---
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: 0.7746195773286169
name: Pearson Cosine
- type: spearman_cosine
value: 0.7690423402274569
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7641811345210845
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.754454714808573
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7621768998872902
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7522944339564277
name: Spearman Euclidean
- type: pearson_dot
value: 0.643272843908074
name: Pearson Dot
- type: spearman_dot
value: 0.6187202562345202
name: Spearman Dot
- type: pearson_max
value: 0.7746195773286169
name: Pearson Max
- type: spearman_max
value: 0.7690423402274569
name: Spearman Max
- type: pearson_cosine
value: 0.7408543477349779
name: Pearson Cosine
- type: spearman_cosine
value: 0.7193195268794856
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7347205138738226
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.716277121285963
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7317357204840789
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7133569462956698
name: Spearman Euclidean
- type: pearson_dot
value: 0.5412116736741877
name: Pearson Dot
- type: spearman_dot
value: 0.5324862690078268
name: Spearman Dot
- type: pearson_max
value: 0.7408543477349779
name: Pearson Max
- type: spearman_max
value: 0.7193195268794856
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)
- **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
### 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-v2-AllSoft")
# 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]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.7746 |
| **spearman_cosine** | **0.769** |
| pearson_manhattan | 0.7642 |
| spearman_manhattan | 0.7545 |
| pearson_euclidean | 0.7622 |
| spearman_euclidean | 0.7523 |
| pearson_dot | 0.6433 |
| spearman_dot | 0.6187 |
| pearson_max | 0.7746 |
| spearman_max | 0.769 |
## 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 5 tokens
- mean: 16.62 tokens
- max: 62 tokens
| - min: 4 tokens
- mean: 9.46 tokens
- max: 29 tokens
|
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------|:-------------------------------------------------|
| A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. |
| Children smiling and waving at camera | There are children present |
| A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | - min: 6 tokens
- mean: 9.81 tokens
- max: 27 tokens
| - min: 5 tokens
- mean: 9.74 tokens
- max: 25 tokens
| - min: 0.0
- mean: 0.54
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
| A plane is taking off. | An air plane is taking off. | 1.0 |
| A man is playing a large flute. | A man is playing a flute. | 0.76 |
| A man is spreading shreded cheese on a pizza. | A man is spreading shredded cheese on an uncooked pizza. | 0.76 |
* Loss: [CoSENTLoss](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: label, sentence1, and sentence2
* Approximate statistics based on the first 1000 samples:
| | label | sentence1 | sentence2 |
|:--------|:-----------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | int | string | string |
| details | | - min: 6 tokens
- mean: 15.76 tokens
- max: 75 tokens
| - min: 8 tokens
- mean: 37.3 tokens
- max: 502 tokens
|
* Samples:
| label | sentence1 | sentence2 |
|:---------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 1 | The film will be screened in 2200 theaters . | In the United States and Canada , pre-release tracking suggest the film will gross $ 7�8 million from 2,200 theaters in its opening weekend , trailing fellow newcomer 10 Cloverfield Lane ( $ 25�30 million projection ) , but similar t |
| 1 | Neighbors 2 : Sorority Rising ( film ) scored over 65 % on Rotten Tomatoes . | On Rotten Tomatoes , the film has a rating of 67 % , based on 105 reviews , with an average rating of 5.9/10 . |
| 1 | Averaged on more than 65 reviews , The Handmaiden scored 94 % . | On Rotten Tomatoes , the film has a rating of 94 % , based on 67 reviews , with an average rating of 8/10 . |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1, sentence2, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 13.64 tokens
- max: 30 tokens
| - min: 6 tokens
- mean: 34.57 tokens
- max: 149 tokens
| |
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| What professors established the importance of Whitehead's work? | Professors such as Wieman, Charles Hartshorne, Bernard Loomer, Bernard Meland, and Daniel Day Williams made Whitehead's philosophy arguably the most important intellectual thread running through the Divinity School. | 0 |
| When did people start living on the edge of the desert? | It was long believed that the region had been this way since about 1600 BCE, after shifts in the Earth's axis increased temperatures and decreased precipitation. | 0 |
| What was the title of Gertrude Stein's 1906-1908 book? | Picasso in turn was an important influence on Stein's writing. | 0 |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence2 and sentence1
* Approximate statistics based on the first 1000 samples:
| | sentence2 | sentence1 |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 7 tokens
- mean: 16.2 tokens
- max: 41 tokens
| - min: 7 tokens
- mean: 14.65 tokens
- max: 33 tokens
|
* Samples:
| sentence2 | sentence1 |
|:-------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
| Ash that enters the air naturally as a result of a volcano eruption is classified as a primary pollutant. | Ash that enters the air naturally as a result of a volcano eruption is classified as what kind of pollutant? |
| Exposure to ultraviolet radiation can increase the amount of pigment in the skin and make it appear darker. | Exposure to what can increase the amount of pigment in the skin and make it appear darker? |
| A lysozyme destroys bacteria by digesting their cell walls. | How does lysozyme destroy bacteria? |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 7 tokens
- mean: 23.6 tokens
- max: 74 tokens
| - min: 7 tokens
- mean: 15.23 tokens
- max: 41 tokens
|
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|
| An atom that gains electrons would be a negative ion. | Atoms that have gained electrons and become negatively charged are called negative ions. |
| Scientists will use data collected during the collisions to explore the particles known as quarks and gluons that make up protons and neutrons. | Protons and neutrons are made of quarks, which are fundamental particles of matter. |
| Watersheds and divides All of the land area whose water drains into a stream system is called the system's watershed. | All of the land drained by a river system is called its basin, or the "wet" term watershed |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 2 tokens
- mean: 350.46 tokens
- max: 512 tokens
| - min: 6 tokens
- mean: 27.13 tokens
- max: 70 tokens
|
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| An eyewitness told BBC Persian that the crowds were sharply divided between hardliners and moderates, but it was clear many people had responded to a call from former President Mohammad Khatami to attend the funeral as a show of support for the opposition reform movement.
Some were chanting opposition slogans, and others carried placards emphasising Mr Rafsanjani's links to the moderate and reformist camps.
"Long live Khatami, Long Live Rouhani. Hashemi, your soul is at peace!" said one banner.
"The circle became too closed for the centre," said another, using a quotation from Persian poetry to underline the growing distance in recent years between Mr Rafsanjani and Iran's hardline political establishment.
At one stage state television played loud music over its live broadcast of the event in order to drown out opposition slogans being chanted by the crowd.
As the official funeral eulogies were relayed to the crowds on the streets, they responded with calls of support for former President Khatami, and opposition leader Mir Hossein Mousavi, and shouts of: "You have the loudspeakers, we have the voice! Shame on you, Shame on State TV!"
On Iranian social media the funeral has been the number one topic with many opposition supporters using the hashtag #weallgathered to indicate their support and sympathy.
People have been posting photos and videos emphasising the number of opposition supporters out on the streets and showing the opposition slogans which state TV has been trying to obscure.
But government supporters have also taken to Twitter to play down the opposition showing at the funeral, accusing them of political opportunism.
"A huge army came out of love of the Supreme Leader," wrote a cleric called Sheikh Reza. "While a few foot soldiers came with their cameras to show off."
Another conversation engaging many on Twitter involved the wording of the prayers used at the funeral.
Did the Supreme Leader Ayatollah Ali Khamenei deliberately leave out a section praising the goodness of the deceased, some opposition supporters asked. And was this a comment on the political tensions between the two?
"No," responded another Twitter user, cleric Abbas Zolghadri. "The words of the prayer can be changed. There are no strict rules."
He followed this with a poignant photo of an empty grave - "Hashemi's final resting place" was the caption, summing up the sense of loss felt by Iranians of many different political persuasions despite the deep and bitter divisions. | Tehran has seen some of the biggest crowds on the streets since the 2009 "Green Movement" opposition demonstrations, as an estimated 2.5 million people gathered to bid farewell to Akbar Hashemi Rafsanjani, the man universally known as "Hashemi". |
| Mark Evans is retracing the same route across the Rub Al Khali, also known as the "Empty Quarter", taken by Bristol pioneer Bertram Thomas in 1930.
The 54-year-old Shropshire-born explorer is leading a three-man team to walk the 800 mile (1,300 km) journey from Salalah, Oman to Doha, Qatar.
The trek is expected to take 60 days.
The Rub Al Khali desert is considered one of the hottest, driest and most inhospitable places on earth.
Nearly two decades after Thomas completed his trek, British explorer and writer Sir Wilfred Thesiger crossed the Empty Quarter - mapping it in detail along the way.
60 days
To cross the Rub' Al Khali desert
* From Salalah in Oman to Doha, Qatar
* Walking with camels for 1,300km
* Area nearly three times the size of the UK
Completed by explorer Bertram Thomas in 1930
Bertram Thomas, who hailed from Pill, near Bristol, received telegrams of congratulation from both King George V and Sultan Taimur, then ruler of Oman.
He went on to lecture all over the world about the journey and to write a book called Arabia Felix.
Unlike Mr Evans, Thomas did not obtain permission for his expedition.
He said: "The biggest challenges for Thomas were warring tribes, lack of water in the waterholes and his total dependence on his Omani companion Sheikh Saleh to negotiate their way through the desert.
"The biggest challenge for those who wanted to make the crossing in recent decades has been obtaining government permissions to walk through this desolate and unknown territory." | An explorer has embarked on a challenge to become only the third British person in history to cross the largest sand desert in the world. |
| An Olympic gold medallist, he was also three-time world heavyweight champion and took part in some of the most memorable fights in boxing history.
He had a professional career spanning 21 years and BBC Sport takes a look at his 61 fights in more detail. | Boxing legend Muhammad Ali, who died at the age of 74, became a sporting icon during his career. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 10 tokens
- mean: 31.89 tokens
- max: 125 tokens
| - min: 5 tokens
- mean: 10.21 tokens
- max: 28 tokens
|
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|
| 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. | USHL completes expansion draft |
| Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month. | Bud Selig to speak at St. Norbert College |
| It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit. | It's cherry time |
* Loss: [AdaptiveLayerLoss](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": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 7 tokens
- mean: 17.26 tokens
- max: 60 tokens
| - min: 2 tokens
- mean: 84.37 tokens
- max: 512 tokens
|
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What type of organism is commonly used in preparation of foods such as cheese and yogurt? | 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. |
| 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? | 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. |
| Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what? | 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. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: id, sentence1, and sentence2
* Approximate statistics based on the first 1000 samples:
| | id | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 17 tokens
- mean: 21.35 tokens
- max: 27 tokens
| - min: 5 tokens
- mean: 11.47 tokens
- max: 25 tokens
| - min: 14 tokens
- mean: 35.55 tokens
- max: 66 tokens
|
* Samples:
| id | sentence1 | sentence2 |
|:--------------------------------------------|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K | What type of water formation is formed by clouds? | beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds. |
| 3LS2AMNW5FPNJK3C3PZLZCPX562OQO | Where do beads of water come from? | 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 |
| 3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3 | What forms beads of water? | beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 3 tokens
- mean: 13.83 tokens
- max: 78 tokens
| - min: 4 tokens
- mean: 11.37 tokens
- max: 30 tokens
|
* Samples:
| sentence1 | sentence2 |
|:-------------------------------------------------|:--------------------------------------------------------------------------|
| The sun is responsible for | the sun is the source of energy for physical cycles on Earth |
| When food is reduced in the stomach | digestion is when stomach acid breaks down food |
| Stars are | a star is made of gases |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 4 tokens
- mean: 8.61 tokens
- max: 27 tokens
| - min: 18 tokens
- mean: 75.09 tokens
- max: 206 tokens
|
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| what are the liberal arts? | 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. |
| what is the mechanism of action of fibrinolytic or thrombolytic drugs? | 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. |
| what is normal plat count | 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). |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 10 tokens
- mean: 11.77 tokens
- max: 21 tokens
| - min: 16 tokens
- mean: 131.57 tokens
- max: 512 tokens
|
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| when did richmond last play in a preliminary final | 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. |
| who sang what in the world's come over you | 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. |
| who produces the most wool in the world | 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. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 15.16 tokens
- max: 48 tokens
| - min: 19 tokens
- mean: 456.87 tokens
- max: 512 tokens
|
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Which American-born Sinclair won the Nobel Prize for Literature in 1930? | 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. |
| Where in England was Dame Judi Dench born? | 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 |
| In which decade did Billboard magazine first publish and American hit chart? | 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 |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 13.53 tokens
- max: 42 tokens
| - min: 6 tokens
- mean: 13.68 tokens
- max: 43 tokens
|
* Samples:
| sentence1 | sentence2 |
|:----------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
| Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me? | I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me? |
| How can I be a good geologist? | What should I do to be a great geologist? |
| How do I read and find my YouTube comments? | How can I see all my Youtube comments? |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 11.6 tokens
- max: 21 tokens
| - min: 13 tokens
- mean: 57.74 tokens
- max: 127 tokens
|
* Samples:
| sentence1 | sentence2 |
|:---------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| is toprol xl the same as metoprolol? | 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. |
| are you experienced cd steve hoffman? | 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. |
| how are babushka dolls made? | 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. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
### 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: anchor and positive
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 5 tokens
- mean: 17.61 tokens
- max: 51 tokens
| - min: 4 tokens
- mean: 9.71 tokens
- max: 29 tokens
|
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|
| Two women are embracing while holding to go packages. | Two woman are holding packages. |
| 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. | Two kids in numbered jerseys wash their hands. |
| A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1, sentence2, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 5 tokens
- mean: 22.43 tokens
- max: 61 tokens
| - min: 8 tokens
- mean: 15.3 tokens
- max: 36 tokens
| |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|:---------------|
| An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions. | Replace another in a molecule happens to atoms during a substitution reaction. | 0 |
| Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase; | Wavelength is the distance between two corresponding points of adjacent waves called. | 1 |
| humans normally have 23 pairs of chromosomes. | Humans typically have 23 pairs pairs of chromosomes. | 1 |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
#### 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: sentence1, sentence2, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 14.15 tokens
- max: 36 tokens
| - min: 4 tokens
- mean: 36.98 tokens
- max: 225 tokens
| |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| What came into force after the new constitution was herald? | As of that day, the new constitution heralding the Second Republic came into force. | 0 |
| What is the first major city in the stream of the Rhine? | The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz. | 0 |
| What is the minimum required if you want to teach in Canada? | In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher. | 0 |
* Loss: [AdaptiveLayerLoss](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": 2,
"prior_layers_weight": 0.1,
"kl_div_weight": 0.5,
"kl_temperature": 1
}
```
### 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`: 1e-06
- `num_train_epochs`: 2
- `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-GeneralSentenceTransformer-v2-2-checkpoints-tmp
- `hub_strategy`: checkpoint
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `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`: 1e-06
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `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-GeneralSentenceTransformer-v2-2-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
Click to expand
| Epoch | Step | Training Loss | nli-pairs loss | qnli-contrastive loss | scitail-pairs-pos loss | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:--------------:|:---------------------:|:----------------------:|:------------------------:|
| 0 | 0 | - | - | - | - | 0.4188 |
| 0.0253 | 71 | 9.7048 | - | - | - | - |
| 0.0503 | 141 | - | 7.9860 | 8.4771 | 6.6165 | - |
| 0.0507 | 142 | 8.6743 | - | - | - | - |
| 0.0760 | 213 | 8.101 | - | - | - | - |
| 0.1006 | 282 | - | 6.8505 | 7.5583 | 4.4099 | - |
| 0.1014 | 284 | 7.5594 | - | - | - | - |
| 0.1267 | 355 | 6.3548 | - | - | - | - |
| 0.1510 | 423 | - | 5.2238 | 6.2964 | 2.3430 | - |
| 0.1520 | 426 | 5.869 | - | - | - | - |
| 0.1774 | 497 | 5.1134 | - | - | - | - |
| 0.2013 | 564 | - | 4.5785 | 5.6786 | 1.8733 | - |
| 0.2027 | 568 | 5.1262 | - | - | - | - |
| 0.2281 | 639 | 3.7625 | - | - | - | - |
| 0.2516 | 705 | - | 3.9531 | 5.1247 | 1.6374 | - |
| 0.2534 | 710 | 4.5256 | - | - | - | - |
| 0.2787 | 781 | 3.8572 | - | - | - | - |
| 0.3019 | 846 | - | 3.5362 | 4.5487 | 1.5215 | - |
| 0.3041 | 852 | 3.9294 | - | - | - | - |
| 0.3294 | 923 | 3.281 | - | - | - | - |
| 0.3522 | 987 | - | 3.1562 | 3.7942 | 1.4236 | - |
| 0.3547 | 994 | 3.2531 | - | - | - | - |
| 0.3801 | 1065 | 3.9305 | - | - | - | - |
| 0.4026 | 1128 | - | 2.7059 | 3.4370 | 1.2689 | - |
| 0.4054 | 1136 | 3.0324 | - | - | - | - |
| 0.4308 | 1207 | 3.3544 | - | - | - | - |
| 0.4529 | 1269 | - | 2.5396 | 3.0366 | 1.2415 | - |
| 0.4561 | 1278 | 3.2331 | - | - | - | - |
| 0.4814 | 1349 | 3.1913 | - | - | - | - |
| 0.5032 | 1410 | - | 2.2846 | 2.7076 | 1.1422 | - |
| 0.5068 | 1420 | 2.7389 | - | - | - | - |
| 0.5321 | 1491 | 2.9541 | - | - | - | - |
| 0.5535 | 1551 | - | 2.1732 | 2.3780 | 1.2127 | - |
| 0.5575 | 1562 | 3.0911 | - | - | - | - |
| 0.5828 | 1633 | 2.932 | - | - | - | - |
| 0.6039 | 1692 | - | 2.0257 | 1.9252 | 1.1056 | - |
| 0.6081 | 1704 | 3.082 | - | - | - | - |
| 0.6335 | 1775 | 3.0328 | - | - | - | - |
| 0.6542 | 1833 | - | 1.9588 | 2.0366 | 1.1187 | - |
| 0.6588 | 1846 | 2.9508 | - | - | - | - |
| 0.6842 | 1917 | 2.7445 | - | - | - | - |
| 0.7045 | 1974 | - | 1.8310 | 1.9980 | 1.0991 | - |
| 0.7095 | 1988 | 2.8922 | - | - | - | - |
| 0.7348 | 2059 | 2.7352 | - | - | - | - |
| 0.7548 | 2115 | - | 1.7650 | 1.5015 | 1.1103 | - |
| 0.7602 | 2130 | 3.2009 | - | - | - | - |
| 0.7855 | 2201 | 2.6261 | - | - | - | - |
| 0.8051 | 2256 | - | 1.6932 | 1.6964 | 1.0409 | - |
| 0.8108 | 2272 | 2.6623 | - | - | - | - |
| 0.8362 | 2343 | 2.8281 | - | - | - | - |
| 0.8555 | 2397 | - | 1.6844 | 1.7854 | 1.0300 | - |
| 0.8615 | 2414 | 2.3096 | - | - | - | - |
| 0.8869 | 2485 | 2.4088 | - | - | - | - |
| 0.9058 | 2538 | - | 1.6698 | 1.8310 | 1.0275 | - |
| 0.9122 | 2556 | 2.6051 | - | - | - | - |
| 0.9375 | 2627 | 2.972 | - | - | - | - |
| 0.9561 | 2679 | - | 1.6643 | 1.8173 | 1.0215 | - |
| 0.9629 | 2698 | 2.4207 | - | - | - | - |
| 0.9882 | 2769 | 2.2772 | - | - | - | - |
| 1.0064 | 2820 | - | 1.7130 | 1.7650 | 1.0496 | - |
| 1.0136 | 2840 | 2.6348 | - | - | - | - |
| 1.0389 | 2911 | 2.8271 | - | - | - | - |
| 1.0567 | 2961 | - | 1.6939 | 2.1074 | 0.9858 | - |
| 1.0642 | 2982 | 2.5215 | - | - | - | - |
| 1.0896 | 3053 | 2.7442 | - | - | - | - |
| 1.1071 | 3102 | - | 1.6633 | 1.5590 | 0.9903 | - |
| 1.1149 | 3124 | 2.6155 | - | - | - | - |
| 1.1403 | 3195 | 2.7053 | - | - | - | - |
| 1.1574 | 3243 | - | 1.6242 | 1.6429 | 0.9740 | - |
| 1.1656 | 3266 | 2.9191 | - | - | - | - |
| 1.1909 | 3337 | 2.1112 | - | - | - | - |
| 1.2077 | 3384 | - | 1.6535 | 1.6226 | 0.9516 | - |
| 1.2163 | 3408 | 2.3519 | - | - | - | - |
| 1.2416 | 3479 | 1.9416 | - | - | - | - |
| 1.2580 | 3525 | - | 1.6103 | 1.6530 | 0.9357 | - |
| 1.2670 | 3550 | 2.0859 | - | - | - | - |
| 1.2923 | 3621 | 2.0109 | - | - | - | - |
| 1.3084 | 3666 | - | 1.5773 | 1.4672 | 0.9155 | - |
| 1.3176 | 3692 | 2.366 | - | - | - | - |
| 1.3430 | 3763 | 1.5532 | - | - | - | - |
| 1.3587 | 3807 | - | 1.5514 | 1.4451 | 0.8979 | - |
| 1.3683 | 3834 | 1.9982 | - | - | - | - |
| 1.3936 | 3905 | 2.4375 | - | - | - | - |
| 1.4090 | 3948 | - | 1.5254 | 1.4050 | 0.8834 | - |
| 1.4190 | 3976 | 1.7548 | - | - | - | - |
| 1.4443 | 4047 | 2.2272 | - | - | - | - |
| 1.4593 | 4089 | - | 1.5186 | 1.3720 | 0.8835 | - |
| 1.4697 | 4118 | 2.2145 | - | - | - | - |
| 1.4950 | 4189 | 1.8696 | - | - | - | - |
| 1.5096 | 4230 | - | 1.5696 | 1.0682 | 0.9336 | - |
| 1.5203 | 4260 | 1.4926 | - | - | - | - |
| 1.5457 | 4331 | 2.1193 | - | - | - | - |
| 1.5600 | 4371 | - | 1.5469 | 0.8180 | 0.9663 | - |
| 1.5710 | 4402 | 2.0298 | - | - | - | - |
| 1.5964 | 4473 | 1.9959 | - | - | - | - |
| 1.6103 | 4512 | - | 1.4656 | 1.1725 | 0.8815 | - |
| 1.6217 | 4544 | 2.3452 | - | - | - | - |
| 1.6470 | 4615 | 1.9529 | - | - | - | - |
| 1.6606 | 4653 | - | 1.4709 | 1.1081 | 0.9079 | - |
| 1.6724 | 4686 | 1.7932 | - | - | - | - |
| 1.6977 | 4757 | 2.1881 | - | - | - | - |
| 1.7109 | 4794 | - | 1.4526 | 0.9851 | 0.9167 | - |
| 1.7231 | 4828 | 2.1128 | - | - | - | - |
| 1.7484 | 4899 | 2.4772 | - | - | - | - |
| 1.7612 | 4935 | - | 1.4204 | 0.8683 | 0.8896 | - |
| 1.7737 | 4970 | 2.4336 | - | - | - | - |
| 1.7991 | 5041 | 1.9101 | - | - | - | - |
| 1.8116 | 5076 | - | 1.3821 | 1.0420 | 0.8538 | - |
| 1.8244 | 5112 | 2.3882 | - | - | - | - |
| 1.8498 | 5183 | 2.2165 | - | - | - | - |
| 1.8619 | 5217 | - | 1.3747 | 1.0753 | 0.8580 | - |
| 1.8751 | 5254 | 1.6554 | - | - | - | - |
| 1.9004 | 5325 | 2.3828 | - | - | - | - |
| 1.9122 | 5358 | - | 1.3637 | 1.0699 | 0.8557 | - |
| 1.9258 | 5396 | 2.3499 | - | - | - | - |
| 1.9511 | 5467 | 2.3972 | - | - | - | - |
| 1.9625 | 5499 | - | 1.3583 | 1.0596 | 0.8536 | - |
| 1.9764 | 5538 | 1.931 | - | - | - | - |
| 2.0 | 5604 | - | 1.3586 | 1.0555 | 0.8543 | 0.7193 |