diff --git "a/last-checkpoint/README.md" "b/last-checkpoint/README.md" new file mode 100644--- /dev/null +++ "b/last-checkpoint/README.md" @@ -0,0 +1,897 @@ +--- +language: +- en +library_name: sentence-transformers +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:975301 +- loss:GISTEmbedLoss +- loss:CoSENTLoss +- loss:OnlineContrastiveLoss +base_model: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1 +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-bm25 +- sentence-transformers/natural-questions +- sentence-transformers/trivia-qa +- sentence-transformers/quora-duplicates +- sentence-transformers/gooaq +widget: +- source_sentence: A man in a Santa Claus costume is sitting on a wooden chair holding + a microphone and a stringed instrument. + sentences: + - a boy looks at his farther + - The man is wearing a costume. + - The man is is near the ball. +- source_sentence: A street vendor selling his art. + sentences: + - A man is selling things on the street. + - A clown is talking into a microphone. + - People are having a picnic. +- source_sentence: The Infrared Detector Laboratory built the Near Infrared Camera + and Multi-Object Spectrometer (NICMOS) instrument for the Hubble Space Telescope + and the Multiband Imaging Photometer (MIPS) instrument for the Spitzer Space Telescope. + sentences: + - Human beings is/are the main cause of recent global warming. + - Lifestyle diseases are diseases that are caused by choices that people make in + their daily lives. + - A telescope is used to make objects in space appear closer. +- source_sentence: Where did France focus its efforts to rebuild its empire? + sentences: + - France took control of Algeria in 1830 but began in earnest to rebuild its worldwide + empire after 1850, concentrating chiefly in North and West Africa, as well as + South-East Asia, with other conquests in Central and East Africa, as well as the + South Pacific. + - In 1890 the government launched a competition to design new buildings for the + museum, with architect Alfred Waterhouse as one of the judges; this would give + the museum a new imposing front entrance. + - Chloroplasts can grow and progress through some of the constriction stages under + poor quality green light, but are slow to complete division—they require exposure + to bright white light to complete division. +- source_sentence: President Johnson issued an executive order to rename the Launch + Operations Center after whom? + sentences: + - 'The label Huguenot was purportedly first applied in France to those conspirators + (all of them aristocratic members of the Reformed Church) involved in the Amboise + plot of 1560: a foiled attempt to wrest power in France from the influential House + of Guise.' + - But an even bigger facility would be needed for the mammoth rocket required for + the manned lunar mission, so land acquisition was started in July 1961 for a Launch + Operations Center (LOC) immediately north of Canaveral at Merritt Island. + - After the war, the new communist authorities of Poland discouraged church construction + and only a small number were rebuilt. +pipeline_tag: sentence-similarity +--- + +# SentenceTransformer based on bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1 + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1](https://huggingface.co/bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1) 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-bm25), [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:** [bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1](https://huggingface.co/bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1) +- **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-bm25) + - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) + - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) + - [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) + - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) +- **Language:** en + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) +) +``` + +## Usage + +### Direct Usage (Sentence Transformers) + +First install the Sentence Transformers library: + +```bash +pip install -U sentence-transformers +``` + +Then you can load this model and run inference. +```python +from sentence_transformers import SentenceTransformer + +# Download from the 🤗 Hub +model = SentenceTransformer("bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step2-checkpoint-tmp") +# Run inference +sentences = [ + 'President Johnson issued an executive order to rename the Launch Operations Center after whom?', + 'But an even bigger facility would be needed for the mammoth rocket required for the manned lunar mission, so land acquisition was started in July 1961 for a Launch Operations Center (LOC) immediately north of Canaveral at Merritt Island.', + 'The label Huguenot was purportedly first applied in France to those conspirators (all of them aristocratic members of the Reformed Church) involved in the Amboise plot of 1560: a foiled attempt to wrest power in France from the influential House of Guise.', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 768] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities.shape) +# [3, 3] +``` + + + + + + + + + + + +## Training Details + +### Training Datasets + +#### nli-pairs + +* Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) +* Size: 100,000 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: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### 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: 50,066 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 | Ace Attorney Trilogy was released on Nintendo Switch and Microsoft Windows before May 2019 . | Additionally , two titles that collect the first three main series games have been released : Ace Attorney : Phoenix Wright Trilogy HD , which was released for iOS and Android in 2012 in Japan and for iOS in 2013 in the West , and Phoenix Wright : Ace Attorney Trilogy , which was released for the Nintendo 3DS in 2014 , and PlayStation 4 , Xbox One , Nintendo Switch and Microsoft Windows on April 9th , 2019. | + | 1 | The movie Live by Night has an approval rating of more than 32 % and has been reviewed by less than 150 critics . | On review aggregation website Rotten Tomatoes , the film has an approval rating of 33 % based on 147 reviews , and an average rating of 5.2/10 . | + | 1 | Lattimore debuted during the season 's opening match . | He made his professional regular season debut during the New Orleans Saints ' season-opener against the Minnesota Vikings and recorded four solo tackles during the 19-29 loss . | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### qnli-contrastive + +* Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) +* Size: 100,000 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 | + |:---------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| + | Where did the attendance at Arsenal games rank in relation to other English clubs? | The club's location, adjoining wealthy areas such as Canonbury and Barnsbury, mixed areas such as Islington, Holloway, Highbury, and the adjacent London Borough of Camden, and largely working-class areas such as Finsbury Park and Stoke Newington, has meant that Arsenal's supporters have come from a variety of social classes. | 0 | + | What defines finite groups of order p, a prime number, as being necessarily cyclic (abelian) groups Zp? | According to Lagrange's theorem, finite groups of order p, a prime number, are necessarily cyclic (abelian) groups Zp. | 0 | + | Who was appointed justiciar? | For some the appointment of Peter des Roches as justiciar was an important factor, as he was considered an "abrasive foreigner" by many of the barons. | 0 | +* Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) + +#### scitail-pairs-qa + +* Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) +* Size: 14,987 training samples +* Columns: sentence2 and sentence1 +* Approximate statistics based on the first 1000 samples: + | | sentence2 | sentence1 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence2 | sentence1 | + |:------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------| + | Pneumonia and emphysema illnesses are caused by damage to the alveoli of the lungs. | What illnesses are caused by damage to the alveoli of the lungs? | + | A synthetic diamond is not considered a mineral because minerals must be created naturally. | Why is a synthetic diamond not considered a mineral? | + | Trans fat added to certain foods to keep them fresher longer, increase(s) the risk of heart disease. | What lipid, added to certain foods to keep them fresher longer, increases the risk of heart disease? | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### 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 | + |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------| + | Natural selection is the idea that the individual organisms with traits most favorable to a given environment will  be able to survive and reproduce. | Natural selection is the process that allows organisms with better traits to survive and produce. | + | Solutions Solutions are homogenous mixtures of two or more substances. | All solutions contain at least two substances. | + | When the moon and the sun are working together to make very big tides, the effect is called Spring Tides. | Spring tides are created when the sun and moon's tides match. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### xsum-pairs + +* Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) +* Size: 100,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------| + | A number of children are among the dead. There were no reports of casualties among Isaf troops.
The police said the attacker was a boy, 14, on a motorbike, who detonated a bomb near an entrance to the HQ.
Kabul security has been tightened as supporters of an anti-Taliban warlord mark 11 years since his assassination.
Ahmad Shah Massoud - a hero of the 1980s war against Soviet occupiers, and later of opposition to the Taliban - was killed by al-Qaeda suicide bombers on 9 September 2001.
Following Saturday's explosion, the Isaf HQ, home to some 2,500 personnel, was placed "on lockdown", the Isaf spokeswoman said.
Child street hawkers are believed to have been caught in the blast and witnesses quoted by Reuters said small bodies could be seen being carried to ambulances.
A police official speaking on condition of anonymity told AFP news agency: "Most of the victims are young children who gather around Isaf to sell small items to soldiers leaving or getting into the base."
The US embassy, the Italian embassy and the presidential palace are also located near the site of the attack.
The Taliban have claimed they were behind the attack, but say it was carried out by a man in his 20s, targeting a building used by the CIA to train Afghan spies.
But the BBC Jonathan Beale, in Kabul, says there is also speculation that it could have been the work of another insurgent group - the Haqqani network - which carried out a series of coordinated attacks in the city earlier this year.
Scores of dignitaries were attending commemorations of Massoud's death in Kabul on Saturday, which is a national public holiday in his honour. | A teenage suicide bomber has killed at least six people near the headquarter of the Nato-led international coalition (Isaf) in Kabul. | + | Although he was never the actual president of Panama, he was the key figure from 1983 to 1989 - and a key US ally in Central America for four decades.
His connection with the United States dated back to the 1950s, when according to various accounts, he was recruited as a CIA informant while studying at a military academy in Peru.
Noriega, now 77, eventually became a prized American "asset" in a region that was becoming politically hostile to US interests in the wake of the Cuban Revolution.
He rose within the ranks of the Panamanian armed forces to become a key supporter of Gen Omar Torrijos, the military ruler who signed a treaty with the US to restore the Panama Canal zone to Panamanian sovereignty in 1977.
After Gen Torrijos's death in a mysterious plane crash in 1981, Noriega became the power behind the scenes as head of the security services.
The US relied on Panama as a regional listening post and Noriega obliged with unfaltering support for the Contras in Nicaragua, and in the fight against the FMLN guerrillas in El Salvador.
At the same time, he began to play an increasingly repressive role internally in Panama, especially after the assassination of Hugo Spadafora, a political opponent who was found beheaded in 1985.
Noriega allegedly played a role in the mid-1980s Iran-Contra affair, which involved the smuggling of weapons and drugs to aid US undercover efforts to support the anti-government forces opposing the Sandinistas in Nicaragua.
However, the US became increasingly suspicious of Noriega amid indications that he was selling his services to other intelligence bodies, not to mention drug-trafficking organisations.
These tensions became public in 1988 when Noriega was indicted in a US federal court on drug-trafficking charges.
He was also accused of rigging elections in 1989.
By mid-December that year, ties had deteriorated so far that President George H W Bush launched an invasion, ostensibly because a US marine had been killed in Panama City, although the operation had been months in the planning.
Noriega sought refuge in the Vatican's diplomatic mission in Panama City. The US tactic to flush him out was to play deafening pop and heavy metal music non-stop outside the building.
By 3 January 1990, it had worked and Noriega surrendered. He was flown to the US with prisoner of war status to face charges of drug-trafficking, money-laundering and racketeering.
His trial there was an international spectacle that revealed titillating details of his personal life.
At the time it was said he wore red underwear to ward off the "evil eye".
In 2007, he concluded his sentence after 17 years of confinement and public silence in a Miami federal jail.
But his legal troubles were far from over and he remained in custody.
In 1999, Noriega had been convicted in absentia in France of using $3m in proceeds from Colombia's Medellin drug cartel drug trade to buy property there.
In March 2010, the US Supreme Court agreed to a French request extradite him to Paris, where he faced a new trial for money-laundering.
Noriega, who denied the charges, was found guilty and sentenced to seven years.
His legal odyssey took another turn on 23 November when a French court approved a request from Panama to send him back home, where he was convicted in absentia of murder, corruption and embezzlement.
He refused the chance to appeal the decision and flew out of Paris on 11 December, escorted by a team of Panamanian officials and a doctor. | For many years, Panama's General Manuel Noriega was the embodiment of the terms "military strongman" and "de facto leader". | + | The actress became a Goodwill Ambassador for the United Nations in 2014, to promote equality and help improve opportunities for women and girls around the world.
As part of her work for the organisation, she made a speech about how important it is to make boys and girls more equal.
Now, she's hoping that a break from acting will give her more time to focus on that role. | Emma Watson, who played Hermione in 'Harry Potter', says that she will take a year off from acting. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### compression-pairs + +* Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) +* Size: 100,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| + | The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints. | USHL completes expansion draft | + | Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month. | Bud Selig to speak at St. Norbert College | + | It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit. | It's cherry time | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### sciq_pairs + +* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) +* Size: 11,679 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | What type of organism is commonly used in preparation of foods such as cheese and yogurt? | Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine. | + | What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere? | Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere. | + | Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what? | Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### qasc_pairs + +* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) +* Size: 8,134 training samples +* Columns: id, sentence1, and sentence2 +* Approximate statistics based on the first 1000 samples: + | | id | sentence1 | sentence2 | + |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | id | sentence1 | sentence2 | + |:--------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | 3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K | What type of water formation is formed by clouds? | beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds. | + | 3LS2AMNW5FPNJK3C3PZLZCPX562OQO | Where do beads of water come from? | beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water | + | 3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3 | What forms beads of water? | beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### openbookqa_pairs + +* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) at [388097e](https://huggingface.co/datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454) +* Size: 2,740 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:-------------------------------------------------|:--------------------------------------------------------------------------| + | The sun is responsible for | the sun is the source of energy for physical cycles on Earth | + | When food is reduced in the stomach | digestion is when stomach acid breaks down food | + | Stars are | a star is made of gases | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### msmarco_pairs + +* Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25) at [ce8a493](https://huggingface.co/datasets/sentence-transformers/msmarco-bm25/tree/ce8a493a65af5e872c3c92f72a89e2e99e175f02) +* Size: 100,000 training samples +* Columns: sentence1, sentence2, and negative +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | negative | + |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | string | + | details | | | | +* Samples: + | sentence1 | sentence2 | negative | + |:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | what are the liberal arts? | liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects. | The New York State Education Department requires 60 Liberal Arts credits in a Bachelor of Science program and 90 Liberal Arts credits in a Bachelor of Arts program. In the list of course descriptions, courses which are liberal arts for all students are identified by (Liberal Arts) after the course number. | + | what is the mechanism of action of fibrinolytic or thrombolytic drugs? | Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure. | Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway. | + | what is normal plat count | 78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL). | Your blood test results should be written in your maternity notes. Your platelet count will look something like Plat. 160x10.9/L, which means you have a platelet count of 160, which is in the normal range.If your platelet count is low, the blood test should be done again.This will keep track of whether or not your count is dropping.our platelet count will look something like Plat. 160x10.9/L, which means you have a platelet count of 160, which is in the normal range. If your platelet count is low, the blood test should be done again. This will keep track of whether or not your count is dropping. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### nq_pairs + +* Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) +* Size: 100,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | when did richmond last play in a preliminary final | Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next. | + | who sang what in the world's come over you | Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel. | + | who produces the most wool in the world | Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### 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: 73,346 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | Which American-born Sinclair won the Nobel Prize for Literature in 1930? | The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. | + | Where in England was Dame Judi Dench born? | Judi Dench - IMDb IMDb Actress | Music Department | Soundtrack Judi Dench was born in York, England, to Eleanora Olive (Jones), who was from Dublin, Ireland, and Reginald Arthur Dench, a doctor from Dorset, England. She attended Mount School in York, and studied at the Central School of Speech and Drama. She has performed with Royal Shakespeare Company, the National Theatre, and at Old Vic Theatre. She is a ... See full bio » Born: a list of 35 people created 02 Jul 2011 a list of 35 people created 19 Apr 2012 a list of 35 people created 28 May 2014 a list of 25 people created 05 Aug 2014 a list of 26 people created 18 May 2015 Do you have a demo reel? Add it to your IMDbPage How much of Judi Dench's work have you seen? User Polls Won 1 Oscar. Another 59 wins & 163 nominations. See more awards  » Known For  2016 The Hollow Crown (TV Series) Cecily, Duchess of York  2015 The Vote (TV Movie) Christine Metcalfe - Total War (1996) ... Narrator (voice) - Stalemate (1996) ... Narrator (voice)  1992 The Torch (TV Mini-Series) Aba  1990 Screen One (TV Series) Anne  1989 Behaving Badly (TV Mini-Series) Bridget  1981 BBC2 Playhouse (TV Series) Sister Scarli  1976 Arena (TV Series documentary) Sweetie Simpkins  1973 Ooh La La! (TV Series) Amélie  1966 Court Martial (TV Series) Marthe  1963 Z Cars (TV Series) Elena Collins  1963 Love Story (TV Series) Pat McKendrick  1960 The Terrible Choice (TV Series) Good Angel Music department (1 credit)   A Fine Romance (TV Series) (theme sung by - 14 episodes, 1981 - 1983) (theme song sung by - 12 episodes, 1983 - 1984) - A Romantic Meal (1984) ... (theme song sung by) - Problems (1984) ... (theme song sung by)  2013 Fifty Years on Stage (TV Movie) (performer: "Send in the Clowns")  2009 Nine (performer: "Folies Bergère") - What's Wrong with Mrs Bale? (1997) ... (performer: "Raindrops Keep Fallin' On My Head" - uncredited) - Misunderstandings (1993) ... (performer: "Walkin' My Baby Back Home" - uncredited)  1982-1984 A Fine Romance (TV Series) (performer - 2 episodes) - The Telephone Call (1984) ... (performer: "Boogie Woogie Bugle Boy" - uncredited) - Furniture (1982) ... (performer: "Rule, Britannia!" - uncredited) Hide   2009 Waiting in Rhyme (Video short) (special thanks)  2007 Expresso (Short) (special thanks)  1999 Shakespeare in Love and on Film (TV Movie documentary) (thanks - as Dame Judi Dench) Hide   2016 Rio Olympics (TV Mini-Series) Herself  2015 In Conversation (TV Series documentary) Herself  2015 Entertainment Tonight (TV Series) Herself  2015 CBS This Morning (TV Series) Herself - Guest  2015 The Insider (TV Series) Herself  1999-2014 Cinema 3 (TV Series) Herself  2013 Good Day L.A. (TV Series) Herself - Guest  2013 Arena (TV Series documentary) Herself  2013 At the Movies (TV Series) Herself  2013 Shooting Bond (Video documentary) Herself  2013 Bond's Greatest Moments (TV Movie documentary) Herself  2012 Made in Hollywood (TV Series) Herself  1999-2012 Charlie Rose (TV Series) Herself - Guest  2008-2012 This Morning (TV Series) Herself - Guest  2012 The Secrets of Skyfall (TV Short documentary) Herself  2012 Anderson Live (TV Series) Herself  2012 J. Edgar: A Complicated Man (Video documentary short) Herself  2011 The Many Faces of... (TV Series documentary) Herself / Various Characters  2011 Na plovárne (TV Series) Herself  2010 BBC Proms (TV Series) Herself  2010 The South Bank Show Revisited (TV Series documentary) Herself - Episode #6.68 (2009) ... Herself - Guest (as Dame Judi Dench)  2007-2009 Breakfast (TV Series)  2009 Larry King Live (TV Series) Herself - Guest  2009 The One Show (TV Series) Herself  2009 Cranford in Detail (Video documentary short) Herself / Miss Matty Jenkins (as Dame Judi Dench)  2005-2008 The South Bank Show (TV Series documentary) Herself  2008 Tavis Smiley (TV Series) Herself - Guest  2007 ITV News (TV Series) Herself - BAFTA Nominee  2007 The Making of Cranford (Video documentary short) Herself / Miss Matty Jenkyns (as Dame Judi Dench)  2006 Becoming Bond (TV Movie documentary) Herself  2006 Corazón de... (TV Series) Hers | + | In which decade did Billboard magazine first publish and American hit chart? | The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day. he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### quora_pairs + +* Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) +* Size: 100,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| + | Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me? | I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me? | + | How can I be a good geologist? | What should I do to be a great geologist? | + | How do I read and find my YouTube comments? | How can I see all my Youtube comments? | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### gooaq_pairs + +* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) +* Size: 100,000 training samples +* Columns: sentence1 and sentence2 +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | + |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| + | type | string | string | + | details | | | +* Samples: + | sentence1 | sentence2 | + |:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | is toprol xl the same as metoprolol? | Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure. | + | are you experienced cd steve hoffman? | The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design. | + | how are babushka dolls made? | Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting. | +* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +### 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: 6,808 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: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### 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: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters: + ```json + {'guide': SentenceTransformer( + (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel + (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) + (2): Normalize() + ), 'temperature': 0.05} + ``` + +#### qnli-contrastive + +* Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) +* Size: 5,463 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: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss) + +#### sts-label + +* Dataset: [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308) +* Size: 1,500 evaluation samples +* Columns: sentence1, sentence2, and score +* Approximate statistics based on the first 1000 samples: + | | sentence1 | sentence2 | score | + |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------| + | type | string | string | float | + | details | | | | +* Samples: + | sentence1 | sentence2 | score | + |:--------------------------------------------------|:------------------------------------------------------|:------------------| + | A man with a hard hat is dancing. | A man wearing a hard hat is dancing. | 1.0 | + | A young child is riding a horse. | A child is riding a horse. | 0.95 | + | A man is feeding a mouse to a snake. | The man is feeding a mouse to the snake. | 1.0 | +* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "pairwise_cos_sim" + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `eval_strategy`: steps +- `per_device_train_batch_size`: 30 +- `per_device_eval_batch_size`: 28 +- `learning_rate`: 5e-06 +- `weight_decay`: 5e-08 +- `num_train_epochs`: 2 +- `lr_scheduler_type`: cosine +- `warmup_ratio`: 0.33 +- `save_safetensors`: False +- `fp16`: True +- `push_to_hub`: True +- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step2-checkpoint-tmp +- `hub_strategy`: checkpoint +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: round_robin + +#### All Hyperparameters +
Click to expand + +- `overwrite_output_dir`: False +- `do_predict`: False +- `eval_strategy`: steps +- `prediction_loss_only`: True +- `per_device_train_batch_size`: 30 +- `per_device_eval_batch_size`: 28 +- `per_gpu_train_batch_size`: None +- `per_gpu_eval_batch_size`: None +- `gradient_accumulation_steps`: 1 +- `eval_accumulation_steps`: None +- `learning_rate`: 5e-06 +- `weight_decay`: 5e-08 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `max_grad_norm`: 1.0 +- `num_train_epochs`: 2 +- `max_steps`: -1 +- `lr_scheduler_type`: cosine +- `lr_scheduler_kwargs`: {} +- `warmup_ratio`: 0.33 +- `warmup_steps`: 0 +- `log_level`: passive +- `log_level_replica`: warning +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `save_safetensors`: False +- `save_on_each_node`: False +- `save_only_model`: False +- `restore_callback_states_from_checkpoint`: False +- `no_cuda`: False +- `use_cpu`: False +- `use_mps_device`: False +- `seed`: 42 +- `data_seed`: None +- `jit_mode_eval`: False +- `use_ipex`: False +- `bf16`: False +- `fp16`: True +- `fp16_opt_level`: O1 +- `half_precision_backend`: auto +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `local_rank`: 0 +- `ddp_backend`: None +- `tpu_num_cores`: None +- `tpu_metrics_debug`: False +- `debug`: [] +- `dataloader_drop_last`: False +- `dataloader_num_workers`: 0 +- `dataloader_prefetch_factor`: None +- `past_index`: -1 +- `disable_tqdm`: False +- `remove_unused_columns`: True +- `label_names`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `fsdp`: [] +- `fsdp_min_num_params`: 0 +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `fsdp_transformer_layer_cls_to_wrap`: None +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `deepspeed`: None +- `label_smoothing_factor`: 0.0 +- `optim`: adamw_torch +- `optim_args`: None +- `adafactor`: False +- `group_by_length`: False +- `length_column_name`: length +- `ddp_find_unused_parameters`: None +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `skip_memory_metrics`: True +- `use_legacy_prediction_loop`: False +- `push_to_hub`: True +- `resume_from_checkpoint`: None +- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step2-checkpoint-tmp +- `hub_strategy`: checkpoint +- `hub_private_repo`: False +- `hub_always_push`: False +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `include_inputs_for_metrics`: False +- `eval_do_concat_batches`: True +- `fp16_backend`: auto +- `push_to_hub_model_id`: None +- `push_to_hub_organization`: None +- `mp_parameters`: +- `auto_find_batch_size`: False +- `full_determinism`: False +- `torchdynamo`: None +- `ray_scope`: last +- `ddp_timeout`: 1800 +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `dispatch_batches`: None +- `split_batches`: None +- `include_tokens_per_second`: False +- `include_num_input_tokens_seen`: False +- `neftune_noise_alpha`: None +- `optim_target_modules`: None +- `batch_eval_metrics`: False +- `batch_sampler`: no_duplicates +- `multi_dataset_batch_sampler`: round_robin + +
+ +### Training Logs +| Epoch | Step | Training Loss | sts-label loss | scitail-pairs-pos loss | nli-pairs loss | qnli-contrastive loss | +|:------:|:----:|:-------------:|:--------------:|:----------------------:|:--------------:|:---------------------:| +| 0.0503 | 74 | 1.047 | - | - | - | - | +| 0.1005 | 148 | 1.0717 | - | - | - | - | +| 0.1508 | 222 | 0.9934 | - | - | - | - | +| 0.2004 | 295 | - | 8.9623 | 0.3904 | 0.2709 | 1.3033 | +| 0.2011 | 296 | 1.1575 | - | - | - | - | +| 0.2514 | 370 | 1.1961 | - | - | - | - | +| 0.3016 | 444 | 1.0785 | - | - | - | - | +| 0.3519 | 518 | 1.0578 | - | - | - | - | +| 0.4008 | 590 | - | 8.8429 | 0.3808 | 0.2699 | 1.2296 | +| 0.4022 | 592 | 1.0735 | - | - | - | - | +| 0.4524 | 666 | 1.0379 | - | - | - | - | + + +### 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", +} +``` + +#### 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} +} +``` + +#### 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}, +} +``` + + + + + + \ No newline at end of file