine runs in total in the first test in Brisbane.
"I haven't decided about that [Sydney] but let's see," he said. "[If I'm not contributing] there's no point in hanging around." | Australia bowled Pakistan out for 163 in Melbourne to win the second Test by an innings and 18 runs on day five. | | The Competition and Markets Authority (CMA) had ordered all insurance companies to split out the extra charges for the additional protection.
But the Co-op was the only firm which missed a deadline to do so, in August last year.
As a result around 120,000 customers received quotations that were unclear.
From 1 February, the Co-op will provide two separate quotations - one with no claims bonus protection, and one without.
"It is very disappointing that a major company such as Co-op Insurance has taken so long to provide this vital information to its customers," said Adam Land, senior director of remedies, business and financial analysis at the CMA.
"Before the order came into force, the price and benefits of NCB [no claims bonus] protection were often unclear to drivers.
"We expect the Co-op to fully comply with the terms of our directions immediately, so that motorists can search more easily for the best deal for them, and decide whether or not they want this optional cover."
The Co-op said most of its quotations do now provide separate details of no claims bonus charges.
"For 90% of our new business customers we are already fully compliant with this order," a spokesperson said.
"We are part way through a major transformation programme, which when complete will allow us to be fully compliant and enable us to provide best in class service to our members." | The Co-op has been ordered to provide clearer insurance quotations, after it failed to tell motorists about separate charges for no claims bonuses. | | The 21-year-old spent last season with the League Two club, scoring six times Argyle reached the play-off final.
Tanner was part of the Reading squad which defeated Derek Adams' side 2-0 in the EFL Cup first round on 9 August.
Tanner, who signed a two-year contract extension with Reading in January 2015, is eligible for Saturday's home fixture against Mansfield Town.
Find all the latest football transfers on our dedicated page. | Plymouth Argyle have re-signed Reading midfielder Craig Tanner on a loan deal until January. | * 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` #### 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: 10,125 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| | Democrat State Senate President John Cullerton will endorse Democrat State Treasurer Alexi Giannoulias for US Senate today. | Cullerton to endorse Giannoulias | | Barbara Walters hospitalised after fall Updated: 18:47, Monday January 21, 2013 Veteran ABC newswoman Barbara Walters has been hospitalised after falling at an inauguration party at the residence of Britain's ambassador to the United States. | Barbara Walters hospitalised after fall | | Girl Next-Door star and Hugh Hefner's ex Bridget Marquardt recently moved out of his Playboy mansion. | Bridget Marquardt moves out of Playboy mansion | * 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` #### compression-pairs2 * Dataset: [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 4,701 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------| | The actress, Natasha Richardson, has died in hospital in New York after suffering a serious head injury in a skiing accident in Canada earlier this week. | Actress Natasha Richardson dies | | 'WHISPERING' Ted Lowe - the most recognisable voice in the history of snooker broadcasting - died four hours before the balls were broken in the final of the sport's most important championship | 'Whispering' Ted Lowe dies | | Nairobi - Somalia's Shabaab Islamists said on Thursday they have executed a French agent they have held since 2009, as France said the hostage was likely killed several days ago in a failed rescue attempt. | Shabaab say they have executed French hostage | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 0.75, "kl_temperature": 0.75 } ``` #### compression-pairs3 * Dataset: [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 4,700 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------| | Pakistan State Oil has started supply of furnace oil to Karachi Electric Supply Company on discounted rates. | Pso starts supply of oil on discounted rates: | | European stocks were little changed as the region's finance chiefs meet today to work on a new strategy to contain the sovereign-debt crisis. | European stocks little changed as finance chiefs meet; | | The body of a missing boater has been found in Lake Okeechobee in southwest Florida. | Body of missing boater found in Lake Okeechobee | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.01, "prior_layers_weight": 10, "kl_div_weight": 3, "kl_temperature": 0.25 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 11,153 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Temperature and what other environmental factor are important in the activity of an enzyme? | | | The sum of the superscripts in an electron configuration is equal to the number of electrons in that atom, which is in turn equal to what number? | Electron configuration notation eliminates the boxes and arrows of orbital filling diagrams. Each occupied sublevel designation is written followed by a superscript that is the number of electrons in that sublevel. For example, the hydrogen configuration is 1 s 1 , while the helium configuration is 1 s 2 . Multiple occupied sublevels are written one after another. The electron configuration of lithium is 1 s 2 2 s 1 . The sum of the superscripts in an electron configuration is equal to the number of electrons in that atom, which is in turn equal to its atomic number. | | What is the most common type of brain injury? | The most common type of brain injury is a concussion. This is a bruise on the surface of the brain. It may cause temporary symptoms such as headache and confusion. Most concussions heal on their own in a few days or weeks. However, repeated concussions can lead to permanent changes in the brain. More serious brain injuries also often cause permanent brain damage. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,767 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | How are organisms able to grow and repair their cells? | Cell division is how organisms grow and repair themselves.. Mitosis is cell division.. Mitosis is how organisms grow and repair themselves. | | What cannot absorb light energy? | chlorophyll is used for absorbing light energy by plants. Fungi have no chlorophyll.. Fungi cannot absorb light energy. | | what effects the colligative properties of solids | adding salt to a solid decreases the freezing point of that solid. Freezing point depression is a colligative property.. salts effect the colligative properties of solids | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 4,505 training samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | What is animal competition? | if two animals eat the same prey then those animals compete for that pey | | If you wanted to make a metal bed frame, where would you start? | alloys are made of two or more metals | | Places lacking warmth have few what | cold environments contain few organisms | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 10,314 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | otc medication meaning | Over-the-counter (OTC) medicines are drugs you can buy without a prescription. Some OTC medicines relieve aches, pains and itches. Some prevent or cure diseases, like tooth decay and athlete's foot. Others help manage recurring problems, like migraines. | | every how many weeks should you get a hair cut | “Somehow people have been taught you need to cut your hair every 4 to 6 weeks and I think that’s way too soon,” she tells InStyle. “If you have a great cut and don’t mind a little added length, the style can last up to 6 months and still look great.”. RELATED: We Tried It: Cindy Crawford’s Cleanse. | | how does a pedometer work | This is pretty much how a pedometer works. Photo: Pedometers can measure your steps because your body swings from side to side as you walk. Each swing counts as one step. Multiplying the number of swings by the average length of your steps tells you how far you've gone. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### msmarco_pairs2 * Dataset: [msmarco_pairs2](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: 6,876 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what should house humidity percentage be? | Lower humidity can cause your heating system to work harder in the winter months. There must be a balance of humidity in your home to make it a comfortable place and preserve your interior as well as you heating and air conditioning systems. During summer months, homes should have between 30 and 45 percent humidity. During the winter, humidity levels should be kept between 45 and 55 percent humidity. How can I regulate my home humidity? The following are some ideas on how you can help to balance your home’s humidity: Install an inside weather station that measures humidity in your home. | | which electrolyte is missing if there is tetany | Hypocalcemia is not a term for tetany but is rather a cause of tetany. Causes. The usual cause of tetany is lack of calcium. An excess of phosphate (high phosphate-to-calcium ratio) can also trigger the spasms. Underfunction of the parathyroid gland can lead to tetany. Low levels of carbon dioxide cause tetany by altering the albumin binding of calcium such that the ionized (physiologically influencing) fraction of calcium is reduced; one common reason for low carbon dioxide levels is hyperventilation. Low levels of magnesium can lead to tetany. | | dimensions of the sea of galilee | Sea of Galilee From Wikipedia, the free encyclopedia. The Sea of Galilee is Israel's largest freshwater lake, approximately 53 kilometers (33 miles) in circumference, about 21 km (13 miles) long, and 13 km (8 miles) wide; it has a total area of 166 sq km, and a maximum depth of approximately 48 meters. At 213 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 0.75, "kl_temperature": 0.75 } ``` #### 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: 12,892 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who did richmond play in the grand 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 – their qualifying final against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. In their first preliminary final 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 for 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 kicked 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.[23] 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 was the first european to translate bhagwat gita into english | Charles Wilkins Sir Charles Wilkins, KH, FRS (1749 – 13 May 1836), was an English typographer and Orientalist, and founding member of The Asiatic Society. He is notable as the first translator of Bhagavad Gita into English, and as the creator, alongside Panchanan Karmakar,[1] of the first Bengali typeface.[2] In 1788, Wilkins was elected a member of the Royal Society.[3] | | who played bruce willis's girlfriend in pulp fiction | Maria de Medeiros Among Medeiros' most memorable film appearances are three early 1990s roles. Her considerable resemblance to Anaïs Nin landed her the primary role in Henry & June (1990), in which she played the author. In 1990, she played the role of Maria in Ken McMullen's film about the rise of the Paris Commune, 1871. In 1994, Medeiros appeared in Quentin Tarantino's Pulp Fiction playing Fabienne, the girlfriend of Butch Coolidge (Bruce Willis). | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### nq_pairs2 * Dataset: [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 4,298 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | when does daylight saving time come into effect | Daylight saving time Start and end dates vary with location and year. Since 1996, European Summer Time has been observed from the last Sunday in March to the last Sunday in October; previously the rules were not uniform across the European Union.[39] Starting in 2007, most of the United States and Canada observe DST from the second Sunday in March to the first Sunday in November, almost two-thirds of the year.[43] The 2007 U.S. change was part of the Energy Policy Act of 2005; previously, from 1987 through 2006, the start and end dates were the first Sunday in April and the last Sunday in October, and Congress retains the right to go back to the previous dates now that an energy-consumption study has been done.[44] Proponents for permanently retaining November as the month for ending DST point to Halloween as a reason to delay the change—to provide extra daylight on October 31. | | when was the words under god added to the pledge of allegiance | Pledge of Allegiance (United States) The phrase "under God" was incorporated into the Pledge of Allegiance on June 14, 1954, by a Joint Resolution of Congress amending § 4 of the Flag Code enacted in 1942.[28] | | who provides the funds for a loan guaranteed by the veterans' administration | VA loan The basic intention of the VA home loan program is to supply home financing to eligible veterans and to help veterans purchase properties with no down payment. The loan may be issued by qualified lenders. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 0.75, "kl_temperature": 0.75 } ``` #### 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: 17,190 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | In which 2004 animated Pixar movie does Violet have powers of invisibility? | Violet - The Incredibles - Pixar movie - Character Profile - Writeups.org Jump to the game stats Content Violet is one of the main characters of the 2004 Pixar animation movie The Incredibles. The movie encountered both critical and commercial success. The Incredibles deals with super-heroism, family life and emotions, violence, and being different. Background Real Name: Violet Parr Other Aliases: Violet. It is very obvious from the film’s plot that a secret ID is incredibly important to a super’s life, so it is thus unlikely that Violet will continue to adventure as “Violet”. I suggest “Invisigirl” because it follows the film’s naming tradition of simple, descriptive names. Marital Status: Single Known Relatives: Robert Parr (Aka Mr. Incredible, father), Helen Parr (Aka Elastigirl, mother), Dashiell Robert Parr (Aka Dash, brother), Jack-Jack (Possibly Jacob) Parr (brother). Group Affiliation: The Incredibles Base Of Operations: Metroville Height: 5’ (Note:Violet’s official height is listed at 4’ 6”. but that is clearly not accurate, by comparing her height to her mother’s and to her boyfriend’s. I went with 5’, which seems accurate, but is still on the short side for a girl her age. Weight: 90lbs Age: 14 Eyes: Dark Blue Hair: Black (probably dyed) Powers and Abilities A pretty much normal teenaged girl otherwise, Violet/Invisigirl can become invisible (partially or fully) and can erect force fields with a high degree of imperviousness to harm (One of her fields protected her family from Syndrome’s crashing plane). Violet has difficulty using her Force Field when she is under stress. Video HD version of the official trailer. History Mr Incredible was his world’s most famed and lauded super-hero (supers, as they are called popularly), until a mishap while preventing a potential suicide led to a lawsuit for damages. This triggered not only an avalanche of personal-injury lawsuits against Mr Incredible personally, but a public backlash of opinion against supers in general, and most of them were forced to go underground to keep from being sued to death. Mr. Incredible and his new wife, Elastigirl , retired and became simple Mr. and Mrs. Parr, and started raising a family. Cut to 15 years later. Bob Parr is an insurance claims specialist with a midlife crisis and a desire to go back to the “old days”. He’s fed up with his pushy boss and his immoral profession. He and his best buddy Lucius Best (aka Frozone ), spend Wednesday nights cruising the city in a car, listening to the police scanner, and saving people on the sly. Helen is trying hard to be a mom to 3 kids, two of whom have superpowers of their own and fight constantly. She has worked too hard to build a normal life for her family to abide his nostalgia for heroism. Violet, their daughter, is having problems relating to people and is withdrawn and moody. Dash , their son, is chafing at the restrictions placed on him, and getting into trouble at school. When Mr. Incredible is offered the chance to play the role of hero again by a mysterious informant, he jumps at the opportunity. But when it turns out to be a trap set by an old nemesis he had a hand in corrupting, the whole family must reveal themselves to save Mr. Incredible and countless innocents. The Incredibles discover that their real power comes from their unity, rather than their superpowers. Description Violet is a tall, skinny girl with a retiring demeanor. She wears a duplicate of her mother’s Iincredisuit in red & black, with the Incredible’s logo on the chest. Before she came to terms with her “differentness”, she wore her hair so that it fell over her face, a figurative shield to hide behind, and wore mostly drab colors. She now wears it pulled back, and wears more bright colors. Personality Violet is a shy, worried teen who wants nothing more then to be normal. She grew up being taught to repress her abilities. Violet wants to be normal so badly that she has difficulty calling on her force field in anything but absolutely safe conditions… ie, at home, in the presence of her family. Her invisibility, a pur | | Where in the body is the Malleus bone? | Malleus Bone Definition, Function & Anatomy | Body Maps Your message has been sent. OK We're sorry, an error occurred. We are unable to collect your feedback at this time. However, your feedback is important to us. Please try again later. Close Malleus The malleus is the outermost and largest of the three small bones in the middle ear, and reaches an average length of about eight millimeters in the typical adult. It is informally referred to as a hammer, owing to it being a hammer-shaped ossicle or small bone that is connected to the ear. It is composed of the head, neck, anterior process, lateral process, and manubrium. When sound reaches the tympanic membrane (eardrum), the malleus transmits these sound vibrations from the eardrum to the incus, and then to the stapes, which is connected to the oval window. Because the malleus is directly connected to the eardrum, it is unlikely that it will be the cause of hearing loss. In cases of atticoantral disease, an inflammatory disease of the middle ear, the ossicular chain (malleus, incus, and stapes) is often affected by abnormal skin growth, called cholesteatoma. This can cause loss of hearing. The malleus and or incus may have to be removed in order to remove all of the cholesteatomas. In cases like these, there may be a second surgery needed for reconstruction purposes. | | Who composed The Resurrection Symphony and The Symphony of a Thousand? | Mahler: The Genius Who Composed the Resurrection Symphony Mahler: The Genius Who Composed the Resurrection Symphony by DavidPaulWagner Gustav Mahler was an outstanding composer and conductor of the post-Romantic era. His lush symphonies included folk music and pastoral elements. Gustav Mahler's romantic music is often heard in modern movies (such as "Death in Venice"). His lush and often world-weary music included nine large-scale symphonies (including the "Resurrection") and cycles of orchestral songs including "The Song of the Earth" and "Songs on the Death of Children". Mahler's music is regarded as the peak of the post-Romantic period of classical music. Mahler's Life Gustav Mahler was born into a large Jewish family (he was one of 14 children) in Kaliste, Bohemia (modern-day Czech Republic) in 1860.  He showed early musical talent (his first public performance was when he was ten) and studied at the Vienna Conservatoire (conservatorium of music). While there he attended occasional lectures by the composer, Anton Bruckner. He was greatly influenced by the music of Richard Wagner. In 1878 he enrolled in Vienna University. He came under the influence of such continental philosophers as Schopenhauer, Nietzsche, Lotse and Fechner. Conducting Career Mahler now commenced his career as an orchestral conductor. He was conductor of (in succession) the Budapest Opera, the Hamburg Opera, the Vienna Court Opera, the Metropolitan Opera (New York) and the New York Philharmonic Society.  As a conductor he showed the influences particularly of Beethoven, Schubert, Wagner, Bruckner and Bach.  Mahler as a Composer Many critics see Mahler's career as a composer as falling into thee sections. In the first part of his composing career (1880-1901), he composed four symphonies, the Lieder eines fahrenden Gesellen (Song of a Wayfarer) song cycle, and other song cycles, which include songs from his Des Knaben Wunderhorn (Youth's Wonder Horn) cycle. In the second part (1901-07), he composed three instrumental symphonies (the 5th, 6th and 7th Symphonies), his Rückert songs (settings of poems by Friedrich Ruckert), his Kindertotenlieder (Songs on the Death of Children), more Wunderhorn arrangements, and finally his choral symphony (8th Symphony). In the third and final part of his composing career (1907-11), Mahler composed Das Lied von der Erde (The Song of the Earth), his 9th Symphony and his unfinished 10th Symphony. These final works show the composer's quiet resignation as he approached his death in 1911. Gustav Mahler's 5th Symphony in Visconti's 1971 film Death in Venice This film was based on Thomas Mann's novella of the same name Mahler's Life (continued) Mahler's Changing Fortunes During his lifetime Mahler's symphonies received wide interest, although he suffered anti-semiticism from various quarters. His songs generally received praise. The premiere of his Eighth Symphony in 1910 was a triumph with applause lasting half an hour. After his death, Mahler's works suffered a decline in popularity and were banned under the Nazi regime. However, since 1960 audiences have been more receptive to romanticism in music and to Mahler. Mahler has influenced a number of major composers including Schoenberg, Berg, Webern, Shostakovich and Britten. Mahler - Symphony No. 2 ("Resurrection") Works of Gustav Mahler | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 4,059 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------|:------------------------------------------------------| | What is the salary of indian president? | What is the President's salary in India? | | As a developer, how can I create a Bitcoin wallet? | Where should I create a bitcoin wallet? | | Do you regularly enjoy anal sex? | Do you like anal sex? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 0.75, "kl_temperature": 0.75 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 12,892 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | are mac addresses case sensitive? | A MAC address consists of six groups of two characters (numbers or letters). ... As you might have noticed, MAC is not case-sensitive, but it tends to appear either all lower case or all upper case. Each time you change a digit or a letter, you'll get a new MAC. | | is azek better than trex? | Trex has a core made of 95% recycled material which includes things like ground up plastic, sawdust, and reclaimed wood. Trex is a much more natural material than Azek. On the other hand, Azek is made of entirely PVC, which is not a natural material. ... It's because of this resistance that Azek is the clear winner. | | what is the best shampoo to use for highlighted hair? | ['Olaplex No. ... ', 'Love Beauty And Planet Blooming Color Shampoo. ... ', 'Rahua Color Full Shampoo. ... ', 'Kerastase Blond Absolu Bain Lumiere Shampoo. ... ', 'David Mallett Shampoo No.2: Le Volume. ... ', 'Fanola No Orange Shampoo. ... ', 'Shu Uemura Color Lustre Shampoo. ... ', 'Living Proof Restore Shampoo.'] | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### gooaq_pairs2 * Dataset: [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 4,298 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how much money do you make for uber? | Uber drivers make an average of $364 a month and a median of $155 a month driving for the ride-sharing company, according to the analysis. | | how do you know when your energizer batteries are charged? | Lift the prongs of the AC plug until fully extended. Plug charger into a standard 110-120 volt AC outlet. The green LED will light during charging. - It is normal for batteries to become warm while charging and is no cause for alarm. | | how long should you run your air conditioner? | An Air Conditioner Should Run for 15-20 Minutes at a Time. In a perfect situation, an air conditioner should run for 15-20 minutes at a time in mild temperatures. Any less than that and your AC could be too large for your home – more on that below. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 0.75, "kl_temperature": 0.75 } ``` #### mrpc_pairs * Dataset: [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 2,474 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------| | In the second quarter last year , the company experienced a net loss of $ 185 million , or 54 cents a share , on sales of $ 600 million . | The company posted a net loss of $ 185 million , or 54 cents per share , in the year-earlier period , it said in a statement Wednesday . | | U.S. District Judge Denny Chin said Fox 's claim was " wholly without merit , both factually and legally . " | " This case is wholly without merit , both factually and legally , " Chin said . | | Pope John Paul has health problems but is still at the helm of the Roman Catholic Church , the pope 's top aide has told Reuters . | Pope John Paul has health problems but is firmly in charge of the Roman Catholic Church , the pope 's top aide told Reuters on Friday . | * 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` ### 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: 100 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 85 evaluation samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dragon Con had over 5000 guests . | Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell . | | COVID-19 has reached more than 185 countries . | As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths . | | In March , Italy had 3.6x times more cases of coronavirus than China . | As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China . | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### sts-label * Dataset: [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) * Size: 100 evaluation 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 man with a hard hat is dancing. | A man wearing a hard hat is dancing. | 1.0 | | A young child is riding a horse. | A child is riding a horse. | 0.95 | | A man is feeding a mouse to a snake. | The man is feeding a mouse to the snake. | 1.0 | * 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" } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 100 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 100 evaluation samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:-----------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------| | A vas deferens is the name of the tube that carries sperm from the epididymis to the urethra. | What is the name of the tube that carries sperm from the epididymis to the urethra? | | A(n) increase in length happens to metal railroad tracks during the heat of a summer day. | What happens to metal railroad tracks during the heat of a summer day? | | Each lymph organ has a different job in the immune system. | Each lymph organ has a different job in what system? | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | 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. | | 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. | | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 18 January 2015 Last updated at 10:03 GMT
The 11-year-old found fame on American TV by appearing on The Ellen DeGeneres Show with cousin, Rosie McClelland, three years ago.
They were plucked from Essex and flown to America after the chat show host fell in love with their version of a Nicki Minaj track.
The pair's YouTube video has been seen by 47 million people.
Sophia Grace and Rosie soon became very popular in the US - interviewing stars like Katy Perry and Taylor Swift.
They also went on to make a movie and tour Australia.
But now, Sophia Grace is going it alone and has just entered America's Billboard music chart for best-selling songs.
She's been telling BBC Radio 1's Newsbeat all about her new song Best Friends and revealed what she would like to do next. | Sophia Grace Brownlee's face is probably one you recognise. | | Four people were taken to hospital on Thursday after a three-vehicle accident in the early hours near the A40 turn for Oxford (junction 8).
On Saturday, a car ended up on its roof on a verge after a four-vehicle crash at junction 10 at 15:22 GMT.
John Callaway from Oxfordshire Fire and Rescue Service said it was "remarkable" that no-one was killed.
He added it was the seventh road accident his Banbury team had been called to in under a week.
Mr Callaway said: "This was a high-speed collision on a fast road, it is remarkable that nobody lost their life.
"Out of the four casualties, two were transferred to hospital by ambulance and two were treated at the scene. Fortunately none of the injuries appear life-threatening.
"With the onset of winter and more difficult driving conditions, I urge drivers to allow more time for their journey and adjust their driving accordingly." | Drivers are being urged to leave more time for their journeys after two crashes on the M40 in recent days. | | Evans is expected to be named on Friday in Michael O'Neill's squad for the World Cup qualifier with Norway in Belfast on 26 March.
The 26-year-old has been sidelined since 2 January because of a chronic groin problem.
Evans has started his country's last two World Cup qualifiers.
New Rovers manager Mowbray made it clear there would be no club-versus-country row if Evans were to face the Norwegians.
"I don't really get involved in the international set-ups. What I do know is footballers like to play for their countries, they want to play for their countries," he said.
"If he gets called up, there will no problem. If anything it will be a benefit if he gets some game time and some intense training to build him up.
"He has trained with us for almost a week now. I would have to say he looks a very talented footballer, my type of footballer. He picks really lovely passes, he's got quick feet and a really good appreciation of the football.
"He needs to get fit. If he gets called up, there will no problem."
Former Celtic boss Mowbray was appointed Blackburn manager on 22 February, succeeding Owen Coyle. | Blackburn boss Tony Mowbray says he has no problem with Corry Evans playing for Northern Ireland despite the midfielder being injured for the past two months. | * 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` #### 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: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | One of America's most famous newspaper publishers, Arthur Ochs Sulzberger, whose family owns the New York Times, died on Saturday at the age of 86. | Arthur Ochs Sulzberger, publisher of the New York Times, dies at 86 | | ``Since vitamin D is generally lower in persons with obesity, it is possible that low vitamin D could account, in part, for the link between obesity and diseases such as cancer, heart disease and diabetes,'' said Caitlin Mason, Ph.D., lead author of the paper, published online May 25 in the American Journal of Clinical Nutrition. | Low vitamin D could account for link between obesity and cancer, heart disease, diabetes | | A 32-year-old Clovis man was sentenced to nine years in prison Wednesday for attempted murder, according to a news release from the office 9th Judicial District Attorney Matthew Chandler. | Clovis man sentenced to nine years | * 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What forms when oceanic crust subducts into the mantle at convergent plate boundaries? | Volcanic mountain ranges form when oceanic crust subducts into the mantle at convergent plate boundaries. The Andes Mountains are a chain of coastal volcanic mountains. They are forming as the Nazca plate subducts beneath the South American plate ( Figure below ). | | Are the joints between the vertebrae contained in your backbone fully movable, partially movable, or unmovable? | Partly movable joints allow only a little movement. Your backbone has partly movable joints between the vertebrae ( Figure below ). | | What rod provides stiffness to counterbalance the pull of muscles? | The notochord lies between the dorsal nerve cord and the digestive tract. It provides stiffness to counterbalance the pull of muscles. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | a living thing with four divisions can be what with woody trunks? | Most modern gymnosperms are trees with woody trunks.. Gymnosperms comprise four divisions.. a living thing with four divisions can be trees with woody trunks. | | What is hair not considered? | Hair helps to insulate and protect the body.. Lean body mass and body fat are derived from total body water.. Hair is not considered fat. | | What can learn behavior that is intended to cause harm or pain? | Aggression is behavior that is intended to cause harm or pain.. If they are around aggressive dogs, they learn to be aggressive.. dogs can learn behavior that is intended to cause harm or pain | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 100 evaluation samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | The thermal production of a stove is generically used for | a stove generates heat for cooking usually | | What creates a valley? | a valley is formed by a river flowing | | when it turns day and night on a planet, what cause this? | a planet rotating causes cycles of day and night on that planet | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how big is a medium sized dog | Medium-size kennels are around 36 inches long, and can accommodate dogs in the 40- to 70-pound range. Bulldogs, cocker spaniels, and American Eskimo dogs at a normal adult size all fit well in these size kennels. Large-size kennels are around 42 inches long, and can accommodate dogs in the 70 to 90 pound range. | | types of wounds | A National Athletic Trainers' Association answered. The five types of wounds are abrasion, avulsion, incision, laceration, and puncture. An abrasion is a wound caused by friction when a body scrapes across a rough surface. An avulsion is characterized by a flap. An incision is a cut with clean edges. A laceration is a cut with jagged edges. | | did mike tyson ever lose | In 2002, Tyson fought for the world heavyweight title at the age of 35, losing by knockout to Lennox Lewis. Tyson retired from professional boxing in 2006, after being knocked out in consecutive matches against Danny Williams and Kevin McBride. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who is playing the fa cup final 2018 | 2018 FA Cup Final The 2018 FA Cup Final was the final match of the 2017–18 FA Cup and the 137th final of the FA Cup, the world's oldest football cup competition. It was played at Wembley Stadium in London, England[3] on 19 May 2018 between Manchester United and Chelsea. It was the second successive final for Chelsea following their defeat by Arsenal the previous year. | | what type of audio file does itunes use | iTunes iTunes keeps track of songs by creating a virtual library, allowing users to access and edit a song's attributes. These attributes, known as metadata, are stored in a binary library file called iTunes Library, which uses a proprietary file format ("ITL"). It caches information like artist and genre from the audio format's tag capabilities (the ID3 tag, for example) and stores iTunes-specific information like play count and rating. iTunes typically reads library data only from this file.[27] A second file can also be created if users activate a preference; the iTunes Music Library.xml file is refreshed whenever information in iTunes is changed. It uses an XML format, allowing third-party apps to access the library information (including play count, last played date, and rating, which are not standard fields in the ID3v2.3 format). Apple's own iDVD, iMovie, and iPhoto applications all access the library.[28] If the first file exists but is corrupted, such as by making it zero-length, iTunes will attempt to reconstruct it from the XML file. Detailed third-party instructions regarding this can be found elsewhere.[29] Beginning with iTunes 10.5.3 this behavior has been changed such that the XML file is not read automatically to recreate the database when the database is corrupted. Rather, the user should load the iTunes Library.xml file via File > Library > Import Playlist.... | | who played the werewolf in the old movies | The Wolf Man (1941 film) The Wolf Man is a 1941 American horror film written by Curt Siodmak and produced and directed by George Waggner. The film features Lon Chaney Jr. in the title role, and also features Claude Rains, Warren William, Ralph Bellamy, Patric Knowles, and Bela Lugosi; with Evelyn Ankers, and Maria Ouspenskaya in supporting roles. The title character has had a great deal of influence on Hollywood's depictions of the legend of the werewolf.[2] The film is the second Universal Pictures werewolf film, preceded six years earlier by the less commercially successful Werewolf of London (1935). | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Italian musician David Rizzio was private secretary to which British monarch? | Rizzio - Memidex dictionary/thesaurus Rizzio David Rizzio | Davide Rizzio | Davide Riccio | Davide Rizzo (Born: about 1533 – Died: 9 March 1566) an Italian courtier, born close to Turin, a descendant of an ancient and noble family still living in Piedmont, the Riccio Counts de San Paolo et Solbrito, who rose to become the private secretary of Mary, Queen of Scots. Mary's husband, Lord Darnley,... | | ‘Full English’ relates to which meal? | Your Guide to a Full English Breakfast (Fry-Up) | Serious Eats Your Guide to a Full English Breakfast (Fry-Up) [ Photograph: Sydney Oland ] Breakfast. The Full English. The Full Monty. A fry-up. Call it what you want, but there are few nations in this world that do breakfast better than the British. Digging into a piping hot fry up is a is an experience that can set you right no matter what situation you may have gotten yourself into. What is included in your fry-up is a matter of taste, as well as region. The following is a breakdown of the fry-up, and the components that are (in my mind, at least) essential—as well as a group of things that are a welcome addition to the party. The Bare Essentials The Meat: Sausage and Bacon The combination of both bacon and sausage is one of the essential elements to a full fry up. A simple pork sausage (like the banger) is ideal and the type of bacon is up to preference. Streaky bacon (or what you Americans just call bacon) is a common sight in a fry up, but back bacon, made from the cured loin of a pig (and often labeled "Irish Bacon" in the States) makes a lovely meaty counterpart to the fatty sausage it sits alongside. Beans The inclusion of beans may seem cursory, but they are the element of the plate that anchors the meal. Don't be ashamed to reach for a can of beans—Heinz is the classic. But if you have time on your side, trying out a homemade version of baked beans can make the humble legume shine. Here are a few recipes to get you started . They won't have the same sweet, tomato-y vinegary flavor of Heinz, but they'll do you well. Tomatoes Much like the beans, the tomato may seem like an optional garnish; I assure you, it is not. The sweetness and acidity that come from a cooked tomato goes a long way in cutting the fattiness that is inherent in the rest of the plate. The way you prepare your tomato is once again a matter of taste—a stewed tomato will work, but simply cutting a tomato in half and frying it in leftover bacon fat, then finishing it with a bit of cracked black pepper, is a quick and delicious way to go. Many proper fry ups will come with grilled tomato (that's broiled, for you American English speakers). Forget The Toast, Bring On The Fried Bread Fried bread is just what it sounds like—sliced bread toasted in a pan with butter, lard, or bacon drippings. It is important to not underestimate the amount of fat you will need to get a perfectly crisp piece of fried bread—a couple tablespoons per slice is not unheard of. It's a bit surprising, but absolutely worth it. You only need a slice or two to satisfy the craving; after that, you may find yourself reaching for a plain piece of toast. The Eggs Lastly, the egg. Normally two, but sometimes three eggs cooked to your preference. The runny yolk to my mind is essential—it's the sauce that brings the whole plate together!—although those die-hard scrambled fans will without a doubt disagree with me. Essential Condiments Some things that should also be included in the essential section of a fry up guide is a hot strong cup of tea and a bottle of vinegary, brown HP sauce, tomato sauce (aka ketchup), or both. If you have a jar of marmite and some nice marmalade, you may want to put that on the table as well. And you will always need a few pieces of extra toast. Optional But Worth It When it comes to things that may go into a fry up, the list is long and varied. These few ingredients make it to the top of most lists, and may in fact belong in the essential category, in many eyes. Black and White Pudding Black pudding, sliced into 1/2-inch pieces and lightly fried. Made with oatmeal, pork fat, and blood, the dark sausage has a strong, minerally flavor, but you only need a few bits of the soft yet crisp disks on your plate to understand why so many of us are big fans. On the other side, white pudding is fatty pork sausage that includes oatmeal, like black pudding—just no blood. Treated the same way, sliced and fried, it has a soft texture like black pudding that leads some people to lovingly refer to it as mealy pudding. Kidney | | What went with Blood and Sweat in the name of the 60s rock band? | Chicago Songs, History, and Biography Does Anybody Really Know What Time It Is? If You Leave Me Now Old Days Hard Habit to Break Where you might have heard them Their early '70s rock hits remain staples on classic rock radio; ditto for their late '70s and early '80s ballads on adult contemporary playlists. Occasionally, however, Chicago's catalog interacts with other realms of entertainment, like the highly ironic use of "If You Leave Me Now" in the classic Gulf War film Three Kings and the zombie spoof Shaun of the Dead, or "Saturday in the Park" being featured in an episode of "The Sopranos," or "Old Days" popping up in the films This is 40 and Starsky & Hutch.  continue reading below our video 5 Urban Myths That Rule the Ages Formed 1967 (Chicago, IL) Styles Jazz-rock, Pop-rock, Classic Rock, Soft-rock, Adult Contemporary, Prog-rock Claims to fame: Did more than any other group to create a commercial fusion of jazz, classical, pop, and rock Their signature sound was the result of several multi-talented singers, songwriters and musicians A socially aware rock band whose lyrical activist sensibilities lasted longer than most Lead guitarist Terry Kath, who died tragically young, is considered one of the most underrated rock guitarists of the era Survived a series of setbacks to re-emerge in the '80s as a successful soft-rock group The classic Chicago lineup: Robert Lamm (born October 13, 1944, Brooklyn, NY): lead and backing vocals, piano, organ, guitar Peter Cetera (born September 13, 1944, Chicago, IL): lead and backing vocals, bass, guitar Terry Kath (born January 31, 1946, Chicago, IL; died January 23, 1978, Woodland Hills, CA): lead and backing vocals, lead guitar, bass Lee Loughnane (born October 21, 1946, Chicago, IL): trumpet, flugelhorn, guitar, percussion, lead and backing vocals  James Pankow (born August 20, 1947, St. Louis, MO): trombone, keyboards, percussion, lead and backing vocals  Walter Parazaider (born March 14, 1945, Chicago, IL): alto and tenor saxophones, flute, clarinet, backing vocals Danny Seraphine (born August 28, 1948, Chicago, IL) drums, percussion, keyboards The History of Chicago Early years Anyone even casually familiar with the band Chicago won't be surprised to learn they were a bunch of guys from the Windy City who took up their instruments at an early age, learning jazz and classical music before being seduced by the money (and women) available to rock and soul party bands. In fact, the members of Chicago, all but two of whom were born and raised in the city or its suburbs, formed the band that was to be their legacy after meeting at the city's famed DePaul University. Walter Parazaider, a classically trained clarinetist who had discovered the joys of the saxophone, was heading up a local rock band called the Missing Links, which at times included Terry Kath, Lee Loughnane and Danny Seraphine. Embolded by the Beatles' recent use of horn sections on songs like "Got to Get You Into My Life," Parazaider began to merge his two loves, expanding the band into a large jazz-rock outfit; Fellow student James Pankow soon joined, then organist and vocalist Robert Lamm, recruited from another local group. As Kath moved from bass to guitar, and with a tenor needed to complete the group's harmony, Peter Cetera was invited to join. Due to the unconventional nature of both their size and scope, they went by the name The Big Thing. Success Parazider's longtime musician friend James William Guercio, by 1967 a producer at Columbia Records, loved the concept and agreed to manage the band. Moving them out to Los Angeles, the group, now renamed Chicago Transit Authority after their hometown's bus line, rehearsed night and day while Guercio produced the second album by Blood, Sweat & Tears, another big rock band with similar ideas. When that album became a Grammy-winning smash, spinning off three hit singles, the stage was set for Chicago. The album Chicago Transit Authority was only successful on the new, free-form FM stations at first, but two years of buzz finally got them a hit with "25 or 6 to 4," and | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### 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: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------|:-----------------------------------------------------------------| | What is it like to attend your high school reunion? | What was it like to go to your high school reunion? | | How do I concentrate in studies? | How can I concentrate in my daily studies? | | How do I become mentally stronger? | How do I become mentally strong? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 3, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 0.75, "kl_temperature": 0.75 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | can drinking too much water make you lose your period? | Not drinking enough water: Keeping yourself dehydrated during periods can lead to cramps and discomfort. During periods, you experience hormonal fluctuations and a bloated belly. As your estrogen and progesterone levels recede, your body retains more water. | | how long do side effects last after stopping medication? | Symptoms of Antidepressant Discontinuation Symptoms of antidepressant withdrawal depend on the specific medication you have been taking. Symptoms most often occur within three days of stopping the antidepressant. They are usually mild and go away within about two weeks. | | is jedediah a biblical name? | In the Hebrew Bible, Jedidiah (Jeddedi in Brenton's Septuagint Translation) was the second or "blessing" name given by God through the prophet Nathan in infancy to Solomon, second son of King David and Bathsheba. | * 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": 2, "prior_layers_weight": 0.25, "kl_div_weight": 1.25, "kl_temperature": 0.9 } ``` #### mrpc_pairs * Dataset: [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | A former employee of a local power company pleaded guilty Wednesday to setting off a bomb that knocked out a power substation during the Winter Olympics last year . | A former Utah Power meter reader pleaded guilty Wednesday to bombing a power substation during the 2002 Winter Olympics . | | Metro , bus and local rail services in France 's four largest towns -- Paris , Lyon , Lille and Marseille -- were severely disrupted , Europe 1 radio reported . | Subway , bus and suburban rail services in France 's four largest cities -- Paris , Lyon , Lille and Marseille -- were severely disrupted , transport authorities said . | | The U.N. troops are in Congo to protect U.N. installations and personnel , and they can only fire in self defense and have been unable to stem the violence . | The troops - whose mandate is to protect U.N. installations and personnel - can only fire in self-defense and have been unable to stem the violence . | * 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": 0.75, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 0.75 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 3e-05 - `weight_decay`: 0.0001 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 2} - `warmup_ratio`: 0.075 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa-ST-AllLayers-v3.1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `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`: 32 - `per_device_eval_batch_size`: 32 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 3e-05 - `weight_decay`: 0.0001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 2} - `warmup_ratio`: 0.075 - `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/DeBERTa-ST-AllLayers-v3.1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `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 - `eval_on_start`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | trivia pairs loss | scitail-pairs-pos loss | vitaminc-pairs loss | qasc pairs loss | scitail-pairs-qa loss | msmarco pairs loss | nq pairs loss | quora pairs loss | qnli-contrastive loss | nli-pairs loss | sts-label loss | compression-pairs loss | xsum-pairs loss | sciq pairs loss | mrpc pairs loss | openbookqa pairs loss | gooaq pairs loss | sts-test_spearman_cosine | |:------:|:-----:|:-------------:|:-----------------:|:----------------------:|:-------------------:|:---------------:|:---------------------:|:------------------:|:-------------:|:----------------:|:---------------------:|:--------------:|:--------------:|:----------------------:|:---------------:|:---------------:|:---------------:|:---------------------:|:----------------:|:------------------------:| | 0.0050 | 32 | 0.6329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0100 | 64 | 0.9693 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0150 | 96 | 0.6548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0201 | 128 | 1.1279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0251 | 160 | 1.0017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0301 | 192 | 0.7571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0351 | 224 | 0.7304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0401 | 256 | 0.7636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0451 | 288 | 0.482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0501 | 320 | 0.6312 | 0.7809 | 0.5141 | 4.6926 | 0.2045 | 0.0761 | 0.5139 | 0.2351 | 0.0392 | 0.1608 | 1.0158 | 3.5502 | 0.0981 | 0.2558 | 0.2562 | 0.0550 | 1.7538 | 0.4713 | 0.7990 | | 0.0552 | 352 | 0.5791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0602 | 384 | 0.6413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0652 | 416 | 0.4319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0702 | 448 | 0.6672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0752 | 480 | 0.459 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0802 | 512 | 0.7621 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0853 | 544 | 0.864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0903 | 576 | 0.5081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0953 | 608 | 0.654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1003 | 640 | 0.6372 | 0.8748 | 0.5404 | 4.7194 | 0.2102 | 0.0754 | 0.5103 | 0.2447 | 0.0782 | 0.1520 | 1.0653 | 3.6123 | 0.1007 | 0.2596 | 0.2645 | 0.0549 | 1.7905 | 0.5236 | 0.7997 | | 0.1053 | 672 | 0.9292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1103 | 704 | 1.3108 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1153 | 736 | 0.9674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1204 | 768 | 0.9226 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1254 | 800 | 0.789 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1304 | 832 | 0.5186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1354 | 864 | 0.6726 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1404 | 896 | 0.5381 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1454 | 928 | 0.581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1504 | 960 | 0.9038 | 0.9380 | 0.4760 | 4.7749 | 0.2327 | 0.0806 | 0.5809 | 0.2808 | 0.0913 | 0.1943 | 1.2173 | 3.1986 | 0.1009 | 0.2758 | 0.2688 | 0.0580 | 1.8053 | 0.5808 | 0.8006 | | 0.1555 | 992 | 0.7964 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1605 | 1024 | 0.8213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1655 | 1056 | 0.5396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1705 | 1088 | 0.9297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1755 | 1120 | 1.169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1805 | 1152 | 0.7486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1856 | 1184 | 0.6821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1906 | 1216 | 0.6125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1956 | 1248 | 0.8061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2006 | 1280 | 0.6918 | 0.9222 | 0.4943 | 4.6790 | 0.1935 | 0.0747 | 0.5744 | 0.3035 | 0.0385 | 0.2037 | 1.1552 | 3.5581 | 0.0974 | 0.2828 | 0.2762 | 0.0562 | 1.6875 | 0.5284 | 0.8014 | | 0.2056 | 1312 | 0.9421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2106 | 1344 | 0.8641 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2156 | 1376 | 1.157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2207 | 1408 | 0.8772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2257 | 1440 | 1.0496 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2307 | 1472 | 0.589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2357 | 1504 | 0.8234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2407 | 1536 | 0.7365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2457 | 1568 | 0.5076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2507 | 1600 | 1.0329 | 0.8292 | 0.5018 | 4.7846 | 0.2132 | 0.0755 | 0.5719 | 0.2738 | 0.0800 | 0.1859 | 1.0577 | 3.6113 | 0.0974 | 0.2749 | 0.2648 | 0.0551 | 1.7900 | 0.5998 | 0.8019 | | 0.2558 | 1632 | 1.4006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2608 | 1664 | 0.5963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2658 | 1696 | 0.7488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2708 | 1728 | 0.8548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2758 | 1760 | 1.3324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2808 | 1792 | 0.5804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2858 | 1824 | 0.7827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2909 | 1856 | 0.5448 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2959 | 1888 | 0.7368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3009 | 1920 | 0.5657 | 0.9042 | 0.4916 | 4.6983 | 0.1963 | 0.0701 | 0.5171 | 0.2481 | 0.0360 | 0.1133 | 1.0244 | 3.1822 | 0.0920 | 0.2594 | 0.2914 | 0.0498 | 1.7960 | 0.5626 | 0.7984 | | 0.3059 | 1952 | 0.7425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3109 | 1984 | 0.7819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3159 | 2016 | 0.5937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3210 | 2048 | 0.8133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3260 | 2080 | 1.0674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3310 | 2112 | 0.6288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3360 | 2144 | 0.5866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3410 | 2176 | 0.6962 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3460 | 2208 | 0.5562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3510 | 2240 | 0.8871 | 0.8545 | 0.5766 | 4.6446 | 0.1923 | 0.0664 | 0.5325 | 0.2721 | 0.0844 | 0.1122 | 1.0148 | 3.9153 | 0.0919 | 0.2596 | 0.2843 | 0.0509 | 1.6234 | 0.5712 | 0.8015 | | 0.3561 | 2272 | 0.6805 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3611 | 2304 | 1.0451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3661 | 2336 | 1.0603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3711 | 2368 | 0.8142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3761 | 2400 | 1.7211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3811 | 2432 | 0.7523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3861 | 2464 | 0.8053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3912 | 2496 | 0.8427 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3962 | 2528 | 0.8204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4012 | 2560 | 0.5343 | 0.8588 | 0.4768 | 4.7234 | 0.1924 | 0.0674 | 0.5183 | 0.3077 | 0.0798 | 0.1508 | 1.0022 | 3.8186 | 0.0960 | 0.2572 | 0.2735 | 0.0512 | 1.6290 | 0.6571 | 0.8039 | | 0.4062 | 2592 | 0.9709 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4112 | 2624 | 0.708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4162 | 2656 | 0.4083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4213 | 2688 | 0.8732 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4263 | 2720 | 1.2616 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4313 | 2752 | 1.3324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4363 | 2784 | 0.6244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4413 | 2816 | 0.6176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4463 | 2848 | 0.6926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4513 | 2880 | 0.8158 | 0.8937 | 0.4763 | 4.7561 | 0.1805 | 0.0703 | 0.5720 | 0.2748 | 0.0799 | 0.1333 | 1.2103 | 3.4280 | 0.0984 | 0.2574 | 0.2824 | 0.0539 | 1.5709 | 0.6211 | 0.8024 | | 0.4564 | 2912 | 1.4753 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4614 | 2944 | 0.5735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4664 | 2976 | 1.2261 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4714 | 3008 | 0.6085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4764 | 3040 | 0.8766 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4814 | 3072 | 1.1824 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4864 | 3104 | 0.7192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4915 | 3136 | 0.6131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4965 | 3168 | 0.7407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5015 | 3200 | 0.5857 | 0.8825 | 0.5107 | 4.7481 | 0.1826 | 0.0667 | 0.5865 | 0.2539 | 0.0626 | 0.1034 | 1.1432 | 3.9403 | 0.0910 | 0.2657 | 0.3079 | 0.0503 | 1.5945 | 0.5953 | 0.8013 | | 0.5065 | 3232 | 0.6212 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5115 | 3264 | 1.1408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5165 | 3296 | 0.6898 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5215 | 3328 | 0.9827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5266 | 3360 | 0.9518 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5316 | 3392 | 0.5584 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5366 | 3424 | 1.3362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5416 | 3456 | 0.4418 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5466 | 3488 | 0.5896 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5516 | 3520 | 0.7951 | 0.9037 | 0.5180 | 4.6285 | 0.1791 | 0.0601 | 0.5547 | 0.2480 | 0.0573 | 0.1186 | 1.0017 | 3.6985 | 0.0899 | 0.2575 | 0.2898 | 0.0476 | 1.6558 | 0.5602 | 0.8003 | | 0.5567 | 3552 | 0.5201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5617 | 3584 | 0.6351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5667 | 3616 | 0.8652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5717 | 3648 | 0.6407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5767 | 3680 | 0.9435 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5817 | 3712 | 0.9295 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5867 | 3744 | 0.6829 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5918 | 3776 | 0.8683 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5968 | 3808 | 1.115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6018 | 3840 | 1.0936 | 0.7936 | 0.4620 | 4.7559 | 0.1861 | 0.0611 | 0.5555 | 0.2324 | 0.0594 | 0.1389 | 0.9106 | 3.4692 | 0.0881 | 0.2492 | 0.2829 | 0.0463 | 1.6010 | 0.5736 | 0.8005 | | 0.6068 | 3872 | 0.8689 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6118 | 3904 | 0.8692 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6168 | 3936 | 0.9083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6218 | 3968 | 1.0782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6269 | 4000 | 0.7711 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6319 | 4032 | 1.0005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6369 | 4064 | 0.7229 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6419 | 4096 | 0.4871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6469 | 4128 | 0.7853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6519 | 4160 | 0.9271 | 0.8565 | 0.4259 | 4.7202 | 0.1750 | 0.0614 | 0.5042 | 0.2456 | 0.0526 | 0.1426 | 0.8980 | 3.9554 | 0.0884 | 0.2565 | 0.2947 | 0.0471 | 1.5555 | 0.5816 | 0.8043 | | 0.6570 | 4192 | 0.5223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6620 | 4224 | 1.0498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6670 | 4256 | 0.6791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6720 | 4288 | 0.8836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6770 | 4320 | 0.6035 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6820 | 4352 | 0.5167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6870 | 4384 | 0.981 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6921 | 4416 | 0.4873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6971 | 4448 | 0.4762 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7021 | 4480 | 0.8201 | 0.7997 | 0.4325 | 4.7335 | 0.1771 | 0.0596 | 0.5347 | 0.2483 | 0.0307 | 0.1156 | 0.8704 | 3.5892 | 0.0871 | 0.2489 | 0.2863 | 0.0446 | 1.5271 | 0.5037 | 0.8043 | | 0.7071 | 4512 | 0.7799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7121 | 4544 | 0.8006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7171 | 4576 | 0.5123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7221 | 4608 | 0.7421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7272 | 4640 | 0.9477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7322 | 4672 | 0.5021 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7372 | 4704 | 0.931 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7422 | 4736 | 0.7777 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7472 | 4768 | 0.9462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7522 | 4800 | 0.5846 | 0.8120 | 0.4563 | 4.6871 | 0.1704 | 0.0585 | 0.5062 | 0.2288 | 0.0621 | 0.1415 | 0.9292 | 3.8014 | 0.0868 | 0.2348 | 0.2816 | 0.0438 | 1.5671 | 0.4848 | 0.8044 | | 0.7572 | 4832 | 0.6735 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7623 | 4864 | 1.1569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7673 | 4896 | 0.9749 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7723 | 4928 | 0.6581 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7773 | 4960 | 0.6979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7823 | 4992 | 0.7582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7873 | 5024 | 1.0082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7924 | 5056 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7974 | 5088 | 0.5165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8024 | 5120 | 0.4914 | 0.7989 | 0.4786 | 4.7251 | 0.1739 | 0.0556 | 0.5132 | 0.2343 | 0.0558 | 0.1053 | 0.8841 | 3.6535 | 0.0838 | 0.2356 | 0.2816 | 0.0417 | 1.5919 | 0.4890 | 0.8042 | | 0.8074 | 5152 | 1.098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8124 | 5184 | 0.821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8174 | 5216 | 0.9351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8224 | 5248 | 0.8784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8275 | 5280 | 0.8326 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8325 | 5312 | 0.7551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8375 | 5344 | 0.8234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8425 | 5376 | 1.0922 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8475 | 5408 | 1.0925 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8525 | 5440 | 1.099 | 0.7568 | 0.4714 | 4.6969 | 0.1696 | 0.0573 | 0.5048 | 0.2302 | 0.0575 | 0.1292 | 0.8909 | 3.7946 | 0.0850 | 0.2318 | 0.2747 | 0.0438 | 1.5425 | 0.4945 | 0.8088 | | 0.8575 | 5472 | 0.5396 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8626 | 5504 | 0.6636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8676 | 5536 | 1.0095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8726 | 5568 | 0.631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8776 | 5600 | 0.5415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8826 | 5632 | 0.9227 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8876 | 5664 | 0.8991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8927 | 5696 | 0.5068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8977 | 5728 | 1.2134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9027 | 5760 | 0.4651 | 0.7480 | 0.4753 | 4.7176 | 0.1701 | 0.0550 | 0.4862 | 0.2252 | 0.0606 | 0.1183 | 0.8765 | 3.7598 | 0.0831 | 0.2294 | 0.2765 | 0.0418 | 1.5488 | 0.4697 | 0.8071 | | 0.9077 | 5792 | 0.6346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9127 | 5824 | 1.1103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9177 | 5856 | 0.7667 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9227 | 5888 | 0.9174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9278 | 5920 | 0.7609 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9328 | 5952 | 0.8993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9378 | 5984 | 0.7587 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9428 | 6016 | 0.935 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9478 | 6048 | 0.8551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9528 | 6080 | 1.4247 | 0.7455 | 0.4640 | 4.7075 | 0.1663 | 0.0553 | 0.4886 | 0.2193 | 0.0537 | 0.1200 | 0.8808 | 3.7687 | 0.0825 | 0.2277 | 0.2470 | 0.0418 | 1.5344 | 0.4775 | 0.8078 | | 0.9578 | 6112 | 0.3377 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9629 | 6144 | 1.163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9679 | 6176 | 1.1638 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9729 | 6208 | 0.7428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9779 | 6240 | 0.3827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9829 | 6272 | 1.0739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9879 | 6304 | 0.7049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9929 | 6336 | 0.9298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9980 | 6368 | 0.6243 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0030 | 6400 | 0.8693 | 0.7522 | 0.4743 | 4.6926 | 0.1651 | 0.0544 | 0.4899 | 0.2182 | 0.0266 | 0.1122 | 0.8765 | 3.7495 | 0.0813 | 0.2270 | 0.2293 | 0.0412 | 1.5506 | 0.4720 | 0.8073 | | 1.0080 | 6432 | 0.731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0130 | 6464 | 0.7662 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0180 | 6496 | 0.5362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0230 | 6528 | 0.9786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0281 | 6560 | 0.9213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0331 | 6592 | 0.7601 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0381 | 6624 | 0.4821 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0431 | 6656 | 0.73 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0481 | 6688 | 0.4139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0531 | 6720 | 0.5152 | 0.7513 | 0.4694 | 4.6802 | 0.1659 | 0.0549 | 0.4901 | 0.2205 | 0.0250 | 0.1132 | 0.8771 | 3.7476 | 0.0817 | 0.2276 | 0.2293 | 0.0415 | 1.5460 | 0.4723 | 0.8081 | | 1.0581 | 6752 | 0.4684 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0632 | 6784 | 0.445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0682 | 6816 | 0.4288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0732 | 6848 | 0.3797 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0782 | 6880 | 0.4304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0832 | 6912 | 0.8562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0882 | 6944 | 0.4902 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0932 | 6976 | 0.4285 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0983 | 7008 | 0.4782 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1033 | 7040 | 0.7503 | 0.9699 | 0.5473 | 4.5217 | 0.1793 | 0.0636 | 0.4798 | 0.2459 | 0.0316 | 0.1796 | 0.9263 | 3.8786 | 0.0929 | 0.2405 | 0.2890 | 0.0485 | 1.7124 | 0.5500 | 0.8109 | | 1.1083 | 7072 | 1.0828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1133 | 7104 | 0.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1183 | 7136 | 0.8111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1233 | 7168 | 0.49 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1283 | 7200 | 0.5289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1334 | 7232 | 0.2983 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1384 | 7264 | 0.5183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1434 | 7296 | 0.3254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1484 | 7328 | 0.5142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1534 | 7360 | 0.5605 | 0.9398 | 0.4742 | 4.8611 | 0.1884 | 0.0625 | 0.5194 | 0.2714 | 0.0587 | 0.2063 | 1.0348 | 3.8329 | 0.0926 | 0.2374 | 0.2771 | 0.0474 | 1.8109 | 0.6362 | 0.8067 | | 1.1584 | 7392 | 0.6993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1635 | 7424 | 0.3437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1685 | 7456 | 0.3281 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1735 | 7488 | 1.0286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1785 | 7520 | 0.6668 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1835 | 7552 | 0.3861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1885 | 7584 | 0.4096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1935 | 7616 | 0.5836 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1986 | 7648 | 0.2649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2036 | 7680 | 0.5884 | 0.9296 | 0.5000 | 4.7865 | 0.1851 | 0.0548 | 0.5506 | 0.2425 | 0.0315 | 0.1508 | 1.0354 | 3.8022 | 0.0859 | 0.2493 | 0.2833 | 0.0428 | 1.6866 | 0.5460 | 0.8000 | | 1.2086 | 7712 | 0.7018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2136 | 7744 | 0.7082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2186 | 7776 | 0.7527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2236 | 7808 | 0.4255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2286 | 7840 | 0.7488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2337 | 7872 | 0.3364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2387 | 7904 | 0.6963 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2437 | 7936 | 0.2829 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2487 | 7968 | 0.7504 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2537 | 8000 | 0.7759 | 0.7162 | 0.4485 | 4.8030 | 0.1880 | 0.0619 | 0.4859 | 0.2364 | 0.0622 | 0.2386 | 0.9781 | 4.1984 | 0.0850 | 0.2367 | 0.2703 | 0.0472 | 1.7419 | 0.6093 | 0.8107 | | 1.2587 | 8032 | 0.5297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2638 | 8064 | 0.4933 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2688 | 8096 | 0.3868 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2738 | 8128 | 0.9955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2788 | 8160 | 0.5548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2838 | 8192 | 0.4924 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2888 | 8224 | 0.3422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2938 | 8256 | 0.4707 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2989 | 8288 | 0.3956 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3039 | 8320 | 0.547 | 0.8857 | 0.4749 | 4.7629 | 0.1739 | 0.0527 | 0.5004 | 0.2118 | 0.0293 | 0.1351 | 0.9302 | 3.5312 | 0.0791 | 0.2362 | 0.2984 | 0.0405 | 1.8043 | 0.5669 | 0.8020 | | 1.3089 | 8352 | 0.5412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3139 | 8384 | 0.3885 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3189 | 8416 | 0.4274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3239 | 8448 | 0.893 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3289 | 8480 | 0.3456 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3340 | 8512 | 0.4292 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3390 | 8544 | 0.4275 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3440 | 8576 | 0.3236 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3490 | 8608 | 0.3961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3540 | 8640 | 0.5146 | 0.8409 | 0.4793 | 4.7572 | 0.1706 | 0.0500 | 0.4634 | 0.2150 | 0.0247 | 0.1045 | 0.9968 | 3.5627 | 0.0777 | 0.2310 | 0.2708 | 0.0391 | 1.7370 | 0.5490 | 0.8042 | | 1.3590 | 8672 | 0.7562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3640 | 8704 | 0.7881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3691 | 8736 | 0.6117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3741 | 8768 | 1.3083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3791 | 8800 | 0.5359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3841 | 8832 | 0.45 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3891 | 8864 | 0.6022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3941 | 8896 | 0.6664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3992 | 8928 | 0.3255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4042 | 8960 | 0.6036 | 0.8256 | 0.3827 | 4.6999 | 0.1707 | 0.0539 | 0.4949 | 0.2415 | 0.0267 | 0.1461 | 0.8724 | 3.5730 | 0.0786 | 0.2278 | 0.2640 | 0.0412 | 1.7219 | 0.5385 | 0.8082 | | 1.4092 | 8992 | 0.4723 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4142 | 9024 | 0.2569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4192 | 9056 | 0.5794 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4242 | 9088 | 1.022 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4292 | 9120 | 1.0539 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4343 | 9152 | 0.4634 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4393 | 9184 | 0.3755 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4443 | 9216 | 0.4033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4493 | 9248 | 0.522 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4543 | 9280 | 1.1067 | 0.7743 | 0.4368 | 4.6339 | 0.1731 | 0.0541 | 0.4894 | 0.2487 | 0.0238 | 0.1774 | 1.0503 | 3.7515 | 0.0801 | 0.2187 | 0.2320 | 0.0442 | 1.6407 | 0.5432 | 0.8089 | | 1.4593 | 9312 | 0.6612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4643 | 9344 | 0.5152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4694 | 9376 | 0.7975 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4744 | 9408 | 0.574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4794 | 9440 | 0.8784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4844 | 9472 | 0.807 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4894 | 9504 | 0.4858 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4944 | 9536 | 0.542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4995 | 9568 | 0.4288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5045 | 9600 | 0.3218 | 0.8712 | 0.4019 | 4.7045 | 0.1558 | 0.0489 | 0.4834 | 0.2272 | 0.0451 | 0.0879 | 0.9722 | 3.9334 | 0.0725 | 0.2280 | 0.2603 | 0.0378 | 1.7225 | 0.5696 | 0.8058 | | 1.5095 | 9632 | 0.7936 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5145 | 9664 | 0.5664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5195 | 9696 | 0.7019 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5245 | 9728 | 0.6887 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5295 | 9760 | 0.5558 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5346 | 9792 | 0.7874 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5396 | 9824 | 0.6661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5446 | 9856 | 0.314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5496 | 9888 | 0.6541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5546 | 9920 | 0.3876 | 0.8161 | 0.4282 | 4.6019 | 0.1474 | 0.0478 | 0.5195 | 0.2488 | 0.0193 | 0.1276 | 0.9812 | 3.7592 | 0.0711 | 0.2148 | 0.2558 | 0.0373 | 1.7022 | 0.5342 | 0.8063 | | 1.5596 | 9952 | 0.4225 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5646 | 9984 | 0.5979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5697 | 10016 | 0.4349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5747 | 10048 | 0.8265 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5797 | 10080 | 0.4669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5847 | 10112 | 0.6543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5897 | 10144 | 0.5953 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5947 | 10176 | 0.7695 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5997 | 10208 | 1.0416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6048 | 10240 | 0.582 | 0.8168 | 0.3981 | 4.6404 | 0.1526 | 0.0492 | 0.4880 | 0.2256 | 0.0500 | 0.1247 | 0.8930 | 3.7979 | 0.0761 | 0.2191 | 0.2541 | 0.0378 | 1.6163 | 0.5045 | 0.8082 | | 1.6098 | 10272 | 0.4853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6148 | 10304 | 0.7606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6198 | 10336 | 0.7573 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6248 | 10368 | 0.8745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6298 | 10400 | 0.5335 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6349 | 10432 | 0.8592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6399 | 10464 | 0.5884 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6449 | 10496 | 0.5912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6499 | 10528 | 0.4696 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6549 | 10560 | 0.6711 | 0.7470 | 0.3632 | 4.6748 | 0.1483 | 0.0474 | 0.4563 | 0.2126 | 0.0224 | 0.1501 | 0.8485 | 4.1075 | 0.0723 | 0.2246 | 0.2475 | 0.0358 | 1.5900 | 0.5061 | 0.8069 | | 1.6599 | 10592 | 0.6604 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6649 | 10624 | 0.7325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6700 | 10656 | 0.5003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6750 | 10688 | 0.7602 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6800 | 10720 | 0.3509 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6850 | 10752 | 0.5256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6900 | 10784 | 0.72 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6950 | 10816 | 0.3566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7000 | 10848 | 0.4914 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7051 | 10880 | 0.803 | 0.7336 | 0.3736 | 4.7360 | 0.1498 | 0.0480 | 0.4678 | 0.2350 | 0.0248 | 0.1196 | 0.8494 | 3.8989 | 0.0745 | 0.2213 | 0.2422 | 0.0366 | 1.5623 | 0.4660 | 0.8067 | | 1.7101 | 10912 | 0.631 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7151 | 10944 | 0.4674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7201 | 10976 | 0.59 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7251 | 11008 | 0.6661 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7301 | 11040 | 0.5495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7352 | 11072 | 0.4449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7402 | 11104 | 0.9734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7452 | 11136 | 0.8756 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7502 | 11168 | 0.5044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7552 | 11200 | 0.4335 | 0.7242 | 0.3770 | 4.7027 | 0.1496 | 0.0460 | 0.4625 | 0.2057 | 0.0449 | 0.1332 | 0.8772 | 4.0420 | 0.0724 | 0.2132 | 0.2311 | 0.0358 | 1.5884 | 0.4569 | 0.8063 | | 1.7602 | 11232 | 0.9002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7652 | 11264 | 0.7993 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7703 | 11296 | 0.7534 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7753 | 11328 | 0.505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7803 | 11360 | 0.5255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7853 | 11392 | 1.1055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7903 | 11424 | 0.4554 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7953 | 11456 | 0.4593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8003 | 11488 | 0.3412 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8054 | 11520 | 0.5286 | 0.7080 | 0.3906 | 4.7211 | 0.1465 | 0.0450 | 0.4674 | 0.2045 | 0.0527 | 0.1149 | 0.8453 | 3.9840 | 0.0704 | 0.2134 | 0.2339 | 0.0343 | 1.5864 | 0.4692 | 0.8070 | | 1.8104 | 11552 | 1.1054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8154 | 11584 | 0.8731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8204 | 11616 | 0.7774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8254 | 11648 | 0.7425 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8304 | 11680 | 0.4233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8354 | 11712 | 1.0839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8405 | 11744 | 1.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8455 | 11776 | 0.9838 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8505 | 11808 | 1.0228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8555 | 11840 | 0.5337 | 0.6966 | 0.3860 | 4.7110 | 0.1449 | 0.0454 | 0.4610 | 0.2012 | 0.0451 | 0.1217 | 0.8483 | 4.0680 | 0.0720 | 0.2120 | 0.2290 | 0.0352 | 1.5618 | 0.4564 | 0.8091 | | 1.8605 | 11872 | 0.4719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8655 | 11904 | 0.9254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8706 | 11936 | 0.4605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8756 | 11968 | 0.5605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8806 | 12000 | 0.804 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8856 | 12032 | 0.8148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8906 | 12064 | 0.6428 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8956 | 12096 | 0.764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9006 | 12128 | 0.8099 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9057 | 12160 | 0.3568 | 0.6930 | 0.3947 | 4.7134 | 0.1439 | 0.0448 | 0.4563 | 0.1980 | 0.0221 | 0.1216 | 0.8413 | 4.0270 | 0.0713 | 0.2107 | 0.2297 | 0.0346 | 1.5734 | 0.4509 | 0.8084 | | 1.9107 | 12192 | 0.6994 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9157 | 12224 | 1.102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9207 | 12256 | 0.7589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9257 | 12288 | 0.8421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9307 | 12320 | 0.6796 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9357 | 12352 | 0.8515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9408 | 12384 | 0.6122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9458 | 12416 | 1.1603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9508 | 12448 | 1.2334 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9558 | 12480 | 0.6642 | 0.6915 | 0.3929 | 4.7102 | 0.1436 | 0.0447 | 0.4545 | 0.1974 | 0.0493 | 0.1189 | 0.8452 | 4.0444 | 0.0710 | 0.2108 | 0.2221 | 0.0345 | 1.5630 | 0.4530 | 0.8084 | | 1.9608 | 12512 | 0.747 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9658 | 12544 | 0.9231 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9709 | 12576 | 1.1242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9759 | 12608 | 0.5239 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9809 | 12640 | 0.697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9859 | 12672 | 0.9842 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9909 | 12704 | 0.8476 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9959 | 12736 | 0.6754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0 | 12762 | - | 0.6916 | 0.3938 | 4.7109 | 0.1435 | 0.0447 | 0.4545 | 0.1969 | 0.0197 | 0.1176 | 0.8446 | 4.0425 | 0.0709 | 0.2108 | 0.2206 | 0.0344 | 1.5648 | 0.4528 | 0.8083 |
### Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.1 - Transformers: 4.42.3 - PyTorch: 2.1.2 - Accelerate: 0.32.1 - Datasets: 2.20.0 - 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} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```