--- base_model: microsoft/deberta-v2-xlarge datasets: - tals/vitaminc - allenai/scitail - allenai/sciq - allenai/qasc - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/gooaq - google-research-datasets/paws language: - en library_name: sentence-transformers metrics: - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:99622 - loss:CachedGISTEmbedLoss widget: - source_sentence: in medical terms what does o.d. mean sentences: - "The SDIF format is a sequence of frames, similar to chunks in the IFF/ AIFF /RIFF\ \ formats, but not strictly compatible. Every frameâ\x80\x99s length is a multiple\ \ of 8 bytes. Every SDIF file or stream must begin with a small opening frame.The\ \ body of the file or stream is a contiguous sequence of time-tagged frames, sorted\ \ in ascending temporal order, with multiple types of frames allowed in a single\ \ file or stream.The most recent version of this format, SDIF-3, is the standard\ \ multi-channel hardware connection employed in DSD recording systems.he body\ \ of the file or stream is a contiguous sequence of time-tagged frames, sorted\ \ in ascending temporal order, with multiple types of frames allowed in a single\ \ file or stream. The most recent version of this format, SDIF-3, is the standard\ \ multi-channel hardware connection employed in DSD recording systems." - Well Phillip, my neighbor had a very similar houseboat and it weighed @ 11-12,000 pounds. It was a 33 foot Nautaline Houseboat, however a 1970, which shouldn't make a difference. Lastly, hopefully some of our readers and visitors will share and post comments about their houseboat trailer and weight experiences. - Jupiterimages/Photos.com/Getty Images. When writing eye exam results on a chart or for a prescription, an eye doctor uses the abbreviations for the Latin terms meaning right and left eye. O.D. stands for oculus dexter, or right eye; it means pertaining to the right eye. O.S. means oculus sinister, or left eye. - source_sentence: More than 273 people have died from the 2019-20 coronavirus outside mainland China . sentences: - 'More than 3,700 people have died : around 3,100 in mainland China and around 550 in all other countries combined .' - 'More than 3,200 people have died : almost 3,000 in mainland China and around 275 in other countries .' - more than 4,900 deaths have been attributed to COVID-19 . - source_sentence: When electrons return to a lower energy level, they emit the excess energy in the form of light. sentences: - When electrons return to a lower energy level, they emit the excess energy in the form of what? - What part of a cell do proteins travel to to be modified for the specific job they will do? - What is the first thing a student should do if an accident happens during a science experiment? - source_sentence: The study of energy and energy transfer involving physical matter is called thermodynamics. sentences: - Photoautotrophs and chemoautotrophs are two basic types of what? - The study of energy and energy transfer involving physical matter is what? - What causes acid rain, ozone depletion, and global warming? - source_sentence: Which Olympic Games were the first to be telecast worldwide? sentences: - 'List of countryside birds in more urban areas - Telegraph Wildlife List of countryside birds in more urban areas Countryside birds are increasingly moving into towns because the big freeze has meant food is harder to find, according to the RSPB.   Follow Here is a list of the unusual visitors, courtesy of the RSPB: Redwing: Most commonly encountered as a winter bird and is Britain''s smallest true thrush. Its creamy strip above the eye and orange-red flank patches make it distinctive. They roam across the countryside, feeding in fields and hedgerows, rarely visiting gardens, except in the coldest weather when snow covers the fields. Fieldfare: Large, colourful thrushes, much like a mistle thrush in general size, shape and behaviour. They stand very upright and move forward with purposeful hops. They are very social birds, spending the winter in flocks of anything from a dozen or two to several hundred strong. Bittern: A thickset heron with all-over bright, pale, buffy-brown plumage covered with dark streaks and bars. It flies on broad, rounded, bowed wings. Very difficult to see, as it moves silently through reeds at water''s edge, looking for fish. The males make a far-carrying, booming sound in spring. Its dependence on reedbeds and very small population make it a Red List species - one of the most threatened in the country. Woodcock: A large bulky wading bird with short legs, and a very long straight tapering bill. It is largely nocturnal, spending most of the day in dense cover. Most of the birds in the UK are residents; in the autumn birds move to the UK from Finland and Russia to winter here. Related Articles' - First televised Olympics | Guinness World Records First televised Olympics When 1936 The Olympics made broadcasting history in 1936 when the Berlin Games was beamed out live in black and white to athletes in the Olympic village and to the wider public in 25 special viewing rooms located in Berlin and Potsdam. This was the first time a sporting event had ever been seen live on television screens and it opened the floodgates. Rome 1960 was the first Olympics to be broadcast live across Europe, while Tokyo 1964 was the first to reach a worldwide audience. Colour pictures arrived four years later at the 1968 Mexico City Games. All records listed on our website are current and up-to-date. For a full list of record titles, please use our Record Application Search. (You will be need to register / login for access) - BBC NEWS | Entertainment | Beverley Sisters' years in limelight Beverley Sisters' years in limelight Teddy, Babs and Joy got their first break in an advertising campaign Veteran singing trio The Beverley Sisters, who have been made MBEs in the New Year Honours List, were one of the most popular acts of the 50s and 60s. Joy - born in 1929 - and the twins Babs and Teddie - born in 1932 - were brought up in Bethnal Green in east London. During the Second World War the girls were evacuated to the Midlands. There they secured a contract to become "Bonnie Babies" in an advertising campaign for the bedtime drink Ovaltine. Radio appearances for the BBC followed, and with support from bandleader Glenn Miller they became professional singers. After the war the siblings were given their own TV show, Those Beverley Sisters. Chart success In 1951 the trio signed a recording contract with Columbia Records that helped them become the highest paid female act in the UK. They were the first British female group to break into the US top 10 and enjoyed chart success with Christmas records like Little Drummer Boy and I Saw Mommy Kissing Santa Claus. Other favourites included Bye Bye Love and Always and Forever. Joy married Billy Wright, then captain of the England football team, in 1958 and had two daughters and a son. Teddie also had a daughter. In 2002 they sang for the Queen and the Duke of Edinburgh at a Jubilee concert, while last year saw them take part in D-Day celebrations in Portsmouth. The trio, now in their 70s, still perform but have had to cut back their workload due to health concerns. model-index: - name: SentenceTransformer based on microsoft/deberta-v2-xlarge results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.9083914036604525 name: Pearson Cosine - type: spearman_cosine value: 0.9121196279131069 name: Spearman Cosine - type: pearson_manhattan value: 0.9168751297887556 name: Pearson Manhattan - type: spearman_manhattan value: 0.9125480317384066 name: Spearman Manhattan - type: pearson_euclidean value: 0.9173432574357514 name: Pearson Euclidean - type: spearman_euclidean value: 0.9124939274633844 name: Spearman Euclidean - type: pearson_dot value: 0.8981723163151856 name: Pearson Dot - type: spearman_dot value: 0.894677286770672 name: Spearman Dot - type: pearson_max value: 0.9173432574357514 name: Pearson Max - type: spearman_max value: 0.9125480317384066 name: Spearman Max - task: type: binary-classification name: Binary Classification dataset: name: allNLI dev type: allNLI-dev metrics: - type: cosine_accuracy value: 0.71875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.834626317024231 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6094808126410836 name: Cosine F1 - type: cosine_f1_threshold value: 0.6867948174476624 name: Cosine F1 Threshold - type: cosine_precision value: 0.5 name: Cosine Precision - type: cosine_recall value: 0.7803468208092486 name: Cosine Recall - type: cosine_ap value: 0.5835584554560046 name: Cosine Ap - type: dot_accuracy value: 0.716796875 name: Dot Accuracy - type: dot_accuracy_threshold value: 792.005615234375 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6130434782608695 name: Dot F1 - type: dot_f1_threshold value: 633.9556274414062 name: Dot F1 Threshold - type: dot_precision value: 0.4912891986062718 name: Dot Precision - type: dot_recall value: 0.815028901734104 name: Dot Recall - type: dot_ap value: 0.5699574122566177 name: Dot Ap - type: manhattan_accuracy value: 0.720703125 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 559.5651245117188 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6072186836518046 name: Manhattan F1 - type: manhattan_f1_threshold value: 784.5065307617188 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.4798657718120805 name: Manhattan Precision - type: manhattan_recall value: 0.8265895953757225 name: Manhattan Recall - type: manhattan_ap value: 0.5843640673658059 name: Manhattan Ap - type: euclidean_accuracy value: 0.716796875 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 17.933330535888672 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.602510460251046 name: Euclidean F1 - type: euclidean_f1_threshold value: 25.284334182739258 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.4721311475409836 name: Euclidean Precision - type: euclidean_recall value: 0.8323699421965318 name: Euclidean Recall - type: euclidean_ap value: 0.583705987979273 name: Euclidean Ap - type: max_accuracy value: 0.720703125 name: Max Accuracy - type: max_accuracy_threshold value: 792.005615234375 name: Max Accuracy Threshold - type: max_f1 value: 0.6130434782608695 name: Max F1 - type: max_f1_threshold value: 784.5065307617188 name: Max F1 Threshold - type: max_precision value: 0.5 name: Max Precision - type: max_recall value: 0.8323699421965318 name: Max Recall - type: max_ap value: 0.5843640673658059 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Qnli dev type: Qnli-dev metrics: - type: cosine_accuracy value: 0.6796875 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.750099241733551 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6803418803418803 name: Cosine F1 - type: cosine_f1_threshold value: 0.6485489010810852 name: Cosine F1 Threshold - type: cosine_precision value: 0.5702005730659025 name: Cosine Precision - type: cosine_recall value: 0.8432203389830508 name: Cosine Recall - type: cosine_ap value: 0.7156359777017246 name: Cosine Ap - type: dot_accuracy value: 0.681640625 name: Dot Accuracy - type: dot_accuracy_threshold value: 724.4273681640625 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6768189509306259 name: Dot F1 - type: dot_f1_threshold value: 601.5821533203125 name: Dot F1 Threshold - type: dot_precision value: 0.5633802816901409 name: Dot Precision - type: dot_recall value: 0.847457627118644 name: Dot Recall - type: dot_ap value: 0.7008881822413993 name: Dot Ap - type: manhattan_accuracy value: 0.6796875 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 667.1034545898438 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6877192982456141 name: Manhattan F1 - type: manhattan_f1_threshold value: 791.084716796875 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.5868263473053892 name: Manhattan Precision - type: manhattan_recall value: 0.8305084745762712 name: Manhattan Recall - type: manhattan_ap value: 0.7161884702166013 name: Manhattan Ap - type: euclidean_accuracy value: 0.6796875 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 21.8489990234375 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.6843033509700176 name: Euclidean F1 - type: euclidean_f1_threshold value: 25.212352752685547 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.5861027190332326 name: Euclidean Precision - type: euclidean_recall value: 0.8220338983050848 name: Euclidean Recall - type: euclidean_ap value: 0.7160572797142014 name: Euclidean Ap - type: max_accuracy value: 0.681640625 name: Max Accuracy - type: max_accuracy_threshold value: 724.4273681640625 name: Max Accuracy Threshold - type: max_f1 value: 0.6877192982456141 name: Max F1 - type: max_f1_threshold value: 791.084716796875 name: Max F1 Threshold - type: max_precision value: 0.5868263473053892 name: Max Precision - type: max_recall value: 0.847457627118644 name: Max Recall - type: max_ap value: 0.7161884702166013 name: Max Ap --- # SentenceTransformer based on microsoft/deberta-v2-xlarge This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) on the negation-triplets, [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), xsum-pairs, [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) and global_dataset datasets. It maps sentences & paragraphs to a 1536-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-v2-xlarge](https://huggingface.co/microsoft/deberta-v2-xlarge) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1536 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - negation-triplets - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - xsum-pairs - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - openbookqa_pairs - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) - global_dataset - **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': 1536, '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/DeBERTa3-0.4B-ST-v1-checkpoints-tmp") # Run inference sentences = [ 'Which Olympic Games were the first to be telecast worldwide?', 'First televised Olympics | Guinness World Records First televised Olympics When 1936 The Olympics made broadcasting history in 1936 when the Berlin Games was beamed out live in black and white to athletes in the Olympic village and to the wider public in 25 special viewing rooms located in Berlin and Potsdam. This was the first time a sporting event had ever been seen live on television screens and it opened the floodgates. Rome 1960 was the first Olympics to be broadcast live across Europe, while Tokyo 1964 was the first to reach a worldwide audience. Colour pictures arrived four years later at the 1968 Mexico City Games. All records listed on our website are current and up-to-date. For a full list of record titles, please use our Record Application Search. (You will be need to register / login for access)', 'BBC NEWS | Entertainment | Beverley Sisters\' years in limelight Beverley Sisters\' years in limelight Teddy, Babs and Joy got their first break in an advertising campaign Veteran singing trio The Beverley Sisters, who have been made MBEs in the New Year Honours List, were one of the most popular acts of the 50s and 60s. Joy - born in 1929 - and the twins Babs and Teddie - born in 1932 - were brought up in Bethnal Green in east London. During the Second World War the girls were evacuated to the Midlands. There they secured a contract to become "Bonnie Babies" in an advertising campaign for the bedtime drink Ovaltine. Radio appearances for the BBC followed, and with support from bandleader Glenn Miller they became professional singers. After the war the siblings were given their own TV show, Those Beverley Sisters. Chart success In 1951 the trio signed a recording contract with Columbia Records that helped them become the highest paid female act in the UK. They were the first British female group to break into the US top 10 and enjoyed chart success with Christmas records like Little Drummer Boy and I Saw Mommy Kissing Santa Claus. Other favourites included Bye Bye Love and Always and Forever. Joy married Billy Wright, then captain of the England football team, in 1958 and had two daughters and a son. Teddie also had a daughter. In 2002 they sang for the Queen and the Duke of Edinburgh at a Jubilee concert, while last year saw them take part in D-Day celebrations in Portsmouth. The trio, now in their 70s, still perform but have had to cut back their workload due to health concerns.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1536] # 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 | 0.9084 | | **spearman_cosine** | **0.9121** | | pearson_manhattan | 0.9169 | | spearman_manhattan | 0.9125 | | pearson_euclidean | 0.9173 | | spearman_euclidean | 0.9125 | | pearson_dot | 0.8982 | | spearman_dot | 0.8947 | | pearson_max | 0.9173 | | spearman_max | 0.9125 | #### Binary Classification * Dataset: `allNLI-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.7188 | | cosine_accuracy_threshold | 0.8346 | | cosine_f1 | 0.6095 | | cosine_f1_threshold | 0.6868 | | cosine_precision | 0.5 | | cosine_recall | 0.7803 | | cosine_ap | 0.5836 | | dot_accuracy | 0.7168 | | dot_accuracy_threshold | 792.0056 | | dot_f1 | 0.613 | | dot_f1_threshold | 633.9556 | | dot_precision | 0.4913 | | dot_recall | 0.815 | | dot_ap | 0.57 | | manhattan_accuracy | 0.7207 | | manhattan_accuracy_threshold | 559.5651 | | manhattan_f1 | 0.6072 | | manhattan_f1_threshold | 784.5065 | | manhattan_precision | 0.4799 | | manhattan_recall | 0.8266 | | manhattan_ap | 0.5844 | | euclidean_accuracy | 0.7168 | | euclidean_accuracy_threshold | 17.9333 | | euclidean_f1 | 0.6025 | | euclidean_f1_threshold | 25.2843 | | euclidean_precision | 0.4721 | | euclidean_recall | 0.8324 | | euclidean_ap | 0.5837 | | max_accuracy | 0.7207 | | max_accuracy_threshold | 792.0056 | | max_f1 | 0.613 | | max_f1_threshold | 784.5065 | | max_precision | 0.5 | | max_recall | 0.8324 | | **max_ap** | **0.5844** | #### Binary Classification * Dataset: `Qnli-dev` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.6797 | | cosine_accuracy_threshold | 0.7501 | | cosine_f1 | 0.6803 | | cosine_f1_threshold | 0.6485 | | cosine_precision | 0.5702 | | cosine_recall | 0.8432 | | cosine_ap | 0.7156 | | dot_accuracy | 0.6816 | | dot_accuracy_threshold | 724.4274 | | dot_f1 | 0.6768 | | dot_f1_threshold | 601.5822 | | dot_precision | 0.5634 | | dot_recall | 0.8475 | | dot_ap | 0.7009 | | manhattan_accuracy | 0.6797 | | manhattan_accuracy_threshold | 667.1035 | | manhattan_f1 | 0.6877 | | manhattan_f1_threshold | 791.0847 | | manhattan_precision | 0.5868 | | manhattan_recall | 0.8305 | | manhattan_ap | 0.7162 | | euclidean_accuracy | 0.6797 | | euclidean_accuracy_threshold | 21.849 | | euclidean_f1 | 0.6843 | | euclidean_f1_threshold | 25.2124 | | euclidean_precision | 0.5861 | | euclidean_recall | 0.822 | | euclidean_ap | 0.7161 | | max_accuracy | 0.6816 | | max_accuracy_threshold | 724.4274 | | max_f1 | 0.6877 | | max_f1_threshold | 791.0847 | | max_precision | 0.5868 | | max_recall | 0.8475 | | **max_ap** | **0.7162** | ## Training Details ### Training Datasets #### negation-triplets * Dataset: negation-triplets * Size: 5,025 training samples * Columns: anchor, entailment, and negative * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | entailment | negative | |:------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------| | um yeah they're they're convenient you know that's that's a | They are convenient. | They are inconvenient. | | The 1978 Formula One season crowned as champion Mario Andretti . | Mario Andretti was the season Champion and the last American to date to win a Formula One race . | Mario Andretti was the season Runner-up and the first American to date to lose a Formula One race. | | a bathroom with urinals sinks and towel dispensers | A couple of urinals mounted to a wall in a restroom. | A couple of urinals not mounted to a wall in a restroom. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 5,025 training samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:----------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------| | The 2018-2019 FA Cup fifth round was on February 19 , 2019 . | The draw for the Fifth Round will take place on 18 February 2019 . | | Shad Moss , who stars in The Fast and the Furious : Tokyo Drift , is also known as Bow Wow . | `` The film stars Lucas Black , Nathalie Kelley , Sung Kang , Shad `` '' Bow Wow '' '' Moss and Brian Tee . '' | | In Drive Angry , Isaiah Oltiano portrays ICE . | She is a waitress at a local bar and has a cheating fiancee Isaiah Oltiano as ICE , whom she abandons to join with Milton to save his granddaughter . * | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------|:--------------------------------------------------------------------| | Most metals exist as solids at room temperature. | Most metals exist in which form at room temperature? | | Monatomic ions form when a single atom gains or loses electrons. | What form when a single atom gains or loses electrons? | | Carbon dioxide chemically weather rocks by creating acids. | How does carbon dioxide chemically weather rocks? | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------| | The small intestine further breaks down the fats through the bile in the gall bladder, which wears out quicker with the large amount of fats. | To solubilize the fats so that they can be absorbed, the gall bladder secretes a fluid called bile into the small intestine. | | Activation energy, which is defined as the amount of energy required to make the reaction start and carry on spontaneously. | The amount of energy required to begin a chemical reaction is known as the activation energy. | | Hydraulic fracturing (fracking) fluid. | Fracking is another term for hydraulic fracturing. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 5,025 training samples * Columns: summary and document * Approximate statistics based on the first 1000 samples: | | summary | document | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | summary | document | |:------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Phil Mickelson has ended his 25-year partnership with caddie Jim Mackay. | Mickelson won five majors and 41 PGA Tour titles with Mackay - nicknamed 'Bones' - on his bag.
The 47-year-old said the separation was "mutual" and confirmed his brother, Tim, will take over from Mackay.
"Our decision is not based on a single incident. We just feel it's the right time for change. Bones is one of the most knowledgeable and dedicated caddies in the world," said Mickelson.
"The next player to work with him will obviously be very lucky. My relationship and history with Bones far exceeds golf. He has been one of the most important and special people in my life since the day we met, and I will always be grateful for everything he has done for me."
Mickelson has earned in excess of $80m (£63m) in prize money since working with Mackay. He has won the Masters three times, the Open Championship and the US PGA, as well as finishing second or tied for second six times at golf's other major, the US Open.
Mackay was at Erin Hills last week to prepare for the US Open in the event Mickelson was able to take part in the tournament, which clashed with his daughter's graduation ceremony.
In a statement Mackay said: "When Phil hired me in 1992, I had one dream: to caddie in a Ryder Cup. Last year, at Hazeltine, Phil played in his 11th straight Ryder Cup. It was so cool to have a front-row seat.
"I wish Phil nothing but the best. His game is still at an elite level, and when he wins in the future (definitely the Masters), I will be among the first to congratulate him." | | Ice cream maker Ben & Jerry's has recalled tubs of one of its best-selling products over concern they may contain small pieces of metal. | Some consumers who bought 500ml cartons of Cookie Dough are being told not to eat it and instead throw it away.
The company has issued an alert over four batches of the ice cream which it says could be affected.
Tubs affected have codes L62110L011, L62111L011, L62112L011 and L62113L011 printed on the bottom of the pots.
The company issued an "important safety notice" after internal quality assurance checks showed that a limited number of products could be affected.
Ben & Jerry's said: "The company has identified a specific production period during which Ben & Jerry's Cookie Dough 500ml may have been affected and, as safety remains a top priority, Ben & Jerry's is voluntarily recalling four batch codes of Cookie Dough 500ml from sale.
"As a precaution, everyone with a 500ml tub of Ben & Jerry's Cookie Dough in their freezers at home should check the batch number on the bottom of their tub to make sure it's not affected.
"And, if it matches the batch numbers listed above, they should not eat the product and, instead, we ask them to discard the product in the usual household bin."
It is unclear how many pots of ice cream could be affected.
The company said that consumers who have to throw away a tub of the ice cream can call 0800 146 252 to find out how to receive a voucher for a free pot. | | Plans to redevelop the former British Sugar factory site that could create more than 400 jobs in Worcestershire have been approved. | Developers St Francis Group submitted plans
to Wyre Forest District Council for the Stourport Road site in April.
The first phase of the plans includes 200 homes, a care home, medical centre, hotel, shops and restaurants.
A spokesperson for the project, called Silverwoods, said building work is likely to start next year.
The second phase would include more homes and the construction of a link road in the town to the Worcester Road, the spokesperson said.
The developers bought the former sugar beet processing and production plant in 2006. British Sugar closed its Kidderminster operation in 2004.
All the buildings on the site have been knocked down apart from 70 metre (230ft) twin silos, which will soon be demolished.
The redevelopment is part of the South Kidderminster Enterprise Park. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Digestive enzymes secreted in the acidic environment (low ph) of the stomach help break down what? | Digestive enzymes secreted in the acidic environment (low pH) of the stomach help break down proteins into smaller molecules. The main digestive enzyme in the stomach is pepsin , which works best at a pH of about 1.5. These enzymes would not work optimally at other pHs. Trypsin is another enzyme in the digestive system, which breaks protein chains in food into smaller parts. Trypsin works in the small intestine, which is not an acidic environment. Trypsin's optimum pH is about 8. | | How much can the bacteria in your gut weigh? | Lastly, keep in mind the small size of bacteria. Together, all the bacteria in your gut may weight just about 2 pounds. | | What are the outpocketings of the digestive tract that remove nitrogenous wastes and function in osmoregulation? | | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Phototropism means growing toward what? | Plants exhibit phototropism, or growing toward a light source.. Green plants use light as their primary energy source.
Phototropism means growing toward the energy of light | | What can cause a thermal conductor to become hot? | if a thermal conductor is exposed to a source of heat then that conductor may become hot. Radiant heat is the way the sun heats the planet.
if a thermal conductor is exposed to the sun then that conductor may become hot | | What kind of feet do echinoids have? | Echinoderms have a unique water vascular system with tube feet.. Another group of echinoderms are the echinoids.
echinoids have tube feet | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 3,029 training samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | Which statement is true? | aluminum is a nonrenewable resource | | Water in a sealed jar might do what when placed in the freezer? | when water freezes , that water expands | | When trees are harvested, the trees | cutting down trees in a forest causes the number of trees to decrease in that forest | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### 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: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | airman leadership school college credit hours | Master Sgt. Joseph P. Cook, the commandant of James M. McCoy Airman Leadership School at Offutt said that the leadership course consists of 192 hours where airmen will also receive nine college credit hours towards their Community College of the Air Force degree. | | how to use a magnehelic gauge | Here’s how to use your Magnehelic Gauge: 1) Prior to cleaning the dryer vent, install our SmartTap Fitting in the transition hose (see pic to the right). Using the same tubing from the LintAlert, connect the SmartTap Fitting to the High Pressure barb on the magnehelic gauge.2) Turn the dryer ON and take note of the high pressure reading on the gauge. 3) After cleaning the dryer vent, turn the dryer back ON and see how much the back pressure has been reduced.sing the same tubing from the LintAlert, connect the SmartTap Fitting to the High Pressure barb on the magnehelic gauge. 2) Turn the dryer ON and take note of the high pressure reading on the gauge. 3) After cleaning the dryer vent, turn the dryer back ON and see how much the back pressure has been reduced. | | cbs television stations phone number | CBS2/KCAL9 is part of CBS Television Stations, a division of CBS Corp. and one of the largest network-owned station groups in the country. CBS Studio City Broadcast Center. 4200 Radford Avenue. Studio City, CA 91604 (818) 655-2000. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### 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: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what the folds in a chefs hat mean | Chef's uniform The toque is a chef's hat that dates back to the 16th century.[1][2] Different heights may indicate rank within a kitchen,[1] and they are designed to prevent hair from falling into the food when cooking.[2] The 100 folds of the toque are said to represent the many different ways a chef knows to cook an egg.[citation needed] | | when did brian come back to family guy | Brian Griffin As a character, Brian has been very well received by critics and fans. When Brian was killed off in the season 12 episode "Life of Brian", the events of the episode received substantial attention from the media and elicited strongly negative reactions from fans of the show. Brian subsequently returned two episodes later, in "Christmas Guy", after Stewie travels back in time to save him.[1][2] | | who plays bart in i can only imagine movie | I Can Only Imagine (film) I Can Only Imagine is a 2018 American Christian drama film directed by the Erwin Brothers and written by Alex Cramer, Jon Erwin, and Brent McCorkle, based on the story behind the MercyMe song of the same name, the best-selling Christian single of all time.[3] The film stars J. Michael Finley as Bart Millard, the lead singer who wrote the song about his relationship with his father (Dennis Quaid). Madeline Carroll, Priscilla Shirer, Cloris Leachman, and Trace Adkins also star. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### 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: 5,025 training samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | The song Diamonds are a Girl’s Best Friend comes from which musical? | Marilyn Monroe — Diamonds Are a Girl's Best Friend — Listen, watch, download and discover music for free at Last.fm Monroe's rendition of the song "Diamonds Are a Girl's Best Friend" (in Gentlemen prefer Blondes) is considered an iconic performance that has been copied by the likes of Madonna, Geri Haliwell, Kylie Minogue, Nicole Kidman and Anna Nicole Smith. Don't want to see ads? Subscribe now Similar Tracks | | What is the Internet country code top-level domain (ccTLD) for Armenia? | .AM Domains • Armenia Domain Names Why choose a .am domain extension? .AM has taken off as a great extension for podcast and audio-related websites. The domain is perfect for radio broadcasting, internet-only radio stations, and broadcasting service providers. Useful for domain hacks, such as greeneggsandh.AM or shaz.AM Why should I register a .AM domain name? Registering ccTLDs offers another way to protect your online identity, as well as your business’ identity. We all know how important it is to protect our personal identity. The same should be true when considering the identity of your business or trademark online. Your assets are important whether in the form of a bank account or a domain name, and they need to be protected. When you register your trademark in a ccTLD domain, you are taking one more step to protect what is critical to you and your livelihood. By purchasing your trademark in a ccTLD, you do not have to worry about a competitor acquiring that name. Think how much easier it is to be the original owner instead of attempting to procure the domain from another party. Are there any restriction for registering a .AM? .AM has no restrictions for registering a domain. Any person from any part of the world and/or any company is welcome to register. However, every domain is subject to review. For religious reasons, Armenian law prohibits its domain names from being used for obscene sites. The right to revoke registrations due to obscenity or other illegal or immoral activity is reserved by the registry. More information | | Na2SO4 is the chemical formula for what compound? | What compound is Na2so4? | Reference.com What compound is Na2so4? A: Quick Answer The compound Na2SO4 is called sodium sulfate. It occurs naturally in many places in the world, including Canada, Mexico, China, South Africa and Spain. It exists in Washington, Wyoming, Nevada and Utah in the United States. Full Answer Sodium sulfate is an important compound in the chemical industry and is used widely in the manufacturing of soap and detergent. Sodium sulfate develops naturally when igneous rocks erode and release sodium into nearby water. When that sodium reacts with sulfur from one of a number of possible sources in the environment, sodium sulfate is the resulting precipitate. The mineral thenardite, a naturally occurring sodium sulfate, occurs in salt lakes, such as the Great Salt Lake in Utah. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how much weight should i gain during each month of pregnancy? | Underweight women should gain 28 to 40 pounds. And overweight women may need to gain only 15 to 25 pounds during pregnancy. In general, you should gain about 2 to 4 pounds during the first three months you're pregnant and 1 pound a week during the rest of your pregnancy. | | what is difference between rebuild index and reorganize in sql server? | REORGANIZE is a pure cleanup operation which leaves all system state as is. There are a number of differences. Basically, rebuilding is a total rebuild of an index - it will build a new index, then drop the existing one, whereas reorganising it will simply, well... it will reorganise it. | | how much cash can i withdraw from a bank? | Canadian banks are legally obligated to report all cash transactions above 10k to FINTRAC, the financial crime intelligence agency. Making multiple deposits/withdrawals under 10k individually, but above 10k together is 100% illegal, even if the reason is legal/legitimate for needing that money. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 5,025 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------| | The branch codice 2 is updated daily , the codice 3 branch is updated every 6 months . | The codice _ 2 branch gets updated daily , and the codice _ 3 branch is updated for every 6 months . | | It ended in 1955 , shortly after the new mayor Norris Poulson opened all new public housing in the city . | It ended in 1955 , shortly after new mayor Norris Poulson opened all new public housing in the city . | | In July 2011 , ARTC transferred the responsibility for the Werris Creek to North Star to the Country Rail Infrastructure Authority . | In July 2011 , responsibility for the Werris Creek to North Star line was transferred from the ARTC to the Country Rail Infrastructure Authority . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### global_dataset * Dataset: global_dataset * Size: 36,293 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------| | Dallas Stars goalie Kari Lehtonen, who has missed four games with a back problem, returned to practice on Wednesday. | Kari Lehtonen returns to practice | | Four children born at one birth are called quadruplets. | What are four children born at one birth called? | | Her movies, including Captain America: Civil War and the Coen Brothers' Hail Caesar!, earned $1.2bn (£978m) globally over the past 12 months.
Johansson's Marvel co-stars Chris Evans and Robert Downey Jr tied for second place with $1.15bn (£938m) each.
Last year, the list was topped by Chris Pratt while, in 2014, Jennifer Lawrence took the top honours.
Forbes curates the annual list by adding up global ticket sales using data from analysis site Box Office Mojo.
Animated movies, where only actors' voices were used, are not included and only top-billed performances are counted.
Johansson is joined in the top five by two other actresses - Australian Margot Robbie, who starred in the successful yet critically mauled Suicide Squad, which grossed $746m ($609m) worldwide, and Amy Adams.
Adams earns her place through her current film, sci-fi hit Arrival, and Batman v Superman: Dawn of Justice, in which she played Lois Lane.
British actress Felicity Jones made her debut on the list after making $805m (£656m) thanks, largely, to Rogue One: A Star Wars Story, only released earlier this month.
She also starred in fantasy film drama A Monster Calls and alongside Tom Hanks in Inferno. | Scarlett Johansson has been named as the top grossing actor of 2016 by Forbes magazine. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` ### Evaluation Datasets #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 128 evaluation samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dragon Con had over 5000 guests . | Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell . | | COVID-19 has reached more than 185 countries . | As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths . | | In March , Italy had 3.6x times more cases of coronavirus than China . | As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### negation-triplets * Dataset: negation-triplets * Size: 128 evaluation samples * Columns: anchor, entailment, and negative * Approximate statistics based on the first 1000 samples: | | anchor | entailment | negative | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | entailment | negative | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-------------------------------------------------------------| | A cafeteria type kitchen that is not in use. | A commercial kitchen with pots several pots on the stove. | A commercial kitchen with no pots on the stove. | | The sightseeing boat streams along the river joined by a plane | a photo of a boat with an airplane in back | a photo of a boat without an airplane in back | | Three men, one holding pipes, another holding a large object above his head, and one resting against the pipe bed on the truck, are looking at the camera. | three men look at the camera | three men ignore the camera | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | | A solution is a homogenous mixture of two or more substances that exist in a single phase. | Solution is the term for a homogeneous mixture of two or more substances. | | Upwelling The physical process in near-shore ocean systems of rising of nutrients and colder bottom waters to the surface because of constant wind patterns along the shoreline. | Upwelling is the term for when deep ocean water rises to the surface. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | The term elastic potential energy is used to describe potential energy due to an object’s shape. | What term is used to describe potential energy due to an object’s shape? | | Structures that protect the coast like barrier islands are called breakwaters. | What are structures that protect the coast like barrier islands called? | | The urinary system system controls the amount of water in the body and removes wastes. | Which human body system controls the amount of water in the body and removes wastes? | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### xsum-pairs * Dataset: xsum-pairs * Size: 128 evaluation samples * Columns: summary and document * Approximate statistics based on the first 1000 samples: | | summary | document | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | summary | document | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | The Russian and Ukrainian leaders have had a telephone conversation over the fate of jailed high-profile prisoners, raising the possibility of a swap. | The call came after Ukraine jailed two alleged Russian special forces soldiers for several crimes including terrorism.
Yevgeny Yerofeyev and Alexander Alexandrov are accused of involvement in Ukraine's conflict with pro-Russian rebels in the east.
A Russian court jailed Ukrainian Nadiya Savchenko for 22 years in March.
She was captured by eastern separatists in 2015 and accused of directing artillery fire that killed two Russian journalists.
Relations between Russia and Ukraine badly deteriorated following Moscow's annexation of the Crimea peninsula in 2014 and its alleged support for pro-Russian rebels in eastern Ukraine.
Russian President Vladimir Putin spoke with Ukrainian counterpart Petro Poroshenko on Monday night, the Kremlin said.
During the phone call, the pair agreed Savchenko would be allowed a consular visit "in the very near future", according to a statement.
Her trial caused an international outcry and her lawyers have said her condition has deteriorated after she started a hunger strike in protest against her treatment.
On Monday a court in Kiev found Yerofeyev and Alexandrov guilty of waging an "aggressive war" against Ukraine, committing a terrorist act and using weapons to provoke an armed conflict.
They were sentenced to 14 years in jail.
Russia has always denied sending troops to eastern Ukraine and said the men were volunteers who had left active service.
Mr Poroshenko has previously proposed swapping the two Russian men for Savchenko. | | Police in the far south of Italy have arrested two top fugitives - 'Ndrangheta mafia bosses who were hiding in a camouflaged bunker. | Giuseppe Ferraro, 47, had been on the run since 1998, and Giuseppe Crea, 37, since 2006. Vegetation concealed their mountain hideout in Reggio Calabria.
Various weapons were seized in the small bunker, which also had cooking equipment and electricity.
The convicted pair have been linked to gangland murders and other crimes.
The police raid took place near the town of Maropati.
The 'Ndrangheta controls much of the world's cocaine trade. Last year Italian police seized several billion euros' worth of 'Ndrangheta assets.
The pair were "living like animals... cut off from society", according to prosecutor Federico Cafiero De Raho, quoted by the AFP news agency. But they were still controlling other gang members from the bunker, he said. | | The Scottish child abuse inquiry will investigate care establishments run by Catholic organisations as part of the second phase of its hearings. | The inquiry will begin its investigation in the autumn of five homes run by the Daughters of Charity of St Vincent de Paul.
In 2018, it will look into a further four children's homes run by the Sisters of Nazareth.
It has asked people with experience of the homes to contact them.
The inquiry is continuing to privately take statements from abuse survivors in Scotland, and will hold its first public sessions in May.
More than 60 institutions, including several top private schools, are being investigated by the inquiry, which is expected to last for four years.
Other institutions being investigated include those run by faith-based organisations and major care providers like Quarriers and Barnardo's.
Daughters of Charity of St Vincent de Paul
• Smyllum Park in Lanark
• Bellevue House in Rutherglen
• St Joseph's Hospital in Rosewell
• St Vincent's School for the Deaf/Blind in Glasgow
• Roseangle Orphanage (St Vincent's) in Dundee.
Sisters of Nazareth
• Nazareth House in Aberdeen
• Nazareth House in Cardonald
• Nazareth House in Kilmarnock
• Nazareth House in Lasswade | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which reproduction produces offspring genetically identical to the one parent? | That is the main difference between sexual and asexual reproduction. Sexual reproduction just means combining genetic material from two parents. Asexual reproduction produces offspring genetically identical to the one parent. | | Transition metals are superior conductors of heat as well as what else? | Transition metals are superior conductors of heat as well as electricity. They are malleable, which means they can be shaped into sheets, and ductile, which means they can be shaped into wires. They have high melting and boiling points, and all are solids at room temperature, except for mercury (Hg), which is a liquid. Transition metals are also high in density and very hard. Most of them are white or silvery in color, and they are generally lustrous, or shiny. The compounds that transition metals form with other elements are often very colorful. You can see several examples in the Figure below . | | Alkanes are nonpolar and therefore do not attract what? | Alkanes are nonpolar; they do not attract ions. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What might fall on land may soak into the ground, becoming groundwater? | Some precipitation that falls on land may soak into the ground, becoming groundwater.. Hail, rain, sleet, and snow are referred to as precipitation .
Hail that falls on land may soak into the ground, becoming groundwater. | | what shares some of the same structures and basic functions? | All cells share some of the same structures and basic functions.. Hair cells are also a type of skin cell.
hair and skin share some of the same structures and basic functions | | Mutation leads to _. | Mutation creates new genetic variation in a gene pool.. Genetic variation is the raw material for evolution.
Mutation leads to evolution. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 128 evaluation samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | The thermal production of a stove is generically used for | a stove generates heat for cooking usually | | What creates a valley? | a valley is formed by a river flowing | | when it turns day and night on a planet, what cause this? | a planet rotating causes cycles of day and night on that planet | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### 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: 128 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 heavy periods called | When you get your period, it may seem like a lot of blood loss. But because it’s impossible (or at least impractical) to actually measure your menstrual flow, it can be hard to know whether yours falls within what’s considered a normal amount — or if it indicates heavy menstrual bleeding, called menorrhagia. | | largest baby born vaginally | In March, a British mother gave birth vaginally to a 15 lb., 7 oz. baby boy named George. According to the Guinness Book of World Records, the heaviest baby ever was born to Anna Bates of Canada in 1879. The 7'5 mother and her 7'11 husband welcomed a 23 lb. 12 oz. baby boy, but he died 11 hours later. | | singapore average manager salary | (Singapore). The average salary for an Information Technology (IT) Manager is S$83,116 per year. Skills that are associated with high pay for this job are UNIX, Information Technology Infrastructure Library (ITIL), and Linux. Experience has a moderate effect on income for this job. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### 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: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | when is the mother day celebrated every year | Mother's Day Mother's Day is a celebration honoring the mother of the family, as well as motherhood, maternal bonds, and the influence of mothers in society. It is celebrated on various days in many parts of the world, most commonly in the months of March or May. It complements similar celebrations honoring family members, such as Father's Day, Siblings Day, and Grandparents Day. | | who wrote the book of john the gospel | Gospel of John Although the Gospel of John is anonymous,[1] Christian tradition historically has attributed it to John the Apostle, son of Zebedee and one of Jesus' Twelve Apostles. The gospel is so closely related in style and content to the three surviving Johannine epistles that commentators treat the four books,[2] along with the Book of Revelation, as a single corpus of Johannine literature, albeit not necessarily written by the same author.[Notes 1] | | where does the blackstone river start and end | Blackstone River The river is formed in Worcester, Massachusetts by the confluence of the Middle River and Mill Brook. From there, it follows a rough southeast course through Millbury, Sutton, Grafton, Northbridge, Uxbridge, Millville, and Blackstone. It then continues into Rhode Island, where it flows through Woonsocket, Cumberland, Lincoln, Central Falls, and Pawtucket, where the river then reaches Pawtucket Falls. After that, the river becomes tidal, and flows into the Seekonk River just north of Providence. Other tributaries join the Blackstone along the way, such as the West and Mumford River, at Uxbridge, and The Branch River in North Smithfield. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### 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: 128 evaluation samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:---------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which county cricket club is based at Grace Road? | Leicestershire County Cricket Club Leicestershire County Cricket Club 0116 283 2128 (Main Switchboard) 0116 2440363 © 2017 by Leicestershire County Cricket Club . All Rights Reserved. Website Design and Build by threebit | | In food, ‘E’ numbers 110 – 119 are which colour? | Food-Info.net : E-numbers : E100- E200 Food Colours E100- E200 Food Colours Alfa-, Beta- and Gamma- Carotene Natural orange-yellow colour | | What orbits the Sun between Earth and Mercury? | What are the orbital lengths and distances of objects in our solar system? How do objects in space travel? What are the orbital lengths and distances of objects in our solar system? Space is huge, and even our immediate environment is gigantic. We are the third planet from the Sun, and the third of three inner planets, all of which are right next to the Sun compared to others. The picture below shows the planets in their orbits on the orbital plane. You have to look carefully to see our home. The four inner planets (Mercury, Venus, Earth and Mars) are in the tiny disk in the center, inside of Jupiter's orbit. Image from The Nine Planets, a Multimedia tour of the Solar System by Bill Arnett http://seds.lpl.arizona.edu/nineplanets/nineplanets/nineplanets.html The planets are far from the Sun, travel huge distances in space, and take a long time to do so. Pluto takes almost 250 years to go around the Sun completely and travels almost 23 billion miles to do so! OBJECT | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | are chewing gums banned in singapore? | Chewing gum ban in Singapore. ... Since 2004, an exception has existed for therapeutic, dental, nicotine chewing gum, which can be bought from a doctor or registered pharmacist. It is currently not illegal to chew gum in Singapore, merely to import it and sell it, apart from the aforementioned exceptions. | | how to change father name in uan account online? | To update an EPF member's name & Date of Birth, the EPFO has launched a new online provision. However, to update or correct EPF member's Father's name then (as of now) there is no online facility to get this done. The PF member has to contact his/her employer, submit a joint declaration form to them. | | how many calories can i burn from swimming? | A 130-pound person swimming freestyle for one hour will burn 590 calories swimming fast, and 413 calories swimming slower. A 155-pound person swimming freestyle for one hour will burn 704 calories swimming fast, and 493 calories swimming slower. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### paws-pos * Dataset: [paws-pos](https://huggingface.co/datasets/google-research-datasets/paws) at [161ece9](https://huggingface.co/datasets/google-research-datasets/paws/tree/161ece9501cf0a11f3e48bd356eaa82de46d6a09) * Size: 128 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | They were there to enjoy us and they were there to pray for us . | They were there for us to enjoy and they were there for us to pray . | | After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month . | In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month . | | From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born . | Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council . | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` #### global_dataset * Dataset: global_dataset * Size: 325 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | cardiotonics definition | Define cardiotonic: tending to increase the tonus of heart muscle — cardiotonic in a sentence tending to increase the tonus of heart muscle… See the full definition | | After the hosts had a goal disallowed when Ben Strevens was flagged offside, Cheek headed in the opener in the 27th minute.
Graham Stack kept Eastleigh in the game by producing a fine double save to deny Reece Hall-Johnson twice on the stroke of half-time.
But Cheek added a second in the 73rd minute to move Braintree seven points above the relegation zone and leave Eastleigh without a win in 2017.
Report supplied by the Press Association.
Match ends, Eastleigh 0, Braintree Town 2.
Second Half ends, Eastleigh 0, Braintree Town 2.
Substitution, Braintree Town. Lee Barnard replaces Michael Cheek.
Jake Goodman (Braintree Town) is shown the yellow card.
Substitution, Braintree Town. Sam Corne replaces Kyron Farrell.
Substitution, Braintree Town. Ian Gayle replaces Sean Clohessy.
Goal! Eastleigh 0, Braintree Town 2. Michael Cheek (Braintree Town).
Sean Clohessy (Braintree Town) is shown the yellow card.
Ben Strevens (Eastleigh) is shown the yellow card.
Substitution, Eastleigh. Sam Muggleton replaces Tyler Garrett.
Substitution, Eastleigh. James Constable replaces Ben Close.
Second Half begins Eastleigh 0, Braintree Town 1.
First Half ends, Eastleigh 0, Braintree Town 1.
Substitution, Eastleigh. Ayo Obileye replaces Reda Johnson.
Goal! Eastleigh 0, Braintree Town 1. Michael Cheek (Braintree Town).
Hakeem Odoffin (Eastleigh) is shown the yellow card.
First Half begins.
Lineups are announced and players are warming up. | Michael Cheek scored twice as Braintree boosted their National League survival hopes with victory over struggling Eastleigh. | | is eeyore at disney world? | At Walt Disney World, Eeyore can be sometimes be met in the Magic Kingdom next to the Pooh attraction, but not all the time. Eeyore features everyday, all day, at the Crystal Palace Buffet on Main Street. ... In California, Eeyore can usually be found in Critter Country near the Pooh attraction. | * Loss: [CachedGISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) 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': 0.025} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 128 - `learning_rate`: 2e-05 - `weight_decay`: 0.001 - `num_train_epochs`: 2 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 4.000000000000001e-06} - `warmup_ratio`: 0.3 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa3-0.4B-ST-v1-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`: 64 - `per_device_eval_batch_size`: 128 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 2 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_min_lr - `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 4.000000000000001e-06} - `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/DeBERTa3-0.4B-ST-v1-checkpoints-tmp - `hub_strategy`: all_checkpoints - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | openbookqa pairs loss | trivia pairs loss | scitail-pairs-pos loss | nq pairs loss | negation-triplets loss | qasc pairs loss | gooaq pairs loss | xsum-pairs loss | scitail-pairs-qa loss | paws-pos loss | msmarco pairs loss | vitaminc-pairs loss | global dataset loss | sciq pairs loss | Qnli-dev_max_ap | allNLI-dev_max_ap | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:---------------------:|:-----------------:|:----------------------:|:-------------:|:----------------------:|:---------------:|:----------------:|:---------------:|:---------------------:|:-------------:|:------------------:|:-------------------:|:-------------------:|:---------------:|:---------------:|:-----------------:|:------------------------:| | 0.0102 | 16 | 7.0465 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0205 | 32 | 9.0583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0307 | 48 | 8.6271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0409 | 64 | 5.6026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0512 | 80 | 4.6908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0614 | 96 | 3.1954 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0716 | 112 | 2.1179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0818 | 128 | 1.2636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0921 | 144 | 0.8325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1004 | 157 | - | 0.8188 | 0.3046 | 0.0716 | 0.9823 | 1.4375 | 0.9191 | 0.4534 | 0.2841 | 0.1112 | 0.0382 | 0.6015 | 3.7648 | 0.4414 | 0.1556 | 0.6679 | 0.5184 | 0.8753 | | 0.1023 | 160 | 0.5674 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1125 | 176 | 0.6077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1228 | 192 | 0.4102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1330 | 208 | 0.4442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1432 | 224 | 0.3306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1535 | 240 | 0.3002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1637 | 256 | 0.2485 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1739 | 272 | 0.274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1841 | 288 | 0.2093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1944 | 304 | 0.3521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2008 | 314 | - | 0.4536 | 0.1331 | 0.0218 | 0.1832 | 0.7851 | 0.1173 | 0.1327 | 0.0783 | 0.0102 | 0.0247 | 0.1522 | 3.2338 | 0.2332 | 0.0630 | 0.7162 | 0.5844 | 0.9121 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.4.0+cu121 - Accelerate: 0.32.1 - Datasets: 2.21.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ```