Training in progress, epoch 1, checkpoint

last-checkpoint/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

last-checkpoint/README.md

@@ -0,0 +1,630 @@
+---
+language:
+- en
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:689221
+- loss:MultipleNegativesRankingLoss
+- loss:CoSENTLoss
+- loss:GISTEmbedLoss
+- loss:OnlineContrastiveLoss
+- loss:MultipleNegativesSymmetricRankingLoss
+base_model: bobox/DeBERTaV3-small-GeneralSentenceTransformer
+datasets:
+- sentence-transformers/all-nli
+- sentence-transformers/stsb
+- tals/vitaminc
+- nyu-mll/glue
+- allenai/scitail
+- sentence-transformers/xsum
+- sentence-transformers/sentence-compression
+widget:
+- source_sentence: A man in a Santa Claus costume is sitting on a wooden chair holding
+    a microphone and a stringed instrument.
+  sentences:
+  - The man is is near the ball.
+  - The man is wearing a costume.
+  - People are having a picnic.
+- source_sentence: A street vendor selling his art.
+  sentences:
+  - A man is selling things on the street.
+  - A woman is walking outside.
+  - A clown is talking into a microphone.
+- source_sentence: A boy looks surly as his father looks at the camera.
+  sentences:
+  - a boy looks at his farther
+  - A dark-haired girl in a spotted shirt is pointing at the picture while sitting
+    next to a boy wearing a purple shirt and jeans.
+  - Man and woman stop and chat with each other.
+- source_sentence: Which company provided streetcar connections between downtown and
+    the hospital?
+  sentences:
+  - In 1914 developers Billings & Meyering acquired the tract, completed street development,
+    provided the last of the necessary municipal improvements including water service,
+    and began marketing the property with fervor.
+  - The war was fought primarily along the frontiers between New France and the British
+    colonies, from Virginia in the South to Nova Scotia in the North.
+  - 'On the basis of CST, Burnet developed a theory of how an immune response is triggered
+    according to the self/nonself distinction: "self" constituents (constituents of
+    the body) do not trigger destructive immune responses, while "nonself" entities
+    (pathogens, an allograft) trigger a destructive immune response.'
+- source_sentence: What language did Tesla study while in school?
+  sentences:
+  - Because of the complexity of medications including specific indications, effectiveness
+    of treatment regimens, safety of medications (i.e., drug interactions) and patient
+    compliance issues (in the hospital and at home) many pharmacists practicing in
+    hospitals gain more education and training after pharmacy school through a pharmacy
+    practice residency and sometimes followed by another residency in a specific area.
+  - Rev. Jimmy Creech was defrocked after a highly publicized church trial in 1999
+    on account of his participation in same-sex union ceremonies.
+  - Tesla was the fourth of five children.
+pipeline_tag: sentence-similarity
+---
+
+# SentenceTransformer based on bobox/DeBERTaV3-small-GeneralSentenceTransformer
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bobox/DeBERTaV3-small-GeneralSentenceTransformer](https://huggingface.co/bobox/DeBERTaV3-small-GeneralSentenceTransformer) 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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [bobox/DeBERTaV3-small-GeneralSentenceTransformer](https://huggingface.co/bobox/DeBERTaV3-small-GeneralSentenceTransformer) <!-- at revision 2a8f28a3e07d490918a6b6668cff3b2215ac4273 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 tokens
+- **Similarity Function:** Cosine Similarity
+- **Training Datasets:**
+    - [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli)
+    - [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)
+- **Language:** en
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("bobox/DeBERTaV3-small-GeneralSentenceTransformer-keepTraining-checkpoints-tmp")
+# Run inference
+sentences = [
+    'What language did Tesla study while in school?',
+    'Tesla was the fourth of five children.',
+    'Rev. Jimmy Creech was defrocked after a highly publicized church trial in 1999 on account of his participation in same-sex union ceremonies.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Datasets
+
+#### nli-pairs
+
+* Dataset: [nli-pairs](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
+* Size: 150,000 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: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                  | sentence2                                        |
+  |:---------------------------------------------------------------------------|:-------------------------------------------------|
+  | <code>A person on a horse jumps over a broken down airplane.</code>        | <code>A person is outdoors, on a horse.</code>   |
+  | <code>Children smiling and waving at camera</code>                         | <code>There are children present</code>          |
+  | <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</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"
+  }
+  ```
+
+#### sts-label
+
+* Dataset: [sts-label](https://huggingface.co/datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co/datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
+* Size: 5,749 training samples
+* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                        | sentence2                                                                        | score                                                          |
+  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
+  | type    | string                                                                           | string                                                                           | float                                                          |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 9.81 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.74 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
+* Samples:
+  | sentence1                                                  | sentence2                                                             | score             |
+  |:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
+  | <code>A plane is taking off.</code>                        | <code>An air plane is taking off.</code>                              | <code>1.0</code>  |
+  | <code>A man is playing a large flute.</code>               | <code>A man is playing a flute.</code>                                | <code>0.76</code> |
+  | <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
+* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "pairwise_cos_sim"
+  }
+  ```
+
+#### 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
+* 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: 17.36 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 37.74 tokens</li><li>max: 224 tokens</li></ul> |
+* Samples:
+  | label          | sentence1                                                                                                                                                                       | sentence2                                                                                                                                                                                                                                                                                                                                                                |
+  |:---------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>1</code> | <code>Baron Waddington was the Home Secretary during the Poll Tax Riots .</code>                                                                                                | <code>Baron Waddington of Read ( Home Secretary during the Poll Tax Riots and former Lord Privy Seal and Leader of the House of Lords ) , Phil Willis , Liberal Democrat MP for Harrogate & Knaresborough , Liberal Democrats official site .</code>                                                                                                                     |
+  | <code>1</code> | <code>Captopril inhibits the catabolism of endogenous opioids and this contributes to its hypotensive action , while naloxone , a mu -opiate antagonist , opposes this .</code> | <code>Captopril inhibits the catabolism of endogenous opioids and this contributes to its hypotensive action , and naloxone a mu -opiate antagonist , opposes this ( 7 ) Goldfrank 's toxicologic emergencies , Lewis R. Goldfrank , Neal Flomenbaum , page 953.Meyler 's Side Effects of Analgesics and Anti-inflammatory Drugs , Jeffrey K. Aronson , page 120.</code> |
+  | <code>1</code> | <code>In under 60 seconds to the end of the first quarter , The Patriots made a more than 79-yard offensive into their opponent 's side .</code>                                | <code>The Patriots countered on their next drive marching 80 yards culminating with a 4-yard touchdown run by Brady ( in the process Brady tied Curtis Martin 's club record for rushing touchdowns in the playoffs ) with less than a minute remaining in the first quarter .</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}
+  ```
+
+#### 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
+* 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: 13.68 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 34.78 tokens</li><li>max: 178 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
+* Samples:
+  | sentence1                                                           | sentence2                                                                                                                                                                 | label          |
+  |:--------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>How many Protestants live in Greece?</code>                   | <code>Greek citizens who are Roman Catholic are estimated to be at around 50,000 with the Roman Catholic immigrant community in the country approximately 200,000.</code> | <code>0</code> |
+  | <code>What restricted the 1870s Child labour in Australia?</code>   | <code>Child labour was restricted by compulsorry schooling.</code>                                                                                                        | <code>0</code> |
+  | <code>Whose assumption of power ended the era of stagnation?</code> | <code>Under his rule, the Russian SFSR and the rest of the Soviet Union went through an era of stagnation.</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
+* 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: 15.72 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 14.86 tokens</li><li>max: 41 tokens</li></ul> |
+* Samples:
+  | sentence2                                                                               | sentence1                                                          |
+  |:----------------------------------------------------------------------------------------|:-------------------------------------------------------------------|
+  | <code>Concave lenses can correct myopia.</code>                                         | <code>What type of lenses can correct myopia?</code>               |
+  | <code>Bases normally have a bitter taste.</code>                                        | <code>What type of taste do bases normally have?</code>            |
+  | <code>An acorn growing into an adult tree will take the longest time to observe.</code> | <code>Which of these will take the LONGEST time to observe?</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"
+  }
+  ```
+
+#### scitail-pairs-pos
+
+* Dataset: [scitail-pairs-pos](https://huggingface.co/datasets/allenai/scitail) at [0cc4353](https://huggingface.co/datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
+* Size: 8,600 training samples
+* Columns: <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: 23.51 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 15.66 tokens</li><li>max: 39 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                  | sentence2                                                                                   |
+  |:---------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------|
+  | <code>One gram of carbohydrate provides four calories.</code>                                                              | <code>One gram of carbohydrates provides four calories of energy.</code>                    |
+  | <code>Our eyes can see a certain spectrum of light, called visible light, starting with red and ending with violet.</code> | <code>Violet and red are two types of visible light.</code>                                 |
+  | <code>Adult-onset Diabetes Former term for noninsulin-dependent or type II diabetes.</code>                                | <code>Type 2 diabetes is also known as noninsulin-dependent or adult-onset diabetes.</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"
+  }
+  ```
+
+#### 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
+* 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: 38 tokens</li><li>mean: 350.51 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 26.76 tokens</li><li>max: 60 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | sentence2                                                                                                                  |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------|
+  | <code>President Barack Obama has worked to improve relations with the communist government in Havana, culminating in his historic visit in March 2016.<br>The president-elect threatened in a tweet to put an end to the detente following the death of Fidel Castro.<br>But the White House bristled at Mr Trump's warning, saying the president was not concerned about the threat.<br>White House Press Secretary Josh Earnest told reporters that reversing the policy would be "a significant economic blow" to Cubans and was "not as easy as a stroke of a pen".<br>President-elect Trump tweeted he would "terminate" Mr Obama's policy on normalising relations with Cuba as thousands of Cubans queued to pay their respects to , who died on Friday.<br>They gathered in Havana's Revolution Square as part of farewell commemorations which will last until Tuesday night, when foreign leaders are due to arrive in Cuba to pay their respects.<br>A cortege will then transport his ashes east across the island to Santiago de Cuba, reversing the route Castro took during the Cuban revolution.<br>They will be laid to rest on Sunday in the city's Santa Ifigenia cemetery.<br>In his tweet, Mr Trump said that if "Cuba is unwilling to make a better deal for the Cuban people, the Cuban/American people and the US as a whole, I will terminate deal".<br>Mr Trump, who takes office in January, said during the election campaign that he would reverse President Barack Obama's rapprochement with Cuba.<br>Under Mr Obama, diplomatic ties were restored in 2015 after being severed in 1961. Some trade restrictions have been eased and the White House has been lobbying the US Congress to terminate an economic embargo that has been in place for decades.<br>What does a Trump presidency mean for US-Cuba relations?<br>Mr Trump's team has accused the Obama administration of giving too much away to Cuba without receiving enough in return.<br>His communications director, Jason Miller, said Mr Trump was seeking "freedom in Cuba for the Cubans and a good deal for Americans where we aren't played for fools".<br>But the White House said that better ties with Cuba served US interests and that reversing the changes would deal "a significant economic blow" to the people of Cuba.<br>"After five decades of not seeing results, the president believed it was time to try something different," said White House spokesman Josh Earnest.<br>In a separate development the first scheduled commercial flight from the US to Havana in more than 50 years has departed from Miami.<br>The American Airlines (AA) flight on Monday morning was the first of a new service to the Cuban capital which will fly from Florida four times a day.<br>"It's a monumental day of great historic relevance with Miami being the epicentre of the Cuban-American community and American's hub for the region," AA Vice President Ralph Lopez was quoted by The Miami Herald  as saying.<br>Several airlines began routes to other parts of Cuba earlier this year, with many more flights and destinations in the offing.</code> | <code>Donald Trump says he will end the thaw between the US and Cuba if the country does not offer a "better deal".</code> |
+  | <code>It will allow members of the public to make complaints about the conduct of Scotland's charities<br>The new measure was recommended by the Fundraising Working Group in Scotland.<br>The Scottish government said it was important confidence was maintained in the country's charities, but stressed that the majority operated to high standards.<br>Communities Secretary Angela Constance said: "Scotland's charities benefit from a great deal of public trust and it's important that that confidence is maintained.<br>"This new phone number and website will be run by Scottish Fundraising Complaints - set up by the Scottish Council for Voluntary Organisations and the Scottish Charity Regulator - and will give people information about how to raise any concerns they may have."<br>Members of the public who are concerned about the fundraising tactics of a charity in Scotland can call 0808 164 2520 or visit the website.<br>Cross-border charities, where charities operate in Scotland but are registered in England and Wales, will continue to be regulated by the Fundraising Regulator.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | <code>A new phone number and website set up to protect the public against aggressive fundraisers has gone live.</code>     |
+  | <code>Carla Whitlock was sprayed with drain cleaner containing sulphuric acid in Southampton on 18 September.<br>Billy Midmore, 23, who was found guilty of causing grievous bodily harm with intent, was jailed for 15 years with a further five years on licence.<br>Geoffrey Midmore, 27, had previously pleaded guilty to the same charge and was jailed for nine years.<br>Southampton Crown Court heard the acid was thrown in the 37-year-old's face after a drug deal went wrong.<br>Sentencing the pair, Judge Peter Ralls QC said: "Your behaviour displays a level of medieval barbarism that is appalling.<br>"You used a weapon that was pernicious and evil. You planned for this, which adds to the culpability."<br>Billy Midmore, who denied any involvement in the attack, admitted sending Ms Whitlock threatening texts after his brother was robbed of drugs and cash worth £2,000.<br>Brothers jailed for Southampton acid attack - as it happened<br>Prosecutors said he held the mother-of-six responsible after she helped broker the deal between the brothers and a Southampton drug dealer.<br>Train CCTV showed him giving a high-five and fist-bumping a friend hours after the attack.<br>The brothers, from London, were arrested in Gillingham, Kent, on 29 September, following a two-week nationwide manhunt.<br>During his trial, Billy Midmore admitted coming to Southampton from London to sell crack cocaine and heroin.<br>The court also heard Geoffrey Midmore had sent a photograph of the drain cleaner on WhatsApp to an acquaintance, with the words: "This is one face melter."<br>The court heard Ms Whitlock needed surgery after the attack and remained in "significant pain".<br>A consultant said her right eye was prone to "breakdown and infection" and her eyesight was unlikely to return.<br>The judge told the brothers: "You bought the drain cleaner because you intended to pour it in her face.<br>"To describe it as the 'face melter' could only have one interpretation."<br>After sentencing, Ch Insp Debra Masson, of Hampshire Constabulary, described the brothers as "men of violence" who "made people's lives a misery".<br>"The evidence shown in court of their behaviour directly after the attack, coupled with their concerted efforts to evade capture, served to expose them as the dangerous criminals they are," she said.<br>"Although things will never be the same for Carla, we hope that the sentence handed to Geoffrey and Billy Midmore today goes some way to giving her closure and allows her to feel that justice has been delivered."</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>Two brothers have been jailed for an acid attack which left a woman scarred and blind in one eye.</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"
+  }
+  ```
+
+#### 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
+* 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: 31.89 tokens</li><li>max: 125 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.21 tokens</li><li>max: 28 tokens</li></ul> |
+* Samples:
+  | sentence1                                                                                                                                                                                                                                          | sentence2                                              |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|
+  | <code>The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints.</code> | <code>USHL completes expansion draft</code>            |
+  | <code>Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.</code>                                                                                                                                      | <code>Bud Selig to speak at St. Norbert College</code> |
+  | <code>It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.</code>                                                                                                                                    | <code>It's cherry time</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"
+  }
+  ```
+
+### 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>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence1                                                                         | sentence2                                                                        |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
+  | 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                                                   |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|
+  | <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> |
+  | <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>                                                                    | <code>A man selling donuts to a customer.</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)
+* Size: 5,463 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: 6 tokens</li><li>mean: 14.13 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.58 tokens</li><li>max: 225 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
+* Samples:
+  | sentence1                                                                 | sentence2                                                                                                                                        | label          |
+  |:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
+  | <code>What came into force after the new constitution was herald?</code>  | <code>As of that day, the new constitution heralding the Second Republic came into force.</code>                                                 | <code>0</code> |
+  | <code>What is the first major city in the stream of the Rhine?</code>     | <code>The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz.</code> | <code>0</code> |
+  | <code>What is the minimum required if you want to teach in Canada?</code> | <code>In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher.</code>             | <code>0</code> |
+* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 20
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 5e-06
+- `weight_decay`: 1e-10
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.33
+- `save_safetensors`: False
+- `fp16`: True
+- `push_to_hub`: True
+- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-keepTraining-checkpoints-tmp
+- `hub_strategy`: checkpoint
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 20
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-06
+- `weight_decay`: 1e-10
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.33
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: False
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: True
+- `resume_from_checkpoint`: None
+- `hub_model_id`: bobox/DeBERTaV3-small-GeneralSentenceTransformer-keepTraining-checkpoints-tmp
+- `hub_strategy`: checkpoint
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step  | Training Loss | nli-pairs loss | qnli-contrastive loss |
+|:------:|:-----:|:-------------:|:--------------:|:---------------------:|
+| None   | 0     | -             | 0.1391         | 0.0068                |
+| 0.1500 | 5170  | 0.2058        | 0.1433         | 0.0066                |
+| 0.3000 | 10340 | 0.1978        | 0.1448         | 0.0053                |
+| 0.4500 | 15510 | 0.2122        | 0.1443         | 0.0063                |
+| 0.6000 | 20680 | 0.1918        | 0.1494         | 0.0053                |
+| 0.7501 | 25850 | 0.2103        | 0.1488         | 0.0082                |
+| 0.9001 | 31020 | 0.2056        | 0.1513         | 0.0039                |
+
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.1.2
+- Accelerate: 0.30.1
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+#### CoSENTLoss
+```bibtex
+@online{kexuefm-8847,
+    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
+    author={Su Jianlin},
+    year={2022},
+    month={Jan},
+    url={https://kexue.fm/archives/8847},
+}
+```
+
+#### GISTEmbedLoss
+```bibtex
+@misc{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, 
+    author={Aivin V. Solatorio},
+    year={2024},
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

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