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Add new SentenceTransformer model.

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  1. README.md +29 -34
  2. model.safetensors +1 -1
README.md CHANGED
@@ -11,40 +11,35 @@ tags:
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  base_model: sentence-transformers/all-MiniLM-L6-v2
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  datasets: []
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  widget:
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- - source_sentence: Truck production was added in May 1993.
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- sentences:
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- - Commercial DNP is unable to be utilized as an antiseptic or as a non-selective
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- bioaccumulating pesticide.
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- - Research has suggested that the attentional focus is variable in size.
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- - Truck creation was removed in May 1993.
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- - source_sentence: This theory is controversial and has been rejected by other researchers.
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  sentences:
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  - The problem of head-of-line blocking using Virtual Output Queues was discussed
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  in the paper.
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- - The theory has been rejected by other researchers.
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- - Omaha Public Power District (OPPD) is considered the state's smallest purchaser
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- of wind energy.
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- - source_sentence: In Yunnan, China, several ethnic minority groups produce Rushan
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- and Rubing from cow's milk.
 
 
 
 
 
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  sentences:
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- - In Yunnan, China, only the ethnic majority groups produce Rushan and Rubing from
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- cow's milk.
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- - I have always used corded headsets and the freedom from the wireless is very helpful.
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- - In 2014, Nextgen fails to achieve KLAS Top Performance Honors for Ambulatory RCM
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- Services.
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- - source_sentence: The sultan Muhammad al-Nasir Ibn Qalawun demolished the minaret
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- permanently after an earthquake in October 1318.
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  sentences:
 
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  - Six other dams were unsuccessful that day, two were small and four were minor
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  in size.
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- - This store was not featured in the SavaCentre TV promotions in 1983.
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- - The sultan Muhammad al-Nasir Ibn Qalawun renovated the minaret after an earthquake
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- in October 1318.
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- - source_sentence: It must be taken at the start of main meals to have maximal effect.
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  sentences:
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- - It must be taken at the start of the main meal.
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- - My name is important.
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- - The species is named in honor of the divorce of Sara Anderson and Malcolm Slaney.
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  pipeline_tag: sentence-similarity
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  ---
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@@ -98,9 +93,9 @@ from sentence_transformers import SentenceTransformer
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  model = SentenceTransformer("LeoChiuu/all-MiniLM-L6-v2-negations")
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  # Run inference
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  sentences = [
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- 'It must be taken at the start of main meals to have maximal effect.',
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- 'It must be taken at the start of the main meal.',
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- 'The species is named in honor of the divorce of Sara Anderson and Malcolm Slaney.',
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  ]
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  embeddings = model.encode(sentences)
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  print(embeddings.shape)
@@ -163,11 +158,11 @@ You can finetune this model on your own dataset.
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  | type | string | string | int |
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  | details | <ul><li>min: 9 tokens</li><li>mean: 16.36 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.55 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~61.33%</li><li>1: ~38.67%</li></ul> |
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  * Samples:
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- | sentence_0 | sentence_1 | label |
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- |:--------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:---------------|
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- | <code>And I tell you something, it's fair.</code> | <code>And I tell you something, it's not fair.</code> | <code>0</code> |
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- | <code>The meeting was commemorated with his image on a stamp from the Vatican post office.</code> | <code>The meeting was commemorated with only his name on a stamp from the Vatican post office.</code> | <code>0</code> |
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- | <code>On June 22, 2009, the SEC also filed civil fraud charges.</code> | <code>On June 22, 2009, the SEC kept itself from filing civil fraud charges.</code> | <code>0</code> |
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  * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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  ```json
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  {
 
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  base_model: sentence-transformers/all-MiniLM-L6-v2
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  datasets: []
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  widget:
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+ - source_sentence: The Turkish language is an official language of the republic.
 
 
 
 
 
 
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  sentences:
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  - The problem of head-of-line blocking using Virtual Output Queues was discussed
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  in the paper.
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+ - He had also written Sonnets in Urdu.
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+ - The Turkish language is an official language of the republic, alongside the Greek
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+ language.
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+ - source_sentence: Spinal locks and cervical locks are allowed and mandatory in IBJJF
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+ Brazilian jiu-jitsu competitions.
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+ sentences:
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+ - He was the head baseball coach at Louisiana State University.
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+ - Spinal locks and cervical locks are forbidden in IBJJF Brazilian jiu-jitsu competitions.
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+ - It must be taken at the start of the main meal.
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+ - source_sentence: The blood samples tested positive for sarin.
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  sentences:
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+ - The blood samples tested negative for sarin.
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+ - It was an inexpensive piece, but I would still have expected better quality.
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+ - Additionally, a church at San Lazaro in Orange Walk District suffered severe damage.
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+ - source_sentence: Several small groups claim to continue to practice this faith.
 
 
 
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  sentences:
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+ - He only extensively held a position of Visiting Lecturer at UCLA in 1967.
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  - Six other dams were unsuccessful that day, two were small and four were minor
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  in size.
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+ - All tiny groups claim to continue to practice this faith.
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+ - source_sentence: The wings are diffuse with scales.
 
 
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  sentences:
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+ - The wings are pale greenish brown, diffused with blackish scales.
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+ - The theory has been rejected by other researchers.
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+ - His views were in a minority at the Westminster Assembly.
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  pipeline_tag: sentence-similarity
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  ---
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  model = SentenceTransformer("LeoChiuu/all-MiniLM-L6-v2-negations")
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  # Run inference
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  sentences = [
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+ 'The wings are diffuse with scales.',
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+ 'The wings are pale greenish brown, diffused with blackish scales.',
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+ 'His views were in a minority at the Westminster Assembly.',
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  ]
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  embeddings = model.encode(sentences)
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  print(embeddings.shape)
 
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  | type | string | string | int |
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  | details | <ul><li>min: 9 tokens</li><li>mean: 16.36 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 16.55 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>0: ~61.33%</li><li>1: ~38.67%</li></ul> |
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  * Samples:
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+ | sentence_0 | sentence_1 | label |
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+ |:------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:---------------|
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+ | <code>It is just very clean when burned.</code> | <code>It is also very dirty when burned.</code> | <code>0</code> |
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+ | <code>The blood samples tested positive for sarin.</code> | <code>The blood samples tested negative for sarin.</code> | <code>0</code> |
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+ | <code>The species is named in honor of the marriage of Sara Anderson and Malcolm Slaney.</code> | <code>The species is named in honor of the divorce of Sara Anderson and Malcolm Slaney.</code> | <code>0</code> |
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  * Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
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  ```json
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  {
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