Sentence Similarity
sentence-transformers
PyTorch
15 datasets
English
deberta-v2
feature-extraction
Generated from Trainer
dataset_size:78183
loss:AdaptiveLayerLoss
loss:CoSENTLoss
loss:GISTEmbedLoss
loss:OnlineContrastiveLoss
loss:MultipleNegativesSymmetricRankingLoss
Eval Results
Inference Endpoints
File size: 136,730 Bytes
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---
language:
- en
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:78183
- loss:AdaptiveLayerLoss
- loss:CoSENTLoss
- loss:GISTEmbedLoss
- loss:OnlineContrastiveLoss
- loss:MultipleNegativesSymmetricRankingLoss
base_model: microsoft/deberta-v3-small
datasets:
- sentence-transformers/all-nli
- sentence-transformers/stsb
- tals/vitaminc
- nyu-mll/glue
- allenai/scitail
- sentence-transformers/xsum
- sentence-transformers/sentence-compression
- allenai/sciq
- allenai/qasc
- allenai/openbookqa
- sentence-transformers/msmarco-msmarco-distilbert-base-v3
- sentence-transformers/natural-questions
- sentence-transformers/trivia-qa
- sentence-transformers/quora-duplicates
- sentence-transformers/gooaq
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: The X and Y chromosomes in human beings that determine the sex
of an individual.
sentences:
- A glacier leaves behind bare rock when it retreats.
- Prokaryotes are unicellular organisms that lack organelles surrounded by membranes.
- Mammalian sex determination is determined genetically by the presence of chromosomes
identified by the letters x and y.
- source_sentence: Police officer with riot shield stands in front of crowd.
sentences:
- A police officer stands in front of a crowd.
- A pair of people play video games together on a couch.
- People are outside digging a hole.
- source_sentence: A young girl sitting on a white comforter on a bed covered with
clothing, holding a yellow stuffed duck.
sentences:
- A man standing in a room is pointing up.
- A Little girl is enjoying cake outside.
- A yellow duck being held by a girl.
- source_sentence: A teenage girl in winter clothes slides down a decline in a red
sled.
sentences:
- A woman preparing vegetables.
- A girl is sliding on a red sled.
- A person is on a beach.
- source_sentence: How many hymns of Luther were included in the Achtliederbuch?
sentences:
- the ABC News building was renamed Peter Jennings Way in 2006 in honor of the recently
deceased longtime ABC News chief anchor and anchor of World News Tonight.
- In early 2009, Disney–ABC Television Group merged ABC Entertainment and ABC Studios
into a new division, ABC Entertainment Group, which would be responsible for both
its production and broadcasting operations.
- Luther's hymns were included in early Lutheran hymnals and spread the ideas of
the Reformation.
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on microsoft/deberta-v3-small
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7584806824433866
name: Pearson Cosine
- type: spearman_cosine
value: 0.7609871232448915
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7572700089811643
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7399684354859064
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7450503391021455
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7278825183625254
name: Spearman Euclidean
- type: pearson_dot
value: 0.5323850336734421
name: Pearson Dot
- type: spearman_dot
value: 0.5125117510725451
name: Spearman Dot
- type: pearson_max
value: 0.7584806824433866
name: Pearson Max
- type: spearman_max
value: 0.7609871232448915
name: Spearman Max
---
# SentenceTransformer based on microsoft/deberta-v3-small
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co./microsoft/deberta-v3-small) on the [nli-pairs](https://huggingface.co./datasets/sentence-transformers/all-nli), [sts-label](https://huggingface.co./datasets/sentence-transformers/stsb), [vitaminc-pairs](https://huggingface.co./datasets/tals/vitaminc), [qnli-contrastive](https://huggingface.co./datasets/nyu-mll/glue), [scitail-pairs-qa](https://huggingface.co./datasets/allenai/scitail), [scitail-pairs-pos](https://huggingface.co./datasets/allenai/scitail), [xsum-pairs](https://huggingface.co./datasets/sentence-transformers/xsum), [compression-pairs](https://huggingface.co./datasets/sentence-transformers/sentence-compression), [sciq_pairs](https://huggingface.co./datasets/allenai/sciq), [qasc_pairs](https://huggingface.co./datasets/allenai/qasc), [openbookqa_pairs](https://huggingface.co./datasets/allenai/openbookqa), [msmarco_pairs](https://huggingface.co./datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3), [nq_pairs](https://huggingface.co./datasets/sentence-transformers/natural-questions), [trivia_pairs](https://huggingface.co./datasets/sentence-transformers/trivia-qa), [quora_pairs](https://huggingface.co./datasets/sentence-transformers/quora-duplicates) and [gooaq_pairs](https://huggingface.co./datasets/sentence-transformers/gooaq) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/deberta-v3-small](https://huggingface.co./microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
- **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)
- [sciq_pairs](https://huggingface.co./datasets/allenai/sciq)
- [qasc_pairs](https://huggingface.co./datasets/allenai/qasc)
- [openbookqa_pairs](https://huggingface.co./datasets/allenai/openbookqa)
- [msmarco_pairs](https://huggingface.co./datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3)
- [nq_pairs](https://huggingface.co./datasets/sentence-transformers/natural-questions)
- [trivia_pairs](https://huggingface.co./datasets/sentence-transformers/trivia-qa)
- [quora_pairs](https://huggingface.co./datasets/sentence-transformers/quora-duplicates)
- [gooaq_pairs](https://huggingface.co./datasets/sentence-transformers/gooaq)
- **Language:** en
<!-- - **License:** Unknown -->
### 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-SenTra-AdaptiveLayers-AllSoft-LowTemp")
# Run inference
sentences = [
'How many hymns of Luther were included in the Achtliederbuch?',
"Luther's hymns were included in early Lutheran hymnals and spread the ideas of the Reformation.",
'the ABC News building was renamed Peter Jennings Way in 2006 in honor of the recently deceased longtime ABC News chief anchor and anchor of World News Tonight.',
]
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.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.7585 |
| **spearman_cosine** | **0.761** |
| pearson_manhattan | 0.7573 |
| spearman_manhattan | 0.74 |
| pearson_euclidean | 0.7451 |
| spearman_euclidean | 0.7279 |
| pearson_dot | 0.5324 |
| spearman_dot | 0.5125 |
| pearson_max | 0.7585 |
| spearman_max | 0.761 |
<!--
## 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: 6,500 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>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### 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: 3,194 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: 6 tokens</li><li>mean: 16.1 tokens</li><li>max: 75 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 38.59 tokens</li><li>max: 260 tokens</li></ul> |
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>1</code> | <code>Based on 31 critics , the film scored more than 43.5 out of 100 .</code> | <code>`` Metacritic gave the film a score of 44 out of 100 , based on 31 critics , indicating `` '' mixed or average reviews '' '' . ''</code> |
| <code>1</code> | <code>Our Brand Is Crisis scored less than 61/100 , with more than eight critics , which means that the audience was divided about the film .</code> | <code>`` On Metacritic , the film has a score of 60 out of 100 , based on 9 critics , indicating `` '' mixed or average reviews '' '' . ''</code> |
| <code>1</code> | <code>The film will be screened in less than 3133 theaters .</code> | <code>In its opening weekend , the film is projected to gross $ 15 million from 3,132 theaters.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### qnli-contrastive
* Dataset: [qnli-contrastive](https://huggingface.co./datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co./datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 4,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.7 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 35.83 tokens</li><li>max: 201 tokens</li></ul> | <ul><li>0: 100.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>When did Father Jose Manuel Perez Castellano die?</code> | <code>The first public library in Montevideo was formed by the initial donation of the private library of Father José Manuel Pérez Castellano, who died in 1815.</code> | <code>0</code> |
| <code>How many singers are typically in the choirs?</code> | <code>The choirs typically range from 40 to 80 singers and are recognized for their efforts to perfect blend, intonation, phrasing and pitch in a large choral setting.</code> | <code>0</code> |
| <code>St. George's Town, St. George's Island and St. George's Parish are all referred to as what?</code> | <code>There is a Hamilton Parish in addition to the City of Hamilton (which is in Pembroke Parish).</code> | <code>0</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "OnlineContrastiveLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### scitail-pairs-qa
* Dataset: [scitail-pairs-qa](https://huggingface.co./datasets/allenai/scitail) at [0cc4353](https://huggingface.co./datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 4,300 training samples
* Columns: <code>sentence2</code> and <code>sentence1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence2 | sentence1 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 16.33 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 14.66 tokens</li><li>max: 34 tokens</li></ul> |
* Samples:
| sentence2 | sentence1 |
|:-------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| <code>You decrease errors in an experiment by take many measurements.</code> | <code>How do you decrease errors in an experiment?</code> |
| <code>A(n) increase in length happens to metal railroad tracks during the heat of a summer day.</code> | <code>What happens to metal railroad tracks during the heat of a summer day?</code> |
| <code>Corals build hard exoskeletons that grow to become coral reefs.</code> | <code>Corals build hard exoskeletons that grow to become what?</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co./datasets/allenai/scitail) at [0cc4353](https://huggingface.co./datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 2,200 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: 23.38 tokens</li><li>max: 64 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 14.97 tokens</li><li>max: 41 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
| <code>In fact, copper is a good conductor of electricity.</code> | <code>Copper conducts electricity the best.</code> |
| <code>The tadpoles feed on aquatic plants and grow quickly eventually developing limbs and lungs as they emerge out of their pools as tiny frogs.</code> | <code>A frog develops (a/an) lungs as it changes from a tadpole to an adult frog.</code> |
| <code>The atomic mass (also referred to as the atomic weight) is the number of protons and neutrons in an atom.</code> | <code>To determine the atomic weight of an element, you should add up protons and neutrons.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### xsum-pairs
* Dataset: [xsum-pairs](https://huggingface.co./datasets/sentence-transformers/xsum) at [788ddaf](https://huggingface.co./datasets/sentence-transformers/xsum/tree/788ddafe04e539956d56b567bc32a036ee7b9206)
* Size: 2,500 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: 2 tokens</li><li>mean: 353.38 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 26.97 tokens</li><li>max: 60 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>The 28-year-old centre-back, who signed on a free transfer from Plymouth in June, has started 17 League One games for Rovers so far this season.<br>He picked up the injury in Monday's 4-1 loss at Charlton Athletic.<br>Meanwhile, on-loan midfielder Charlie Colkett, 20, has been recalled to his parent club Chelsea, after 17 Rovers appearances in all competitions.</code> | <code>Bristol Rovers defender Peter Hartley has been sidelined for up to three months with a foot injury.</code> |
| <code>Although the 2014 Academy Awards ceremony has come and gone, its after-effects are still being felt in Kenya.<br>The first wave of activity happened soon after Lupita Nyong'o was announced Oscar winner for best actress in a supporting role in 12 Years A Slave.<br>The media, especially on Twitter, went wild with jubilant Kenyans sending and sharing the news, joy and jokes.<br>One Kenyan created much laughter by asking: "Which is this Oscar guy that Lupita has won over?"<br>Yet others began to wonder whether it was now time to seriously analyse what the waters of Lake Victoria - on the shores of Kisumu in Western Kenya - actually contained.<br>That region of the country and its Luo community have made global history for a second time.<br>It was the home of both Barack Obama's father and Lupita Nyong'o's family. Indeed, her father, Anyang Nyong'o is the Senator for Kisumu county and some of Kenya's sharpest minds come from this region.<br>And thanks to her comments, we now have a blue Nairobi. The Kenyan capital has always been known as the green city in the sun, because of its parks and trees. Although with weather patterns running amok, it often feels like the great sun in the city.<br>But not any more. Thanks to Lupita's comment that the beautiful light blue dress she wore at the Oscars reminded her of Nairobi, we now have Nairobi blue.<br>Whether this is a new colour, city, dress or state of mind, I can't tell.<br>What I know for certain is that Lupita's "your dreams are valid" statement in her acceptance speech has become a catch-phrase and a rallying call for Kenyans, especially the youth, to dream big.<br>You'll now hear young artists and upcoming entrepreneurs saying that they will push themselves to rise to the highest ranks - because their dreams are valid.<br>Lupita's victory at the Oscars is a demonstration that the African craft of telling or portraying stories is at the highest global standard.<br>The fact that she won and faced the world without lightening her dark skin complexion, or extending her short African hair, makes another statement - that an authentic African identity does not have to be negotiable for Africa to be heard loud and clear across the planet.<br>Proudly African, Lupita even gave the Western world collective heart failure as they struggled to pronounce her second name correctly.<br>As an ever-optimistic believer that Africa will soon take over the world, I feel the time has come for the continent to dream mega and now use its own voice to narrate the African experience.<br>Hollywood is one of the biggest factories and exporters of Western culture and the American experience. And Africa is a big importer of the same.<br>But the boot is gradually shifting to the other foot with Africa already exporting its sporting talent, its innovations and some of its culture from sources such as the Nigerian Nollywood film industry.<br>Now we must export our thinking. When the world begins to think what we think and why we think it, our story will move from what author Chimamanda Ngozi Adichie describes as the single story, to being understood in its truly complex weave of the African.<br>For this to happen we have to believe that our dreams are valid and that only we can validate those dreams.<br>It's also about believing in ourselves as Africans - that we can change the world, even if it's one colour at a time.<br>Lupita's Nairobi blue certainly seems to have the potential of becoming the new black on the global fashion scene.<br>If you would like to comment on Joseph Warungu's column, please do so below.</code> | <code>In our series of letters from African journalists, broadcaster and media trainer Joseph Warungu reflects on the aftermath of Lupita Nyong'o's historic success at the Oscars.</code> |
| <code>He told Newsnight it was "unrealistic" to assume the poll - likely to be held later this year - would be repeated.<br>Mr Osborne, who described himself as a Eurosceptic, said he was "optimistic" about reaching a deal on EU reforms.<br>The chancellor's comments come as Leader of the Commons Chris Grayling said staying in the EU under the current terms would be "disastrous".<br>The in/out referendum on EU membership has been promised by the end of 2017.<br>Prime Minister David Cameron has said he wants to campaign for the UK to stay in a reformed EU, but says he "rules nothing out" if his demands are refused by other EU leaders - a line Mr Osborne reiterated in his interview.<br>"I think anyone who votes out on the assumption that a year or two later you can have another vote to vote back in... is being unrealistic about the nature of the choice," Mr Osborne said.<br>"And I think it's really important that the British people focus on the fact this is the once-in-a-lifetime decision."<br>Timeline: What will happen when?<br>Guide: All you need to know the referendum<br>Explained: What does Britain want from Europe?<br>Analysis: Cameron tries to avert slanging match<br>More: BBC News EU referendum special<br>Mr Cameron has said he hopes to reach a deal on his reform demands, which include curbs on EU migrants' welfare entitlement, at next month's European Council meeting, .<br>His proposal for a four-year freeze on some payments has been resisted by other EU leaders, but Mr Osborne said he saw "the essential pieces of the deal falling into place".<br>Some of the alternative arrangements to remaining within the EU "do not look very attractive", added Mr Osborne.<br>He said: "I want us to be able to stay in a reformed European Union.<br>"And so establishing these principles - that Britain can't be discriminated against because it's not part of the euro, can't pick up the bill for eurozone bailouts, crucially can't have imposed on it changes the eurozone want to make without our consent - these things really matter and they're part of that resettlement."<br>The chancellor - who has a key role in the UK's negotiating team - said the Treasury was "100% focused" on the talks, rather than planning for a UK exit from the EU.<br>He also said the UK would be demanding a permanent guarantee that it would not have to contribute to future eurozone bailout payments.<br>He added: "I've been concerned about some of the things that have happened in the European Union, that's why I want to make those changes.<br>"It's a perfectly respectable position to say 'let's seek those changes, let's achieve those changes, let's have that new settlement, and then we can have the best of both worlds'.<br>"We can be in the European Union, but not run by the European Union."<br>Mr Cameron has said his ministers will be able to campaign for either side in the referendum, but must back the government until negotiations are complete.<br>Leader of the Commons Mr Grayling, writing in the Daily Telegraph on Thursday, said remaining within the European Union under the UK's current membership terms would be "disastrous".<br>He intervention has been seen as the first sign of a minister preparing to campaign to leave the EU in the UK's referendum.<br>Mr Grayling said the UK was at "a crucial crossroads" and "cannot be left in a position where we have no ability to defend our national interest" within the EU".<br>He backed Mr Cameron to secure the reforms he is demanding, a stance supported by Mayor of London Boris Johnson.<br>Mr Johnson has said he remains "very confident" the prime minister will "get a good deal" for the UK in his renegotiation with EU leaders.<br>Asked about Mr Grayling's article in the Telegraph, Mr Johnson said: "He's totally right to say unless you get reform, Europe will continue to be a zone of low growth and stagnation."<br>You can watch Newsnight's full interview with George Osborne on iPlayer .</code> | <code>The UK's EU referendum represents a "once-in-a-lifetime decision", Chancellor George Osborne has said.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### compression-pairs
* Dataset: [compression-pairs](https://huggingface.co./datasets/sentence-transformers/sentence-compression) at [605bc91](https://huggingface.co./datasets/sentence-transformers/sentence-compression/tree/605bc91d95631895ba25b6eda51a3cb596976c90)
* Size: 4,000 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>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "MultipleNegativesSymmetricRankingLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### sciq_pairs
* Dataset: [sciq_pairs](https://huggingface.co./datasets/allenai/sciq) at [2c94ad3](https://huggingface.co./datasets/allenai/sciq/tree/2c94ad3e1aafab77146f384e23536f97a4849815)
* Size: 6,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 17.26 tokens</li><li>max: 60 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 84.37 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What type of organism is commonly used in preparation of foods such as cheese and yogurt?</code> | <code>Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine.</code> |
| <code>What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?</code> | <code>Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere.</code> |
| <code>Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what?</code> | <code>Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### qasc_pairs
* Dataset: [qasc_pairs](https://huggingface.co./datasets/allenai/qasc) at [a34ba20](https://huggingface.co./datasets/allenai/qasc/tree/a34ba204eb9a33b919c10cc08f4f1c8dae5ec070)
* Size: 6,500 training samples
* Columns: <code>id</code>, <code>sentence1</code>, and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | id | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 17 tokens</li><li>mean: 21.35 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.47 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 35.55 tokens</li><li>max: 66 tokens</li></ul> |
* Samples:
| id | sentence1 | sentence2 |
|:--------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K</code> | <code>What type of water formation is formed by clouds?</code> | <code>beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds.</code> |
| <code>3LS2AMNW5FPNJK3C3PZLZCPX562OQO</code> | <code>Where do beads of water come from?</code> | <code>beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water</code> |
| <code>3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3</code> | <code>What forms beads of water? </code> | <code>beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### openbookqa_pairs
* Dataset: [openbookqa_pairs](https://huggingface.co./datasets/allenai/openbookqa) at [388097e](https://huggingface.co./datasets/allenai/openbookqa/tree/388097ea7776314e93a529163e0fea805b8a6454)
* Size: 2,740 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 13.83 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.37 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------|:--------------------------------------------------------------------------|
| <code>The sun is responsible for</code> | <code>the sun is the source of energy for physical cycles on Earth</code> |
| <code>When food is reduced in the stomach</code> | <code>digestion is when stomach acid breaks down food</code> |
| <code>Stars are</code> | <code>a star is made of gases</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### msmarco_pairs
* Dataset: [msmarco_pairs](https://huggingface.co./datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3) at [28ff31e](https://huggingface.co./datasets/sentence-transformers/msmarco-msmarco-distilbert-base-v3/tree/28ff31e4c97cddd53d298497f766e653f1e666f9)
* Size: 6,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.61 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 75.09 tokens</li><li>max: 206 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what are the liberal arts?</code> | <code>liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.</code> |
| <code>what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code> | <code>Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.</code> |
| <code>what is normal plat count</code> | <code>78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### nq_pairs
* Dataset: [nq_pairs](https://huggingface.co./datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co./datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 6,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.77 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 131.57 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>when did richmond last play in a preliminary final</code> | <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.</code> |
| <code>who sang what in the world's come over you</code> | <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> |
| <code>who produces the most wool in the world</code> | <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### trivia_pairs
* Dataset: [trivia_pairs](https://huggingface.co./datasets/sentence-transformers/trivia-qa) at [a7c36e3](https://huggingface.co./datasets/sentence-transformers/trivia-qa/tree/a7c36e3c8c8c01526bc094d79bf80d4c848b0ad0)
* Size: 6,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 15.16 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 456.87 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</code> | <code>The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. <http://www.nobelprize.org/nobel_prizes/literature/laureates/1930/></code> |
| <code>Where in England was Dame Judi Dench born?</code> | <code>Judi Dench - IMDb IMDb Actress | Music Department | Soundtrack Judi Dench was born in York, England, to Eleanora Olive (Jones), who was from Dublin, Ireland, and Reginald Arthur Dench, a doctor from Dorset, England. She attended Mount School in York, and studied at the Central School of Speech and Drama. She has performed with Royal Shakespeare Company, the National Theatre, and at Old Vic Theatre. She is a ... See full bio » Born: a list of 35 people created 02 Jul 2011 a list of 35 people created 19 Apr 2012 a list of 35 people created 28 May 2014 a list of 25 people created 05 Aug 2014 a list of 26 people created 18 May 2015 Do you have a demo reel? Add it to your IMDbPage How much of Judi Dench's work have you seen? User Polls Won 1 Oscar. Another 59 wins & 163 nominations. See more awards » Known For 2016 The Hollow Crown (TV Series) Cecily, Duchess of York 2015 The Vote (TV Movie) Christine Metcalfe - Total War (1996) ... Narrator (voice) - Stalemate (1996) ... Narrator (voice) 1992 The Torch (TV Mini-Series) Aba 1990 Screen One (TV Series) Anne 1989 Behaving Badly (TV Mini-Series) Bridget 1981 BBC2 Playhouse (TV Series) Sister Scarli 1976 Arena (TV Series documentary) Sweetie Simpkins 1973 Ooh La La! (TV Series) Amélie 1966 Court Martial (TV Series) Marthe 1963 Z Cars (TV Series) Elena Collins 1963 Love Story (TV Series) Pat McKendrick 1960 The Terrible Choice (TV Series) Good Angel Music department (1 credit) A Fine Romance (TV Series) (theme sung by - 14 episodes, 1981 - 1983) (theme song sung by - 12 episodes, 1983 - 1984) - A Romantic Meal (1984) ... (theme song sung by) - Problems (1984) ... (theme song sung by) 2013 Fifty Years on Stage (TV Movie) (performer: "Send in the Clowns") 2009 Nine (performer: "Folies Bergère") - What's Wrong with Mrs Bale? (1997) ... (performer: "Raindrops Keep Fallin' On My Head" - uncredited) - Misunderstandings (1993) ... (performer: "Walkin' My Baby Back Home" - uncredited) 1982-1984 A Fine Romance (TV Series) (performer - 2 episodes) - The Telephone Call (1984) ... (performer: "Boogie Woogie Bugle Boy" - uncredited) - Furniture (1982) ... (performer: "Rule, Britannia!" - uncredited) Hide 2009 Waiting in Rhyme (Video short) (special thanks) 2007 Expresso (Short) (special thanks) 1999 Shakespeare in Love and on Film (TV Movie documentary) (thanks - as Dame Judi Dench) Hide 2016 Rio Olympics (TV Mini-Series) Herself 2015 In Conversation (TV Series documentary) Herself 2015 Entertainment Tonight (TV Series) Herself 2015 CBS This Morning (TV Series) Herself - Guest 2015 The Insider (TV Series) Herself 1999-2014 Cinema 3 (TV Series) Herself 2013 Good Day L.A. (TV Series) Herself - Guest 2013 Arena (TV Series documentary) Herself 2013 At the Movies (TV Series) Herself 2013 Shooting Bond (Video documentary) Herself 2013 Bond's Greatest Moments (TV Movie documentary) Herself 2012 Made in Hollywood (TV Series) Herself 1999-2012 Charlie Rose (TV Series) Herself - Guest 2008-2012 This Morning (TV Series) Herself - Guest 2012 The Secrets of Skyfall (TV Short documentary) Herself 2012 Anderson Live (TV Series) Herself 2012 J. Edgar: A Complicated Man (Video documentary short) Herself 2011 The Many Faces of... (TV Series documentary) Herself / Various Characters 2011 Na plovárne (TV Series) Herself 2010 BBC Proms (TV Series) Herself 2010 The South Bank Show Revisited (TV Series documentary) Herself - Episode #6.68 (2009) ... Herself - Guest (as Dame Judi Dench) 2007-2009 Breakfast (TV Series) 2009 Larry King Live (TV Series) Herself - Guest 2009 The One Show (TV Series) Herself 2009 Cranford in Detail (Video documentary short) Herself / Miss Matty Jenkins (as Dame Judi Dench) 2005-2008 The South Bank Show (TV Series documentary) Herself 2008 Tavis Smiley (TV Series) Herself - Guest 2007 ITV News (TV Series) Herself - BAFTA Nominee 2007 The Making of Cranford (Video documentary short) Herself / Miss Matty Jenkyns (as Dame Judi Dench) 2006 Becoming Bond (TV Movie documentary) Herself 2006 Corazón de... (TV Series) Hers</code> |
| <code>In which decade did Billboard magazine first publish and American hit chart?</code> | <code>The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day. he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### quora_pairs
* Dataset: [quora_pairs](https://huggingface.co./datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co./datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 4,000 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 13.53 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 13.68 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
| <code>Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?</code> | <code>I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?</code> |
| <code>How can I be a good geologist?</code> | <code>What should I do to be a great geologist?</code> |
| <code>How do I read and find my YouTube comments?</code> | <code>How can I see all my Youtube comments?</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### gooaq_pairs
* Dataset: [gooaq_pairs](https://huggingface.co./datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co./datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
* Size: 6,500 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 11.6 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 57.74 tokens</li><li>max: 127 tokens</li></ul> |
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>is toprol xl the same as metoprolol?</code> | <code>Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.</code> |
| <code>are you experienced cd steve hoffman?</code> | <code>The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.</code> |
| <code>how are babushka dolls made?</code> | <code>Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
### 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: 750 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 17.61 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.71 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> |
| <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>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### scitail-pairs-pos
* Dataset: [scitail-pairs-pos](https://huggingface.co./datasets/allenai/scitail) at [0cc4353](https://huggingface.co./datasets/allenai/scitail/tree/0cc4353235b289165dfde1c7c5d1be983f99ce44)
* Size: 750 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 5 tokens</li><li>mean: 22.43 tokens</li><li>max: 61 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 15.3 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
| <code>An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions.</code> | <code>Replace another in a molecule happens to atoms during a substitution reaction.</code> | <code>0</code> |
| <code>Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase;</code> | <code>Wavelength is the distance between two corresponding points of adjacent waves called.</code> | <code>1</code> |
| <code>humans normally have 23 pairs of chromosomes.</code> | <code>Humans typically have 23 pairs pairs of chromosomes.</code> | <code>1</code> |
* Loss: [<code>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "GISTEmbedLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
#### qnli-contrastive
* Dataset: [qnli-contrastive](https://huggingface.co./datasets/nyu-mll/glue) at [bcdcba7](https://huggingface.co./datasets/nyu-mll/glue/tree/bcdcba79d07bc864c1c254ccfcedcce55bcc9a8c)
* Size: 750 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: 14.15 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 36.98 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>AdaptiveLayerLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#adaptivelayerloss) with these parameters:
```json
{
"loss": "OnlineContrastiveLoss",
"n_layers_per_step": -1,
"last_layer_weight": 1.5,
"prior_layers_weight": 0.15,
"kl_div_weight": 2,
"kl_temperature": 0.5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 28
- `per_device_eval_batch_size`: 18
- `learning_rate`: 2e-05
- `weight_decay`: 7.5e-07
- `lr_scheduler_type`: cosine_with_restarts
- `lr_scheduler_kwargs`: {'num_cycles': 3}
- `warmup_ratio`: 0.25
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTaV3-small-SenTra-AdaptiveLayers-AllSoft-LowTemp-n
- `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`: 28
- `per_device_eval_batch_size`: 18
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 7.5e-07
- `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_with_restarts
- `lr_scheduler_kwargs`: {'num_cycles': 3}
- `warmup_ratio`: 0.25
- `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-SenTra-AdaptiveLayers-AllSoft-LowTemp-n
- `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 | scitail-pairs-pos loss | qnli-contrastive loss | nli-pairs loss | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:----------------------:|:---------------------:|:--------------:|:------------------------:|
| 0.0378 | 106 | 24.0169 | - | - | - | - |
| 0.0757 | 212 | 12.3216 | - | - | - | - |
| 0.1135 | 318 | 8.0841 | - | - | - | - |
| 0.1513 | 424 | 7.3781 | - | - | - | - |
| 0.1892 | 530 | 7.0623 | - | - | - | - |
| 0.2270 | 636 | 6.5592 | - | - | - | - |
| 0.2648 | 742 | 6.3105 | - | - | - | - |
| 0.3001 | 841 | - | 4.0438 | 6.8184 | 5.7654 | - |
| 0.3026 | 848 | 5.703 | - | - | - | - |
| 0.3405 | 954 | 5.14 | - | - | - | - |
| 0.3783 | 1060 | 5.1272 | - | - | - | - |
| 0.4161 | 1166 | 4.6389 | - | - | - | - |
| 0.4540 | 1272 | 4.628 | - | - | - | - |
| 0.4918 | 1378 | 4.2676 | - | - | - | - |
| 0.5296 | 1484 | 3.9344 | - | - | - | - |
| 0.5675 | 1590 | 4.0826 | - | - | - | - |
| 0.6003 | 1682 | - | 2.4293 | 3.9794 | 3.2967 | - |
| 0.6053 | 1696 | 3.8582 | - | - | - | - |
| 0.6431 | 1802 | 3.8492 | - | - | - | - |
| 0.6809 | 1908 | 3.6339 | - | - | - | - |
| 0.7188 | 2014 | 3.6057 | - | - | - | - |
| 0.7566 | 2120 | 3.7662 | - | - | - | - |
| 0.7944 | 2226 | 3.3745 | - | - | - | - |
| 0.8323 | 2332 | 3.3909 | - | - | - | - |
| 0.8701 | 2438 | 3.032 | - | - | - | - |
| 0.9004 | 2523 | - | 1.9019 | 3.7526 | 2.5329 | - |
| 0.9079 | 2544 | 3.2439 | - | - | - | - |
| 0.9458 | 2650 | 3.4244 | - | - | - | - |
| 0.9836 | 2756 | 3.6424 | - | - | - | - |
| 1.0214 | 2862 | 3.7585 | - | - | - | - |
| 1.0592 | 2968 | 2.9868 | - | - | - | - |
| 1.0971 | 3074 | 2.9724 | - | - | - | - |
| 1.1349 | 3180 | 3.0862 | - | - | - | - |
| 1.1727 | 3286 | 3.0445 | - | - | - | - |
| 1.2006 | 3364 | - | 1.6621 | 2.9946 | 2.2784 | - |
| 1.2106 | 3392 | 2.8753 | - | - | - | - |
| 1.2484 | 3498 | 2.5852 | - | - | - | - |
| 1.2862 | 3604 | 2.6861 | - | - | - | - |
| 1.3241 | 3710 | 2.7259 | - | - | - | - |
| 1.3619 | 3816 | 2.5023 | - | - | - | - |
| 1.3997 | 3922 | 2.8295 | - | - | - | - |
| 1.4375 | 4028 | 2.7043 | - | - | - | - |
| 1.4754 | 4134 | 2.8 | - | - | - | - |
| 1.5007 | 4205 | - | 1.5995 | 2.7823 | 2.1823 | - |
| 1.5132 | 4240 | 2.3898 | - | - | - | - |
| 1.5510 | 4346 | 2.8008 | - | - | - | - |
| 1.5889 | 4452 | 2.4967 | - | - | - | - |
| 1.6267 | 4558 | 2.7084 | - | - | - | - |
| 1.6645 | 4664 | 2.5479 | - | - | - | - |
| 1.7024 | 4770 | 2.5035 | - | - | - | - |
| 1.7402 | 4876 | 2.6293 | - | - | - | - |
| 1.7780 | 4982 | 2.6069 | - | - | - | - |
| 1.8009 | 5046 | - | 1.4964 | 2.4063 | 2.0049 | - |
| 1.8158 | 5088 | 2.4453 | - | - | - | - |
| 1.8537 | 5194 | 2.2469 | - | - | - | - |
| 1.8915 | 5300 | 2.0528 | - | - | - | - |
| 1.9293 | 5406 | 2.4979 | - | - | - | - |
| 1.9672 | 5512 | 2.6698 | - | - | - | - |
| 2.0050 | 5618 | 3.2147 | - | - | - | - |
| 2.0428 | 5724 | 2.4885 | - | - | - | - |
| 2.0807 | 5830 | 2.5061 | - | - | - | - |
| 2.1010 | 5887 | - | 1.4211 | 2.3481 | 1.8698 | - |
| 2.1185 | 5936 | 2.285 | - | - | - | - |
| 2.1563 | 6042 | 2.6148 | - | - | - | - |
| 2.1941 | 6148 | 2.4811 | - | - | - | - |
| 2.2320 | 6254 | 2.0681 | - | - | - | - |
| 2.2698 | 6360 | 2.4426 | - | - | - | - |
| 2.3076 | 6466 | 2.5273 | - | - | - | - |
| 2.3455 | 6572 | 2.1097 | - | - | - | - |
| 2.3833 | 6678 | 2.8945 | - | - | - | - |
| 2.4011 | 6728 | - | 1.3394 | 2.6094 | 1.8919 | - |
| 2.4211 | 6784 | 2.2264 | - | - | - | - |
| 2.4590 | 6890 | 2.5986 | - | - | - | - |
| 2.4968 | 6996 | 2.3359 | - | - | - | - |
| 2.5346 | 7102 | 1.857 | - | - | - | - |
| 2.5724 | 7208 | 2.0381 | - | - | - | - |
| 2.6103 | 7314 | 2.0267 | - | - | - | - |
| 2.6481 | 7420 | 2.0914 | - | - | - | - |
| 2.6859 | 7526 | 1.9207 | - | - | - | - |
| 2.7013 | 7569 | - | 1.2556 | 2.2631 | 1.7135 | - |
| 2.7238 | 7632 | 2.034 | - | - | - | - |
| 2.7616 | 7738 | 2.2729 | - | - | - | - |
| 2.7994 | 7844 | 1.936 | - | - | - | - |
| 2.8373 | 7950 | 2.1102 | - | - | - | - |
| 2.8751 | 8056 | 1.6607 | - | - | - | - |
| 2.9129 | 8162 | 1.9579 | - | - | - | - |
| 2.9507 | 8268 | 2.4587 | - | - | - | - |
| 2.9886 | 8374 | 2.78 | - | - | - | - |
| 3.0 | 8406 | - | - | - | - | 0.7610 |
### 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",
}
```
#### AdaptiveLayerLoss
```bibtex
@misc{li20242d,
title={2D Matryoshka Sentence Embeddings},
author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
year={2024},
eprint={2402.14776},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### 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}
}
```
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