Add new SentenceTransformer model
Browse files- README.md +160 -128
- model.safetensors +1 -1
README.md
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- feature-extraction
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- dense
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- generated_from_trainer
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- dataset_size:
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- loss:AnglELoss
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- loss:CoSENTLoss
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- loss:CachedMultipleNegativesRankingLoss
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\ pediatrician, or paediatrician. The word pediatrics and its cognates mean healer\
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\ of children; they derive from two Greek words: Ï\x80αá¿\x96Ï\x82 (pais child)\
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\ and ἰαÏ\x84Ï\x81Ï\x8CÏ\x82 (iatros doctor, healer)."
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- source_sentence:
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Coffin Nevins and David Nevins , Jr..
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sentences:
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sentences:
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sentences:
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sentences:
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datasets:
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- google-research-datasets/paws
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- nyu-mll/glue
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# SentenceTransformer based on jhu-clsp/ettin-encoder-17m
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on
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## Model Details
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- sentence-transformers/s2orc
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- sentence-transformers/codesearchnet
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- sentence-transformers/stackexchange-duplicates
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- **Language:** en
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<!-- - **License:** Unknown -->
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@@ -155,12 +159,12 @@ from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("tasksource/ettin-17m-embed")
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# Run inference
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queries = [
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"
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documents = [
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'
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]
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query_embeddings = model.encode_query(queries)
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document_embeddings = model.encode_document(documents)
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# Get the similarity scores for the embeddings
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similarities = model.similarity(query_embeddings, document_embeddings)
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print(similarities)
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# tensor([[ 0.
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```
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<!--
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| | sentence1 | sentence2 | label |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 11 tokens</li><li>mean: 27.
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* Samples:
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| sentence1
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| <code>
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| <code>
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
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```json
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{
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| | sentence1 | sentence2 | label |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 10 tokens</li><li>mean: 27.
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* Samples:
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| sentence1
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| <code>
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
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```json
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{
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| | sentence1 | sentence2 | label |
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|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 6 tokens</li><li>mean: 13.
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* Samples:
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| sentence1
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| <code>
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
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```json
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{
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* Size: 22,650 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2
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| type | string | string
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| details | <ul><li>min: 6 tokens</li><li>mean: 22.
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* Samples:
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| sentence1
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
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```json
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{
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* Size: 10,047 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| | sentence1 | sentence2
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| type | string | string
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| details | <ul><li>min: 4 tokens</li><li>mean: 17.
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* Samples:
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
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```json
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{
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| | sentence1 | sentence2 | label |
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|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
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| type | string | string | float |
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| details | <ul><li>min: 6 tokens</li><li>mean: 14.
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* Samples:
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| sentence1
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| <code>
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| <code>A
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
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```json
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{
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* Size: 13,317 training samples
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* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
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* Approximate statistics based on the first 1000 samples:
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| details | <ul><li>min:
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* Samples:
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| sentence1
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| <code>A woman is
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
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```json
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{
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* Size: 14,280 training samples
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* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
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* Approximate statistics based on the first 1000 samples:
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| details | <ul><li>min: 0.0</li><li>mean: 3.
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* Samples:
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* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
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```json
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{
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}
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```
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</details>
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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### Training Logs
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| Epoch | Step | Training Loss |
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### Framework Versions
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- feature-extraction
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- dense
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- generated_from_trainer
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- dataset_size:7376192
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- loss:AnglELoss
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- loss:CoSENTLoss
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- loss:CachedMultipleNegativesRankingLoss
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\ pediatrician, or paediatrician. The word pediatrics and its cognates mean healer\
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\ of children; they derive from two Greek words: Ï\x80αá¿\x96Ï\x82 (pais child)\
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\ and ἰαÏ\x84Ï\x81Ï\x8CÏ\x82 (iatros doctor, healer)."
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sentences:
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- As a result of this success , other multinational companies such as Unilever ,
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Microsoft , Digital Equipment , Schlumberger or Lazard have approached the services
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of Leclercq 's company .
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- These buried TBMs were then used to provide an electrical earth .
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and at Long Beach Oil Field in 1921 .
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struck out three in the ninth and allowed only an infield single by Greg Norton.
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sentences:
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- Closer Eric Gagne earned his 17th save in as many opportunities as he struck out
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three of the four batters he faced in the ninth.
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- Syrian soldiers killed in bomb attack
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- Two puppies play with a red chew toy in a field.
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- source_sentence: I think I want to learn French.
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sentences:
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- GOOD
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- In my experience, I have observed them not achieve their goals very quickly but
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I have also witnessed them succeed.
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- my way of thinking I want to learn French
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sentences:
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- House of Cards (season 6) The sixth and final season of the American political
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drama web television series House of Cards was confirmed by Netflix on December
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4, 2017, and is scheduled to be released in late 2018.[1] Unlike previous seasons
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that consisted of thirteen episodes each, the sixth season will consist of only
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eight. The season will not include former lead actor Kevin Spacey, who was fired
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from the show due to sexual misconduct allegations.
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- 'Maggie Elizabeth Jones Maggie Elizabeth Jones (born October 10, 2003) is an American
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child actress, best known for her roles in We Bought a Zoo, the Fox sitcom Ben
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and Kate,[1] and as Lea Clark in American Girl: Lea to the Rescue. [2]'
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in the poem. He was once the most beautiful of all angels, and is a tragic figure
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who famously declares: "Better to reign in Hell than serve in Heaven." Following
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his failed rebellion against God, he is cast out from Heaven and condemned to
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Hell. Satan''s desire to rebel against his creator stems from his unwillingness
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to be subjugated by God and his Son, claiming that angels are "self-begot, self-raised,"[13]
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and thereby denying God''s authority over them as their creator.'
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datasets:
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- google-research-datasets/paws
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- nyu-mll/glue
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# SentenceTransformer based on jhu-clsp/ettin-encoder-17m
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on 20 datasets. It maps sentences & paragraphs to a 256-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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- sentence-transformers/s2orc
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- sentence-transformers/codesearchnet
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- sentence-transformers/stackexchange-duplicates
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- tasksource/flan
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- **Language:** en
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<!-- - **License:** Unknown -->
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model = SentenceTransformer("tasksource/ettin-17m-embed")
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# Run inference
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queries = [
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"who said better to reign in hell than serve in heaven",
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documents = [
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'Paradise Lost Satan, formerly called Lucifer, is the first major character introduced in the poem. He was once the most beautiful of all angels, and is a tragic figure who famously declares: "Better to reign in Hell than serve in Heaven." Following his failed rebellion against God, he is cast out from Heaven and condemned to Hell. Satan\'s desire to rebel against his creator stems from his unwillingness to be subjugated by God and his Son, claiming that angels are "self-begot, self-raised,"[13] and thereby denying God\'s authority over them as their creator.',
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'Maggie Elizabeth Jones Maggie Elizabeth Jones (born October 10, 2003) is an American child actress, best known for her roles in We Bought a Zoo, the Fox sitcom Ben and Kate,[1] and as Lea Clark in American Girl: Lea to the Rescue. [2]',
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'House of Cards (season 6) The sixth and final season of the American political drama web television series House of Cards was confirmed by Netflix on December 4, 2017, and is scheduled to be released in late 2018.[1] Unlike previous seasons that consisted of thirteen episodes each, the sixth season will consist of only eight. The season will not include former lead actor Kevin Spacey, who was fired from the show due to sexual misconduct allegations.',
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]
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query_embeddings = model.encode_query(queries)
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document_embeddings = model.encode_document(documents)
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# Get the similarity scores for the embeddings
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similarities = model.similarity(query_embeddings, document_embeddings)
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print(similarities)
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# tensor([[ 0.6853, -0.1319, -0.0476]])
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```
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<!--
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| | sentence1 | sentence2 | label |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
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| type | string | string | int |
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| details | <ul><li>min: 11 tokens</li><li>mean: 27.89 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 27.86 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>0: ~55.30%</li><li>1: ~44.70%</li></ul> |
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* Samples:
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| sentence1 | sentence2 | label |
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|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
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| <code>Wesley Enoch , the eldest son of Doug and Lyn Enoch from Stradbroke Island , grew up in Brisbane and is the brother of the Minister of Queensland , Leeanne Enoch .</code> | <code>Wesley Enoch , the eldest son of Doug and Lyn Enoch from Brisbane , grew up in Stradbroke Island and is the brother of the Minister of Queensland , Leeanne Enoch .</code> | <code>0</code> |
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| <code>The idea of the ensemble is further discussed in the article Statistical Ensemble ( Mathematical Physics ) .</code> | <code>The idea of the ensemble is further discussed in the mathematical ensemble ( statistical physics ) article .</code> | <code>0</code> |
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| <code>Clatsop County comprises the Astoria , OR Micropolitan Statistical Area and is located in the northwest - Oregon .</code> | <code>Clatsop County comprises the Astoria , OR Micropolitan Statistical Area and is located in Northwest Oregon .</code> | <code>1</code> |
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* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
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```json
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{
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| | sentence1 | sentence2 | label |
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|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
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| type | string | string | int |
|
| 256 |
+
| details | <ul><li>min: 10 tokens</li><li>mean: 27.03 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 27.13 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>0: ~32.90%</li><li>1: ~67.10%</li></ul> |
|
| 257 |
* Samples:
|
| 258 |
+
| sentence1 | sentence2 | label |
|
| 259 |
+
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
| 260 |
+
| <code>Colorado Attorney-General Ken Salazar later said his office has also filed suit against Invesco , charging it with violations of the state 's consumer protection act .</code> | <code>Colorado Attorney General Ken Salazar also filed a lawsuit Tuesday against Invesco , accusing it of violating the state 's Consumer Protection Act .</code> | <code>1</code> |
|
| 261 |
+
| <code>At first , the animals ’ performance declined compared to the sessions on the joystick .</code> | <code>At first , the animals ' performance declined compared with the joystick sessions .</code> | <code>1</code> |
|
| 262 |
+
| <code>For the first quarter , HP pulled in $ 2.94 billion and captured 27.9 percent of the market .</code> | <code>H-P came in second with nearly $ 3.32 billion in sales and 26 percent of the market 's revenue .</code> | <code>1</code> |
|
| 263 |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
|
| 264 |
```json
|
| 265 |
{
|
|
|
|
| 279 |
| | sentence1 | sentence2 | label |
|
| 280 |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:------------------------------------------------|
|
| 281 |
| type | string | string | int |
|
| 282 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 13.48 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 28 tokens</li><li>mean: 331.34 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>0: ~32.20%</li><li>1: ~67.80%</li></ul> |
|
| 283 |
* Samples:
|
| 284 |
+
| sentence1 | sentence2 | label |
|
| 285 |
+
|:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
| 286 |
+
| <code>Dopamine is released by neurons.</code> | <code>Dopamine -LRB- DA , contracted from 3,4-dihydroxyphenethylamine -RRB- is an organic chemical of the catecholamine and phenethylamine families that plays several important roles in the brain and body .. organic chemical. organic compound. catecholamine. catecholamine. phenethylamine. phenethylamine. brain. brain. Dopamine. Dopamine ( medication ). It is an amine synthesized by removing a carboxyl group from a molecule of its precursor chemical L-DOPA , which is synthesized in the brain and kidneys .. L-DOPA. L-DOPA. amine. amine. carboxyl group. C-terminus. precursor chemical. precursor ( chemistry ). synthesized. biosynthesis. brain. brain. Dopamine is also synthesized in plants and most animals .. synthesized. biosynthesis. Dopamine. Dopamine ( medication ). In the brain , dopamine functions as a neurotransmitter -- a chemical released by neurons -LRB- nerve cells -RRB- to send signals to other nerve cells .. brain. brain. neurotransmitter. neurotransmitter. The brain includes several...</code> | <code>0</code> |
|
| 287 |
+
| <code>Tanzania shares borders with several landlocked countries.</code> | <code>Mah-Adhur Gushnasp , also known by the Arabicized form of Mahadharjushnas , was an Iranian nobleman who served as the wuzurg framadar -LRB- vizier or prime minister -RRB- of the Sasanian Empire during the reign of the child ruler Ardashir III -LRB- r. 628 -- 629 -RRB- .. Arabicized. Arabicized. Iranian. Iranian people. wuzurg framadar. Wuzurg framadar. vizier. vizier. prime minister. prime minister. Sasanian Empire. Sasanian Empire. Ardashir III. Ardashir III</code> | <code>1</code> |
|
| 288 |
+
| <code>Naga Chaitanya was in Dhada.</code> | <code>BMW 320 TC is a racing car built under Super 2000 specifications , which is currently competing in the FIA World Touring Car Championship .. World Touring Car Championship. World Touring Car Championship. BMW. BMW. Super 2000. Super 2000. FIA. FIA</code> | <code>1</code> |
|
| 289 |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
|
| 290 |
```json
|
| 291 |
{
|
|
|
|
| 302 |
* Size: 22,650 training samples
|
| 303 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| 304 |
* Approximate statistics based on the first 1000 samples:
|
| 305 |
+
| | sentence1 | sentence2 | label |
|
| 306 |
+
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
|
| 307 |
+
| type | string | string | int |
|
| 308 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 22.13 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 22.1 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>0: ~53.00%</li><li>1: ~47.00%</li></ul> |
|
| 309 |
* Samples:
|
| 310 |
+
| sentence1 | sentence2 | label |
|
| 311 |
+
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
| 312 |
+
| <code>a statistical statement of how likely it is that an obtained result occurred by chance (p value should be less than or equal to 3)</code> | <code>refers to the cutoff point (i.e., critical value); any value that exceeds the cutoff point will be noted as statistically significant</code> | <code>0</code> |
|
| 313 |
+
| <code>a website that finds webpages that match a word of phase of a given search expression</code> | <code>search tool that allows you to find specific documents through keyword searches and menu choices, in contrast to directories, which are lists of websites classified by topic.</code> | <code>1</code> |
|
| 314 |
+
| <code>explains a phenomenon using assumptions and a philosophical stance. similar to theory but more abstract, generally not testable. connects concepts</code> | <code>broadly presents an understanding of a phenomena and reflects the assumptions of he model's designer</code> | <code>1</code> |
|
| 315 |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
|
| 316 |
```json
|
| 317 |
{
|
|
|
|
| 328 |
* Size: 10,047 training samples
|
| 329 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| 330 |
* Approximate statistics based on the first 1000 samples:
|
| 331 |
+
| | sentence1 | sentence2 | label |
|
| 332 |
+
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:------------------------------------------------|
|
| 333 |
+
| type | string | string | int |
|
| 334 |
+
| details | <ul><li>min: 4 tokens</li><li>mean: 17.38 tokens</li><li>max: 82 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 17.38 tokens</li><li>max: 143 tokens</li></ul> | <ul><li>0: ~36.60%</li><li>1: ~63.40%</li></ul> |
|
| 335 |
* Samples:
|
| 336 |
+
| sentence1 | sentence2 | label |
|
| 337 |
+
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
|
| 338 |
+
| <code>The Embraer jets are scheduled to be delivered by September 2006.</code> | <code>Joseph Biden Jr. is an American politician and the president-elect of the United States.</code> | <code>0</code> |
|
| 339 |
+
| <code>Attackers detonated a second roadside bomb later Sunday as a U.S. convoy was traveling near Fallujah, killing an American soldier and wounding three others.</code> | <code>ATTAACKERS DETONATED A SECOND ROADSIDE BOMB LATER SUNDAY AS A U S CONVOY WAS TRAVELING NEAR FALLUJAH, AND AMERICAN SOLDER WOUNDING OTHERS</code> | <code>0</code> |
|
| 340 |
+
| <code>You want something to eat?</code> | <code>Do you want to go get some food</code> | <code>0</code> |
|
| 341 |
* Loss: [<code>AnglELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#angleloss) with these parameters:
|
| 342 |
```json
|
| 343 |
{
|
|
|
|
| 357 |
| | sentence1 | sentence2 | label |
|
| 358 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
|
| 359 |
| type | string | string | float |
|
| 360 |
+
| details | <ul><li>min: 6 tokens</li><li>mean: 14.85 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.79 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.68</li><li>max: 5.0</li></ul> |
|
| 361 |
* Samples:
|
| 362 |
+
| sentence1 | sentence2 | label |
|
| 363 |
+
|:-------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:-------------------------------|
|
| 364 |
+
| <code>New Syria opposition chief wants no-strings aid</code> | <code>Syria opposition unites as Israel fires warning shots</code> | <code>1.0</code> |
|
| 365 |
+
| <code>A spokesman said: "Since November, we have co-operated fully with the police.</code> | <code>It added it had "co-operated fully" with police since November.</code> | <code>4.25</code> |
|
| 366 |
+
| <code>A small boy in a bathrobe is sitting in a metal chair.</code> | <code>A boy in a robe sits in a chair.</code> | <code>4.199999809265137</code> |
|
| 367 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
| 368 |
```json
|
| 369 |
{
|
|
|
|
| 380 |
* Size: 13,317 training samples
|
| 381 |
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
|
| 382 |
* Approximate statistics based on the first 1000 samples:
|
| 383 |
+
| | sentence1 | sentence2 | label |
|
| 384 |
+
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
|
| 385 |
+
| type | string | string | float |
|
| 386 |
+
| details | <ul><li>min: 5 tokens</li><li>mean: 12.26 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.22 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 1.0</li><li>mean: 3.5</li><li>max: 5.0</li></ul> |
|
| 387 |
* Samples:
|
| 388 |
+
| sentence1 | sentence2 | label |
|
| 389 |
+
|:----------------------------------------------------|:---------------------------------------------------------|:-------------------------------|
|
| 390 |
+
| <code>A woman is cooking eggs</code> | <code>The woman is cooking something</code> | <code>4.199999809265137</code> |
|
| 391 |
+
| <code>A great dog is climbing a steep hill</code> | <code>A great dog is wildly climbing a steep hill</code> | <code>4.5</code> |
|
| 392 |
+
| <code>A man is passionately playing a guitar</code> | <code>A person is singing and playing a guitar</code> | <code>4.199999809265137</code> |
|
| 393 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
| 394 |
```json
|
| 395 |
{
|
|
|
|
| 406 |
* Size: 14,280 training samples
|
| 407 |
* Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
|
| 408 |
* Approximate statistics based on the first 1000 samples:
|
| 409 |
+
| | label | sentence1 | sentence2 |
|
| 410 |
+
|:--------|:---------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
|
| 411 |
+
| type | float | string | string |
|
| 412 |
+
| details | <ul><li>min: 0.0</li><li>mean: 3.13</li><li>max: 5.0</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 18.59 tokens</li><li>max: 80 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.19 tokens</li><li>max: 77 tokens</li></ul> |
|
| 413 |
* Samples:
|
| 414 |
+
| label | sentence1 | sentence2 |
|
| 415 |
+
|:------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
| 416 |
+
| <code>0.0</code> | <code>they "have" fox because fox news reports on newsworthy events.</code> | <code>i saved up for it because i knew i was going to need it.</code> |
|
| 417 |
+
| <code>4.25</code> | <code>Each time, radical improvements in technology made the threat evaporate.</code> | <code>Each time, of radical technological progress leave these threats.</code> |
|
| 418 |
+
| <code>3.4</code> | <code>I call on Prague to respond to this signal, this challenge, this plea for dialogue, to grant our request and to ensure, together with our House, that this legacy of a nationalist era can be consigned to the past.</code> | <code>I invite Prague to seize this, this invitation, this request for dialogue, to respond and to ensure that, together with this House, these relics of a nationalist age can be abandoned.</code> |
|
| 419 |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
|
| 420 |
```json
|
| 421 |
{
|
|
|
|
| 730 |
}
|
| 731 |
```
|
| 732 |
</details>
|
| 733 |
+
<details><summary>tasksource/flan</summary>
|
| 734 |
+
|
| 735 |
+
#### tasksource/flan
|
| 736 |
+
|
| 737 |
+
* Dataset: tasksource/flan
|
| 738 |
+
* Size: 200,000 training samples
|
| 739 |
+
* Columns: <code>anchor</code>, <code>positive</code>, <code>rejected</code>, <code>task</code>, and <code>template_type</code>
|
| 740 |
+
* Approximate statistics based on the first 1000 samples:
|
| 741 |
+
| | anchor | positive | rejected | task | template_type |
|
| 742 |
+
|:--------|:--------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
|
| 743 |
+
| type | string | string | string | string | string |
|
| 744 |
+
| details | <ul><li>min: 72 tokens</li><li>mean: 513.33 tokens</li><li>max: 1024 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.69 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.59 tokens</li><li>max: 74 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 16.38 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 5.97 tokens</li><li>max: 7 tokens</li></ul> |
|
| 745 |
+
* Samples:
|
| 746 |
+
| anchor | positive | rejected | task | template_type |
|
| 747 |
+
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------|:------------------------------------------|:---------------------------------------------------------|:--------------------|
|
| 748 |
+
| <code>Please answer the following question: Given the following passage "A healthy, and legal, publishing industry existed throughout Europe, although established publishers and book sellers occasionally ran afoul of the law. The Encyclopédie, for example, condemned not only by the King but also by Clement XII, nevertheless found its way into print with the help of the aforementioned Malesherbes and creative use of French censorship law. But many works were sold without running into any legal trouble at all. Borrowing records from libraries in England, Germany and North America indicate that more than 70 percent of books borrowed were novels. Less than 1 percent of the books were of a religious nature, indicating the general trend of declining religiosity.", answer the following question. Note that the answer is present within the text. Question: What country was omitted from the borrowing records?<br>Answer:</code> | <code>French</code> | <code>US</code> | <code>adversarial_qa_dbert_answer_the_following_q</code> | <code>zs_opt</code> |
|
| 749 |
+
| <code>Problem: Given the question: Given the following passage "On 3 December, Chopin complained about his bad health and the incompetence of the doctors in Majorca: "Three doctors have visited me ... The first said I was dead; the second said I was dying; and the third said I was about to die." He also had problems having his Pleyel piano sent to him. It finally arrived from Paris in December. Chopin wrote to Pleyel in January 1839: "I am sending you my Preludes [(Op. 28)]. I finished them on your little piano, which arrived in the best possible condition in spite of the sea, the bad weather and the Palma customs." Chopin was also able to undertake work on his Ballade No. 2, Op. 38; two Polonaises, Op. 40; and the Scherzo No. 3, Op. 39.", answer the following question. Note that the answer is present within the text. Question: Which doctor did not think Chopin was dead?<br>++++++++++++++++++++++++++++++++<br>The answer is:<br>the third<br><br><br>Problem: Given the question: Given the following passage "...</code> | <code>Jehovah</code> | <code>Ashkenazi and Sephardic Jews</code> | <code>adversarial_qa_dbert_answer_the_following_q</code> | <code>fs_opt</code> |
|
| 750 |
+
| <code>input: Please answer the following: Given the following passage "Jehovah's Witnesses are perhaps best known for their efforts to spread their beliefs, most notably by visiting people from house to house, distributing literature published by the Watch Tower Society in 700 languages. The objective is to start a regular "Bible study" with any person who is not already a member, with the intention that the student be baptized as a member of the group; Witnesses are advised to consider discontinuing Bible studies with students who show no interest in becoming members. Witnesses are taught they are under a biblical command to engage in public preaching. They are instructed to devote as much time as possible to their ministry and are required to submit an individual monthly "Field Service Report". Baptized members who fail to report a month of preaching are termed "irregular" and may be counseled by elders; those who do not submit reports for six consecutive months are termed "inactive".", ...</code> | <code>reformed</code> | <code>The Verge</code> | <code>adversarial_qa_dbert_answer_the_following_q</code> | <code>fs_opt</code> |
|
| 751 |
+
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
|
| 752 |
+
```json
|
| 753 |
+
{
|
| 754 |
+
"scale": 20.0,
|
| 755 |
+
"similarity_fct": "cos_sim",
|
| 756 |
+
"mini_batch_size": 32,
|
| 757 |
+
"gather_across_devices": false
|
| 758 |
+
}
|
| 759 |
+
```
|
| 760 |
+
</details>
|
| 761 |
|
| 762 |
### Training Hyperparameters
|
| 763 |
#### Non-Default Hyperparameters
|
|
|
|
| 896 |
### Training Logs
|
| 897 |
| Epoch | Step | Training Loss |
|
| 898 |
|:------:|:-----:|:-------------:|
|
| 899 |
+
| 0.0347 | 500 | 5.015 |
|
| 900 |
+
| 0.0694 | 1000 | 3.6072 |
|
| 901 |
+
| 0.1041 | 1500 | 3.6206 |
|
| 902 |
+
| 0.1387 | 2000 | 3.5205 |
|
| 903 |
+
| 0.1734 | 2500 | 3.8717 |
|
| 904 |
+
| 0.2081 | 3000 | 3.2363 |
|
| 905 |
+
| 0.2428 | 3500 | 3.0776 |
|
| 906 |
+
| 0.2775 | 4000 | 3.1094 |
|
| 907 |
+
| 0.3122 | 4500 | 3.3586 |
|
| 908 |
+
| 0.3468 | 5000 | 3.2504 |
|
| 909 |
+
| 0.3815 | 5500 | 2.9393 |
|
| 910 |
+
| 0.4162 | 6000 | 2.8626 |
|
| 911 |
+
| 0.4509 | 6500 | 3.1186 |
|
| 912 |
+
| 0.4856 | 7000 | 2.9852 |
|
| 913 |
+
| 0.5203 | 7500 | 2.8228 |
|
| 914 |
+
| 0.5549 | 8000 | 2.9656 |
|
| 915 |
+
| 0.5896 | 8500 | 2.6737 |
|
| 916 |
+
| 0.6243 | 9000 | 2.6191 |
|
| 917 |
+
| 0.6590 | 9500 | 2.7254 |
|
| 918 |
+
| 0.6937 | 10000 | 2.6937 |
|
| 919 |
+
| 0.7284 | 10500 | 2.8813 |
|
| 920 |
+
| 0.7630 | 11000 | 2.8221 |
|
| 921 |
+
| 0.7977 | 11500 | 2.9132 |
|
| 922 |
+
| 0.8324 | 12000 | 2.4675 |
|
| 923 |
+
| 0.8671 | 12500 | 2.9528 |
|
| 924 |
+
| 0.9018 | 13000 | 2.4298 |
|
| 925 |
+
| 0.9365 | 13500 | 2.3748 |
|
| 926 |
+
| 0.9711 | 14000 | 2.6557 |
|
| 927 |
|
| 928 |
|
| 929 |
### Framework Versions
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 67193928
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:42965efac128bdb06678f2ffbda2054bb31f286f68c10a5eea5796a170fd68b4
|
| 3 |
size 67193928
|