diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -7,7 +7,7 @@ tags:
 - sentence-similarity
 - feature-extraction
 - generated_from_trainer
-- dataset_size:689221
+- dataset_size:131566
 - loss:MultipleNegativesRankingLoss
 - loss:CoSENTLoss
 - loss:GISTEmbedLoss
@@ -22,6 +22,14 @@ datasets:
 - allenai/scitail
 - sentence-transformers/xsum
 - sentence-transformers/sentence-compression
+- allenai/sciq
+- allenai/qasc
+- allenai/openbookqa
+- sentence-transformers/msmarco-msmarco-distilbert-base-v3
+- sentence-transformers/natural-questions
+- sentence-transformers/trivia-qa
+- sentence-transformers/quora-duplicates
+- sentence-transformers/gooaq
 metrics:
 - pearson_cosine
 - spearman_cosine
@@ -34,43 +42,34 @@ metrics:
 - pearson_max
 - spearman_max
 widget:
-- source_sentence: What are the exceptions in the constitution  that require special
-    considerations to amend?
+- source_sentence: Centrosome-independent mitotic spindle formation in  vertebrates.
   sentences:
-  - The river makes a distinctive turn to the north near Chur.
-  - The Victorian Constitution can be amended by the Parliament of Victoria, except
-    for certain "entrenched" provisions that require either an absolute majority in
-    both houses, a three-fifths majority in both houses, or the approval of the Victorian
-    people in a referendum, depending on the provision.
-  - A new arrangement of the theme, once again by Gold, was introduced in the 2007
-    Christmas special episode, "Voyage of the Damned"; Gold returned as composer for
-    the 2010 series.
-- source_sentence: What is the name of a Bodhisattva vow?
+  - Birds pair up with the same bird in mating season.
+  - We use voltage to keep track of electric potential energy.
+  - A mitotic spindle forms from the centrosomes.
+- source_sentence: A dog carrying a stick in its mouth runs through a snow-covered
+    field.
   sentences:
-  - In Tibetan Buddhism the teachers of Dharma in Tibet are most commonly called a
-    Lama.
-  - This origin of chloroplasts was first suggested by the Russian biologist Konstantin
-    Mereschkowski in 1905 after Andreas Schimper observed in 1883 that chloroplasts
-    closely resemble cyanobacteria.
-  - The announcement came a day after Setanta Sports confirmed that it would launch
-    in March as a subscription service on the digital terrestrial platform, and on
-    the same day that NTL's services re-branded as Virgin Media.
-- source_sentence: Two dogs run around inside a fence.
+  - The children played on the floor.
+  - A pair of people play video games together on a couch.
+  - A animal carried a stick through a snow covered field.
+- source_sentence: A guy on a skateboard, jumping off some steps.
   sentences:
-  - A young woman tennis player have many tennis balls.
-  - Two dogs are inside a fence.
-  - A little girl in red plays tennis.
-- source_sentence: A little boy wearing a blue stiped shirt has a party hat on his
-    head and is playing in a puddle.
+  - A woman is making music.
+  - a guy with a skateboard making a jump
+  - A dog holds an object in the water.
+- source_sentence: A photographer with bushy dark hair takes a photo of a skateboarder
+    at an indoor park.
   sentences:
-  - The party boy is playing in a puddle.
-  - There is a crowd
-  - Four people are skiing
-- source_sentence: Two wrestlers jump in a ring while an official watches.
+  - The person with the camera photographs the person skating.
+  - A man starring at a piece of paper.
+  - The man is riding a bike in sand.
+- source_sentence: Why did oil start getting priced in terms of gold?
   sentences:
-  - The man was walking.
-  - Two men are dressed in makeup
-  - Two wrestlers were just tagged in on a tag team match.
+  - Because oil was priced in dollars, oil producers' real income decreased.
+  - This allows all set top boxes in a household to share recordings and other media.
+  - Only the series from 2009 onwards are available on Blu-ray, except for the 1970
+    story Spearhead from Space, released in July 2013.
 pipeline_tag: sentence-similarity
 model-index:
 - name: SentenceTransformer based on microsoft/deberta-v3-small
@@ -83,40 +82,70 @@ model-index:
       type: sts-test
     metrics:
     - type: pearson_cosine
-      value: 0.7827777535990615
+      value: 0.7740200646402275
       name: Pearson Cosine
     - type: spearman_cosine
-      value: 0.7930096932283699
+      value: 0.7726824843726025
       name: Spearman Cosine
     - type: pearson_manhattan
-      value: 0.7959463678643859
+      value: 0.7871287254831608
       name: Pearson Manhattan
     - type: spearman_manhattan
-      value: 0.792182337344966
+      value: 0.7758049644234141
       name: Spearman Manhattan
     - type: pearson_euclidean
-      value: 0.7948115210006163
+      value: 0.7842462717672578
       name: Pearson Euclidean
     - type: spearman_euclidean
-      value: 0.7907409787879929
+      value: 0.7723622369393174
       name: Spearman Euclidean
     - type: pearson_dot
-      value: 0.7150471304135075
+      value: 0.705919446324648
       name: Pearson Dot
     - type: spearman_dot
-      value: 0.6966062484321753
+      value: 0.6867859662226861
       name: Spearman Dot
     - type: pearson_max
-      value: 0.7959463678643859
+      value: 0.7871287254831608
       name: Pearson Max
     - type: spearman_max
-      value: 0.7930096932283699
+      value: 0.7758049644234141
+      name: Spearman Max
+    - type: pearson_cosine
+      value: 0.7740200646402275
+      name: Pearson Cosine
+    - type: spearman_cosine
+      value: 0.7726824843726025
+      name: Spearman Cosine
+    - type: pearson_manhattan
+      value: 0.7871287254831608
+      name: Pearson Manhattan
+    - type: spearman_manhattan
+      value: 0.7758049644234141
+      name: Spearman Manhattan
+    - type: pearson_euclidean
+      value: 0.7842462717672578
+      name: Pearson Euclidean
+    - type: spearman_euclidean
+      value: 0.7723622369393174
+      name: Spearman Euclidean
+    - type: pearson_dot
+      value: 0.705919446324648
+      name: Pearson Dot
+    - type: spearman_dot
+      value: 0.6867859662226861
+      name: Spearman Dot
+    - type: pearson_max
+      value: 0.7871287254831608
+      name: Pearson Max
+    - type: spearman_max
+      value: 0.7758049644234141
       name: Spearman Max
 ---
 
 # SentenceTransformer based on microsoft/deberta-v3-small
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) and [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression) 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.
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail), [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co/datasets/allenai/sciq), [qasc_pairs](https://huggingface.co/datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa), [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) and [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
 
 ## Model Details
 
@@ -135,6 +164,14 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [m
     - [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail)
     - [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum)
     - [compression-pairs](https://huggingface.co/datasets/sentence-transformers/sentence-compression)
+    - [sciq_pairs](https://huggingface.co/datasets/allenai/sciq)
+    - [qasc_pairs](https://huggingface.co/datasets/allenai/qasc)
+    - [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa)
+    - [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
+    - [nq_pairs](https://huggingface.co/datasets/sentence-transformers/natural-questions)
+    - [trivia_pairs](https://huggingface.co/datasets/sentence-transformers/trivia-qa)
+    - [quora_pairs](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
+    - [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq)
 - **Language:** en
 <!-- - **License:** Unknown -->
 
@@ -171,9 +208,9 @@ from sentence_transformers import SentenceTransformer
 model = SentenceTransformer("bobox/DeBERTaV3-small-GeneralSentenceTransformer")
 # Run inference
 sentences = [
-    'Two wrestlers jump in a ring while an official watches.',
-    'Two wrestlers were just tagged in on a tag team match.',
-    'Two men are dressed in makeup',
+    'Why did oil start getting priced in terms of gold?',
+    "Because oil was priced in dollars, oil producers' real income decreased.",
+    'This allows all set top boxes in a household to share recordings and other media.',
 ]
 embeddings = model.encode(sentences)
 print(embeddings.shape)
@@ -217,18 +254,35 @@ You can finetune this model on your own dataset.
 * Dataset: `sts-test`
 * Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
 
-| Metric              | Value     |
-|:--------------------|:----------|
-| pearson_cosine      | 0.7828    |
-| **spearman_cosine** | **0.793** |
-| pearson_manhattan   | 0.7959    |
-| spearman_manhattan  | 0.7922    |
-| pearson_euclidean   | 0.7948    |
-| spearman_euclidean  | 0.7907    |
-| pearson_dot         | 0.715     |
-| spearman_dot        | 0.6966    |
-| pearson_max         | 0.7959    |
-| spearman_max        | 0.793     |
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.774      |
+| **spearman_cosine** | **0.7727** |
+| pearson_manhattan   | 0.7871     |
+| spearman_manhattan  | 0.7758     |
+| pearson_euclidean   | 0.7842     |
+| spearman_euclidean  | 0.7724     |
+| pearson_dot         | 0.7059     |
+| spearman_dot        | 0.6868     |
+| pearson_max         | 0.7871     |
+| spearman_max        | 0.7758     |
+
+#### Semantic Similarity
+* Dataset: `sts-test`
+* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| pearson_cosine      | 0.774      |
+| **spearman_cosine** | **0.7727** |
+| pearson_manhattan   | 0.7871     |
+| spearman_manhattan  | 0.7758     |
+| pearson_euclidean   | 0.7842     |
+| spearman_euclidean  | 0.7724     |
+| pearson_dot         | 0.7059     |
+| spearman_dot        | 0.6868     |
+| pearson_max         | 0.7871     |
+| spearman_max        | 0.7758     |
 
 <!--
 ## Bias, Risks and Limitations
@@ -249,7 +303,7 @@ You can finetune this model on your own dataset.
 #### 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: 150,000 training samples
+* Size: 10,000 training samples
 * Columns: <code>sentence1</code> and <code>sentence2</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence1                                                                         | sentence2                                                                        |
@@ -297,19 +351,19 @@ You can finetune this model on your own dataset.
 #### vitaminc-pairs
 
 * Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
-* Size: 75,142 training samples
+* Size: 4,943 training samples
 * Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | label                        | sentence1                                                                         | sentence2                                                                         |
-  |:--------|:-----------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | int                          | string                                                                            | string                                                                            |
-  | details | <ul><li>1: 100.00%</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.44 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 38.0 tokens</li><li>max: 151 tokens</li></ul> |
+  |         | label                        | sentence1                                                                         | sentence2                                                                          |
+  |:--------|:-----------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | int                          | string                                                                            | string                                                                             |
+  | details | <ul><li>1: 100.00%</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 16.56 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 37.97 tokens</li><li>max: 184 tokens</li></ul> |
 * Samples:
-  | label          | sentence1                                                                                                                                                     | sentence2                                                                                                                                                                                                           |
-  |:---------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>1</code> | <code>Penguins has a rating of less than 92 % , defined by more than 20 reviews on Rotten Tomatoes .</code>                                                   | <code>On review aggregator Rotten Tomatoes , the film holds an approval rating of 91 % based on 22 reviews , with an average rating of 7.14/10 .</code>                                                             |
-  | <code>1</code> | <code>Fluoxetine , acts as a positive allosteric modulator of the GABAA receptor at high concentrations , as does norfluoxetine though more potently .</code> | <code>In addition , it acts as a positive allosteric modulator of the GABAA receptor at high concentrations , and norfluoxetine does the same but more potently , actions which may be clinically-relevant .</code> |
-  | <code>1</code> | <code>Andrew Robertson is considered by many experts to be one of the best left backs .</code>                                                                | <code>He is considered by many pundits to be one of the best left backs in the world due to his pace and crossing ability.</code>                                                                                   |
+  | label          | sentence1                                                                  | sentence2                                                                                                                  |
+  |:---------------|:---------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
+  | <code>1</code> | <code>The film scored above 85.5 % based on more than 127 reviews .</code> | <code>On Rotten Tomatoes the film has a rating of 86 % , based on 128 reviews , with an average rating of 7.2/10 .</code>  |
+  | <code>1</code> | <code>The film has more than 59 reviews .</code>                           | <code>On Rotten Tomatoes , the film has a rating of 47 % , based on 60 reviews , with an average rating of 5.4/10 .</code> |
+  | <code>1</code> | <code>More than 100 cases of COVID-19 have been reported .</code>          | <code>Tamil Nadu reported more than 190 cases in a day , all who had been to the event.</code>                             |
 * Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
   ```json
   {'guide': SentenceTransformer(
@@ -322,37 +376,37 @@ You can finetune this model on your own dataset.
 #### qnli-contrastive
 
 * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
-* Size: 104,743 training samples
+* Size: 10,000 training samples
 * Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence1                                                                         | sentence2                                                                          | label                        |
   |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
   | type    | string                                                                            | string                                                                             | int                          |
-  | details | <ul><li>min: 3 tokens</li><li>mean: 13.82 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 34.56 tokens</li><li>max: 110 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.69 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 35.17 tokens</li><li>max: 499 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
 * Samples:
-  | sentence1                                                                          | sentence2                                                                                                                                                                                                                       | label          |
-  |:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
-  | <code>Which Formula One racing team developed the C-X75's used for filming.</code> | <code>The C-X75s used for filming were developed by the engineering division of Formula One racing team Williams, who built the original C-X75 prototype for Jaguar.</code>                                                     | <code>0</code> |
-  | <code>When did the University of Michigan leave Detroit?</code>                    | <code>In June 2009, the Michigan State University College of Osteopathic Medicine which is based in East Lansing opened a satellite campus located at the Detroit Medical Center.</code>                                        | <code>0</code> |
-  | <code>When did the Vlachs migrate into the region?</code>                          | <code>The Gorals of southern Poland and northern Slovakia are partially descended from Romance-speaking Vlachs who migrated into the region from the 14th to 17th centuries and were absorbed into the local population.</code> | <code>0</code> |
+  | sentence1                                                                                                            | sentence2                                                                                                                                                                                               | label          |
+  |:---------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>What year was the Southampton Docks company created?</code>                                                    | <code>The Southampton Docks company had been formed in 1835.</code>                                                                                                                                     | <code>0</code> |
+  | <code>what happens to the reflected wave which allows the antenna to reach its asympotic feedpoint impedance?</code> | <code>Thus the antenna's impedance, given by the ratio of feedpoint voltage to current, is altered due to the antenna's proximity to the ground.</code>                                                 | <code>0</code> |
+  | <code>West 132nd Street is interrupted by St. Nicholas Park and which college?</code>                                | <code>The main portion of 132nd Street runs eastbound from Frederick Douglass Boulevard to northern end of Park Avenue where there is a southbound exit from/entrance to the Harlem River Drive.</code> | <code>0</code> |
 * Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
 
 #### scitail-pairs-qa
 
 * Dataset: [scitail-pairs-qa](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
-* Size: 14,987 training samples
+* Size: 6,595 training samples
 * Columns: <code>sentence2</code> and <code>sentence1</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence2                                                                         | sentence1                                                                         |
   |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 7 tokens</li><li>mean: 16.04 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.14 tokens</li><li>max: 34 tokens</li></ul> |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 16.18 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.15 tokens</li><li>max: 41 tokens</li></ul> |
 * Samples:
-  | sentence2                                                                             | sentence1                                                                               |
-  |:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
-  | <code>Voltage is not the same as energy, but means the energy per unit charge.</code> | <code>What term is not the same as energy, but means the energy per unit charge?</code> |
-  | <code>A jellyfish does not have a circulatory system.</code>                          | <code>Name the type of system that a jellyfish does not have?</code>                    |
-  | <code>Insight learning is based on past experience and reasoning.</code>              | <code>What type of learning is based on past experience and reasoning?</code>           |
+  | sentence2                                                                                                                 | sentence1                                                                                                                             |
+  |:--------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Nearly five million people die yearly due to air pollution.</code>                                                  | <code>Nearly how many millions of people die yearly due to air pollution?</code>                                                      |
+  | <code>Exercising every day is an example of a good health habit.</code>                                                   | <code>Which activity is an example of a good health habit?</code>                                                                     |
+  | <code>A(n) fungus causes ergot, a disease that impacts crops directly and has more devastating effects on animals.</code> | <code>A type of what organism causes ergot, a disease that impacts crops directly and has more devastating effects on animals?</code> |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -364,19 +418,19 @@ You can finetune this model on your own dataset.
 #### 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
+* Size: 3,405 training samples
 * Columns: <code>sentence1</code> and <code>sentence2</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                         | sentence2                                                                         |
-  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                            | string                                                                            |
-  | details | <ul><li>min: 6 tokens</li><li>mean: 23.99 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.54 tokens</li><li>max: 39 tokens</li></ul> |
+  |         | sentence1                                                                         | sentence2                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 23.97 tokens</li><li>max: 65 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.1 tokens</li><li>max: 39 tokens</li></ul> |
 * Samples:
-  | sentence1                                                                                                        | sentence2                                                                                                         |
-  |:-----------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------|
-  | <code>A) A calorie is a unit of measure used to express the amount of energy a food produces in the body.</code> | <code>Another unit of energy, used widely in the health professions and everyday life, is calorie ( cal )?</code> |
-  | <code>solid 1 A state that retains shape independent of the shape of the container it occupies.</code>           | <code>Solid takes neither the shape nor the volume of its container.</code>                                       |
-  | <code>Sometimes the two sides of a fracture moved due to the pressure and a fault was formed.</code>             | <code>A fault is the fracture caused when rocks on both sides move.</code>                                        |
+  | sentence1                                                                                                                              | sentence2                                                                                                |
+  |:---------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|
+  | <code>Tornadoes Tornadoes are most common during the period of spring and early summer, but can occur in any month of the year.</code> | <code>Tornadoes can occur in any.</code>                                                                 |
+  | <code>A watershed is the land area that drains water into a river system or other body of water.</code>                                | <code>All of the land drained by a river system is called its basin, or the "wet" term  watershed</code> |
+  | <code>Humans have 23 pairs of chromosomes (one set from each parent).</code>                                                           | <code>Humans have 23 pairs pairs of chromosomes.</code>                                                  |
 * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
   ```json
   {
@@ -388,19 +442,19 @@ You can finetune this model on your own dataset.
 #### xsum-pairs
 
 * Dataset: [xsum-pairs](https://huggingface.co/datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co/datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206)
-* Size: 150,000 training samples
+* Size: 10,000 training samples
 * Columns: <code>sentence1</code> and <code>sentence2</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                            | sentence2                                                                         |
-  |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
-  | type    | string                                                                               | string                                                                            |
-  | details | <ul><li>min: 13 tokens</li><li>mean: 346.32 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.95 tokens</li><li>max: 66 tokens</li></ul> |
+  |         | sentence1                                                                           | sentence2                                                                          |
+  |:--------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                              | string                                                                             |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 355.4 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 27.07 tokens</li><li>max: 70 tokens</li></ul> |
 * Samples:
-  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | sentence2                                                                                                                                                      |
-  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------|
-  | <code>Jacob Murphy fired in his 10th goal of the season from inside the box to give the Canaries the lead at the break.<br>Adam Hammill, Angus MacDonald and Marley Watkins all went close for the visitors after the restart.<br>Norwich then stretched their lead thanks to MacDonald's own goal to leave them five points behind sixth-placed Sheffield Wednesday.<br>Victory means caretaker boss Alan Irvine has now claimed four points from a possible six since the departure of Alex Neil.<br>The hosts dominated the early proceedings, with Jonny Howson and Alex Pritchard both being denied by Barnsley keeper Adam Davies.<br>After Hammill had a goal ruled out for a clear offside at the other end, winger Murphy gave the Canaries a deserved lead moments before the break when, having being picked out by Cameron Jerome, he drilled a shot low and into the corner of the net.<br>Hammill was unlucky to not get a strong enough flick on Andy Yiadom's cross to make it 1-1 after the restart and MacDonald saw a close-range effort well saved by Michael McGovern from the resulting corner.<br>But, after Steven Naismith fired over for the Canaries with just the keeper to beat, they doubled their lead in fortunate circumstances as an effort from Murphy deflected off MacDonald into the net.<br>Jerome and Howson then went close to adding a third as Norwich coasted to three points.<br>Norwich caretaker manager Alan Irvine:<br>"I was asked to take charge for two games and I have done that. I haven't heard anything more about what happens going forward, but I should imagine I will be speaking to someone soon to find out what happens next week.<br>"If that is to be my last game in charge it was a good way to finish - and the win certainly makes it interesting as far as the play-offs are concerned.<br>"Being five points behind sounds a lot better than being eight points behind - and as I said last week there are still plenty of points to play for."<br>Barnsley manager Paul Heckingbottom:<br>"The take-away message from that game is hit the target, score goals.<br>"There were plenty of positives to take away from it, but if you are going to get anything in this league you have got to be clinical in front of goal.<br>"It's frustrating, but there is still plenty to play for. We will keep striving to get that perfect performance and obviously want to win as many games as possible between now and the end of the season."<br>Match ends, Norwich City 2, Barnsley 0.<br>Second Half ends, Norwich City 2, Barnsley 0.<br>Hand ball by Nélson Oliveira (Norwich City).<br>Attempt missed. Ryan Kent (Barnsley) right footed shot from the centre of the box is close, but misses to the left. Assisted by Ryan Hedges with a cross.<br>Attempt saved. Nélson Oliveira (Norwich City) left footed shot from outside the box is saved in the centre of the goal.<br>Alex Pritchard (Norwich City) wins a free kick in the attacking half.<br>Foul by Alex Mowatt (Barnsley).<br>Corner,  Barnsley. Conceded by Jonny Howson.<br>Foul by Graham Dorrans (Norwich City).<br>Matthew James (Barnsley) wins a free kick in the defensive half.<br>Attempt missed. Tom Bradshaw (Barnsley) left footed shot from the centre of the box is too high. Assisted by Gethin Jones with a cross.<br>Attempt missed. Steven Naismith (Norwich City) right footed shot from the right side of the box misses to the left. Assisted by Alex Pritchard.<br>Corner,  Norwich City. Conceded by Angus MacDonald.<br>Attempt blocked. Jonny Howson (Norwich City) right footed shot from the right side of the box is blocked. Assisted by Graham Dorrans with a through ball.<br>Substitution, Norwich City. Graham Dorrans replaces Jacob Murphy.<br>Substitution, Norwich City. Nélson Oliveira replaces Cameron Jerome.<br>Substitution, Barnsley. Ryan Hedges replaces Adam Hammill.<br>Attempt missed. Ryan Kent (Barnsley) left footed shot from the centre of the box is high and wide to the left. Assisted by Matthew James with a cross.<br>Attempt saved. Cameron Jerome (Norwich City) right footed shot from the centre of the box is saved in the bottom right corner. Assisted by Jacob Murphy with a through ball.<br>Substitution, Barnsley. Alex Mowatt replaces Marley Watkins.<br>Corner,  Barnsley. Conceded by Ivo Pinto.<br>Corner,  Barnsley. Conceded by Russell Martin.<br>Attempt blocked. Tom Bradshaw (Barnsley) right footed shot from the right side of the box is blocked. Assisted by Ryan Kent.<br>Own Goal by Angus MacDonald, Barnsley.  Norwich City 2, Barnsley 0.<br>Attempt saved. Jacob Murphy (Norwich City) right footed shot from the centre of the box is saved in the bottom right corner. Assisted by Alex Pritchard.<br>Attempt saved. Steven Naismith (Norwich City) left footed shot from the centre of the box is saved in the bottom left corner. Assisted by Steven Whittaker with a cross.<br>Ivo Pinto (Norwich City) wins a free kick in the defensive half.<br>Foul by Adam Hammill (Barnsley).<br>Attempt saved. Ryan Kent (Barnsley) right footed shot from outside the box is saved in the centre of the goal. Assisted by Marley Watkins.<br>Attempt missed. Josh Scowen (Barnsley) right footed shot from outside the box is high and wide to the right. Assisted by Adam Hammill.<br>Jacob Murphy (Norwich City) wins a free kick in the attacking half.<br>Foul by Angus MacDonald (Barnsley).<br>Ryan Bennett (Norwich City) wins a free kick in the defensive half.<br>Foul by Marc Roberts (Barnsley).<br>Ivo Pinto (Norwich City) is shown the yellow card for a bad foul.<br>Foul by Ivo Pinto (Norwich City).<br>Ryan Kent (Barnsley) wins a free kick in the attacking half.<br>Foul by Ryan Bennett (Norwich City).<br>Tom Bradshaw (Barnsley) wins a free kick in the attacking half.<br>Attempt missed. Steven Naismith (Norwich City) left footed shot from the left side of the box is too high. Assisted by Alex Pritchard.</code> | <code>Norwich City kept their Championship play-off hopes alive by beating Barnsley at Carrow Road.</code>                                                     |
-  | <code>Political reporter Samantha Maiden said the offensive text, which also contained strong language, was intended for disgraced ex-minister Jamie Briggs.<br>She said Mr Dutton apologised for the message about her article referring to Mr Briggs' recent resignation.<br>The BBC has approached Mr Dutton's office for comment.<br>He reportedly told News Corp in a statement he is expecting a "tough time" in Ms Maiden's next article.<br>"Sam and I have exchanged some robust language over the years so we had a laugh after this and I apologised to her straightaway, which she took in good faith," Mr Dutton was quoted as saying.<br>Former Cities Minister Jamie Briggs resigned last week following a complaint from a female public servant over his alleged conduct during a night out in Hong Kong.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>Australia's Immigration Minister Peter Dutton has reportedly apologised for mistakenly sending an SMS to a journalist, calling her a "mad witch".</code> |
-  | <code>Demonstrators have moved around several sites since April to highlight a crisis in temporary housing.<br>The council's lawyer told the court "trespass, highways and planning laws" were the grounds for the case.<br>The cost to the council in terms of additional policing, security and legal costs has exceeded £100,000, he added.<br>Ahead of the hearing, tents were set up and a banner reading "The homeless resistance" was hung outside Manchester Civil Justice Centre.<br>'Grave and serious'<br>Protesters said they hoped to be offered "permanent, suitable accommodation".<br>Some had earlier refused temporary accommodation offered by the council because they said it was "not suitable" and they felt unsafe.<br>The council said it had engaged with the protestors and had offered them support, but it could not accept anti-social behaviour and disruption to residents and businesses.<br>Councillor Nigel Murphy added the exclusion order was "designed to prevent the recurrence of camps and not targeted at individual rough sleepers".<br>He said the council would work with police and court bailiffs to "regain possession" of areas taken over by camps in St Ann's Square and Castlefield as soon as possible.<br>John Clegg, from Unison's community branch, said there was a lack of social housing in Manchester.<br>He added: "There is a large amount of money for building private flats, more hotels are going up all the time, but there are no plans to build any social housing. That's wrong. That's absolutely wrong."<br>"In our view an injunction is a form of gating, and sending out a message that poor people are not wanted and should not be coming in to the city centre."</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>A Manchester City Council application for an injunction to stop the setting up of homeless camps in the city centre has been granted.</code>             |
+  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | sentence2                                                                                                                                              |
+  |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Former Royal Marine Stephen Gough, 55, was found guilty by a jury at Winchester Crown Court of breaching an anti-social behaviour order (Asbo).<br>The order bans him from taking off his clothes in public.<br>He had refused to put on clothes as he left Winchester Prison after being imprisoned for a previous Asbo breach.<br>Judge Jane Miller QC suggested moves should be made to find Gough a closed nudist community to live in to prevent the cycle of imprisonment which has seen him jailed for much of the past eight years.<br>Gough earned his nickname when he completed a naked trek from Land's End to John O'Groats in 2003.<br>A BBC documentary team followed his journey.<br>He had previously argued there was nothing intimidating about him appearing "in his natural human state".</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | <code>A man known as the "naked rambler" has been jailed for two-and-a-half years after walking out of prison wearing only his boots and socks.</code> |
+  | <code>Colbert, 49, is host of Comedy Central's acclaimed late-night satire programme The Colbert Report.<br>Letterman, 66, said last week he was retiring after 21 years hosting the CBS show and 11 years on NBC's Late Night.<br>On his show, Colbert plays a satirical version of himself to mock right-wing pundits. He has suggested he will retire the character for the new show.<br>"Stephen Colbert is one of the most inventive and respected forces on television," CBS president Leslie Moonves wrote in a statement following the announcement.<br>"David Letterman's legacy and accomplishments are an incredible source of pride for all of us here, and today's announcement speaks to our commitment of upholding what he established for CBS in late night."<br>On his popular and influential Emmy-winning Comedy Central programme, Colbert's biting brand of satire has drawn critical acclaim as well as provoking ire, often from the Republicans and conservatives he skewers.<br>Recently he was attacked on social media for a joke some viewed as disparaging toward Asian Americans but which he meant as a satirical jab at the owner of the Washington Redskins American football team.<br>Of his hiring to the Late Show top spot, Colbert said, "simply being a guest on David Letterman's show has been a highlight of my career."<br>"I never dreamed that I would follow in his footsteps, though everyone in late night follows Dave's lead."</code> | <code>Stephen Colbert will succeed Late Show host David Letterman upon his retirement next year, CBS has said.</code>                                  |
+  | <code>The pair are both through to the last eight of the Welsh Open at Cardiff, with White beating defending champion John Higgins 4-1.<br>"Michael White is an amazing player. He has absolutely everything," O'Sullivan, 40, told BBC Wales.<br>"Wales is going to have another world champion... maybe a multiple world champion."<br>O'Sullivan added: "John Higgins behind Stephen Hendry is the best player I have seen and he has lost to Michael.<br>"He [White] is fearless, he plays everybody like they are the same. He doesn't care who is sat in that seat against him and that is a gift.<br>"Michael is now a top player. He's definitely knocking on the doo. If he won the world title this year I wouldn't be surprised."<br>Englishman O'Sullivan had little trouble beating China's Yu Delu 4-1 after losing the opening frame.<br>White, 24, described the victory over four-time world champion Higgins as a career highlight.<br>"It is a fantastic win, [it] would have to be up there with the best of my career," White said.<br>"I am feeling very relaxed and laid back and it showed in my performance.<br>"To get that win in my home event makes it more special. It is good for Wales and the tournament.<br>"Who knows, maybe a Welsh player could get to the final."</code>                                                                                                                                                                           | <code>Welshman Michael White can become a world champion, according to five-time winner Ronnie O'Sullivan.</code>                                      |
 * Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
   ```json
   {
@@ -412,7 +466,7 @@ You can finetune this model on your own dataset.
 #### 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: 180,000 training samples
+* Size: 10,000 training samples
 * Columns: <code>sentence1</code> and <code>sentence2</code>
 * Approximate statistics based on the first 1000 samples:
   |         | sentence1                                                                           | sentence2                                                                         |
@@ -433,20 +487,213 @@ You can finetune this model on your own dataset.
   }
   ```
 
+#### sciq_pairs
+
+* Dataset: [sciq_pairs](https://huggingface.co/datasets/allenai/sciq) at [2c94ad3](https://huggingface.co/datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
+* Size: 10,000 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                          |
+  |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 7 tokens</li><li>mean: 17.26 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 84.37 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                   | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What type of organism is commonly used in preparation of foods such as cheese and yogurt?</code>                                                                                      | <code>Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+  | <code>What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?</code> | <code>Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | <code>Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what?</code>                                                                       | <code>Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+#### qasc_pairs
+
+* Dataset: [qasc_pairs](https://huggingface.co/datasets/allenai/qasc) at [a34ba20](https://huggingface.co/datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
+* Size: 8,134 training samples
+* Columns: <code>id</code>, <code>sentence1</code>, and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | id                                                                                 | sentence1                                                                         | sentence2                                                                          |
+  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                            | string                                                                             |
+  | details | <ul><li>min: 17 tokens</li><li>mean: 21.35 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.47 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.55 tokens</li><li>max: 66 tokens</li></ul> |
+* Samples:
+  | id                                          | sentence1                                                      | sentence2                                                                                                                                                                          |
+  |:--------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K</code> | <code>What type of water formation is formed by clouds?</code> | <code>beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds.</code>                                         |
+  | <code>3LS2AMNW5FPNJK3C3PZLZCPX562OQO</code> | <code>Where do beads of water come from?</code>                | <code>beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water</code> |
+  | <code>3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3</code> | <code>What forms beads of water? </code>                       | <code>beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water.</code>                                                 |
+* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
+  ```json
+  {'guide': SentenceTransformer(
+    (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+    (2): Normalize()
+  ), 'temperature': 0.05}
+  ```
+
+#### openbookqa_pairs
+
+* Dataset: [openbookqa_pairs](https://huggingface.co/datasets/allenai/openbookqa) at [388097e](https://huggingface.co/datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454)
+* Size: 2,740 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 13.83 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.37 tokens</li><li>max: 30 tokens</li></ul> |
+* Samples:
+  | sentence1                                        | sentence2                                                                 |
+  |:-------------------------------------------------|:--------------------------------------------------------------------------|
+  | <code>The sun is responsible for</code>          | <code>the sun is the source of energy for physical cycles on Earth</code> |
+  | <code>When food is reduced in the stomach</code> | <code>digestion is when stomach acid breaks down food</code>              |
+  | <code>Stars are</code>                           | <code>a star is made of gases</code>                                      |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+#### msmarco_pairs
+
+* Dataset: [msmarco_pairs](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co/datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9)
+* Size: 10,000 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 4 tokens</li><li>mean: 8.61 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 75.09 tokens</li><li>max: 206 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                           | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  |:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>what are the liberal arts?</code>                                             | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> | <code>Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code>                                                                                                                                                                                                                                                                                                                                           |
+  | <code>what is normal plat count</code>                                              | <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+#### 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: 10,000 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                          | sentence2                                                                            |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 10 tokens</li><li>mean: 11.77 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 131.57 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence1                                                       | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+  |:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.</code> |
+  | <code>who sang what in the world's come over you</code>         | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+  | <code>who produces the most wool in the world</code>            | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+#### 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: 10,000 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                            |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 15.16 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 456.87 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                 | sentence2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  |:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</code>     | <code>The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. <http://www.nobelprize.org/nobel_prizes/literature/laureates/1930/></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
+  | <code>Where in England was Dame Judi Dench born?</code>                                   | <code>Judi Dench - IMDb IMDb Actress | Music Department | Soundtrack Judi Dench was born in York, England, to Eleanora Olive (Jones), who was from Dublin, Ireland, and Reginald Arthur Dench, a doctor from Dorset, England. She attended Mount School in York, and studied at the Central School of Speech and Drama. She has performed with Royal Shakespeare Company, the National Theatre, and at Old Vic Theatre. She is a ... See full bio » Born: a list of 35 people created 02 Jul 2011 a list of 35 people created 19 Apr 2012 a list of 35 people created 28 May 2014 a list of 25 people created 05 Aug 2014 a list of 26 people created 18 May 2015 Do you have a demo reel? Add it to your IMDbPage How much of Judi Dench's work have you seen? User Polls Won     1     Oscar. Another    59 wins & 163 nominations. See more awards  » Known For  2016 The Hollow Crown (TV Series) Cecily, Duchess of York  2015 The Vote (TV Movie) Christine Metcalfe - Total War (1996) ... Narrator (voice) - Stalemate (1996) ... Narrator (voice)  1992 The Torch (TV Mini-Series) Aba  1990 Screen One (TV Series) Anne  1989 Behaving Badly (TV Mini-Series) Bridget  1981 BBC2 Playhouse (TV Series) Sister Scarli  1976 Arena (TV Series documentary) Sweetie Simpkins  1973 Ooh La La! (TV Series) Amélie  1966 Court Martial (TV Series) Marthe  1963 Z Cars (TV Series) Elena Collins  1963 Love Story (TV Series) Pat McKendrick  1960 The Terrible Choice (TV Series) Good Angel Music department (1 credit)   A Fine Romance (TV Series) (theme sung by - 14 episodes, 1981 - 1983) (theme song sung by - 12 episodes, 1983 - 1984) - A Romantic Meal (1984) ... (theme song sung by) - Problems (1984) ... (theme song sung by)  2013 Fifty Years on Stage (TV Movie) (performer: "Send in the Clowns")  2009 Nine (performer: "Folies Bergère") - What's Wrong with Mrs Bale? (1997) ... (performer: "Raindrops Keep Fallin' On My Head" - uncredited) - Misunderstandings (1993) ... (performer: "Walkin' My Baby Back Home" - uncredited)  1982-1984 A Fine Romance (TV Series) (performer - 2 episodes) - The Telephone Call (1984) ... (performer: "Boogie Woogie Bugle Boy" - uncredited) - Furniture (1982) ... (performer: "Rule, Britannia!" - uncredited) Hide   2009 Waiting in Rhyme (Video short) (special thanks)  2007 Expresso (Short) (special thanks)  1999 Shakespeare in Love and on Film (TV Movie documentary) (thanks - as Dame Judi Dench) Hide   2016 Rio Olympics (TV Mini-Series) Herself  2015 In Conversation (TV Series documentary) Herself  2015 Entertainment Tonight (TV Series) Herself  2015 CBS This Morning (TV Series) Herself - Guest  2015 The Insider (TV Series) Herself  1999-2014 Cinema 3 (TV Series) Herself  2013 Good Day L.A. (TV Series) Herself - Guest  2013 Arena (TV Series documentary) Herself  2013 At the Movies (TV Series) Herself  2013 Shooting Bond (Video documentary) Herself  2013 Bond's Greatest Moments (TV Movie documentary) Herself  2012 Made in Hollywood (TV Series) Herself  1999-2012 Charlie Rose (TV Series) Herself - Guest  2008-2012 This Morning (TV Series) Herself - Guest  2012 The Secrets of Skyfall (TV Short documentary) Herself  2012 Anderson Live (TV Series) Herself  2012 J. Edgar: A Complicated Man (Video documentary short) Herself  2011 The Many Faces of... (TV Series documentary) Herself / Various Characters  2011 Na plovárne (TV Series) Herself  2010 BBC Proms (TV Series) Herself  2010 The South Bank Show Revisited (TV Series documentary) Herself - Episode #6.68 (2009) ... Herself - Guest (as Dame Judi Dench)  2007-2009 Breakfast (TV Series)  2009 Larry King Live (TV Series) Herself - Guest  2009 The One Show (TV Series) Herself  2009 Cranford in Detail (Video documentary short) Herself / Miss Matty Jenkins (as Dame Judi Dench)  2005-2008 The South Bank Show (TV Series documentary) Herself  2008 Tavis Smiley (TV Series) Herself - Guest  2007 ITV News (TV Series) Herself - BAFTA Nominee  2007 The Making of Cranford (Video documentary short) Herself / Miss Matty Jenkyns (as Dame Judi Dench)  2006 Becoming Bond (TV Movie documentary) Herself  2006 Corazón de... (TV Series) Hers</code> |
+  | <code>In which decade did Billboard magazine first publish and American hit chart?</code> | <code>The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day.  he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul</code>                                            |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+#### 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: 10,000 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 13.53 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.68 tokens</li><li>max: 43 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                           | sentence2                                                                                               |
+  |:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
+  | <code>Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?</code> | <code>I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?</code> |
+  | <code>How can I be a good geologist?</code>                                                         | <code>What should I do to be a great geologist?</code>                                                  |
+  | <code>How do I read and find my YouTube comments?</code>                                            | <code>How can I see all my Youtube comments?</code>                                                     |
+* Loss: [<code>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+#### gooaq_pairs
+
+* Dataset: [gooaq_pairs](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
+* Size: 10,000 training samples
+* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                           |
+  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                              |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 11.6 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 57.74 tokens</li><li>max: 127 tokens</li></ul> |
+* Samples:
+  | sentence1                                          | sentence2                                                                                                                                                                                                                                                                                                                                       |
+  |:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>is toprol xl the same as metoprolol?</code>  | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code>                                                                                                    |
+  | <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> |
+  | <code>how are babushka dolls made?</code>          | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code>                                           |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
 ### Evaluation Datasets
 
 #### nli-pairs
 
 * Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
 * Size: 6,808 evaluation samples
-* Columns: <code>sentence1</code> and <code>sentence2</code>
+* Columns: <code>anchor</code> and <code>positive</code>
 * Approximate statistics based on the first 1000 samples:
-  |         | sentence1                                                                         | sentence2                                                                        |
+  |         | anchor                                                                            | positive                                                                         |
   |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
   | type    | string                                                                            | string                                                                           |
   | details | <ul><li>min: 5 tokens</li><li>mean: 17.64 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.67 tokens</li><li>max: 29 tokens</li></ul> |
 * Samples:
-  | sentence1                                                                                                                                                                      | sentence2                                                   |
+  | anchor                                                                                                                                                                         | positive                                                    |
   |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|
   | <code>Two women are embracing while holding to go packages.</code>                                                                                                             | <code>Two woman are holding packages.</code>                |
   | <code>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.</code> | <code>Two kids in numbered jerseys wash their hands.</code> |
@@ -459,6 +706,30 @@ You can finetune this model on your own dataset.
   }
   ```
 
+#### scitail-pairs-pos
+
+* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
+* Size: 1,304 evaluation samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                         | label                                           |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | int                                             |
+  | details | <ul><li>min: 5 tokens</li><li>mean: 22.52 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.34 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~47.50%</li><li>1: ~52.50%</li></ul> |
+* Samples:
+  | sentence1                                                                                                                         | sentence2                                                                                          | label          |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
+  | <code>An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions.</code> | <code>Replace another in a molecule happens to atoms during a substitution reaction.</code>        | <code>0</code> |
+  | <code>Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase;</code>                       | <code>Wavelength is the distance between two corresponding points of adjacent waves called.</code> | <code>1</code> |
+  | <code>humans normally have 23 pairs of chromosomes.</code>                                                                        | <code>Humans typically have 23 pairs pairs of chromosomes.</code>                                  | <code>1</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
 #### qnli-contrastive
 
 * Dataset: [qnli-contrastive](https://huggingface.co/datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co/datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
@@ -481,8 +752,8 @@ You can finetune this model on your own dataset.
 #### Non-Default Hyperparameters
 
 - `eval_strategy`: steps
-- `per_device_train_batch_size`: 94
-- `per_device_eval_batch_size`: 32
+- `per_device_train_batch_size`: 28
+- `per_device_eval_batch_size`: 16
 - `learning_rate`: 2e-05
 - `weight_decay`: 1e-10
 - `num_train_epochs`: 2
@@ -491,7 +762,7 @@ You can finetune this model on your own dataset.
 - `save_safetensors`: False
 - `fp16`: True
 - `push_to_hub`: True
-- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-checkpoints-tmp
+- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp
 - `hub_strategy`: checkpoint
 - `batch_sampler`: no_duplicates
 
@@ -502,8 +773,8 @@ You can finetune this model on your own dataset.
 - `do_predict`: False
 - `eval_strategy`: steps
 - `prediction_loss_only`: True
-- `per_device_train_batch_size`: 94
-- `per_device_eval_batch_size`: 32
+- `per_device_train_batch_size`: 28
+- `per_device_eval_batch_size`: 16
 - `per_gpu_train_batch_size`: None
 - `per_gpu_eval_batch_size`: None
 - `gradient_accumulation_steps`: 1
@@ -577,7 +848,7 @@ You can finetune this model on your own dataset.
 - `use_legacy_prediction_loop`: False
 - `push_to_hub`: True
 - `resume_from_checkpoint`: None
-- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-checkpoints-tmp
+- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp
 - `hub_strategy`: checkpoint
 - `hub_private_repo`: False
 - `hub_always_push`: False
@@ -610,38 +881,38 @@ You can finetune this model on your own dataset.
 </details>
 
 ### Training Logs
-| Epoch  | Step  | Training Loss | qnli-contrastive loss | nli-pairs loss | sts-test_spearman_cosine |
-|:------:|:-----:|:-------------:|:---------------------:|:--------------:|:------------------------:|
-| None   | 0     | -             | 20.1737               | 4.0959         | -                        |
-| 0.1001 | 734   | 4.796         | -                     | -              | -                        |
-| 0.2001 | 1468  | 1.3015        | 0.0358                | 0.9115         | -                        |
-| 0.3002 | 2202  | 0.89          | -                     | -              | -                        |
-| 0.4002 | 2936  | 0.716         | 0.0168                | 0.5944         | -                        |
-| 0.5003 | 3670  | 0.6365        | -                     | -              | -                        |
-| 0.6003 | 4404  | 0.5883        | 0.0164                | 0.4975         | -                        |
-| 0.7004 | 5138  | 0.5192        | -                     | -              | -                        |
-| 0.8004 | 5872  | 0.4961        | 0.0288                | 0.4450         | -                        |
-| 0.9005 | 6606  | 0.6035        | -                     | -              | -                        |
-| 1.0005 | 7340  | 0.4733        | 0.0110                | 0.4215         | -                        |
-| 1.1006 | 8074  | 0.4002        | -                     | -              | -                        |
-| 1.2007 | 8808  | 0.3929        | 0.0454                | 0.3796         | -                        |
-| 1.3007 | 9542  | 0.3826        | -                     | -              | -                        |
-| 1.4008 | 10276 | 0.3522        | 0.0178                | 0.3714         | -                        |
-| 1.5008 | 11010 | 0.3627        | -                     | -              | -                        |
-| 1.6009 | 11744 | 0.3553        | 0.0257                | 0.3629         | -                        |
-| 1.7009 | 12478 | 0.3406        | -                     | -              | -                        |
-| 1.8010 | 13212 | 0.3288        | 0.0289                | 0.3575         | -                        |
-| 1.9010 | 13946 | 0.4563        | -                     | -              | -                        |
-| 2.0    | 14672 | -             | 0.0320                | 0.3551         | 0.7930                   |
+| Epoch | Step | Training Loss | nli-pairs loss | qnli-contrastive loss | scitail-pairs-pos loss | sts-test_spearman_cosine |
+|:-----:|:----:|:-------------:|:--------------:|:---------------------:|:----------------------:|:------------------------:|
+| None  | 0    | -             | 2.9404         | 4.1500                | 2.3949                 | -                        |
+| 0.1   | 471  | 3.3296        | 1.8879         | 2.2598                | 1.3439                 | -                        |
+| 0.2   | 942  | 1.8704        | 0.9546         | 1.8402                | 0.5629                 | -                        |
+| 0.3   | 1413 | 1.2621        | 0.7152         | 1.3887                | 0.4553                 | -                        |
+| 0.4   | 1884 | 1.2512        | 0.5274         | 0.8418                | 0.3621                 | -                        |
+| 0.5   | 2355 | 1.1724        | 0.4927         | 0.1424                | 0.3924                 | -                        |
+| 0.6   | 2826 | 0.9036        | 0.4621         | 0.3409                | 0.3777                 | -                        |
+| 0.7   | 3297 | 1.0374        | 0.4111         | 0.2125                | 0.3417                 | -                        |
+| 0.8   | 3768 | 0.9259        | 0.3853         | 0.1646                | 0.2819                 | -                        |
+| 0.9   | 4239 | 0.8709        | 0.3749         | 0.1157                | 0.2912                 | -                        |
+| 1.0   | 4710 | 0.8686        | 0.3636         | 0.0961                | 0.3109                 | -                        |
+| 1.1   | 5181 | 0.726         | 0.3744         | 0.0453                | 0.3424                 | -                        |
+| 1.2   | 5652 | 0.8151        | 0.3502         | 0.1835                | 0.2602                 | -                        |
+| 1.3   | 6123 | 0.7127        | 0.3362         | 0.1089                | 0.2460                 | -                        |
+| 1.4   | 6594 | 0.8408        | 0.3184         | 0.0701                | 0.2784                 | -                        |
+| 1.5   | 7065 | 0.7845        | 0.3191         | 0.0318                | 0.2822                 | -                        |
+| 1.6   | 7536 | 0.5766        | 0.3056         | 0.0566                | 0.2774                 | -                        |
+| 1.7   | 8007 | 0.7304        | 0.2991         | 0.0542                | 0.2736                 | -                        |
+| 1.8   | 8478 | 0.6639        | 0.2949         | 0.0515                | 0.2694                 | -                        |
+| 1.9   | 8949 | 0.6153        | 0.2938         | 0.0589                | 0.2718                 | -                        |
+| 2.0   | 9420 | 0.6665        | 0.2937         | 0.0569                | 0.2724                 | 0.7727                   |
 
 
 ### Framework Versions
-- Python: 3.10.12
+- Python: 3.10.13
 - Sentence Transformers: 3.0.1
 - Transformers: 4.41.2
-- PyTorch: 2.3.0+cu121
-- Accelerate: 0.31.0
-- Datasets: 2.20.0
+- PyTorch: 2.1.2
+- Accelerate: 0.30.1
+- Datasets: 2.19.2
 - Tokenizers: 0.19.1
 
 ## Citation