--- base_model: microsoft/deberta-v3-small datasets: - sentence-transformers/all-nli - jinaai/negation-dataset-v2 - tals/vitaminc - nyu-mll/glue - allenai/scitail - sentence-transformers/xsum - sentence-transformers/sentence-compression - allenai/sciq - allenai/qasc - sentence-transformers/msmarco-msmarco-distilbert-base-v3 - sentence-transformers/natural-questions - sentence-transformers/trivia-qa - sentence-transformers/quora-duplicates - sentence-transformers/gooaq - sentence-transformers/simple-wiki 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 - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - 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:147277 - loss:AdaptiveLayerLoss - loss:GISTEmbedLoss - loss:TripletLoss - loss:OnlineContrastiveLoss - loss:MultipleNegativesSymmetricRankingLoss - loss:MultipleNegativesRankingLoss widget: - source_sentence: how to make gums stop bleeding after wisdom tooth extraction? sentences: - If bleeding continues or begins again, sit upright or in a recliner, avoid physical activity, use ice packs on the sides of the face where surgery was performed and bite gauze for 1 hour or on a moistened tea bag for 30 minutes. The tannic acid in the tea bag helps to form a clot by constricting bleeding vessels. - The heat detector is an instrument that warns of fire when the temperature around the smoke detector reaches a certain level. But heat detectors do not detect smoke. On the other hand, a smoke detector warns of fire when it comes across combustion or soot products in the atmosphere. - '[''diary.'', ''diagnosis.'', ''dialogue.'', ''diameter.'', ''diagram.'', ''diamond.'', ''diarrhoea.'', ''diagnostic.'']' - source_sentence: 'A row of park benches on the side of a green grassy hill. ' sentences: - A train traveling on a track through a nature park. - A train parking on a track through a nature park. - An airplane is ascending into the white sky - source_sentence: what is a minnesota multiphasic personality inventory test sentences: - Minnesota Multiphasic Personality Inventory The Minnesota Multiphasic Personality Inventory (MMPI) is a standardized psychometric test of adult personality and psychopathology.[1] Psychologists and other mental health professionals use various versions of the MMPI to help develop treatment plans; assist with differential diagnosis; help answer legal questions (forensic psychology); screen job candidates during the personnel selection process; or as part of a therapeutic assessment procedure.[2] - Paulie Paulie is a 1998 American adventure fantasy comedy film about a disobedient bird named Paulie, starring Tony Shalhoub, Gena Rowlands, Hallie Eisenberg, and Jay Mohr. Mohr performs both the voice of Paulie and the on-screen supporting role of Benny, a character who has a lot of dialogue with Paulie. - Sunday Bloody Sunday "Sunday Bloody Sunday" is a song by Irish rock band U2. It is the opening track from their 1983 album War and was released as the album's third single on 21 March 1983 in Germany and the Netherlands.[3] "Sunday Bloody Sunday" is noted for its militaristic drumbeat, harsh guitar, and melodic harmonies.[4] One of U2's most overtly political songs, its lyrics describe the horror felt by an observer of the Troubles in Northern Ireland, mainly focusing on the Bloody Sunday incident in Derry where British troops shot and killed unarmed civil rights protesters and bystanders. At the same time, the lyrics reject hate and revenge as a response, as noted in the line "There's many lost, but tell me who has won." Along with "New Year's Day," the song helped U2 reach a wider listening audience. It was generally well received by critics on the album's release.[5][6] - source_sentence: what is lifelock protection sentences: - Large and in charge. As expected, Apple has unveiled a larger iPhone, and it's called the iPhone 6 Plus. It also happens to be the biggest iPhone to date. The iPhone 6 Plus features a new Retina HD display technology; according to Apple's Phil Schiller, the iPhone 6 Plus and iPhone 6 screens are new in every way. The iPhone 6 Plus measures 5.5 inches, just as was rumored. Its display has a resolution of 1920x1080 with 401ppi. The iPhone 6 Plus also managed to pack two million pixels in its display, over a million more than the iPhone 6. - 'LifeLock Inc. (NYSE:LOCK) is an American identity theft protection company based in Tempe, Arizona. The company offers the LifeLock identity theft protection system, which is intended to detect fraudulent applications for various credit and non-credit related services.In July 2015, CNN headlines FTC: LifeLock protection service doesn''t live up to its name.ifeLock was co-founded in 2005 by Robert J. Maynard and Todd Davis. Amid controversy, Maynard left the company in 2007.' - 'Great Idea: Drama and role play. Drama and role play can be fun and used successfully in any area of the curriculum. Drama is a very valuable tool for exploring issues, making learning memorable, encouraging co-operation and empathy.' - source_sentence: By what name was the Mongol army that finally conquered Bulgaria known? sentences: - It was commissioned by Odo, the Bishop of Bayeux and first Earl of Kent, employing natives from Kent who were learned in the Nordic traditions imported in the previous half century by the Danish Vikings. - The famous cavalry expedition led by Subutai and Jebe, in which they encircled the entire Caspian Sea defeating all armies in their path, remains unparalleled to this day, and word of the Mongol triumphs began to trickle to other nations, particularly Europe. - Newcastle Student Radio is run by students from both of the city's universities, broadcasting from Newcastle University's student's union building during term time. model-index: - name: SentenceTransformer based on microsoft/deberta-v3-small results: - task: type: semantic-similarity name: Semantic Similarity dataset: name: sts test type: sts-test metrics: - type: pearson_cosine value: 0.816293607681843 name: Pearson Cosine - type: spearman_cosine value: 0.8161010743022479 name: Spearman Cosine - type: pearson_manhattan value: 0.8129923580109858 name: Pearson Manhattan - type: spearman_manhattan value: 0.8099359329667263 name: Spearman Manhattan - type: pearson_euclidean value: 0.8022637024623361 name: Pearson Euclidean - type: spearman_euclidean value: 0.8001752377809467 name: Spearman Euclidean - type: pearson_dot value: 0.7700870243703964 name: Pearson Dot - type: spearman_dot value: 0.7831264441454899 name: Spearman Dot - type: pearson_max value: 0.816293607681843 name: Pearson Max - type: spearman_max value: 0.8161010743022479 name: Spearman Max - task: type: triplet name: Triplet dataset: name: negationNLI test type: negationNLI-test metrics: - type: cosine_accuracy value: 1.0 name: Cosine Accuracy - type: dot_accuracy value: 0.0 name: Dot Accuracy - type: manhattan_accuracy value: 1.0 name: Manhattan Accuracy - type: euclidean_accuracy value: 1.0 name: Euclidean Accuracy - type: max_accuracy value: 1.0 name: Max Accuracy - task: type: binary-classification name: Binary Classification dataset: name: mrpc test type: mrpc-test metrics: - type: cosine_accuracy value: 0.7333333333333333 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7723015546798706 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.8137931034482758 name: Cosine F1 - type: cosine_f1_threshold value: 0.7615677118301392 name: Cosine F1 Threshold - type: cosine_precision value: 0.7405857740585774 name: Cosine Precision - type: cosine_recall value: 0.9030612244897959 name: Cosine Recall - type: cosine_ap value: 0.847047462638431 name: Cosine Ap - type: dot_accuracy value: 0.6766666666666666 name: Dot Accuracy - type: dot_accuracy_threshold value: 71.28694915771484 name: Dot Accuracy Threshold - type: dot_f1 value: 0.7967479674796748 name: Dot F1 - type: dot_f1_threshold value: 60.0638427734375 name: Dot F1 Threshold - type: dot_precision value: 0.6621621621621622 name: Dot Precision - type: dot_recall value: 1.0 name: Dot Recall - type: dot_ap value: 0.7597503410099737 name: Dot Ap - type: manhattan_accuracy value: 0.7166666666666667 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 144.24057006835938 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.8079470198675496 name: Manhattan F1 - type: manhattan_f1_threshold value: 158.62255859375 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.7120622568093385 name: Manhattan Precision - type: manhattan_recall value: 0.9336734693877551 name: Manhattan Recall - type: manhattan_ap value: 0.8379421798530551 name: Manhattan Ap - type: euclidean_accuracy value: 0.72 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 6.903799057006836 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.8025751072961373 name: Euclidean F1 - type: euclidean_f1_threshold value: 8.285726547241211 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.6925925925925925 name: Euclidean Precision - type: euclidean_recall value: 0.9540816326530612 name: Euclidean Recall - type: euclidean_ap value: 0.832934238772353 name: Euclidean Ap - type: max_accuracy value: 0.7333333333333333 name: Max Accuracy - type: max_accuracy_threshold value: 144.24057006835938 name: Max Accuracy Threshold - type: max_f1 value: 0.8137931034482758 name: Max F1 - type: max_f1_threshold value: 158.62255859375 name: Max F1 Threshold - type: max_precision value: 0.7405857740585774 name: Max Precision - type: max_recall value: 1.0 name: Max Recall - type: max_ap value: 0.847047462638431 name: Max Ap - task: type: binary-classification name: Binary Classification dataset: name: Vitaminc test type: Vitaminc-test metrics: - type: cosine_accuracy value: 0.5566666666666666 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7603035569190979 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.6635071090047393 name: Cosine F1 - type: cosine_f1_threshold value: 0.5236827731132507 name: Cosine F1 Threshold - type: cosine_precision value: 0.5054151624548736 name: Cosine Precision - type: cosine_recall value: 0.9655172413793104 name: Cosine Recall - type: cosine_ap value: 0.5291811141478275 name: Cosine Ap - type: dot_accuracy value: 0.5633333333333334 name: Dot Accuracy - type: dot_accuracy_threshold value: 92.43148803710938 name: Dot Accuracy Threshold - type: dot_f1 value: 0.6575342465753424 name: Dot F1 - type: dot_f1_threshold value: 56.82046127319336 name: Dot F1 Threshold - type: dot_precision value: 0.49146757679180886 name: Dot Precision - type: dot_recall value: 0.993103448275862 name: Dot Recall - type: dot_ap value: 0.5319629878299388 name: Dot Ap - type: manhattan_accuracy value: 0.5566666666666666 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 171.0796661376953 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.6605922551252847 name: Manhattan F1 - type: manhattan_f1_threshold value: 271.766357421875 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.4931972789115646 name: Manhattan Precision - type: manhattan_recall value: 1.0 name: Manhattan Recall - type: manhattan_ap value: 0.5296616128404239 name: Manhattan Ap - type: euclidean_accuracy value: 0.5466666666666666 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 8.31692123413086 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.662037037037037 name: Euclidean F1 - type: euclidean_f1_threshold value: 12.49872875213623 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.49825783972125437 name: Euclidean Precision - type: euclidean_recall value: 0.9862068965517241 name: Euclidean Recall - type: euclidean_ap value: 0.522357181501639 name: Euclidean Ap - type: max_accuracy value: 0.5633333333333334 name: Max Accuracy - type: max_accuracy_threshold value: 171.0796661376953 name: Max Accuracy Threshold - type: max_f1 value: 0.6635071090047393 name: Max F1 - type: max_f1_threshold value: 271.766357421875 name: Max F1 Threshold - type: max_precision value: 0.5054151624548736 name: Max Precision - type: max_recall value: 1.0 name: Max Recall - type: max_ap value: 0.5319629878299388 name: Max Ap --- # SentenceTransformer based on microsoft/deberta-v3-small This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [nli-pairs2](https://huggingface.co/datasets/sentence-transformers/all-nli), [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [xsum-pairs2](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc), openbookqa_pairs, [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates), [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq), [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq), [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue), [simple_wiki_pairs](https://huggingface.co/datasets/sentence-transformers/simple-wiki) and [simple_wiki_pairs2](https://huggingface.co/datasets/sentence-transformers/simple-wiki) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Datasets:** - [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) - [nli-pairs2](https://huggingface.co/datasets/sentence-transformers/all-nli) - [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) - [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) - [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) - [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) - [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) - [xsum-pairs2](https://huggingface.co/datasets/sentence-transformers/xsum) - [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression) - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) - [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc) - openbookqa_pairs - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions) - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) - [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) - [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) - [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) - [simple_wiki_pairs](https://huggingface.co/datasets/sentence-transformers/simple-wiki) - [simple_wiki_pairs2](https://huggingface.co/datasets/sentence-transformers/simple-wiki) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bobox/DeBERTa-ST-AllLayers-v3.5-checkpoints-tmp") # Run inference sentences = [ 'By what name was the Mongol army that finally conquered Bulgaria known?', 'The famous cavalry expedition led by Subutai and Jebe, in which they encircled the entire Caspian Sea defeating all armies in their path, remains unparalleled to this day, and word of the Mongol triumphs began to trickle to other nations, particularly Europe.', 'It was commissioned by Odo, the Bishop of Bayeux and first Earl of Kent, employing natives from Kent who were learned in the Nordic traditions imported in the previous half century by the Danish Vikings.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Semantic Similarity * Dataset: `sts-test` * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.8163 | | **spearman_cosine** | **0.8161** | | pearson_manhattan | 0.813 | | spearman_manhattan | 0.8099 | | pearson_euclidean | 0.8023 | | spearman_euclidean | 0.8002 | | pearson_dot | 0.7701 | | spearman_dot | 0.7831 | | pearson_max | 0.8163 | | spearman_max | 0.8161 | #### Triplet * Dataset: `negationNLI-test` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:-------------------|:--------| | cosine_accuracy | 1.0 | | dot_accuracy | 0.0 | | manhattan_accuracy | 1.0 | | euclidean_accuracy | 1.0 | | **max_accuracy** | **1.0** | #### Binary Classification * Dataset: `mrpc-test` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:----------| | cosine_accuracy | 0.7333 | | cosine_accuracy_threshold | 0.7723 | | cosine_f1 | 0.8138 | | cosine_f1_threshold | 0.7616 | | cosine_precision | 0.7406 | | cosine_recall | 0.9031 | | cosine_ap | 0.847 | | dot_accuracy | 0.6767 | | dot_accuracy_threshold | 71.2869 | | dot_f1 | 0.7967 | | dot_f1_threshold | 60.0638 | | dot_precision | 0.6622 | | dot_recall | 1.0 | | dot_ap | 0.7598 | | manhattan_accuracy | 0.7167 | | manhattan_accuracy_threshold | 144.2406 | | manhattan_f1 | 0.8079 | | manhattan_f1_threshold | 158.6226 | | manhattan_precision | 0.7121 | | manhattan_recall | 0.9337 | | manhattan_ap | 0.8379 | | euclidean_accuracy | 0.72 | | euclidean_accuracy_threshold | 6.9038 | | euclidean_f1 | 0.8026 | | euclidean_f1_threshold | 8.2857 | | euclidean_precision | 0.6926 | | euclidean_recall | 0.9541 | | euclidean_ap | 0.8329 | | max_accuracy | 0.7333 | | max_accuracy_threshold | 144.2406 | | max_f1 | 0.8138 | | max_f1_threshold | 158.6226 | | max_precision | 0.7406 | | max_recall | 1.0 | | **max_ap** | **0.847** | #### Binary Classification * Dataset: `Vitaminc-test` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:----------| | cosine_accuracy | 0.5567 | | cosine_accuracy_threshold | 0.7603 | | cosine_f1 | 0.6635 | | cosine_f1_threshold | 0.5237 | | cosine_precision | 0.5054 | | cosine_recall | 0.9655 | | cosine_ap | 0.5292 | | dot_accuracy | 0.5633 | | dot_accuracy_threshold | 92.4315 | | dot_f1 | 0.6575 | | dot_f1_threshold | 56.8205 | | dot_precision | 0.4915 | | dot_recall | 0.9931 | | dot_ap | 0.532 | | manhattan_accuracy | 0.5567 | | manhattan_accuracy_threshold | 171.0797 | | manhattan_f1 | 0.6606 | | manhattan_f1_threshold | 271.7664 | | manhattan_precision | 0.4932 | | manhattan_recall | 1.0 | | manhattan_ap | 0.5297 | | euclidean_accuracy | 0.5467 | | euclidean_accuracy_threshold | 8.3169 | | euclidean_f1 | 0.662 | | euclidean_f1_threshold | 12.4987 | | euclidean_precision | 0.4983 | | euclidean_recall | 0.9862 | | euclidean_ap | 0.5224 | | max_accuracy | 0.5633 | | max_accuracy_threshold | 171.0797 | | max_f1 | 0.6635 | | max_f1_threshold | 271.7664 | | max_precision | 0.5054 | | max_recall | 1.0 | | **max_ap** | **0.532** | ## Training Details ### Training Datasets #### nli-pairs * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 4,689 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------| | middle-aged women (maroon shirt) holding child (white shirt) and a young women (green shirt) in the not too far distance. | She is holding the child. | | A gray bird is flying through the trees. | An animal is flying through some trees. | | A woman in a black suit stands smiling in front of another woman in a red jacket. | A woman is posing for a picture. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### nli-pairs2 * Dataset: [nli-pairs2](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 2,310 training samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------| | Two children are enjoying themselves on a trampoline. | Two kids are playing outside. | | A man in a blue shirt with glasses and a shaved down head is looking at a microscope in a laboratory. | The man is a scientist. | | A Boston Celtics fan having lunch before the game between Boston and Lakers. | The fan is eating lunch between games. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### negation-triplets * Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) * Size: 9,100 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 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------|:------------------------------------------------------------| | BUDAPEST, Hungary Andor Lilienthal, the last surviving member of 27 original grandmaster chess players, died Saturday in Budapest at the age of 99, the Hungarian Chess Federation said. | Andor Lilienthal, chess grandmaster, dies at 99 | Andor Lilienthal, chess novice, lives past 99 | | The press is right to focus on character. | The press focuses on character in their coverage. | The press ignores character in their coverage. | | Yakup Barokas is its editor-in-chief . | Yakup Barokas is its editor in chief . | Yakup Barokas is its assistant editor. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "TripletLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 1.1 } ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 8,049 training samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Celebrity Skin 's three tracks topped the Hot Modern Rock Tracks chart . | In the United State , three songs appeared on various Billboard charts , with all three achieving the greatest success on the Hot Modern Rock Tracks chart . | | Dos Santos fought against Ben Rothwell on 10 April 2016 . | Dos Santos is expected to face Ben Rothwell on April 10 , 2016 at UFC Fight Night 86. | | Susan Blakely was born before 1949 . | Susan Blakely ( born September 7 , 1948 ) is an American actress and model . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 9,100 training samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | What was Henry Brown's nickname? | The resistance to the slave trade was growing by the mid-nineteenth century; in one famous case in 1848, Henry "Box" Brown made history by having himself nailed into a small box and shipped from Richmond to abolitionists in Philadelphia, Pennsylvania, escaping slavery. | 0 | | Although "Superstorm Sandy" left minimal damage to any of the tourist areas it did cause what two other things to occur? | The source of the misinformation was a widely circulated photograph of a damaged section of the Boardwalk that was slated for repairs, prior to the storm, and incorrect news reports at the time of the disaster. | 0 | | If someone contemplates God forever, what type of knowledge will they obtain? | Thus, contemplation forever produces a mystified, imperfect knowledge of God. | 0 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "OnlineContrastiveLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 1, "kl_div_weight": 1.1, "kl_temperature": 0.9 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 7,807 training samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:-------------------------------------------------------------------|:--------------------------------------------------------------------| | Evidence allows theories to be widely accepted. | What allows theories to be widely accepted? | | The earth's crust is composed of igneuos rock. | What is the earths crust composed of? | | The axis of the plant has a root end and a shoot end. | What part of the plant has a root end and a shoot end? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 8,600 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------| | The right atrium collects blood and the right ventricle then pumps it to the lungs where it is oxygenated. | Blood from the body enters right atrium of the heart before it is pumped to the right ventricle and then to the lungs. | | 1 gram of sugar provides 4 Kcal of energy. | One gram of sugar or starch provides 4 calories of energy. | | enzymes Protein molecules that act as catalysts in biochemical reactions. | In a biological reaction, proteins act as catalysts. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 5,267 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------| | Chaudhry Muhammad Shakeel is accused of murdering Ms Shahid, 28, from Bradford, and Chaudhry Muhammad Shahid is being held as an accessory to murder.
At the hearing the men were presented with the police evidence against them.
They will have the chance to challenge the case on 7 October when the court will decide whether to bring charges.
Both men were remanded back in to custody ahead of their next appearance.
Ms Shahid died in July in northern Punjab. It is thought she had travelled to Pakistan to visit family in the village of Pandori after being told her father was ill.
Her relatives initially said she had suffered a heart attack but a post-mortem examination confirmed she died as a result of being strangled.
Her second husband, Syed Mukhtar Kazim, believes she was the victim of a so-called "honour killing" as her family did not approve of their marriage. | The first husband and father of alleged "honour killing" victim Samia Shahid have appeared in court in Pakistan in connection with her death. | | The 25-year-old American born striker made 37 appearances and scored seven goals during his 12 months at Tynecastle.
Johnsen also made his first international appearance for Norway while a Hearts player.
The Edinburgh club has strengthened up front, signing Kyle Lafferty during this transfer window.
Johnsen was brought to Hearts by former head coach Robbie Neilson after leaving Bulgarian club Litex Lovech.
And Neilson's successor, Ian Cathro, had to deny reports of an argument with the striker at half-time during a game against St Johnstone in May.
Den Haag, managed by Alfons Groenendijk, the former Manchester City midfielder and former Ajax assistant, finished 11th in the Eredivisie last season. | Hearts have sold Bjorn Johnsen to Dutch top-rlight club ADO Den Haag for an undisclosed transfer fee. | | 30 December 2015 Last updated at 12:29 GMT
Fishermen and beach-goers, along with the police and the navy, helped get the 20-metre-long mammal safely back out to deeper water. | A blue whale has been rescued after getting stranded on a beach in the port city of Iquique in northern Chile, South America. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### xsum-pairs2 * Dataset: [xsum-pairs2](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 1,732 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------| | Ms Kumari is the daughter of the maharaja of Dungarpur in the northern state of Rajasthan.
"They knew each other for years, they were childhood friends," Yaduveer's father Swaroopanand Urs told BBC Hindi.
About 1,000 guests witnessed the elaborate ceremony and a further 2,500 were invited to an evening reception.
They include Karnataka state Chief Minister K Siddaramaiah.
The centuries-old wedding traditions involving the royal family took place at Mysore's City Palace.
Yaduveer, 24, was crowned the new maharaja of Mysore, the titular head of the 600-year-old Wadiyar dynasty, in May last year after the death of his grand uncle Maharaja Srikantadatta Narasimharaja Wadiyar.
Srikantadatta Wadiyar, who died in December 2013, was childless and did not name an heir, but his widow Pramodadevi Wadiyar adopted Yaduveer Gopalraj Urs, a relative, to ensure continuity.
Yaduveer's grandaunt, Kamashidevi, said: "It was a typical south Indian Mysorean wedding. There was a small pooja [religious ritual] for the bride before the wedding ceremony began. Normally, in our families, the girl adapts to the traditions of the boy's family."
The couple are the custodian of more than 1,500 acres of land spread across the south Indian cities of Mysore, Bangalore, Hassan, Channapatna and other places in the state of Karnataka.
The CEO of Mysore Palace, M Lakhsminarayana, declined to comment on the cost of the wedding, but added that "there was no extravagance".
Another reception will be held on Saturday in Bangalore, the capital of Karnataka state.
India's royalty lost their official powers when the nation gained independence in 1947 but the modern-day maharajas are still wealthy and influential.
All pictures by Anurag Basavaraj | The maharaja of Mysore in southern India, Yaduveer Krishnadatta Chamaraja Wadiyar, has married princess Trishika Kumari at the royal palace. | | Annette Gration, 58, was told she could not stay at Searles of Hunstanton, as the company had a policy against people staying by themselves.
Mrs Gration, of Skegness, Lincolnshire, was eventually allowed to stay after saying she would be joined by her son.
The company said the issue would be addressed at its next policy review.
Mrs Gration, whose husband Phil died from cancer last July, said she decided to speak out after failing to receive a response to her complaint to Searles.
More on this and other Norfolk stories
She said she went to the camping site last November in her camper van with two friends who were travelling in their own van.
When she arrived she said she was told she was "was not allowed on because I was a single person".
"I felt I was an oddity and not welcome because of my marital status," said Mrs Gration.
"Why was I discriminated against as a single person? I'm quite angry."
Jean McQueen, of Cleethorpes, Lincolnshire, said she had also been told she could not stay as a single person at the camp site.
Searles issued two statements after the BBC contacted them in connection with the concerns.
The spokesman for the firm said they were "prepared to exercise discretion" on issues like single person bookings "if we feel it appropriate."
The "question of single occupancy on touring pitches" would be addressed during its next review of customer feedback, the company added. | A widow says she felt like an "oddity" after she was told she could not stay in a motor home at a Norfolk holiday park because she was single. | | The St David's Day speech was an internet sensation, with mobile phone clips being shared on social media.
He told BBC Wales he was not affiliated to any political party and that "they're all doing terrible jobs on the whole".
Sheen said he would not relish getting involved in party politics.
Asked if he was surprised by the reaction to the speech, he said: "I didn't know it was being filmed.
"It was a cold and very wet day. The fact that anyone turned up at all was amazing and that they stayed around was amazing.
"I think probably because I was so cold and so wet I gave it a little bit of extra oomph maybe, but you know, I was inspired by the fact it was in Tredegar, the home of Nye Bevan, the man who was the architect of the National Health Service, right at the heart of the welfare state and I think it represents something."
Asked if he might be tempted to give up the day job for a political career, Sheen said he was under "no illusions" about what was involved in becoming a politician.
"They're having to do with all kinds of bureaucracy and all kinds of difficulties - it's not something that I relish getting involved in but nevertheless I'm going to say what I feel about what I see whenever I get the chance." | Actor Michael Sheen says the passion displayed in his recent speech on the NHS was inspired by Aneurin Bevan - and the Tredegar weather. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### compression-pairs * Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 4,418 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------| | Roads, bring forth with it development, Sujeewa Mudalige, senior partner PriceWaterhouseCoopers Sri Lanka, an audit firm, told this reporter. | Roads bring development | | Chemical company BASF SE said Wednesday it is temporarily closing 80 plants worldwide due to slumping demand and cutting production at 100 more, including facilities in Texas and Louisiana. | BASF temporarily closing 80 plants | | Big East Commissioner John Marinatto resigned Monday after less than three years on the job and a wave of departures by high-profile schools. | Big East commissioner John Marinatto resigns | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### compression-pairs2 * Dataset: [compression-pairs2](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 2,165 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------| | Brazilian supermodel Gisele Bundchen says she can never imagine retiring from her work and that she will be working hard until the end. | Gisele Bundchen says she will never retire | | Celebrities hit the Gibson Amphitheater at Universal Studios Sunday night in Universal City, California to attend the 2010 MTV Movie Awards, hosted by actor/comedian Aziz Ansari. | Celebrities attend the 2010 MTV Movie Awards: | | Ajmal of Anjathey and Thiru Thiru Thuru fame, is upbeat with Ko's bumper opening. | Ajmal is upbeat | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### compression-pairs3 * Dataset: [compression-pairs3](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 2,165 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------| | Business confidence declined in March, the SA Chamber of Commerce and Industry said on Wednesday as it released its Business Confidence Index. | Business confidence declines in March: | | An army helicopter with two pilots and two soldiers on board went missing during a routine training exercise while flying over Shivmandir area above Yumesamdong in north Sikkim near the Chinese border today, army sources said. | Army helicopter with two pilots, two soldiers missing in Sikkim | | Pleasantville Police are investigating whether a shattered car window could be connected to a shots fired call Friday morning. | Police investigating if shattered car window is connected to shots fired call | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 3, "last_layer_weight": 0.1, "prior_layers_weight": 10, "kl_div_weight": 4, "kl_temperature": 0.25 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 8,750 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Muscles that move what long bone originate on the pelvic girdle? | Gluteal Region Muscles That Move the Femur Most muscles that insert on the femur (the thigh bone) and move it, originate on the pelvic girdle. The psoas major and iliacus make up the iliopsoas group. Some of the largest and most powerful muscles in the body are the gluteal muscles or gluteal group. The gluteus maximus is the largest; deep to the gluteus maximus is the gluteus medius, and deep to the gluteus medius is the gluteus minimus, the smallest of the trio (Figure 11.29 and Figure 11.30). | | Matter can exist either as a pure substance or as a combination of what? | The properties of matter, both physical and chemical, depend on the substances that matter is made of. Matter can exist either as a pure substance or as a combination of different substances. | | Where does the small intestine begin? | The small intestine a is narrow tube that starts at the stomach and ends at the large intestine ( Figure above ). In adults, the small intestine is about 23 feet long. Chemical digestion takes place in the first part of the small intestine. Many enzymes and other chemicals are secreted here. The small intestine is also where most nutrients are absorbed into the blood. The later sections of the small intestines are covered with tiny projections called villi ( Figure below ). Villi contain very tiny blood vessels. Nutrients are absorbed into the blood through these tiny vessels. There are millions of villi, so, altogether, there is a very large area for absorption to take place. In fact, villi make the inner surface area of the small intestine 1,000 times larger than it would be without them. The entire inner surface area of the small intestine is about as big as a basketball court!. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 7,889 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | if an animal lives under ground then that animal will be what from wild combustion? | if an animal lives under ground then that animal will be protected from a wild fire. Fire is a chemical reaction called combustion.. if an animal lives under ground then that animal will be protected from wild combustion | | Flowers produce spores that develop into what? | Flowers produce spores that develop into gametophytes.. Pollen is the gametophyte generation of seed plants.. Flowers produce spores that develop into seed plants | | where does a range of reproductive systems occur? | Roundworms make up the phylum Nematoda.. Within the Nematoda, a range of reproductive systems occur.. within roundworms, a range of reproductive systems occur | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### qasc_facts_sym * Dataset: [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 5,600 training samples * Columns: combinedfact and facts * Approximate statistics based on the first 1000 samples: | | combinedfact | facts | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | combinedfact | facts | |:-------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------| | Mature worms go through a major transformation to develop reproductive organs | Adult worms go through a major transformation to develop reproductive organs.. Mature adult worms are 5cm long.. | | Adult tulip bulbs are barrel-shaped | Adult tunicates are barrel-shaped.. Tulip bulbs are tunicated.. | | Yeast and molds can decompose wood | Fungi are the only organisms that can decompose wood.. Fungi include yeasts, molds, and fleshy fungi.. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 4,505 training samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | What is animal competition? | if two animals eat the same prey then those animals compete for that pey | | If you wanted to make a metal bed frame, where would you start? | alloys are made of two or more metals | | Places lacking warmth have few what | cold environments contain few organisms | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 5,627 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 to subscribe to tunein radio | Share on Facebook Tweet Share Pin Share. TuneIn is sweetening the pot to get you to subscribe to its $7.99 per month premium service. Starting today, subscribers will get access to radio feeds for all NFL games, from the pre-season through the Super Bowl. | | what makes my legs weak | Feeling weak in the knees or experiencing “jelly” or “rubber” legs is a common stress response reaction. Most people in fearful or stressful situations experience weak legs. The degree of weakness often is directly proportional to the degree of fear or stress. | | how long are rn refresher courses in arizona | N:\EDUCATION\Refresher programs\REFRESHER COURSE LIST\2014. 1. ARIZONA STATE BOARD OF NURSING. Approved Refresher Courses. PROFESSIONAL AND PRACTICAL NURSING. The following is a list of all refresher programs and course descriptions currently approved by the Arizona State. Board of Nursing. While the programs appearing on this list are under a four (4) year approval period, be. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### msmarco_pairs2 * Dataset: [msmarco_pairs2](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 2,772 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is a conch? | if it is open end up it is a girl the conch is really a sea animal in a large shell something like a giant snail the meat is edible and used to make conch fritters conch salad and conch chowderthe foods we call island cuisine are actually bahamian in origin foods like conch chowder conch fritters conch steaks and conch saladf it is open end up it is a girl the conch is really a sea animal in a large shell something like a giant snail the meat is edible and used to make conch fritters conch salad and conch chowder | | can pandora rings be engraved | Personalised Pandora style charms which can be personalised with any engraving of your choice. Suitable for all popular types of snake chain bracelets and necklaces. All our charms come with a velour free gift pouch and card.ersonalised Pandora style charms which can be personalised with any engraving of your choice. Suitable for all popular types of snake chain bracelets and necklaces. All our charms come with a velour free gift pouch and card. | | what is the purpose of the investigator brochure | ABSTRACT. The Investigator’s Brochure (IB) is a multidisciplinary document that summarises the main elements of an entire development programme to date. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 2,772 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who has the most around the horn wins | Around the Horn Around the Horn premiered on November 4, 2002.[2] From its premiere until January 2004, the show was hosted by Max Kellerman, who at the time was largely known strictly as a contributor to ESPN's Friday Night Fights. Kellerman departed from the network for Fox Sports[3] [4] and after the show tried out several replacements, current host Tony Reali was named the permanent host in February 2004.[5] As of August 2017, Woody Paige has the most wins in the history of the show, with more than five hundred.[6] Despite early negative reviews due to its argumentative formatting,[7] the show has lasted more than fifteen years on the air, remaining a staple on ESPN.[8] | | when does johnny english strikes again come out | Johnny English Strikes Again Johnny English Strikes Again was scheduled to be released in both the United Kingdom and United States on 12 October 2018 by Universal Pictures;[7][13] the date for America was later moved up to 20 September 2018.[14] | | how many teeth does a zebra shark have | Zebra shark The zebra shark has a cylindrical body with a large, slightly flattened head and a short, blunt snout. The eyes are small and placed on the sides of the head; the spiracles are located behind them and are as large or larger. The last 3 of the 5 short gill slits are situated over the pectoral fin bases, and the fourth and fifth slits are much closer together than the others. Each nostril has a short barbel and a groove running from it to the mouth.[8] The mouth is nearly straight, with three lobes on the lower lip and furrows at the corners. There are 28–33 tooth rows in the upper jaw and 22–32 tooth rows in the lower jaw; each tooth has a large central cusp flanked by two smaller ones.[3] | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### nq_pairs2 * Dataset: [nq_pairs2](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 2,772 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who played the original anne of green gables | Anne of Green Gables (1934 film) Anne of Green Gables is a 1934 film directed by George Nicholls, Jr., based upon the novel, Anne of Green Gables by Lucy Maud Montgomery. The actress Dawn O'Day who portrayed the title character of Anne Shirley changed her stage name to Anne Shirley after making this film. There was also a sequel; Anne of Windy Poplars. | | the irregular at magic high school are they really siblings | The Irregular at Magic High School The story takes place in an alternate history where magic exists and is polished through modern technology. It follows Tatsuya and Miyuki Shiba, siblings who enroll into First High magic high school. While keeping their connections to the infamous Yotsuba clan secret, they attempt to live their daily life in peace where Tatsuya is shunned for his apparent ineptness and Miyuki is validated for her magical abilities. | | where is game 7 of the world series played | 2017 World Series This was the first World Series Game 7 to be played at Dodger Stadium (and the first postseason Game 7 at the stadium since the 1988 NLCS).[91] It was also the first time since the 1931 World Series that a Game 7 occurred in a Series with both teams having won at least 100 games during the season.[92] This was the first time since the 2001 World Series and 2002 World Series that back-to-back Fall Classics had a Game 7.[93] | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 6,790 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:---------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | In humans, plumbism is chronic poisoning due to the absorption of what into the body? | Plumbism | definition of plumbism by Medical dictionary Plumbism | definition of plumbism by Medical dictionary http://medical-dictionary.thefreedictionary.com/plumbism Related to plumbism: lead poisoning lead1  (Pb) [led] a chemical element, atomic number 82, atomic weight 207.19. Excessive ingestion or absorption causes lead poisoning . (See also Appendix 6.) l. poisoning poisoning caused by the presence of lead or lead salts in the body; it affects the brain, nervous system, blood, and digestive system and can be either chronic or acute. Called also plumbism and saturnism . Chronic Lead Poisoning. This was once fairly common among painters, and was called “painter's colic.” It became less frequent as lead-free paints were substituted for lead-based ones and as plastic toys replaced lead ones. The disease is still seen among children with pica (a craving for unnatural articles of food) who may eat lead paint chips or coatings. The Centers for Disease Control and Prevention defines an elevated blood lead level as >10 μg/dL for children younger than six years of age. However, there is evidence that there are subtle effects even at lower levels Symptoms include weight loss, anemia, stomach cramps (lead colic), a bluish black line at the edge of the gums, and constipation. Other symptoms may be mental depression and, in children, irritability and convulsions. In addition to the poisoning, the anemia and weight loss must also be treated, usually by providing an adequate diet. In serious cases, EDTA (calcium disodium edetate) may be prescribed. Acute Lead Poisoning. This rare condition can be caused in two ways: lead may accumulate in the bones, liver, kidneys, brain, and muscles and then be released suddenly to produce an acute condition; or large amounts of lead may be inhaled or ingested at one time. Symptoms are a metallic taste in the mouth, vomiting, bloody or black diarrhea, and muscle cramps. Diagnosis is made by examination of the blood and urine. Treatment. Immediate removal of unabsorbed lead in the intestinal tract through the administration of mild saline cathartics and enemas. EDTA is given and in most cases measures must be taken to reduce the increased intracranial pressure that accompanies acute lead poisoning. Prevention. An awareness of the prevalence of lead poisoning among children of preschool age who live in poorly maintained housing has led to neighborhood screening surveys in high-risk areas. An important aspect of prevention of lead poisoning is determination of sources of lead in the environment and efforts to remove them. Sources include peeling paint from window sills, walls, floors, and bannisters, and from soil around old houses that have shed exterior paint through the years. An often unsuspected source is the glaze of certain pottery and “leaded glass;” lead can leach out into food and beverages from such vessels. A vital factor in coping with the problem of lead contamination is public education and development of a community awareness of possible sources and of the need for elimination of these hazards from the environment. lead poi·son·ing acute or chronic intoxication by lead or any of its salts; symptoms of acute lead poisoning usually are those of acute gastroenteritis in adults or encephalopathy in children; chronic lead poisoning is manifested chiefly by anemia, constipation, colicky abdominal pain, neuropathy with paralysis with wrist-drop involving the extensor muscles of the forearm, bluish lead line of the gums, and interstitial nephritis; saturnine gout, convulsions, and coma may occur. | | Vehicles from which country use the international registration letter L? | Vehicle documents required for international road haulage - GOV.UK GOV.UK Vehicle documents required for international road haulage From: Vehicle documents drivers need to legally cross international borders in a UK-registered vehicle. Contents Further Information When you drive a goods vehicle from one country to another, you must make sure that you have certain documents on board. This guide provides information about the documents you will need as a driver to make sure that your vehicle is legally able to cross international borders. Vehicle registration documents If you take a UK-registered vehicle out of the country for less than 12 months, you must take documentation to show that you are authorised to possess the vehicle. This means you must carry the original Vehicle Registration Certificate (V5C) with you. If you have not received the V5C certificate, or the original has been lost, stolen or defaced, you can download the application for a vehicle registration certificate (V62) . If you take your vehicle out of the UK for more than 12 months (permanent export), you must notify the Driver and Vehicle Licensing Agency ( DVLA ) by completing the purple section, part 11 (V5C/4) of the VC5. It’s important that you take your registration certificate with you as you may have to hand it to the relevant authority when the vehicle is registered abroad. Read about taking a vehicle out of the UK permanently or temporarily . If your vehicle is hired or leased, the supplier company is unlikely to let you have the original VC5. Instead you can apply for a Vehicle on Hire Certificate (VE103). This certificate is authenticated proof of permission from the owner to take the vehicle abroad. A Vehicle on Hire Certificate is valid for one year and you can buy one from motoring organisations such as: Automobile Association (AA) Road Haulage Association Vehicle insurance documents The basic EU legal requirement is third party vehicle insurance. This covers injury to other people, including your passengers, damage to or loss of other peoples’ property resulting from an accident caused by you. It doesn’t cover any costs incurred by you as a result of an accident. Third party, fire and theft provides the same cover as third party but also includes fire damage and theft of the vehicle. Fully comprehensive provides the same cover as third party, fire and theft and additionally covers any damage to your vehicle. Every motor insurance policy issued in the EU must provide the minimum insurance cover required by law in any other EU country. Green Card In many countries, even those within the EU where a UK insurance certificate is acceptable, you may be asked to produce a Green Card. The Green Card is not an insurance cover. It simply provides proof, in those countries where the Green Card is valid, that the minimum third party liability cover required by law in the visited country is in force. If your insurers aren’t able to issue a Green Card, you can find alternative suppliers on the MIB website . The MIB operates the Green Card system in the UK. Insurance for goods in transit In some countries, you may need to produce a certificate of insurance for the goods carried to avoid paying a premium. See the guide on moving goods by road . It’s also important to ensure that the risk of goods being damaged, delayed, perished, lost or stolen in transit is properly managed. See the guide on transport insurance . Goods vehicle operator’s licence To transport goods abroad in an HGV for hire or reward you must have a standard international operators licence. This allows you to carry goods both in the UK and on international journeys. The licence comes into force once the fee has been paid and the licence documents are issued. Providing the 5 yearly renewal fee is paid and there are no infringements, the licence lasts indefinitely. Identity discs are also issued and must be displayed in each specified motor vehicle. The identity discs show the: operator’s name type of licence Community licences A valid Community Licence is required for all hire or reward op | | Which US President went to the same London university as Mick Jagger? | London Universities With Celebrity Students | Spotahome Blog London Universities With Celebrity Students April 29, 2016 Did you share your university with a celebrity? It is highly likely that a celebrity has walked the halls of your university. You may have even used the same desk as your idol. Find out if a celebrity attended YOUR university! London is home to some of the best universities in the world. It is therefore no surprise that several celebrities once chose this location as their place of study.  Spotahome has selected our favourite celebrities who went to a university in London… …Mick Jagger 1943 – Present Celebrity Musician & LSE Student Mick Jagger, otherwise known as the front-man of the Rolling Stones, studied Business at London School of Economics during the 1960s. Somewhat predictably, he dropped out in 1963 to pursue one of the most successful music careers in the history of Rock’n’Roll! …David Attenborough 1926 – Present Nature Documentarist & LSE Student Sir David Attenborough is the nation’s favourite documentary-making celebrity. His soothing voice and exquisitely-captured nature footage has attracted a large following of fans throughout the world.  Attenborough reportedly studied Anthropology at the London School of Economics in 1964, but packed it in for a post as the Director of BBC2. …John F Kennedy   1917 – 1963 Former US President  & LSE Student This may be news for some, but John F Kennedy attended the London School of Economics in 1935. Before returning to the USA, America’s former President apparently attended LSE  to pursue a General course and is arguably one of the university’s most surprising celebrities. …Mahatma Gandhi 1869 – 1948 Leader of Indian Independence & UCL Student Unbeknownst to many, Mahatma Gandhi supposedly studied Law at University College London  before returning to India where he enabled his country to achieve independence. Gandhi is remembered as an activist whose policy of nonviolence influenced many leaders of the civil rights movement. …Vivienne Westwood 1941 – Present Fashion Designer & Student at Middlesex, Westminster & Goldsmith Universities Often praised for merging politics with punk and new wave fashion, Vivienne Westwood is renowned for being one of the most influential celebrity fashion designers of all time. Before launching her fruitful career, Westwood studied at Middlesex University , The University of Westminster and Goldsmiths University . …Nelson Mandela 1918 – 2013 Anti-Apartheid Activist & University of London Student He is not a celebrity in the traditional sense, but Nelson Mandela’s name is among those who studied at the University of London. His path at this institution was certainly unconventional as he studied Law as a distance student during his 27-years of imprisonment. …Gael Garcia Bernal 1978 – Present Actor, Director & Central School of Speech and Drama Graduate Have you seen The Motorcycle Diaries? If so, you may be aware of Gael Garcia Bernal. The Mexican actor/film director graduated from the Central School of Speech and Drama in London – he was the first Mexican to ever attend this institution. …Sir Elton John 1947 – Present Musician & Former Royal Academy of Music Student Before becoming a celebrity musician in the late 1960s, Elton John honed his musical technique at the Royal Academy of Music.  The singer values the inspirational training he received at this institution during his teenage years. …Kit Harington 1986 – Present Actor & Central School of Speech and Drama Student Unless you’ve been living under a rock, you’re probably familiar with this Game of Thrones celebrity. Kit Harington is the British heartthrob who made Jon Snow a household name. Before landing a role on the 2nd highest IMDB rated TV series after Breaking Bad, Kit Harington attended the Central School of Speech and Drama  in London. …Virginia Woolf 1882 – 1941 Author & Kings College London Graduate Virginia Woolf was a prominent english modernist writer who is highly respected in literary circles. Woolf attended  Kings College London  between 1897 and 1902 an | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### quora_pairs * Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 17,926 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------| | How are the Indians affected if Donald Trump wins the presidential election in the US? | If Mr Donald Trump wins the US Presidential election, will it impact India economically? | | What was the significance of the battle of Somme, and how did this battle compare and contrast to the Battle of Port Arthur? | What was the significance of the battle of Somme, and how did this battle compare and contrast to the Battle of Nanshan? | | What are the advantages of taking a nap in the afternoon? | What are he advantages of taking a nap in the afternoon? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 5,627 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | are all mattress protectors waterproof? | Most mattress protectors cover your mattress like a fitted sheet on your bed. They don't cover the entire mattress, but do protect it against most accidental spills, bacteria and some allergens. They're made of water-resistant materials that still allow for considerable airflow and breathability. | | are praying mantis endangered in pa? | Are Praying Mantises Endangered? Globally, there are an amazing 2,000 species of mantis. None of those species are considered to be threatened, likely to die out in the future or become endangered. ... However, in North America, none of the species are endangered. | | what is the tire pressure for hyundai santa fe? | Hyundai Santa Fe Tire Pressure: Front and rear tire pressures should be at 33 PSI, if properly inflated. Hyundai Santa Fe Sport Tire Pressure: Front and rear tires should be inflated to 34 PSI. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### gooaq_pairs2 * Dataset: [gooaq_pairs2](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 2,772 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | are heirloom tomatoes good for you? | Not only are they delicious, tomatoes (regular and heirloom alike) are arguably one of the healthiest foods on earth. They are rich in Vitamin C, Vitamin A, and Vitamin K, and potassium, among many other vitamins and minerals. | | is aye a real word? | Interjection. yes; yea; a word expressing assent, or an affirmative answer to a question. | | how long after a miscarriage will you get a positive pregnancy test? | Experiencing a miscarriage can be an emotional rollercoaster and the confusion over a persistently positive pregnancy test can add to this already difficult situation. Be assured that it can take a variable amount of time (on average two weeks) for a woman's hCG level to disappear after a miscarriage. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### mrpc_pairs * Dataset: [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 2,474 training samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | She said Jane Doe 's lawyers asked Verizon to withhold her name because she was planning on challenging the subpoena . | Jane Doe , deciding to fight the subpoena , asked Verizon to withhold her name . | | Mr. Gettelfinger said at that news conference that " we are going to continue to hammer away at the negotiations process until we reach an agreement , " with Ford and G.M. | " We are going to continue to hammer away at the negotiations process until we reach an agreement , " he said . | | Cisco has signed similar deals with AT & T Corp. T.N , SBC Communications Inc . SBC.N and Sprint Corp. FON.N . | Cisco has similar relationships with BellSouth competitors SBC Communications , AT & T and Sprint Communications . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### simple_wiki_pairs * Dataset: [simple_wiki_pairs](https://huggingface.co/datasets/sentence-transformers/simple-wiki) at [60fd9b4](https://huggingface.co/datasets/sentence-transformers/simple-wiki/tree/60fd9b4680642ace0e2604cc2de44d376df419a7) * Size: 3,751 training samples * Columns: text and simplified * Approximate statistics based on the first 1000 samples: | | text | simplified | |:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | text | simplified | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | An elastic modulus , or modulus of elasticity , is the mathematical description of an object or substance 's tendency to be deformed elastically ( i.e. , non-permanently ) when a force is applied to it . | An elastic modulus , or modulus of elasticity , is the mathematical description of an object or substance 's tendency to be deformed elastically ( i.e. non-permanently ) when a force is applied to it . | | Malcolm Gladwell ( born September 3 , 1963 ) is a Canadian writer for The New Yorker and best-selling author based in New York City . | Malcolm Gladwell ( born September 3 , 1963 ) is a British-born Canadian journalist , author , and pop sociologist , living in New York City . | | Nicotine is the addictive drug in tobacco products . | Nicotine is the addictive drug in tobacco products , so there is a chance when using NRT products , to become addicted to those products in place of the tobacco habit . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### simple_wiki_pairs2 * Dataset: [simple_wiki_pairs2](https://huggingface.co/datasets/sentence-transformers/simple-wiki) at [60fd9b4](https://huggingface.co/datasets/sentence-transformers/simple-wiki/tree/60fd9b4680642ace0e2604cc2de44d376df419a7) * Size: 1,848 training samples * Columns: text and simplified * Approximate statistics based on the first 1000 samples: | | text | simplified | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | text | simplified | |:--------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------| | In reward for good behavior , Johns was issued with a ticket of leave on arrival , and on 10 March 1855 he received a conditional pardon . | He was a well behaved prisoner , and as a reward Johns was given a ticket of leave . On 10 March 1855 , he was given a conditional pardon . | | Blue Lines is generally considered the first trip hop album , although the term was not coined until years later . | Blue Lines is usually considered the first trip hop album , although the term was not used commonly until years later . | | Later it was dedicated to protecting other species , including marine species . | Later it began protecting other species , including some in the ocean . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "n_layers_per_step": 3, "last_layer_weight": 0.1, "prior_layers_weight": 10, "kl_div_weight": 4, "kl_temperature": 0.25 } ``` ### Evaluation Datasets #### nli-pairs * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab) * Size: 1,000 evaluation samples * Columns: anchor and positive * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | anchor | positive | |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------| | Two women are embracing while holding to go packages. | Two woman are holding packages. | | Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | | A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### vitaminc-pairs * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0) * Size: 100 evaluation samples * Columns: claim and evidence * Approximate statistics based on the first 1000 samples: | | claim | evidence | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | claim | evidence | |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Dragon Con had over 5000 guests . | Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell . | | COVID-19 has reached more than 185 countries . | As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths . | | In March , Italy had 3.6x times more cases of coronavirus than China . | As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### negation-triplets * Dataset: [negation-triplets](https://huggingface.co/datasets/jinaai/negation-dataset-v2) * Size: 67 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 bicycle store shows two males leaning toward a bike. | A man adjust a bicycle in a bike shop with a child. | A man damaging a bicycle in a bike shop with a child. | | A bathroom scene with focus on the toilet. | A bathroom with a toilet, sink and red tile flooring. | A bathroom without a toilet, sink and red tile flooring. | | A building with a black and gold clock on it. | A large clock towers next to a forest filled with green trees. | A small clock tower next to a forest filled with dead trees. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "TripletLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 1, "kl_div_weight": 0.9, "kl_temperature": 1.1 } ``` #### qnli-contrastive * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 100 evaluation samples * Columns: sentence1, sentence2, and label * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | label | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------| | type | string | string | int | | details | | | | * Samples: | sentence1 | sentence2 | label | |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------| | What came into force after the new constitution was herald? | As of that day, the new constitution heralding the Second Republic came into force. | 0 | | What is the first major city in the stream of the Rhine? | The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz. | 0 | | What is the minimum required if you want to teach in Canada? | In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher. | 0 | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "OnlineContrastiveLoss", "n_layers_per_step": -1, "last_layer_weight": 1, "prior_layers_weight": 1, "kl_div_weight": 1.1, "kl_temperature": 0.9 } ``` #### scitail-pairs-qa * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 100 evaluation samples * Columns: sentence2 and sentence1 * Approximate statistics based on the first 1000 samples: | | sentence2 | sentence1 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence2 | sentence1 | |:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------| | Air is made of atoms. | Which of the following is made of atoms? | | All solutions contain at least two substances. | All solutions contain at least how many substances? | | Carbon 14 isotope of carbon is typically used to date ancient items. | What isotope of carbon is typically used to date ancient items? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### scitail-pairs-pos * Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------| | An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions. | Replace another in a molecule happens to atoms during a substitution reaction. | | Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase; | Wavelength is the distance between two corresponding points of adjacent waves called. | | humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### xsum-pairs * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------| | Andrew Stringer, from Gloucestershire Wildlife Trust, said it has the "perfect habitat" for the mammals, which could be "beneficial" in reducing grey squirrel numbers.
A feasibility study is being carried out by trust and Forestry Commission.
If the reintroduction goes ahead, 60 pine martens would be released over three years from next spring.
According to Mr Stringer, pine martens were driven to extinction in Gloucestershire by Victorian gamekeepers.
"They're our second rarest carnivore but it really wasn't that long ago - a couple of hundred years ago - that they were abundant here," he said.
"Reintroducing them and establishing a population would be fantastic for their conservation status in the UK."
A feasibility study is being carried out to check for "suitable habitats" in the forest and ensure "abundant populations" of the animal's prey.
It is also ensuring pine martens will not have "any detrimental effects" on the area.
"On the face of it, the Forest of Dean looks like prime habitat for pine martens," Mr Stringer said.
"They eat what's most common, for instance grey squirrels and wood pigeons, and because they're impacting on those really common species they let rarer species thrive.
"If we've got the support of the local communities, then we'd be looking to reintroduce 20 pine martens per year over a three-year period and that should create a stable population."
A final decision is due to be made early next year. | Pine martens could be reintroduced into the Forest of Dean after an absence of 200 years. | | Osvaldas Rimsa, 27, died at the scene of the collision on Rocky Hill at 13:45 GMT.
His Yamaha R1 motorcycle crashed with a black Alfa Romeo Giuliett, Kent Police said. The car driver, a man in his 50s, suffered minor injuries.
The West Kent Biker group paid tribute to Mr Rimsa on Facebook, saying he had a "real passion for motorcycling".
The group wrote: "It's with a very heavy heart that I tell you we sadly lost one of our own yesterday tragically.
"Osvaldas Rimsa had been with West Kent Biker right from the start and attended virtually every meet... the guy was dedicated and had a real passion for motorcycling.
"You will never be forgotten brother. Ride free mate and sleep tight."
Dean's Place Hotel in Alfriston, East Sussex, said Mr Rimsa worked for them as head waiter.
On Facebook, the hotel described him as "a young, intelligent and funny man with a very bright future ahead of him taken far too early."
Police urged witnesses to contact them.
Sgt Chris Wade said: "This collision happened at a very busy location in the town and I believe there may be other witnesses who have not yet come forward." | A "dedicated" motorcyclist was killed in a crash with a car in Maidstone on Friday. | | It has been in continuous use since World War II and is polluted with heavy oils and diesels, as well as small amounts of heavy metals.
Japanese knotweed, which can undermine foundations, is also present.
Work on cleaning up the 14 acre site is expected to start later in the summer and finish in 2012.
Lawrence McCullough, the development manager at Fort George, said it was normal for a site which had been used as a shipyard and military base for many years to have some pollution.
"Contamination on the site consists mainly of heavy oils and diesels and small bits of very dangerous chemicals like arsenic," he said.
"It's a site that has to have work done to clean it up to make it suitable for future use. As part of the planning requirements we have to do it."
A plant that needs to be disposed of along with the hazardous waste is also present on the site.
"Japanese knotweed can be dealt with and will be dealt with," Mr McCullough said.
"It will be sifted and burnt.
"It's a painstaking operation, but if it's left untreated it can return and undermine foundations of the buildings and cause considerable damage to buildings in the future."
He said that the MoD funding for cleaning up the site was a result of a commitment the ministry had made in 2001 when it gave up Fort George. | The Ministry of Defence is spending more than three million pounds decontaminating the former Fort George army base in Londonderry. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### compression-pairs * Dataset: [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co/datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------| | Tommy Thompson, the Republican former Wisconsin governor and George W. Bush's first secretary of Health and Human Services, has told friends he plans to run for the open Senate seat in Wisconsin, according to top Wisconsin sources. | Tommy Thompson may run for Senate | | Arynga announces that they have joined the GENIVI Alliance to collaborate with automotive OEMs and Tier One suppliers on keeping the growing amount of software in the car up-to-date. | Arynga joins GENIVI Alliance | | Lionsgate's 3:10 to Yuma topped both the national home video sales and rental charts for the week ending Jan. 13. | 3:10 to Yuma tops sales and rental charts | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### sciq_pairs * Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which region of dna consists of one or more genes that encode the proteins needed for a specific function? | Regulation of transcription in prokaryotes typically involves operons. An operon is a region of DNA that consists of one or more genes that encode the proteins needed for a specific function. The operon also includes a promoter and an operator. The operator is a region of the operon where regulatory proteins bind. It is located near the promoter and helps regulate transcription of the operon genes. | | In places like the grand canyon, hard rocks that are resistant to weathering form what, while softer rocks that weather more easily form slopes? | The rocks in this photo of the Grand Canyon are all sedimentary. Hard rocks that are resistant to weathering form cliffs. Softer rocks that weather more easily form slopes. | | What type of gas is a harmless gas that living things add to the atmosphere during respiration? | A: You can tell that they are different compounds from their very different properties. Carbon dioxide is a harmless gas that living things add to the atmosphere during respiration. Carbon monoxide is a deadly gas that can quickly kill people if it becomes too concentrated in the air. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### qasc_pairs * Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | What requires food? | Evaporation of sweat uses energy, and the energy comes from body heat.. Because food contains energy.. evaporation of sweat requires food | | What happens as mitosis slows for good? | Aging occurs as cells lose their ability to divide.. Cell division is by mitosis .. aging occurs as mitosis slows for good | | Building housing developments usually requires replacing what? | building housing developments usually requires replacing animal habitats. Habitat Hairy-nosed wombats are terrestrial and build burrows.. building housing developments usually requires replacing animal burrows | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### qasc_facts_sym * Dataset: [qasc_facts_sym](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070) * Size: 100 evaluation samples * Columns: combinedfact and facts * Approximate statistics based on the first 1000 samples: | | combinedfact | facts | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | combinedfact | facts | |:----------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------| | space travel is used for moving people | transportation technology is used for moving people. Technology permits space travel and transportation.. | | plants use unsaturated fatty acids to store sunlight | Plants use unsaturated fatty acids to store energy.. Sunlight is turned into energy by plants.. | | Cancer cells divide more often than normal cells, and often develop into tumors. | Cancer cells divide more often than normal cells, and grow out of control.. Cancer cells, however, grow out of control and develop into a tumor.. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` #### openbookqa_pairs * Dataset: openbookqa_pairs * Size: 100 evaluation samples * Columns: question and fact * Approximate statistics based on the first 1000 samples: | | question | fact | |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | question | fact | |:-----------------------------------------------------------------------|:-----------------------------------------------------------------------------| | The thermal production of a stove is generically used for | a stove generates heat for cooking usually | | What creates a valley? | a valley is formed by a river flowing | | when it turns day and night on a planet, what cause this? | a planet rotating causes cycles of day and night on that planet | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### msmarco_pairs * Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what is the space called between your eyebrows | The space between your eyebrows is called the glabella.. Chinese Face Reading-There is a special name for the space between the eyebrows, called the ‘Yin Tang’. | | what is a cow chow | The Chow Chow or Chow (from Chinese: 獢) is a dog breed originally from northern China, where it is now known as the Fluffy Lion-dog (sōng shÄ« quǎn 松狮犬). The breed has also been called the Tang Quan, Dog of the Tang Empire .It is believed that the Chow Chow is one of the native dogs used as the model for the Foo Dog, the traditional stone guardians found in front of Buddhist temples and palaces. Chow Chow cream coat, the only dog breed with this distinctive bluish color in its lips and oral cavity. A Chow Chow puppy. The Chow Chow is a sturdily built dog, square in profile. | | how long do growing pains usually last | The duration of the pain is usually between 10 and 30 minutes, although it might range from minutes to hours. The degree of pain can be mild or very severe. Growing pains are intermittent, with pain-free intervals from days to months.In some children the pain can occur daily.ecause these pains most often occur during years when the child's growth is not at its fastest rate, the pains are NOT associated with growing. The name was given in the 1930s to 1940s when the pains were thought to be from faster growth of the bones when compared to the growth of the tendons. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### nq_pairs * Dataset: [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | what was the code name for the overall invasion of normandy | Operation Overlord Operation Overlord was the codename for the Battle of Normandy, the Allied operation that launched the successful invasion of German-occupied Western Europe during World War II. The operation was launched on 6 June 1944 with the Normandy landings (Operation Neptune, commonly known as D-Day). A 1,200-plane airborne assault preceded an amphibious assault involving more than 5,000 vessels. Nearly 160,000 troops crossed the English Channel on 6 June, and more than two million Allied troops were in France by the end of August. | | when did come from away open in toronto | Come from Away Another Canadian production opened in a sold-out, four-week run in Winnipeg at the Royal Manitoba Theatre Centre in January 2018.[19] The production began performances at the Royal Alexandra Theatre in Toronto on February 13, 2018.[20] | | who has the most title wins in wwe | List of WWE Champions Overall, there have been 50 different official champions, with John Cena having the most reigns at thirteen. Seven men in history have held the championship for a continuous reign of one year (365 days) or more: Bruno Sammartino, Pedro Morales, Bob Backlund, Hulk Hogan, Randy Savage, John Cena and CM Punk.[10] | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### trivia_pairs * Dataset: [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co/datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Cynology is the study of which animals? | Cytology in Dogs Cytology in Dogs Last Modified: August 02, 2015 Share This Article Read by: 141,681 pet lovers Cytology is the examination and study of blood or tissue cells under a microscope that can be done in dogs and other animals. Cytology can be used to detect inflammation, infection, bacteria, fungi, parasites and cancer. Cytology involves examination of a tissue or fluid sample. Often cytology is used to identify a lump or mass noted on physical examination. Certain skin diseases or hair loss situations – such as mange mite infection – can be diagnosed using cytology. Cytology is usually done when abnormal fluid is detected in a body cavity. Cytology may follow an abdominal ultrasound examination or surgical procedure that reveals abnormal organ tissue. Cytology of vaginal fluid can be used to guide breeding in female dogs. There is no real contraindication to performing this test. Negative results can exclude the presence of certain diseases. For example, a skin tumor might be malignant or benign. If the cytology shows the mass to be a simple fatty tumor, it may be left alone and followed. In contrast, identification of a dangerous skin cancer, such as a mast cell tumor, would indicate the need for surgical removal of the mass. As with all tests, a cytologic examination is neither 100 percent sensitive nor specific. Should a cytology exam be negative or inconclusive, your veterinarian may recommend a full tissue biopsy sample be obtained and submitted for analysis. What Does Cytology Reveal in Dogs? Cytology can identify the presence of inflammation, infection, cancer, parasites, bacteria and fungi (molds and yeast). Following cytology, additional diagnostic tests, procedures, or medications may be recommended. How Is Cytology Done in Dogs? Cytology testing involves obtaining a sample of suspicious material. The material can be obtained by pressing a microscope slide against the tissue, by gently scraping the area with a scalpel blade, or by inserting a needle or sterile Q-tip into the tissue to obtain fluid or tissue. Once obtained, the material is spread thinly over a microscope slide and allowed to dry. Fluid samples may be placed in a centrifuge first to concentrate the cells before they are transferred to the slide. The sample is then dyed with special biological stains to ease identification of the cells. The sample is once again allowed to dry. Once the dye has dried, the slide is ready for microscopic evaluation. Some veterinarians are sufficiently experienced to evaluate cytology specimens. Most veterinarians submit the cytology specimen to a diagnostic laboratory for evaluation by a veterinary pathologist. Even if your veterinarian provides you a presumptive diagnosis based on his/her evaluation of the slide, the final diagnosis is typically made after the pathologist reviews the sample. The cytology test generally takes 20 to 30 minutes to perform if done in the veterinarian's office. If the sample is submitted to a laboratory, results may not be available for 2 to 3 days. Is Cytology Painful to Dogs? In obtaining a sample with a needle, some pain may be involved, but very small needles typically are used. As with humans, the pain perceived from a needle stick varies among individual dogs, but it should not be any more painful than an injection or a blood sample. Is Sedation or Anesthesia Needed for Cytology? Sedation or anesthesia is not typically needed, but might be necessary depending on how the cytology sample is collected. Those samples obtained from skin scrapings or aspirations typically do not require sedation. Obviously, a sample obtained during a surgical procedure will require anesthesia for the surgery. (?) | | Which English football team are nicknamed the Hornets? | Official Website of the Hornets | Watford Football Club 09:00 AM - 20 Jan 2017 ⚽️🎉 It's Friday & #watfordfc are back in @premierleague action tomorrow when they travel to @afcbournemouth 🍒… https://t.co/S3Cf2bv8kd 09:00 AM - 20 Jan 2017 01:08 AM - 20 Jan 2017 Mauro Zárate cerca de llegar al #WatfordFC 🐝. #Fichajes https://t.co/j2XmAk2Fy6 01:08 AM - 20 Jan 2017 Watford FC Blog @WatfordFCBlog 19:50 PM - 19 Jan 2017 The Zarate deal seems to be getting closer. Heard some promising things about him, but will judge him when I see him play. #WatfordFC 19:50 PM - 19 Jan 2017 Watford FC Blog @WatfordFCBlog 19:49 PM - 19 Jan 2017 Hope Gomes is fit enough to play on Saturday. Not 100% sold on Pantilimon really if I'm being honest. #WatfordFC 19:49 PM - 19 Jan 2017 Watford Ladies FC @watfordladiesfc 19:35 PM - 19 Jan 2017 RT @donnybelles: NEWS: Our @WomensFACup tie with @Watfordladiesfc will be played at @RMFC1919 Full story 👉🏻👉🏻https://t.co/EqTbohBziU http… 19:35 PM - 19 Jan 2017 | | Rocinante is the name of which fictional character’s horse? | Rocinante | fictional character | Britannica.com Rocinante fictional character THIS ARTICLE IS A STUB. You can learn more about this topic in the related articles below. Similar Topics Rocinante, fictional character, the spavined half-starved horse that Don Quixote designates his noble steed in the classic novel Don Quixote (1605, 1615) by Miguel de Cervantes . Learn More in these related articles: horse a hoofed, herbivorous mammal of the family Equidae. It comprises a single species, Equus caballus, whose numerous varieties are called breeds. Before the advent of mechanized vehicles, the horse was widely used as a draft animal, and riding on horseback was one of the chief means of transportation.... Don Quixote (novel by Cervantes) novel published in two parts (Part I, 1605; Part II, 1615) by Miguel de Cervantes, one of the most widely read classics of Western literature. Originally conceived as a comic satire against the chivalric romances then in literary vogue, it describes realistically what befalls an elderly knight who,... Miguel de Cervantes September 29?, 1547 Alcalá de Henares, Spain April 22, 1616 Madrid Spanish novelist, playwright, and poet, the creator of Don Quixote (1605, 1615) and the most important and celebrated figure in Spanish literature. His novel Don Quixote has been translated, in full or in part, into more than... Corrections? Updates? Help us improve this article! Contact our editors with your feedback. MEDIA FOR: You have successfully emailed this. Error when sending the email. Try again later. Edit Mode Submit Tips For Editing We welcome suggested improvements to any of our articles. You can make it easier for us to review and, hopefully, publish your contribution by keeping a few points in mind. Encyclopædia Britannica articles are written in a neutral objective tone for a general audience. You may find it helpful to search within the site to see how similar or related subjects are covered. Any text you add should be original, not copied from other sources. At the bottom of the article, feel free to list any sources that support your changes, so that we can fully understand their context. (Internet URLs are the best.) Your contribution may be further edited by our staff, and its publication is subject to our final approval. Unfortunately, our editorial approach may not be able to accommodate all contributions. Submit Thank You for Your Contribution! Our editors will review what you've submitted, and if it meets our criteria, we'll add it to the article. 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Close Date Published: February 05, 2016 URL: https://www.britannica.com/topic/Rocinante Access Date: December 28, 2016 Share | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### quora_pairs * Dataset: [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb) * Size: 1,680 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------| | Who will be a better president Donald Trump or Hillary Clinton? | How is Hillary Clinton a better choice than Donald Trump? | | Can I hack a Facebook account when I am logged in but don’t have password or email? | How can I log in to Facebook if I forgot my email? | | Why do dogs wag their tails when they are happy? | Why do some dogs wag their tail in circles? | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": -1, "last_layer_weight": 0.25, "prior_layers_weight": 2, "kl_div_weight": 1.75, "kl_temperature": 0.75 } ``` #### gooaq_pairs * Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:-------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | how much do you get paid during basic training in the army? | Competitive Salary As a new direct entry recruit, you could earn anywhere from $35,820 - $62,424 annually while you complete basic training. Once you are fully trained for your chosen occupation, your salary will continue to increase based on your time in the military, rank and acquired skills. | | where are all clay pigeons in fortnite? | There's a Fortnite Clay Pigeon to be found right in between Risky Reels, Lazy Links, and Tomato Town, to the east of the river. You can find a Fortnite Clay Pigeon on the northeastern shore of Loot Lake. West and slightly south of Pleasant Park, there's another Fortnite Clay Pigeon just south of the large hill. | | how late do you have to stay for jury duty? | A jury panel usually is in place for approximately four weeks. During this time jurors could be chosen from the panel and be sworn to sit on more than one trial. Trials can last a few days or a number of weeks. Jurors will be informed if a trial is expected to sit for a longer duration. | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "GISTEmbedLoss", "n_layers_per_step": 1, "last_layer_weight": 1.75, "prior_layers_weight": 0.75, "kl_div_weight": 0.85, "kl_temperature": 1.15 } ``` #### mrpc_pairs * Dataset: [mrpc_pairs](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c) * Size: 100 evaluation samples * Columns: sentence1 and sentence2 * Approximate statistics based on the first 1000 samples: | | sentence1 | sentence2 | |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | sentence1 | sentence2 | |:--------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | He said the foodservice pie business doesn 't fit the company 's long-term growth strategy . | " The foodservice pie business does not fit our long-term growth strategy . | | The AFL-CIO is waiting until October to decide if it will endorse a candidate . | The AFL-CIO announced Wednesday that it will decide in October whether to endorse a candidate before the primaries . | | Wal-Mart said it would check all of its million-plus domestic workers to ensure they were legally employed . | It has also said it would review all of its domestic employees more than 1 million to ensure they have legal status . | * Loss: [AdaptiveLayerLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters: ```json { "loss": "MultipleNegativesSymmetricRankingLoss", "n_layers_per_step": -1, "last_layer_weight": 0.75, "prior_layers_weight": 1.25, "kl_div_weight": 1.33, "kl_temperature": 0.75 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 50 - `per_device_eval_batch_size`: 50 - `learning_rate`: 3.5e-05 - `weight_decay`: 0.0001 - `num_train_epochs`: 10 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 4} - `warmup_ratio`: 0.1 - `save_safetensors`: False - `fp16`: True - `push_to_hub`: True - `hub_model_id`: bobox/DeBERTa-ST-AllLayers-v3.5-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`: 50 - `per_device_eval_batch_size`: 50 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 3.5e-05 - `weight_decay`: 0.0001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {'num_cycles': 4} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: False - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: True - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bobox/DeBERTa-ST-AllLayers-v3.5-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 | trivia pairs loss | negation-triplets loss | gooaq pairs loss | mrpc pairs loss | xsum-pairs loss | compression-pairs loss | qnli-contrastive loss | qasc pairs loss | scitail-pairs-pos loss | vitaminc-pairs loss | sciq pairs loss | openbookqa pairs loss | nq pairs loss | qasc facts sym loss | msmarco pairs loss | scitail-pairs-qa loss | nli-pairs loss | quora pairs loss | Vitaminc-test_max_ap | mrpc-test_max_ap | negationNLI-test_max_accuracy | sts-test_spearman_cosine | |:------:|:----:|:-------------:|:-----------------:|:----------------------:|:----------------:|:---------------:|:---------------:|:----------------------:|:---------------------:|:---------------:|:----------------------:|:-------------------:|:---------------:|:---------------------:|:-------------:|:-------------------:|:------------------:|:---------------------:|:--------------:|:----------------:|:--------------------:|:----------------:|:-----------------------------:|:------------------------:| | None | 0 | - | 13.7719 | 8.5566 | 16.0717 | 8.5447 | 10.7531 | 13.4215 | 8.9584 | 13.8239 | 6.0245 | 7.3822 | 3.5277 | 9.1746 | 18.2946 | 8.7014 | 20.0609 | 5.5183 | 11.2934 | 8.7693 | 0.5189 | 0.8213 | 1.0 | 0.1064 | | 0.0250 | 74 | 12.9253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0501 | 148 | 9.5557 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0751 | 222 | 9.2788 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1001 | 296 | 8.6849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1251 | 370 | 7.7612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1502 | 444 | 7.1482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1752 | 518 | 6.5551 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2002 | 592 | 6.2869 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2252 | 666 | 5.092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2503 | 740 | 4.7866 | 6.1790 | 5.8781 | 4.7197 | 1.2289 | 2.8964 | 2.1322 | 4.7184 | 3.1045 | 1.6439 | 6.3893 | 2.1849 | 4.3320 | 6.4909 | 2.4956 | 6.1475 | 0.7982 | 7.2085 | 1.2508 | 0.5415 | 0.8350 | 1.0 | 0.6094 | | 0.2753 | 814 | 4.9352 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3003 | 888 | 4.6466 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3253 | 962 | 4.5722 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3504 | 1036 | 4.3531 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3754 | 1110 | 4.2219 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4004 | 1184 | 4.7228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4254 | 1258 | 4.1036 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4505 | 1332 | 3.0328 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4755 | 1406 | 3.6321 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5005 | 1480 | 3.6522 | 4.9682 | 4.7611 | 2.8734 | 0.6950 | 1.9395 | 1.3952 | 3.2326 | 1.3577 | 0.8932 | 6.6551 | 1.6005 | 2.7664 | 4.6862 | 1.5507 | 3.4870 | 0.2729 | 4.2440 | 0.9854 | 0.5286 | 0.8387 | 1.0 | 0.7711 | | 0.5255 | 1554 | 3.6262 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5506 | 1628 | 3.314 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5756 | 1702 | 3.0849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6006 | 1776 | 2.9041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6256 | 1850 | 3.1351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6507 | 1924 | 3.1039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6757 | 1998 | 3.1698 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7007 | 2072 | 2.5172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7257 | 2146 | 2.4682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7508 | 2220 | 2.6695 | 4.5662 | 4.3310 | 2.5801 | 0.4526 | 1.5002 | 1.0249 | 2.5176 | 1.0030 | 0.8153 | 5.9182 | 1.4846 | 2.2422 | 4.2798 | 0.9864 | 3.1296 | 0.1621 | 3.2204 | 0.7614 | 0.5320 | 0.8470 | 1.0 | 0.8161 | | 0.7758 | 2294 | 2.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8008 | 2368 | 3.1122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8258 | 2442 | 2.7361 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8509 | 2516 | 2.4439 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8759 | 2590 | 2.5529 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9009 | 2664 | 2.4578 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9259 | 2738 | 2.6346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9510 | 2812 | 2.4693 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9760 | 2886 | 2.4323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.42.4 - PyTorch: 2.3.1+cu121 - Accelerate: 0.32.1 - Datasets: 2.20.0 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### AdaptiveLayerLoss ```bibtex @misc{li20242d, title={2D Matryoshka Sentence Embeddings}, author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li}, year={2024}, eprint={2402.14776}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### GISTEmbedLoss ```bibtex @misc{solatorio2024gistembed, title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, author={Aivin V. Solatorio}, year={2024}, eprint={2402.16829}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```