diff --git "a/checkpoint-3712/README.md" "b/checkpoint-3712/README.md" new file mode 100644--- /dev/null +++ "b/checkpoint-3712/README.md" @@ -0,0 +1,1413 @@ +--- +language: +- en +library_name: sentence-transformers +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:197788 +- loss:AdaptiveLayerLoss +- loss:CoSENTLoss +- loss:GISTEmbedLoss +- loss:OnlineContrastiveLoss +- loss:MultipleNegativesSymmetricRankingLoss +base_model: microsoft/deberta-v3-small +datasets: +- sentence-transformers/all-nli +- sentence-transformers/stsb +- tals/vitaminc +- nyu-mll/glue +- allenai/scitail +- sentence-transformers/xsum +- sentence-transformers/sentence-compression +- allenai/sciq +- allenai/qasc +- allenai/openbookqa +- sentence-transformers/msmarco-msmarco-distilbert-base-v3 +- sentence-transformers/natural-questions +- sentence-transformers/trivia-qa +- sentence-transformers/quora-duplicates +- sentence-transformers/gooaq +metrics: +- pearson_cosine +- spearman_cosine +- pearson_manhattan +- spearman_manhattan +- pearson_euclidean +- spearman_euclidean +- pearson_dot +- spearman_dot +- pearson_max +- spearman_max +widget: +- source_sentence: Winter in the northern hemisphere is occurring on + sentences: + - December is during the winter in the northern hemisphere + - a glacier moves slowly + - seasons cause change to the environment +- source_sentence: Two young men in unusual clothing are jumping in a gym. + sentences: + - A woman shows her profile in front of a white luxury car. + - Two people are in the gym + - A woman sits outside. +- source_sentence: and carbon-14 ( 14 C), which also contains 6 protons and electrons, + but has 8 neutrons. + sentences: + - Particles in the gas state of matter do not experience any force of mutual attraction + or repulsion. + - Gas has no definite volume and no definite shape. + - A carbon atom with 6 protons and 8 neutrons is more specifically known as carbon + 14. +- source_sentence: Constructive interference is when two waves combine to create a + larger wave. + sentences: + - What is it called when two waves combine to create a larger wave? + - Which of these describes water in a solid state? + - A light bulb converts electrical energy to light and what? +- source_sentence: what is the main idea of the communist manifesto + sentences: + - Jury selection Jury selection is the selection of the people who will serve on + a jury during a jury trial. The group of potential jurors (the "jury pool", also + known as the venire) is first selected from among the community using a reasonably + random method. Jury lists are compiled from voter registrations and driver license + or ID renewals. From those lists, summons are mailed. A panel of jurors is then + assigned to a courtroom. The prospective jurors are randomly selected to sit in + the jury box. At this stage they will be questioned in court by the judge and/or + attorneys in the United States. Depending on the jurisdiction, attorneys may have + an opportunity to mount a challenge for cause argument or use one of a limited + number of peremptory challenges. In some jurisdictions that have capital punishment, + the jury must be death-qualified to remove those who are opposed to the death + penalty. Jury selection and techniques for voir dire are taught to law students + in trial advocacy courses. However, attorneys sometimes use expert assistance + in systematically choosing the jury, although other uses of jury research are + becoming more common. The jury selected is said to have been "empaneled". + - UNICEF The United Nations International Children's Emergency Fund was created + by the United Nations General Assembly on 11 December 1946, to provide emergency + food and healthcare to children in countries that had been devastated by World + War II. The Polish physician Ludwik Rajchman is widely regarded as the founder + of UNICEF and served as its first chairman from 1946. On Rajchman's suggestion, + the American Maurice Pate was appointed its first executive director, serving + from 1947 until his death in 1965.[5][6] In 1950, UNICEF's mandate was extended + to address the long-term needs of children and women in developing countries everywhere. + In 1953 it became a permanent part of the United Nations System, and the words + "international" and "emergency" were dropped from the organization's name, making + it simply the United Nations Children's Fund, retaining the original acronym, + "UNICEF".[3] + - The Communist Manifesto The Communist Manifesto summarises Marx and Engels' theories + concerning the nature of society and politics, that in their own words, "The history + of all hitherto existing society is the history of class struggles". It also briefly + features their ideas for how the capitalist society of the time would eventually + be replaced by socialism. Near the end of the Manifesto, the authors call for + "forcible overthrow of all existing social conditions", which served as the justification + for all communist revolutions around the world. +pipeline_tag: sentence-similarity +model-index: +- name: SentenceTransformer based on microsoft/deberta-v3-small + results: + - task: + type: semantic-similarity + name: Semantic Similarity + dataset: + name: sts test + type: sts-test + metrics: + - type: pearson_cosine + value: 0.7355921455984917 + name: Pearson Cosine + - type: spearman_cosine + value: 0.7175266639957124 + name: Spearman Cosine + - type: pearson_manhattan + value: 0.7409243217735156 + name: Pearson Manhattan + - type: spearman_manhattan + value: 0.7250251572437655 + name: Spearman Manhattan + - type: pearson_euclidean + value: 0.7293547151744354 + name: Pearson Euclidean + - type: spearman_euclidean + value: 0.7122336979289147 + name: Spearman Euclidean + - type: pearson_dot + value: 0.6441608711789287 + name: Pearson Dot + - type: spearman_dot + value: 0.6242096516614428 + name: Spearman Dot + - type: pearson_max + value: 0.7409243217735156 + name: Pearson Max + - type: spearman_max + value: 0.7250251572437655 + name: Spearman Max +--- + +# SentenceTransformer based on microsoft/deberta-v3-small + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa), [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer +- **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 768 tokens +- **Similarity Function:** Cosine Similarity +- **Training Datasets:** + - [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) + - [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) + - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) + - [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) + - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) + - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) + - [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) + - [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) + - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) + - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) + - [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) + - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) + - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) + - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) + - [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) + - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) +- **Language:** en + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("bobox/DeBERTa-ST-AllLayers-testing-v2-checkpoints-tmp") +# Run inference +sentences = [ + 'what is the main idea of the communist manifesto', + 'The Communist Manifesto The Communist Manifesto summarises Marx and Engels\' theories concerning the nature of society and politics, that in their own words, "The history of all hitherto existing society is the history of class struggles". It also briefly features their ideas for how the capitalist society of the time would eventually be replaced by socialism. Near the end of the Manifesto, the authors call for "forcible overthrow of all existing social conditions", which served as the justification for all communist revolutions around the world.', + 'Jury selection Jury selection is the selection of the people who will serve on a jury during a jury trial. The group of potential jurors (the "jury pool", also known as the venire) is first selected from among the community using a reasonably random method. Jury lists are compiled from voter registrations and driver license or ID renewals. From those lists, summons are mailed. A panel of jurors is then assigned to a courtroom. The prospective jurors are randomly selected to sit in the jury box. At this stage they will be questioned in court by the judge and/or attorneys in the United States. Depending on the jurisdiction, attorneys may have an opportunity to mount a challenge for cause argument or use one of a limited number of peremptory challenges. In some jurisdictions that have capital punishment, the jury must be death-qualified to remove those who are opposed to the death penalty. Jury selection and techniques for voir dire are taught to law students in trial advocacy courses. However, attorneys sometimes use expert assistance in systematically choosing the jury, although other uses of jury research are becoming more common. The jury selected is said to have been "empaneled".', +] +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 | 0.7356 | +| **spearman_cosine** | **0.7175** | +| pearson_manhattan | 0.7409 | +| spearman_manhattan | 0.725 | +| pearson_euclidean | 0.7294 | +| spearman_euclidean | 0.7122 | +| pearson_dot | 0.6442 | +| spearman_dot | 0.6242 | +| pearson_max | 0.7409 | +| spearman_max | 0.725 | + + + + + +## 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: 20,000 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": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 18,977 training samples +* Columns: label, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | label | sentence1 | sentence2 | + |:--------|:-----------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | int | string | string | + | details | | | | +* Samples: + | label | sentence1 | sentence2 | + |:---------------|:--------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | 1 | Frozen II has more than 46 reviews and is rated below 65 % . | `` On Metacritic , the film has a weighted average score of 64 out of 100 , based on 47 critics , indicating `` '' generally favorable reviews '' '' . '' | + | 1 | The Man from U.N.C.L.E ( film ) has mixed reviews . | The film premiered at Barcelona on August 2 , 2015 and was released on August 14 , 2015 , by Warner Bros. , receiving mixed reviews from critics and grossing $ 109 million worldwide on a $ 75–84 million budget. | + | 1 | Colleen O'Shaughnessey voiced Karen in Ponyo . | Emi Hiraoka and Nozomi ? hashi voice Kumiko and Karen , attendants of Himawari Nursery School ; their respective English voice actresses are Jennessa Rose and Colleen O'Shaughnessey , who are also credited. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### qnli-contrastive + +* Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) +* Size: 10,000 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 | + |:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | Which two companies were involved in the "War of the Speeds"? | The commercial rivalry between RCA Victor and Columbia Records led to RCA Victor's introduction of what it had intended to be a competing vinyl format, the 7-inch (175 mm) 45 rpm disc. | 0 | + | What may impact social dynamics and technical development? | Diffusion of innovations theory presents a research-based model of why and when individuals and cultures adopt new ideas, practices, and products. | 0 | + | During what period did the governmental monopolies become privatized once again? | To eliminate the influence of such private entrepreneurs, Emperor Wu nationalized the salt and iron industries in 117 BC and allowed many of the former industrialists to become officials administering the monopolies. | 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": 1.75, + "kl_div_weight": 2.5, + "kl_temperature": 0.25 + } + ``` + +#### scitail-pairs-qa + +* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) +* Size: 14,237 training samples +* Columns: sentence2 and sentence1 +* Approximate statistics based on the first 1000 samples: + | | sentence2 | sentence1 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence2 | sentence1 | + |:----------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------| + | Am radio waves exist in the 540 to 1600 khz frequency range. | What type of radio waves exist in the 540 to 1600 khz frequency range? | + | Rounding is when one or more ending digits are dropped to get the correct number of significant figures. | What is it called when one or more ending digits are dropped to get the correct number of significant figures? | + | Distance over time in a given direction determines velocity. | What determines velocity? | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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 | + |:-------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| + | Simply, the total work is Force time Distance (ie. | Force times distance is the equation for work. | + | MARS Mars, otherwise known as the Red Planet is the fourth planet away from the sun. | Mars is the fourth planet from the sun. | + | Obviously it is biologically human, genetically human, a distinct member of the species homo sapiens. | Humans belong to the species homo sapiens. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### xsum-pairs + +* Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) +* Size: 3,800 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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+ | But not quite yet - with their lordships, in particular, cranking up for a considerable clash over the Higher Education Bill. And watch out for a Commons-Lords clash on peers' call for an independent inquiry into the way the police complaints system dealt with allegations of corrupt relationships between the police and newspapers... a modest appetiser, with a government decision looming on press regulation.
The one imponderable is the eagerly-awaited Supreme Court decision on whether there has to be a Parliamentary vote on the triggering of Article 50 - if that arrives (and the timing is unclear), there will be huge pressure for a rapid Commons statement setting out the next moves.
If the ruling goes against the government, it will be a highly-charged event - and if the Supremes require a full scale bill to be passed, a frantic re-jigging of the legislative timetable will follow.
Here's my run-down of the week ahead:
The Commons re-opens (2.30pm) with Work and Pensions questions - doubtless to be followed by the usual crop of post-recess ministerial statements and urgent questions.
Then MPs are on a running three-line whip for the report stage, Legislative Grand Committee and third reading of the Technical and Further Education Bill - where the only amendments down are from Labour's Gordon Marsden, who is proposing a series of changes, mostly aiming to give learners a voice on the panels regulating their teaching, and to create a new duty for the government to publish strategy on careers education.
The select committee of the day is the Communities and Local Government hearing (at 4pm) on the Casey Review into Integration, with its author, Dame Louise Casey.
Her review was commissioned by David Cameron and recommended a new strategy to help bridge divides in the UK, including an "integration oath" to encourage immigrants to embrace British values, and greater focus on promoting the English language and securing "women's emancipation in communities where they are being held back by regressive cultural practices". It has been criticised for focusing on Muslim communities.
In the Lords (2.30pm) peers celebrate their return with an interesting crop of questions to ministers. There is (but of course) a Brexit question, this time from the Lib Dem, Lord Maclennan, on the government's intentions for publishing a green paper on Brexit negotiations; the Green Party peer, Baroness Jones of Moulsecoomb has a question about the Pitchford inquiry into undercover policing, and infiltration of various environmentalists and radical groups; and Labour's Lord Grocott wants to highlight the fact that the vast majority of hereditary peerages exclude women from inheriting, with a question about the Register of Hereditary Peers' compatibility with equalities legislation.
But that is just a preliminary skirmish before what may become full-on trench warfare over the Higher Education and Research Bill.
A cross-party alliance of peers fears that this could open the way for the full "marketisation" of higher education - and that could, unusually, lead to a series of votes being forced at committee stage (this is the first of two committee stage days timetabled for this week).
First up is a 'before Clause 1' amendment from Labour's Lord Stevenson. The tell-tale sign here is that it is co-signed by heavy hitters from other groups including Crossbenchers Baroness Brown of Cambridge, who was Vice-Chancellor of Aston University from 2006 to 2016 and Baroness Wolf of Dulwich, a professor at King's College, London; plus the Lib Dem Baroness Garden, which sets out core principles of academic freedom and independence as a preface to the bill.
That's closely followed by a second amendment from Lord Stevenson and Baroness Garden, specifying that UK universities should operate "on a not-for-profit basis". Labour sources are already warning that if the government does not amend the bill to meet their concerns, it can expect a long drawn out battle of a kind not seen since the Health and Social Care Bill, during the Coalition years.
Also on the agenda is a short debate on the role of the Armed Forces Covenant in ensuring those who serve and their families are treated with fairness and respect - led by the Bishop of Portsmouth.
The Commons meets at 11.30am for Foreign Office questions - after which the post-holiday backlog of statements and UQs may continue. Then comes a Ten Minute Rule Bill on Mutual Guarantee Societies from Labour and Co-Operative Party MP Christina Rees . The bill encourages small and medium size enterprises to join collectively to create a Mutual Guarantee Society to negotiate a better deal from banks.
MPs then polish off their consideration of the Commonwealth Development Corporation Bill, which will raise the limit on the financial support that can be provided to the CDC, the UK's development finance institution.
Amendments include a proposal from the Labour front bench to prohibit any new investment from going to an investment vehicle or company which uses or seems likely to use tax havens.
More detailed legislating follows and the Commons considers Lords' amendments to the Policing and Crime Bill. The government suffered four defeats on this measure in the Upper House - on issues including the maximum sentence for stalking, a "Hillsborough" provision on financial support for bereaved families at inquests involving the police, and on an independent inquiry into the way the police complaints handled allegations of corrupt relationships between the police and newspapers. Will the Home Secretary, Amber Rudd, seek to reverse those changes, and if she does, will peers bat the bill back to MPs again?
In Westminster Hall, the Lib Dem Norman Lamb, a health minister under the Coalition, has a debate on supporting children's wellbeing and mental health in a school environment (9.30am-11am). Conservative MP Justin Tomlinson raises the issue of allocation of funding from the soft drinks industry levy for sport in schools (2.30pm-4pm) and the DUP's Sir Jeffrey Donaldson has a debate on the implementation of the Stormont House Agreement (4pm -5.30pm)
Committee of the day is the Home Affairs hearing (2.15pm) on hate crime and its violent consequences. The witnesses include Joanna Mludzinska of the Polish Social and Cultural Association, and Taduesz K Stenzel of the Federation of Poles in Great Britain, plus Professor Matthew Feldman of the Centre for Fascist, Anti-Fascist and Post-Fascist Studies, Teeside University, and Professor Matthew Goodwin, of the University of Kent.
In the Lords (2.30pm), the main legislating is on the detail of the Wales Bill.
MPs gather at 11.30am for International Development questions, followed, at noon by the first Prime Minister's question time of 2017. Next comes an interesting Ten Minute Rule Bill on Guardianship (Missing Persons) from the Conservative, Kevin Hollinrake.
Families with a missing loved one have been campaigning for guardianship powers for the past five years, to enable them to manage a missing relative's finances and property until they return, and ensure any dependents are looked after. Up to 1,500 adults are missing for longer than a year and, he says, the lack of guardianship powers means that families are powerless to deal with the practical challenges like ensuring bills are paid, homes are protected and dependents are looked after.
The result, he says, is that families are forced to stand by and watch as the life they hope their missing loved one will return to falls apart.
The main debate will be on a Labour Opposition Day motion, to be announced.
In Westminster Hall (9.30am-11am), the Conservative Anne Main leads a debate on pharmacies and integrated healthcare in England - she will talk about what pharmacies are doing at the moment, how they are funded, and make the case that pharmacies should be treating more minor ailments and chronic conditions to take pressure off GPs - and she will argue that pharmacies want to do more, and this should be reflected in their funding.
Labour's Rob Marris leads a debate on access to justice (2.30pm-4pm) and Karl Turner, who had a brief spell as Labour's shadow attorney general, raises the funding of Crown Prosecution Service (4.30pm -5.30pm).
Committee of the day is the Work and Pensions hearing (9.30am) on victims of modern slavery - with former senior judge, Baroness Butler-Sloss and Kevin Hyland, the Independent Anti-Slavery Commissioner. And keep an eye out for the Treasury Committee (2.15pm) which has one of its regular hearings with the Governor of the Bank of England, Mark Carney, and a supporting cast of BoE officials - always worth watching.
In the Lords (3pm) the main event is the week's second committee stage day on the Higher Education and Research Bill (see above). And there will be a short debate on equine welfare standards led by the Conservative, Lord Higgins.
It's Transport questions in the Commons (from 9.30am), followed by the weekly Business Statement from the Leader of the House - which may, among other things, confirm a date for the long-awaited vote on plans to move MPs out of the Victorian Palace of Westminster for five years, or more, to allow for a multi-billion pound renovation programme.
Or are ministers getting cold feet about the cost and gathering backbench opposition?
The main debates are on subjects chosen by the Backbench Business Committee - first, on the humanitarian crisis in Yemen, and then on security and the political situation in the African Great Lakes region.
In Westminster Hall (1.30pm) there's a debate on the Justice Select Committee's report on Restorative Justice, led by Committee Chair Bob Neill - and that's followed (3pm) by a debate on the future of the UK maritime industry led by the Lib Dem former Scottish Secretary, Alistair Carmichael.
In the Lords (11am) the main events are debates led by backbench Labour peers; Baroness Massey of Darwen, on the Institute for Public Policy Research's annual State of the North report; the former International Development minister Baroness Kinnock of Holyhead on the treatment of the Muslim Rohingya minority in Burma, and the former defence secretary and Nato Secretary General, George Robertson on the future capability of the UK's armed forces in the current international situation.
The House will also wave through the Savings (Government Contributions) Bill - which, as a money bill is considered untouchable by the Lords and will go through all its stages of consideration at a single gulp.
It's private members' bill time again in the Commons, from 9.30am. First up is the second reading for the Conservative Kevin Foster's Broadcasting (Radio Multiplex Services) Bill. He argues that at present, the costs and licensing system don't help small-scale radio stations and radio services to access to the DAB digital radio network and his bill aims to make digital access available to all small-scale operators.
Next comes the Civil Partnership Act 2004 (Amendment) Bill - the Conservative Tim Loughton aims to make the option of civil partnership available to straight couples (he was accused of trying to wreck the Gay Marriage Bill when he introduced an amendment on this issue, in 2013).
Third in the running order is the Workers' Rights (Maintenance of EU Standards) Bill, from Labour's Melanie Onn, which will be launched at an event on Wednesday, with Labour Shadow Brexit Secretary Keir Starmer and Liz Snape (President of the TUC and Assistant General Secretary of Unison) speaking.
The bill targets a key Brexit issue for Labour, so I can't help wondering if some of the usual suspects will be padding out the previous debates, to minimise, or even eliminate the time available to it.
And at 2.30pm, when the names of undebated bills are read out for their ritual parliamentary pole-axing, watch out for a shout of "Object" against Peter Bone's Withdrawal from the European Union (Article 50) Bill.
If no-one objected, it would be deemed to have had an unopposed second reading and could go forward for consideration in committee. But someone will....a little end of day theatre, with points of order and show indignation. | They're back and with Brexit looming, it may not be long before the ramifications of leaving the EU begins to crowd out almost all other issues from the agenda in both Lords and Commons. | + | Media playback is not supported on this device
So for now, the thousands of fans who turned up to chant his name from the grandstands on a baking hot summer afternoon - and the thousands more around the world willing him on - will have to wait to see how this remarkable story will end.
In truth, even to have got this far is incredible.
Driving around the Hungaroring, setting apparently competitive times, completing long and short runs, giving "excellent" feedback to the Renault engineers, the 32-year-old Pole looked for all the world like any other grand prix driver.
He is anything but.
Six and a half years ago, Kubica was in hospital in northern Italy fighting for his life. A horrific rally accident, in which a steel roadside barrier penetrated his car, and then its driver, left him with multiple fractures down the right-hand side of his body and a partially severed right arm.
That arm and hand bear the effects of that accident to this day - visibly atrophied, held awkwardly, it has clearly limited strength and partial movement.
More than two years after the accident, Kubica referred to a potential F1 return as a "nearly impossible" dream. Yet here he was completing two grand prix distances on one of the toughest tracks on the calendar.
One can only imagine the difficulties Kubica has faced, the determination and mental fortitude it has taken to get to this point.
When he crashed his rally car in February 2011, he was weeks away from starting his fifth full F1 season. He was regarded as one of the shining talents of his generation, a man whose ability could be regarded in the same bracket as superstar world champions Lewis Hamilton and Fernando Alonso.
He was driving in the rally because he enjoyed it, but also because he believed it would make him a better driver. But then he lost control, and the pictures of the aftermath of the accident tell their own story.
It took an hour to get him out of the car. Once in hospital, the first operation - he has since had 17 more - was seven hours long.
"The reality was the first big moment I was fighting to be alive," Kubica said in a BBC Sport interview on the eve of Wednesday's test.
"People are concentrating only on my arm because it is the biggest limitation. But the reality is I had fractures from my feet up to my shoulders on the right-hand side.
"I had many fractures and that's why it was so complicated and takes so long to recover. But of course my arm was the most damaged.
"The first two months were tough. I was lucky I was a sportsman and driving F1. That's probably why my arm is still there.
"But on the other hand there are moments when you have to forget who you are but you are a human being. This is maybe something where the situation was not easy to cope."
Eighteen months after the accident, Kubica was back in a rally car - and he won the first event he took part in.
"People were seeing me and concentrating on getting me as fast as possible back to the car," he said. "In the end, I decided first I have to wake up in the morning happy, then I can start to be a racing driver.
"It probably took me over two years to get back to a reasonable level. I had for months, even a full year, pain everywhere depending on the conditions I was in.
"You have to first of all feel good with yourself before doing something which requires being fast or driving a racing car. It is not that I lost my biggest passion - it is still racing. But also my general life has changed a lot and this was crucial."
By 2013, Kubica's arm was sufficiently recovered for him to do some work in the Mercedes F1 simulator, but it did not have the necessary movement for him to drive an F1 car. He was not able to rotate his wrist enough - he could turn left only by lifting his elbow, which is not possible in an F1 cockpit.
Instead, he turned to rallying, and spent three seasons competing in the world championship, proving blisteringly fast and brave, but prone to big crashes.
By the end of 2015, the money had run out.
"I didn't know if I would get the chance to return to F1," Kubica said, "but after rally time I had a difficult period. I was weighing 10kg, perhaps 15kg, over normal weight. So I started preparing."
He systematically tried a range of racing cars to see if he could be competitive in them, explored the idea of returns in DTM German touring cars and the World Endurance Championship.
The turning point was around last December, when he spent some time in the simulator at the Italian racing car constructor Dallara, and realised an F1 return might now be a realistic possibility.
"I needed to get back in a proper rhythm of my life and if the chance will come I need to get the maximum out of it," he said. "In most of the cars I was able to achieve what was my target and four months ago nobody could expect this and that's why I really appreciate the chance Renault are giving me. But I want to do my best."
The guys at Renault had kept in touch, and they suggested a one-off test in a 2012 F1 car.
This came in Valencia in June. It was about completing the circle more than anything else, just to give him a chance to try it again after so long. But he impressed so much - completing more than 100 laps, quicker than the team's reserve driver - that a second test, this time much more serious, was arranged.
At Paul Ricard in the south of France, on the Wednesday before the British Grand Prix, Renault held a test so focused on a potential return that it included hairpins, both left and right, to see if he could negotiate them. He did.
He passed the extrication test - when a driver has to get himself out of the cockpit within five seconds - at the first attempt and was again quick and consistent, and provided technical feedback of remarkable sharpness and insight.
"Part of this testing is also to know better myself," Kubica said. "My life has changed. I know what a big influence the injury has on daily life.
"Everyone sees me as a driver, but in the end I am a human being, I do usual stuff at home. I train, I cycle, most of the stuff most of the people do. My limitations have a bigger influence on daily life than in driving cars."
And in the car?
"From the sensitivity point of view, I am at a good level. The biggest problem is not only the strength, it is the movement limitations. On the front arm I don't have proper supination, so I cannot twist my front arm and wrist, so this is actually the biggest limitation."
Kubica won just one race in his F1 career - the 2008 Canadian Grand Prix. So why, some may wonder, is there such a fuss about his potential comeback?
One one level, the answer to that is obvious. If a man with this sort of disability can return to F1 after more than six years and be competitive, it would rank as one of the greatest comebacks in the history of sport.
But beyond that, there is the possibility of a mega-talent being back in F1.
"Robert's one of the quickest drivers I've ever raced against," said Hamilton. "He's one of the best drivers I've driven against.
"Just raw, natural talent, which I think as a sport it's a shame we don't have here with us - because there's not a lot that comes through. Not a lot of great, great drivers come through. You have some that are much better than the rest, but still not the greatest, and then you have real special drivers like him."
Kubica's last season was his finest. In the Renault, not a fully competitive car, he put in some stunning performances, the best ones at the three greatest drivers' circuits on the calendar - Monaco, Spa and Suzuka. He qualified second, third and fourth at those races, places the car had no right to be. And was equally impressive in the grands prix.
Kubica had to stop his TV interviews immediately after qualifying in Japan because he found he could not speak. He went away to sit by himself for 10 minutes while he contemplated what he had just done.
Renault sporting director Alan Permane, who has been instrumental in organising Kubica's tests, says: "Suzuka qualifying in 2010 was a lap like I've never seen from anyone else, ever. He came in absolutely white, having scared the life out of himself."
From a man who has been in F1 for more than 25 years and worked alongside Michael Schumacher and Alonso, that is quite a compliment.
Kubica's comeback has developed a momentum of its own since that first test in Valencia for no other reason than the strength of his performances.
The seriousness with which Renault are taking this is evident from how little they are saying about it - details are hard to come by; team members simply will not speak about the tests other than in the broadest terms.
The last question in the minds of both Kubica and the team was whether he could handle a 2017 car.
On Wednesday, he completed 142 laps in temperatures in the mid-to-high 30Cs. His fastest lap times were a little slower than those set by Jolyon Palmer, the slower of the two Renault race drivers, in qualifying at last weekend's race.
But it is notoriously hard to make a judgement from lap times in testing, because the teams do not give details of the specifications they run in. Track conditions also vary.
It was hotter, and therefore slower, on Wednesday than on the race weekend. But there was more rubber on the track, which would make it faster.
Fuel loads also have a big effect. Had, for example, Kubica been running with 30kg of fuel on those laps, a normal sort of testing fuel load, that is worth an extra second of lap time.
At the same time, Kubica was nearly two seconds quicker than what Canadian Nicholas Latifi, a Formula Two driver, managed on the first day of the test.
And when he did a long run shortly after lunch, his laps were quicker than the best times Palmer and team-mate Nico Hulkenberg set in the race on the same type of tyres.
In a team statement, Kubica said it was "too early" to say what the next step might be. But was there a hint when he told the media, "I would like more opportunities but the reality is that we have to wait and see"?
He also said he was happy "but not 100% happy" with how the day went, and that he would be quicker if he drove again. But then he has always had a reputation for demanding a lot - of both himself and the people he works with.
Renault will know what they have seen, and if that is anything like the Kubica of old, it would be no surprise to see him in the car in place of Palmer at some point this year, perhaps even from the next race in Belgium at the end of this month.
"I don't know if it will happen and I don't know how big a chance I have," Kubica told the BBC on Tuesday. "Most of the people would love it. It is a nice story. As a fan, someone coming back six years after a big injury, I would have big admiration.
"But in the end the one who is risking all that I have is myself, because first of all if I come back I don't want to do it just to come back. I need to be sure I am able at least to come as close as possible to the level I was before my accident. This will be the target. Before doing it, I need to be sure I am able to do this.
"People who know me, they know if I am here, they know I think I can do it. To be honest, even if I am not racing, I am just testing. The last three months are probably the best three months of my life in motorsport." | Robert Kubica was giving nothing away after his highly anticipated return to the wheel of a contemporary Formula 1 car on Wednesday. | + | Media playback is unsupported on your device
11 August 2015 Last updated at 20:02 BST
After fleeing Syria, he said he spent three weeks camped in Calais finally getting onto a train, after 15 attempts, at the end of July.
The 23-year-old, now in Birmingham, is seeking asylum in the UK.
He said he has come to England for peace and freedom, not for financial reward.
He is receiving help from charity St Chad's Sanctuary which provides food and clothing for asylum seekers. | Two weeks ago, migrant Ahmed Tawil was fighting to get over fences into the Channel Tunnel. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "MultipleNegativesSymmetricRankingLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1, + "prior_layers_weight": 1.75, + "kl_div_weight": 0.5, + "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: 9,500 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| + | Kat Dennings has joined the cast of Thor -- according to the movie's leading lady, Natalie Portman. | Kat Dennings joins cast of 'Thor' | + | Sumitomo Electric Industries, a supplier of solutions for optical components, modules and sub-systems, is extending its 10Gbps portfolio with the introduction of a CWDM-SFP+ transceiver module that can provide parallel transmission at 80Gbps. | Sumitomo Electric extends 10Gbps portfolio | + | The Fuel and Energy Ministry believes that the increase in the retail prices of light petroleum products in Ukraine corresponds to the trends on the European market. | Increase in prices of petroleum products in Ukraine corresponds to trends on European market | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "MultipleNegativesSymmetricRankingLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1, + "prior_layers_weight": 1.75, + "kl_div_weight": 0.5, + "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: 11,095 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:-------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Where do skeletal muscles usually attach? | Many skeletal muscles are attached to the ends of bones where they meet at a joint. The muscles span the joint and connect the bones. When the muscles contract, they pull on the bones, causing them to move. | + | Which infection turns ant's abdomen red and fruit-like and an easy prey for birds? | neotropicum infects the ant Cephalotes atratus, leading to dramatic changes in the infected ant's morphology and behavior. The infected ant’s abdomen turns red and is held raised up, which makes it resemble a fruit and increases the likelihood of the infected ant being eaten by birds (→). The birds transport the worms, which survive in their digestive systems until they are excreted; they are then eaten by, and infect new ants to complete the worm’s life cycle.78 Perhaps the most famous example of this type of behavior occurs in wasps of the family Ichneumonidae. Female wasps deposit their fertilized eggs into the bodies of various types of caterpillars. The wasp eggs hatch out and produce larvae which then feed on the living caterpillar, consuming it from the inside out. Charles Darwin, in a letter to the American naturalist Asa Gray, remarked “There seems to me too much misery in the world. I cannot persuade myself that a beneficent & omnipotent God would have designedly created the Ichneumonidae with the express intention of their feeding within the living bodies of caterpillars, or that a cat should play with mice. ” Rather than presume that a supernatural creator was responsible for such apparently cruel behaviors, Darwin and others sought alternative, morally neutral naturalistic processes that could both generate biological diversity and explain biological behaviors. As the diversity of organisms became increasingly apparent and difficult to ignore, another broad and inescapable conclusion began to emerge from anatomical studies: many different organisms displayed remarkable structural similarities. For example, as naturalists characterized various types of animals, they found that they either had an internal skeleton (the vertebrates) or did not (the invertebrates). Comparative studies revealed that there were often many similarities between quite different types of organisms. A classic work, published in 1555, compared the skeletons of a human and a bird, both vertebrates.79 While many bones have different shape and relative sizes, what was most striking is how many bones are at least superficially similar between the two organisms (→). This type of “comparative anatomy” revealed many similarities between apparently unrelated organisms. For example, the skeleton of the dugong (a large aquatic mammal) appears quite similar to that of the European mole, a small terrestrial mammal that tunnels underground on land. In fact, there are general skeletal similarities between all vertebrates. The closer we look, the more similarities we find. These similarities run deeper than the. | + | With a shape that specially suits its function of sending nerve signals to other cells, the human nerve cell is an example of what? | The human nerve cell in Figure below is a good example of a specialized animal cell. Its shape suits it for its function of sending nerve signals to other cells. A nerve cell couldn’t take this shape if it were surrounded by a rigid cell wall. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### qasc_pairs + +* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) +* Size: 7,727 training samples +* Columns: id, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | id | sentence1 | sentence2 | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | id | sentence1 | sentence2 | + |:--------------------------------------------|:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------| + | 3EA3QWIZ4IUQFEK1MYGBKK4YUTZITT | What reduces a human's bodily water? | if an animal sweats then that animal 's bodily water decreases. Human beings are group animals.. sweating reduces a human's bodily water | + | 32SCWG5HIH3CU2WT3GY2I7DWVIR6PR | What is stored in the stem of the saguaro? | a cactus stem is used for storing water. Saguaro cactus grow very slowly.. Water is stored in the stem of the saguaro | + | 392CY0QWG1Q6YT5B7XF3CCS61DBI4F | What are parts of the ecosystem? | Most ecosystems get energy from sunlight.. Sunlight Green plants require sunlight in order to grow.. Plants are part of the ecosystem. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### openbookqa_pairs + +* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) at [388097e](https://huggingface.co/datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454) +* Size: 2,603 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:-----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------| + | A tuna would prefer to consume | tuna eat fish | + | DNA is a vehicle for passing inherited characteristics from parent to what? | DNA is a vehicle for passing inherited characteristics from parent to offspring | + | Sea anemones | poisonous darts are used for defense by sea anemones | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 19,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | what's your favorite color and why is it | Blue is my absolute favorite color. Blue is a very tranquil color, and is comforting to me. Pretty much everything in my life is blue such as: my vehicle, couch, curtains, sheet sets, dishes, silverware, bookshelves, the majority of my clothing, and lots of other stuff. I also enjoy all things sliver. | + | average rent in bethlehem pa | Average Rent in . Bethlehem, PAStudio: $9311 bedroom: $11582 bedrooms: $13983 bedrooms: $ 1573 | + | how long does botox last | How long does a BOTOX® injection last? You can expect the effect of your BOTOX® treatment to last anywhere from four to six months. As acetylcholine production returns to normal, your muscles will once again begin to contract and the wrinkles will reappear. That's the bad news. The good news is that your wrinkles may be less prominent after BOTOX® treatment because your muscles may be somewhat trained to be in the more relaxed state. As the wrinkles reappear, you will have to have repeat BOTOX® treatments to get them to disappear again. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 19,000 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 mayor in nightmare before christmas | List of The Nightmare Before Christmas characters The Mayor of Halloween Town is depicted as a short, fat man, who has the appearance of a giant candy corn with a cone-shaped head, wearing an impossibly tall top hat, a spider bolo tie, and a ribbon of office that says "Mayor" on it. His cone-shaped head has two faces. One face is peach-skinned, rosy-cheeked, and smiling while the other face is white-skinned, pale and frowning with pointed teeth. Depending on the Mayor's mood, his head swivels around to display the right face with a loud clicking sound; the other face, when not in use, has its eyes closed and is considered dormant. This split personality is likely inspired by Dr Jekyll and Mr Hyde, and is a literal interpretation of the phrase "two-faced politicians". | + | when did pitbulls become illegal in the uk | Dangerous Dogs Act 1991 Under the 1991 act (and as amended in 1997) it is illegal to own any Specially Controlled Dogs without specific exemption from a court. The dogs have to be muzzled and kept on a lead in public, they must be registered and insured, neutered, tattooed and receive microchip implants. The Act also bans the breeding, sale and exchange of these dogs, even if they are on the Index of Exempted Dogs.[1] | + | who is the lead singer of doobie brothers | The Doobie Brothers The band's history can be roughly divided into three eras. From 1970 to 1975 it featured lead vocalist Tom Johnston and featured a mainstream rock and roll sound with elements of folk, country and R&B. Johnston quit the group in 1975, and was replaced with Michael McDonald whose interest in soul music changed the sound of the band until it broke up in 1982. The Doobie Brothers reformed in 1987 with Johnston back in the fold and are still active to the present with occasional contributions from McDonald. Every incarnation of the group emphasized vocal harmonies from the band's members. The Doobie Brothers were inducted into the Vocal Group Hall of Fame in 2004.[6] | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 19,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Actress Maria Schneider who died in 2011 age 58 was famous for her role in what iconic sexually explicit film? | Maria Schneider, Brando’s Lover in ‘Last Tango,’ Dies at 58 - The New York Times The New York Times Movies |Maria Schneider, Actress in ‘Last Tango,’ Dies at 58 Search Continue reading the main story Maria Schneider , the French actress whose sex scenes with Marlon Brando in “Last Tango in Paris” set a new standard for explicitness on screen, died on Thursday in Paris. She was 58. A spokesman for her agency, Act 1, said she had died after a long illness but provided no other details. The baby-faced, voluptuous Ms. Schneider was only 19 when the Italian director Bernardo Bertolucci chose her for the role of the free-spirited, mysterious Jeanne in “Last Tango.” She seemed, he said in explaining the choice, “like a Lolita, but more perverse.” The part was originally intended for Dominique Sanda, who dropped out after becoming pregnant. In the film, Jeanne enters into a brief but torrid affair with a recently widowed American businessman, played by Brando . Their erotically charged relationship, played out in an empty apartment near the Bir-Hakeim Bridge in Paris, shocked audiences on the film’s release in 1972, especially a scene in which Brando pins Ms. Schneider to the floor and, taking a stick of butter, seems to perform anal intercourse. The Motion Picture Association of America gave the film an X rating. Photo Ms. Schneider with Marlon Brando in “Last Tango in Paris,” (1972); its sexually explicit scenes shocked some audiences. Credit UNITED ARTISTS, via Public Theater “Last Tango” fixed Ms. Schneider in the public mind as a symbol of the sexual revolution. She spent years trying to move beyond the role, for which she was paid $4,000, and the notoriety that came with it. “I felt very sad because I was treated like a sex symbol,” she told The Daily Mail of London in 2007. “I wanted to be recognized as an actress, and the whole scandal and aftermath of the film turned me a little crazy and I had a breakdown. Now, though, I can look at the film and like my work in it.” Advertisement Continue reading the main story The famous scene, she said, was not in the script and made it into the film only at Brando’s insistence. “I felt humiliated, and to be honest I felt a little raped, both by Marlon and by Bertolucci,” she said. “After the scene, Marlon didn’t console me or apologize. Thankfully, there was just one take.” Ms. Schneider later appeared opposite Jack Nicholson in “The Passenger” (1975) , directed by Michelangelo Antonioni, playing an architecture student known simply as the Girl. Although she went on to work with important directors like René Clément in “The Baby Sitter” (1975) and Jacques Rivette in “Merry-Go-Round” (1981), her film career declined after the mid- 1970s, in part because of a turbulent personal life that included drug abuse, at least one suicide attempt and messy affairs with both men and women. Photo Maria Schneider in 2003. Credit Abdelhak Senna/Agence France-Presse -- Getty Images She walked off the set of “The Baby Sitter” (also known as “Scar Tissue” ) in Rome and checked herself into a mental hospital to be with her girlfriend at the time. In 1977 she was cast as Conchita in Luis Buñuel’s “That Obscure Object of Desire” but left the film after arguing with Buñuel. Her part was assigned to two actresses, Ángela Molina and Carole Bouquet. Maria Schneider was born on March 27, 1952, in Paris, the illegitimate daughter of Marie-Christine Schneider, a Romanian-born model, and the prominent actor Daniel Gélin. She did not meet her father, who refused to acknowledge her, until she was in her teens. She was reared by her mother in a town near the German border and left home at 15 for Paris, where she scratched out a living as a film extra and a model. Brigitte Bardot, who had worked with Mr. Gélin on several films, was appalled at the girl’s situation and intervened, giving her a room in her house and helping find her an agent with William Morris. Ms. Schneider played small parts in “The Christmas Tree,” with William Holden and Virna Lisi, and “The Love Mates,” with Alain Delon, before being cast in “L | + | "Which 60s song starts, ""You've got a lot of nerve?""" | Bob Dylan — Positively 4th Street — Listen, watch, download and discover music for free at Last.fm 60s "Positively 4th Street" is a song written and performed by Bob Dylan, first recorded by Dylan in New York City on July 29, 1965. It was released as a single by Columbia Records on September 7, 1965, reaching #1 on Canada's RPM chart, #7 on the U.S. Billboard Hot 100, and #8 on the UK Singles Chart. Rolling Stone magazine ranked the song as #206 in their 500 Greatest Songs of All Time list. The song was released between… read more Don't want to see ads? Subscribe now Similar Tracks | + | What is a person who makes barrels or casks called? | Barrel making Search billions of records on Ancestry.com Cooper or Barrel maker On early census records an occupation listed was "Cooper." I thought it might be fun to let you see what your ancestor did for a living if he was thus employed. Having read many pages on the subject, I set forth, here a synopis of the vocation, trusting that you will folow links for more reading. If anyone would like to add to this, please feel free to write the Baker Block Museum. You will find many of the tools for this trade at the museum. Enjoy! Cooper - n. - a person whose work is making or repairing barrels and casks (Webster" New World Dictionary). Cooperage, from same source indicates it is the workshop of or work done by a Cooper. For a first-rate look at the process go to: http://www.beekmanwine.com/prevtopah.htm In the 1800s in Europe as well as in our fledgling country a man skilled at making barrels was an important person. Many goods were shipped and stored in barrels (also called casks, tuns, kegs or hogsheads depending on the area of the world and the size of the container). We often think in terms of wine or whiskey when we think of the things likely to be contained in a barrel. But, all sorts of foods were stored in barrels. Sauerkraut was fermented and stored in them. Fish, meats and some vegetables were dried and salted then stored and transported in them. Most any item that could be stored for a length of time would be stored in a barrel to keep out vermin. Fragile items such as eggs would be packed in them among layers of straw to keep them cooler as well as to keep them from breaking. Barrels were great -- they could be rolled down ship gangplanks; have wheels and handles attached to them so a man could cart them about; be strapped onto a pack animal; be strapped together to float behind a raft down a river. One could bury them in a stream or cool earth as refrigerating units. They have been cut in half  to feed or water stock, make a cradle for a child, or act as a large mixing bowl for any number of reasons. They were made of any tree that could be worked. Oak was the preferred wood for wine and whiskey casks as the grain is fine and the containers could more easily be made waterproof. Modified, they become butter churns, buckets and wash tubs. One of the biggest uses for barrels in Northwest Florida in the nineteenth century was in the Turpentine Industry. Differing sized barrels were necessary for the storage and shipping of the products (called Naval Stores), pitch, tar, turpentine and such. Most of the larger camps had their own Cooper, often trained by his father or grandfather. Many were Scots while some were blacks who had been taught by the barrel maker on the plantation where they lived. These men were skilled technicians, fashioning barrels from raw wood through many processes. Often they had to fell a tree, cut it into boards, fashion the correctly sized and shaped staves for a particular type of barrel and dry the wood before they could even start building a barrel. A keen eye was needed to assure there were no knots or weak grain in the wood used. Each stave must be strong and well made. Selecting the right tree for the job was quite a knack and took a lot of experience. Staves had to be planned for smoothness on the outside and slightly cupped inside. The Bevel on their edges must be precise or they would neither fit together nor hold water. Also the tapering of each stave is critical to its fit. Hoops were made by the local blacksmith. (Earlier barrels were made with wooden hoops.) Staves were soaked so they could be curved properly. Some barrels were burned inside while others might be sealed with glue or pitch. Any that were to be used for food storage, needed to be relatively airtight (tight cooperage) so sealing material would not contaminate foodstuffs. "Slack cooperage" refers to containers that were not tight but would be fine for flour, grains and other non-liquids. There is a very interesting book called "The 'Possum Hunter and the Tar Heels" -- historical novel of post Civil War days. The auth | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 9,500 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:--------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | What can be done if I forgot to pay my traffic ticket? | What are the legal consequences if I forgot to pay a speeding ticket? | + | How can I be a motivational speaker? | How do I become motivational speaker? | + | Can you die from a lack of sleep? | Can I die from lack of sleep? | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### gooaq_pairs + +* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) +* Size: 19,000 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 book house of leaves about? | The plot is centered on a (possibly fictional) documentary about a family whose house is impossibly larger on the inside than the outside. The format and structure of House of Leaves is unconventional, with unusual page layout and style, making it a prime example of ergodic literature. | + | which wbc has red colored granules? | You can see that eosinophils only have two lobes to their nucleus. These cells have large acidophilic specific granules - these stain bright red, or reddish-purple. These granules contain proteins that are 'destructive' and toxic. | + | how long do you have to be in college to be a photographer? | Photography certificate programs vary by school and can either be offered as entry-level programs requiring anywhere from six to 10 classes or as four-year programs. Associate degrees in photography usually take two years to finish and are roughly 90 credit hours. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +### Evaluation Datasets + +#### nli-pairs + +* Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) +* Size: 1,500 evaluation samples +* Columns: anchor and positive +* Approximate statistics based on the first 1000 samples: + | | anchor | positive | + |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | anchor | positive | + |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| + | Two women are embracing while holding to go packages. | Two woman are holding packages. | + | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | + | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### vitaminc-pairs + +* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) +* Size: 999 evaluation samples +* Columns: label, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | label | sentence1 | sentence2 | + |:--------|:-----------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | int | string | string | + | details | | | | +* Samples: + | label | sentence1 | sentence2 | + |:---------------|:-----------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | 1 | Jonathan Nolan attended Loyola Academy in Wilmette ( Illinois ) and Georgetown University in Washington , D.C . | Nolan attended Loyola Academy in Wilmette , Illinois , graduating in 1994 and then graduated from Georgetown University in Washington , D.C. , in 1999 , where he majored in English and was a staff writer for The Hoya. | + | 1 | Snoop Dogg is an American rap artist . | Doggystyle is the debut studio album from American rapper Snoop Dogg , then known as Snoop Doggy Dogg , released by Death Row Records on November 23 , 1993 . | + | 1 | The Kansas City Chiefs lost more than 11 games in their last 2018-19 playoff games . | As of the conclusion of the 2018–19 playoffs , they have lost 12 of their last 16 playoff games , including eight straight from 1993–2015. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### qnli-contrastive + +* Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) +* Size: 1,500 evaluation samples +* Columns: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | What came into force after the new constitution was herald? | As of that day, the new constitution heralding the Second Republic came into force. | 0 | + | What is the first major city in the stream of the Rhine? | The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz. | 0 | + | What is the minimum required if you want to teach in Canada? | In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher. | 0 | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "OnlineContrastiveLoss", + "n_layers_per_step": -1, + "last_layer_weight": 0.75, + "prior_layers_weight": 1.75, + "kl_div_weight": 2.5, + "kl_temperature": 0.25 + } + ``` + +#### scitail-pairs-qa + +* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) +* Size: 750 evaluation samples +* Columns: sentence2 and sentence1 +* Approximate statistics based on the first 1000 samples: + | | sentence2 | sentence1 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence2 | sentence1 | + |:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| + | Dinosaurs filled the niches that mammals fill today during the mesozoic era. | Dinosaurs filled the niches that mammals fill today during which era? | + | Distance is defined as the magnitude or size of displacement between two positions. | What property is defined as the magnitude or size of displacement between two positions? | + | The sound in a loud classroom is an example of a form of energy. | Which of the following is an example of a form of energy? | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### scitail-pairs-pos + +* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) +* Size: 1,304 evaluation samples +* Columns: sentence1, sentence2, and label +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | label | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------| + | type | string | string | int | + | details | | | | +* Samples: + | sentence1 | sentence2 | label | + |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------| + | An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions. | Replace another in a molecule happens to atoms during a substitution reaction. | 0 | + | Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase; | Wavelength is the distance between two corresponding points of adjacent waves called. | 1 | + | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | 1 | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### xsum-pairs + +* Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) +* Size: 200 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------| + | Many mortgages do not allow homeowners to leave and rent out their property on a short-term basis.
Accommodation booking sites like AirBnB make it very easy for people to let out all or part of their home.
It is a popular, and potentially lucrative business, for Edinburgh residents during the festival season and over Hogmanay.
The average AirBnB rental in Edinburgh is reportedly £2,187 per month.
But experts have warned there are many things homeowners should consider before sub-letting.
David Marshall, operations director at Warners Solicitors & Estate Agents, said: "For regular homeowners who are thinking of moving out of their property for a short period to make it available for festival let, it's vital that they check the terms of their mortgage to ensure that this is permitted.
"They would also have to consider the risks of renting their property out for a short period of time, and how easily they would be able to recover costs of any damage caused by tenants."
Those looking to make a quick profit during the festival season should also be wary of damage to their property.
Neil McInnes, director with Edinburgh-based Umega Lettings, said specific holiday-let mortgages were not sold on the high street and required larger deposits. They also have higher interest rates.
"With long-term letting, the legal obligations on the landlord are clearer and the tenant is responsible as the resident of the property, whereas visitors or guests in holiday accommodation expect a higher level of service from their host and take less responsibility for the property as a result," he said.
"In other words, you have people taking less care of the property as they are only in it for a limited time, compared to long-term tenants who want to enjoy comfortable surroundings and decor."
AirBnB advises all hosts who use their service to check with their mortgage lenders first in case subletting is prohibited. | Edinburgh Fringe sub-letters could be breaching their mortgage agreement, leading property experts have warned. | + | Thomas Gallagher, 16, was discovered by a member of the public in Old Kays Park in Tottington, Bury, in July 2015.
An inquest concluded the schoolboy took his own life and police shortcomings did not contribute to his death.
But an Independent Police Complaints Commission (IPCC) report found Greater Manchester Police's (GMP) conduct was "below the standard expected."
The teenager's parents criticised police, saying there had been serious failings in the handling of the case, the Manchester Evening News reports.
In a statement they said: "We explained his vulnerabilities concerning his recent mental health history. Subsequently two members of the public found Tom five hours later. GMP failed to respond at all."
The IPCC found police delayed looking for the schoolboy because of staffing shortages in the Bury division, which were "foreseeable".
"No officers were deployed...until the discovery of Thomas' body some five hours later," the investigator found.
The report said the response of four officers and a radio operator was "unsatisfactory".
It said the conduct of an inspector, a police sergeant, an acting police sergeant, a police constable and a civilian radio operator, "whilst not amounting to misconduct, did fall below the standard expected."
IPCC Associate Commissioner, Guido Liguori, recommended GMP "should address" the "under-resourcing" of the police division in Bury as well as "ensuring police officers and staff are properly supported" for "the benefit of the local community."
Ch Supt Chris Sykes from GMP's Bury division said: "Thomas Gallagher's death was a tragedy and our deepest condolences remain with his family.
"Unfortunately, it was not possible for GMP to resource the original missing report, due to staff abstractions and a higher than usual number of incidents on the night."
Changes have already been implemented in Bury, including new shift patterns, to ensure the force could respond to unanticipated spikes in demand in future, he added.
He said: "Whilst the jury concluded that none of the shortcomings in GMP's response caused or contributed in any way to Tom's death, we fully acknowledge the comments made by the jury in delivering their conclusion at today's inquest. We will also await the coroner's report on any recommendations." | No officers were deployed to search for a vulnerable teenager who was later found dead, the police watchdog said. | + | Sir Nicholas Winton was 29 when he smuggled 669 boys and girls, destined for concentration camps, out of Czechoslovakia in 1939.
The 101-year-old attended the service earlier at Maidenhead railway station in front of a crowd of onlookers.
The piece, forming part of a bench, is on the station's platform three.
It was unveiled by Maidenhead MP Theresa May.
Sir Nicholas, of Pinkneys Green, was joined by members of the Maidenhead Rotary Club, where he is also a member.
Scrapbook found
A motion was unanimously passed to install the £20,000 statue, created by local sculptor Lydia Karpinska, by the Royal Borough of Windsor and Maidenhead Council last year.
It depicts Sir Nicholas relaxing on a park bench, reading a book which contains images of the children he saved and the trains used to evacuate them.
Councillor Derek Wilson, who put forward the motion, called Sir Nicholas "a true hero".
He added: "He played a valuable contribution in evacuating these children at a time when it was extremely difficult.
"We should never forget the contribution of the members of our community that put their own lives at risk.
"He is extremely modest but I felt it was important that in Maidenhead we recognised his achievements."
Sir Nicholas kept quiet about his work for 50 years until his wife found a scrapbook.
Realising the danger that the imminent Nazi invasion posed, he worked to find British families willing to put up £50 to rescue the children and look after them until they were 17.
His efforts have been likened to the work of the world famous "saviour" of Jewish prisoners Oskar Schindler.
Sir Nicholas was knighted by the Queen in March 2003 and a year earlier was finally reunited with hundreds of the children he saved - including Labour peer Lord Dubbs and film director Karel Reisz. | A statue has been unveiled to honour the man dubbed the "British Schindler" for his work saving Jewish children from Nazi invasion. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "MultipleNegativesSymmetricRankingLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1, + "prior_layers_weight": 1.75, + "kl_div_weight": 0.5, + "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: 500 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:---------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------| + | Actress Christina Ricci is all set to make her Broadway debut when she appears in the Donald Margulies play Time Stands Still. | Christina Ricci to make Broadway debut | + | A Thursday morning fire destroyed a single-story home north of Dallas, though no one was injured. | Fire destroys home north of Dallas | + | A USAirways captain piloting a Pittsburgh-bound flight was taken to a hospital Wednesday evening after smelling fumes in the cockpit. | Pilot taken to hospital after smelling fumes | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "MultipleNegativesSymmetricRankingLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1, + "prior_layers_weight": 1.75, + "kl_div_weight": 0.5, + "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: 584 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Instead of a nervous system, plant responses are generally controlled by what type of molecules, which serves as chemical messengers? | Like all organisms, plants detect and respond to stimuli in their environment. Unlike animals, plants can’t run, fly, or swim toward food or away from danger. They are usually rooted to the soil. Instead, a plant’s primary means of response is to change how it is growing. Plants also don’t have a nervous system to control their responses. Instead, their responses are generally controlled by hormones , which are chemical messenger molecules. | + | The most common mode of asexual reproduction is through the formation of asexual these? | The most common mode of asexual reproduction is through the formation of asexual spores, which are produced by one parent only (through mitosis) and are genetically identical to that parent (Figure 24.8). Spores allow fungi to expand their distribution and colonize new environments. They may be released from the parent thallus either outside or within a special reproductive sac called a sporangium. | + | Experiments have shown that acceleration is exactly inversely proportional to mass, just as it is exactly linearly proportional to what? | where m is the mass of the system. Experiments have shown that acceleration is exactly inversely proportional to mass, just as it is exactly linearly proportional to the net external force. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### qasc_pairs + +* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) +* Size: 407 evaluation samples +* Columns: id, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | id | sentence1 | sentence2 | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | id | sentence1 | sentence2 | + |:--------------------------------------------|:------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | 39JEC7537U0EF32QZJK4AZUO2ICCVO | what do adult sponges produce? | Adult sponges produce eggs and sperm.. Sperm and eggs are cells known as gametes.. adult sponges produce gametes | + | 34Q075JO1XCEZZRCGP7V8AL730A10I | What type of hormones are produced and controlled in the glands? | Most pituitary hormones control other endocrine glands.. Hormones are produced in Endocrine Glands .. Pituitary hormones are controlled and produced in glands. | + | 333U7HK6I9EFT08AIQ1WRH1C3AUJDS | pushing with strokes on a bike cause that bike to what | pushing on the pedals of a bike cause that bike to move. Bike shoes allow a rider to pull during the whole pedal stroke.. pushing with strokes on a bike cause that bike to move | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### openbookqa_pairs + +* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) at [388097e](https://huggingface.co/datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454) +* Size: 137 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:---------------------------------------------------------------------------------------|:----------------------------------------------------------------------------| + | What contains chlorophyll? | a chloroplast contains chlorophyll | + | What animal is cold-blooded? | an amphibian is cold-blooded | + | if a person needed two objects to be more alike in appearance, they could | painting an object a color causes that object to be that color | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 1,000 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:---------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | what year did fearless come out | Fearless (Taylor Swift album) Fearless is the second studio album by the American singer-songwriter Taylor Swift. The album was released on November 11, 2008, by Big Machine Records. As with her first album, Taylor Swift, Swift wrote or co-wrote all thirteen tracks on Fearless. | + | how to choose ERP system | Here are seven steps to help you choose the right software for your organization: 1 Conduct a process review and analysis. 2 Since ERP is first and foremost a business initiative, you should first define and document your current business processes, pain points, and strengths. Since ERP is first and foremost a business initiative, you should first define and document your current business processes, pain points, and strengths. 2 This analysis should also include what you think your processes should look like in the future (your to-be state) and the corresponding business requirements. | + | when was tdap approved | April 20, 2009 — The tetanus toxoid/reduced diphtheria toxoid/acellular pertussis single-dose booster vaccine (Tdap; Boostrix, GlaxoSmithKline Biologicals) was approved for an expanded age indication (10 – 64 years) by the US Food and Drug Administration (FDA), on December 4, 2008, according to an article published in the April 17 issue of the Morbidity and Mortality Weekly Report. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 1,000 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | who plays garcia's boyfriend on criminal minds | Penelope Garcia Garcia is unabashedly emotional, which sometimes makes her job with the BAU more difficult. She has broken down, crying several times while listening to and watching terrifying things in her office as she analyzes them for the team. However, according to Agent Hotchner, she "fills her office with figurines and color to remind herself to smile as the horror fills her screens". Garcia is, on the whole, an optimist. She has managed to remain so, even though the job occasionally requires her to dig into people's secret lives, to "find the god-awful thing that happened to them that made them do the god-awful thing to someone else".[7] Many team members have commented in various ways, that her optimism is an aid to them, that (as Hotch said) they would never want her to change. He once commented to Reid and JJ, after contacting Garcia during a case, "Remind me to have her drug tested," indicating her optimism could even be too much at times. Garcia was once shot by a man with whom she had just gone on a date, but survived when the bullet missed her heart and ricocheted into her abdomen. The attacker (who turned out to be a type of serial killer known as a 'hero homicide') was later killed.[8] After this incident, Morgan insisted she keep a gun; however, it is never shown whether she took this advice. She was romantically involved with fellow FBI Technical Analyst Kevin Lynch (played by Nicholas Brendon). | + | where was the battle of little bighorn fought | Battle of the Little Bighorn The Battle of the Little Bighorn, known to the Lakota and other Plains Indians as the Battle of the Greasy Grass[10] and also commonly referred to as Custer's Last Stand, was an armed engagement between combined forces of the Lakota, Northern Cheyenne, and Arapaho tribes and the 7th Cavalry Regiment of the United States Army. The battle, which resulted in the defeat of US forces, was the most significant action of the Great Sioux War of 1876. It took place on June 25–26, 1876, along the Little Bighorn River in the Crow Indian Reservation in southeastern Montana Territory.[11] | + | who played kirby in suite life on deck | Windell Middlebrooks Windell Dwain Middlebrooks, Jr. (January 8, 1979 – March 9, 2015) was an American actor and singer. He was most well known for his role in The Suite Life on Deck as Kirby Morris, a security guard on the cruise ship. He also starred in Body of Proof and Ace in the Hole. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 1,000 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 the name of the National Lottery draw where players pick five numbers from a range of 1 to 39 and one number from a range of 1 to 14? | Lotteries | Popular UK Lottery Games Lotto The UK's first and most popular lottery game changed its name to "Lotto" in 2002 after being known as the National Lottery since 1994. Players pick six numbers from 1 to 59 and match two or more to win a prize. Draws take place every Wednesday and Saturday, and if the jackpot isn't won, it can roll over until it hits the cap of �50 million. Lotto Millionaire Raffle Lotto Millionaire Raffle replaced Lotto Raffle in October 2015 and guarantees to give away a top tier prize of �1 million in every draw. There are also 20 awards of �20,000 on offer in this supplementary game where you receive one entry for every Lotto line you play. EuroMillions EuroMillions is one of the biggest lotteries in the world and is played every Tuesday and Friday in nine European countries. The minimum jackpot is �15 million, but it can roll over to a maximum of �190 million. To win the jackpot, a player must match the five main numbers from 1 to 50 and two 'Lucky Star' numbers from 1 to 11. Millionaire Maker Millionaire Maker is a supplementary game to the main EuroMillions draw that UK players are automatically entered into when they purchase a EuroMillions ticket. A raffle number consisting of three letters and six digits is generated for every line of numbers played and one player in the UK is guaranteed to win �1 million in every draw. The last Friday of every month is Mega Friday, when the �1 million prize is accompanied by an amazing non-cash prize, such as a luxurious holiday. Health Lottery The Health Lottery is drawn three times a week on Wednesday, Thursday and Saturday and has six prize tiers. Players must match all five main numbers drawn to win the top prize of �100,000. Out of every �1 played, 20p is donated to health-related good causes across Great Britain. Thunderball Thunderball has a top prize of �500,000 available in the draws which take place on Wednesday, Fridays and Saturdays. The top prize remains constant no matter how many people win. Select five numbers between 1 and 39 and one Thunderball between 1 and 14 in order to play and the jackpot is yours if you match all six numbers drawn. Irish Lotto Choose six numbers from the range 1 to 45 and if you match all of your selections, you can win a jackpot guaranteed to be worth at least �2 million. The odds of winning the jackpot are impressive when compared to other lotteries, and there are two draws every week on Wednesdays and Saturdays. HotPicks Lotto HotPicks is played every Wednesday and Saturday and requires that you predict how many numbers you will match from 1 to 59. State correctly that you�ll match all five numbers and you�ll snap up the top prize �350,000. Prizes are static for HotPicks, so the jackpot can be won by multiple players and each would still receive a �350,000 cash prize. Postcode Lottery The Postcode Lottery gives players the chance to land various prizes based on their address and has been running in Britain since 2005. It is a subscription lottery where a different number of postcodes are drawn every day and the amount you receive depends on how many winning entries you have bought. Non-cash prizes are also up for grabs every month. | + | In which TV series might Lieutenant Green say Spectrum is green? | Cast of Characters:  Symphony Angel CAST OF CHARACTERS    CAPTAIN SCARLET and the MYSTERONS UNOFFICIAL WEBSITE by Chris Bishop UPDATES: April 12, 2015: Minor modifications   "I don't think I have a head for heights." Undercover Symphony (voice by Janna Hill) to a Mysteronised model who had asked her if she would have liked to be a pilot in "Model Spy".       (Sources:  TV Century 21 material (Annuals, books and magazines), Engale Marketing's Century 21 magazine, Issue 15, Winter 1995, Fleetway Magazines, Captain Scarlet & the Mysterons book by Chris Drake & Graham Bassett, Complete Book of Captain Scarlet by Chris Bentley� all related to TV Century 21 material - Photo-montages provided by dedicated fans.)   Angel aircraft fighter pilot, sometimes helicopter pilot. Real name : Symphony Angel's voice was provided by Janna Hill.   History:   Born January 6, 2042, in Cedar Rapids Iowa, Karen Wainwright displayed such amazing gifted talents at her unnamed high school in Boston, Massachusetts, where she often was top of the class, that consequently, still a teenager, she was sent at 16 to Yale University in New Haven, Connecticut.  There, she showed herself better than other students much older than she was, gaining seven degrees in the study and employment of mathematics and technology.  So amazing were her academic abilities that Karen was nominated �student of the year� by the combined university committee.   Her abilities attracted the attention of the Universal Secret Service and soon after she graduated from Yale, Karen was contacted and offered employment by them.  An adventurous young person eager for excitement, the USS offer greatly appealed to Karen and she readily accepted it. She followed the USS comprehensive training course and such was her adaptability and swift and intelligent thinking that she passed it in only two years, while the norm was at least five years.  By early 2062, Karen was a fully fledged USS field agent, dealing solely with industrial espionage, and she became a great credit to the organisation.  During her career, she handled many tricky assignments, and in five years had become the USS�s number one secret agent.  This allowed her to develop techniques that helped revamp the way the USS was dealing with espionage, and these methods became a model to be used by other espionage and security agencies.   While training as an aircraft pilot for a special USS mission, Karen literally fell in love with flying, and it wasn�t long before she came to realise that the one ambition in her life was to become an aircraft pilot.  She left the service in early 2067, and joined up with an unnamed charter company, dealing with worldwide passenger transport.  Karen�s flying skills were so good that she soon gained worldwide recognition, and even became headline news.    Her many talents and her potential were recognised by the Spectrum selection committee, and, she was offered the chance to become one of their ace pilots (some sources say at late as late 2067). Karen passed the entrance exam with ease and was enlisted by Spectrum to become Symphony Angel.     Personality profile: On duty, Symphony is proficient and skilful, and totally dedicated to her role as Spectrum pilot. Intuitive, capable, and brave to the point of recklessness, her impetuosity, however, can get the better of her, and she sometimes finds herself in tricky situations (�Manhunt�, �The Trap�).   Off duty, Symphony is quick-witted, and sympathetic to her fellow Ang | + | The vitelus is which part of an egg? | vitellus - Wiktionary vitellus vitellus ‎( countable and uncountable , plural vitelli or vitelluses ) (biology) The contents or substance of the ovum ; egg yolk . 1861, F. Rymer Jones, The General Structure of the Animal Kingdom (page 48) In the sarcodo there takes place a process which may be in some measure compared with what occurs in the vitellus after the fecundation of an ovum. The granules becoming united together form groups, which soon divide and subdivide […] (botany) Perisperm in an early condition. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### 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: 500 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 the difference between speaking and singing? | What is the difference between talking and singing? | + | Should I learn Node.js or Ruby on Rails? | Should I learn Ruby on Rails or Node.js? | + | Who made you realize you were gay? | When did you first realize that you were gay? | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +#### gooaq_pairs + +* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) +* Size: 1,000 evaluation samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | will a queen bed frame fit a king? | Unless you have an adjustable bed frame that can be moved from queen to king size, a king size mattress will not fit on a queen frame width-wise. This means not only purchasing a new bed frame but also removing it and disposing of the pieces. | + | what is the relationship between frequency and time period? | Frequency, f, is how many cycles of an oscillation occur per second and is measured in cycles per second or hertz (Hz). The period of a wave, T, is the amount of time it takes a wave to vibrate one full cycle. These two terms are inversely proportional to each other: f = 1/T and T = 1/f. | + | how many times a day can you take rescue remedy? | RESCUE PLUS®** Dietary Supplements RESCUE PLUS® Gum – Adults chew 2 pieces for 30 minutes twice daily. RESCUE PLUS® Lozenge1 – Dissolve one lozenge as needed. RESCUE PLUS® Sleep Gummy – Adults, chew 2 gummies 30 minutes before bedtime. Not to exceed 4 per day. | +* Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: + ```json + { + "loss": "GISTEmbedLoss", + "n_layers_per_step": -1, + "last_layer_weight": 1.5, + "prior_layers_weight": 0.75, + "kl_div_weight": 0.85, + "kl_temperature": 1.15 + } + ``` + +### 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`: 1e-05 +- `lr_scheduler_type`: cosine_with_restarts +- `lr_scheduler_kwargs`: {'num_cycles': 3} +- `warmup_ratio`: 0.3 +- `save_safetensors`: False +- `fp16`: True +- `push_to_hub`: True +- `hub_model_id`: bobox/DeBERTa-ST-AllLayers-testing-v2-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`: 1e-05 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 3 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine_with_restarts +- `lr_scheduler_kwargs`: {'num_cycles': 3} +- `warmup_ratio`: 0.3 +- `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-testing-v2-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 +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: proportional + +
+ +### Training Logs +| Epoch | Step | Training Loss | scitail-pairs-pos loss | trivia pairs loss | scitail-pairs-qa loss | sciq pairs loss | xsum-pairs loss | gooaq pairs loss | nli-pairs loss | qnli-contrastive loss | nq pairs loss | compression-pairs loss | quora pairs loss | vitaminc-pairs loss | msmarco pairs loss | openbookqa pairs loss | qasc pairs loss | sts-test_spearman_cosine | +|:------:|:----:|:-------------:|:----------------------:|:-----------------:|:---------------------:|:---------------:|:---------------:|:----------------:|:--------------:|:---------------------:|:-------------:|:----------------------:|:----------------:|:-------------------:|:------------------:|:---------------------:|:---------------:|:------------------------:| +| 0.0150 | 93 | 11.7791 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0301 | 186 | 10.5226 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0451 | 279 | 9.3586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0601 | 372 | 8.4548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0752 | 465 | 8.3449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.0900 | 557 | - | 4.9304 | 8.6893 | 4.2781 | 7.7079 | 4.7787 | 7.5741 | 7.4502 | 8.8638 | 8.4397 | 3.4898 | 4.5476 | 5.4037 | 8.5196 | 7.6071 | 12.3202 | 0.3694 | +| 0.0902 | 558 | 7.4107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1052 | 651 | 7.0111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1203 | 744 | 5.9488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1353 | 837 | 6.3051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1503 | 930 | 5.2056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1654 | 1023 | 5.0794 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1801 | 1114 | - | 2.5082 | 5.1203 | 1.3340 | 6.7728 | 2.7278 | 3.7513 | 4.6932 | 11.7594 | 5.1936 | 1.7700 | 1.4383 | 3.5585 | 4.5863 | 5.4420 | 10.4294 | 0.6364 | +| 0.1804 | 1116 | 4.362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.1954 | 1209 | 4.3245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2105 | 1302 | 4.359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2255 | 1395 | 4.0488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2405 | 1488 | 4.0499 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2556 | 1581 | 3.7256 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2701 | 1671 | - | 1.9771 | 4.0200 | 0.8966 | 6.4250 | 2.1290 | 2.8735 | 3.6349 | 9.6884 | 4.1148 | 1.2427 | 1.0029 | 2.8114 | 3.6553 | 4.7841 | 9.2392 | 0.6666 | +| 0.2706 | 1674 | 3.6978 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.2856 | 1767 | 3.9547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3007 | 1860 | 4.162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3157 | 1953 | 3.739 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3307 | 2046 | 4.2688 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3458 | 2139 | 3.3462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3602 | 2228 | - | 1.6728 | 3.4190 | 0.6640 | 6.1646 | 1.7385 | 2.4531 | 3.1179 | 6.6514 | 3.3717 | 1.0110 | 0.8420 | 2.4077 | 3.0671 | 4.3699 | 7.8982 | 0.6845 | +| 0.3608 | 2232 | 3.8272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3758 | 2325 | 3.4532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.3909 | 2418 | 3.7391 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4059 | 2511 | 3.4451 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4210 | 2604 | 3.2086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4360 | 2697 | 3.153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4502 | 2785 | - | 1.3816 | 3.0526 | 0.4701 | 5.9720 | 1.4568 | 2.1218 | 2.7248 | 6.1082 | 2.9799 | 0.8825 | 0.7020 | 2.1636 | 2.6956 | 3.8901 | 7.1430 | 0.7139 | +| 0.4510 | 2790 | 2.9093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4661 | 2883 | 2.6223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4811 | 2976 | 2.8362 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.4961 | 3069 | 2.6383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5112 | 3162 | 2.6265 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5262 | 3255 | 2.8718 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5403 | 3342 | - | 1.2885 | 2.8283 | 0.3320 | 5.8096 | 1.3039 | 1.9127 | 2.4382 | 3.9993 | 2.5974 | 0.6816 | 0.5710 | 1.8587 | 2.3674 | 3.8856 | 6.9195 | 0.7175 | +| 0.5412 | 3348 | 2.4683 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5563 | 3441 | 2.3363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5713 | 3534 | 2.474 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| 0.5863 | 3627 | 2.4987 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | + + +### Framework Versions +- Python: 3.10.13 +- Sentence Transformers: 3.0.1 +- Transformers: 4.41.2 +- PyTorch: 2.1.2 +- Accelerate: 0.30.1 +- Datasets: 2.19.2 +- Tokenizers: 0.19.1 + +## Citation + +### BibTeX + +#### Sentence Transformers +```bibtex +@inproceedings{reimers-2019-sentence-bert, + title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", + author = "Reimers, Nils and Gurevych, Iryna", + booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", + month = "11", + year = "2019", + publisher = "Association for Computational Linguistics", + url = "https://arxiv.org/abs/1908.10084", +} +``` + +#### AdaptiveLayerLoss +```bibtex +@misc{li20242d, + title={2D Matryoshka Sentence Embeddings}, + author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, + year={2024}, + eprint={2402.14776}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +``` + +#### CoSENTLoss +```bibtex +@online{kexuefm-8847, + title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT}, + author={Su Jianlin}, + year={2022}, + month={Jan}, + url={https://kexue.fm/archives/8847}, +} +``` + +#### GISTEmbedLoss +```bibtex +@misc{solatorio2024gistembed, + title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, + author={Aivin V. Solatorio}, + year={2024}, + eprint={2402.16829}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + + + + + \ No newline at end of file