---
base_model: microsoft/deberta-v3-small
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:32500
- loss:GISTEmbedLoss
widget:
- source_sentence: What was the name of Jed's nephew in The Beverly Hillbillies?
sentences:
- Jed Clampett - The Beverly Hillbillies Characters - ShareTV Buddy Ebsen began
his career as a dancer in the late 1920s in a Broadway chorus. He later formed
a vaudeville ... Character Bio Although he had received little formal education,
Jed Clampett had a good deal of common sense. A good-natured man, he is the apparent
head of the family. Jed's wife (Elly May's mother) died, but is referred to in
the episode "Duke Steals A Wife" as Rose Ellen. Jed was shown to be an expert
marksman and was extremely loyal to his family and kinfolk. The huge oil pool
in the swamp he owned was the beginning of his rags-to-riches journey to Beverly
Hills. Although he longed for the old ways back in the hills, he made the best
of being in Beverly Hills. Whenever he had anything on his mind, he would sit
on the curbstone of his mansion and whittle until he came up with the answer.
Jedediah, the version of Jed's name used in the 1993 Beverly Hillbillies theatrical
movie, was never mentioned in the original television series (though coincidentally,
on Ebsen's subsequent series, Barnaby Jones, Barnaby's nephew J.R. was also named
Jedediah). In one episode Jed and Granny reminisce about seeing Buddy Ebsen and
Vilma Ebsen—a joking reference to the Ebsens' song and dance act. Jed appears
in all 274 episodes. Episode Screenshots
- a stove generates heat for cooking usually
- Miss Marple series by Agatha Christie Miss Marple series 43 works, 13 primary
works Mystery series in order of publication. Miss Marple is introduced in The
Murder at the Vicarage but the books can be read in any order. Mixed short story
collections are included if some are Marple, often have horror, supernatural,
maybe detective Poirot, Pyne, or Quin. Note that "Nemesis" should be read AFTER
"A Caribbean Holiday"
- source_sentence: A recording of folk songs done for the Columbia society in 1942
was largely arranged by Pjetër Dungu .
sentences:
- Someone cooking drugs in a spoon over a candle
- A recording of folk songs made for the Columbia society in 1942 was largely arranged
by Pjetër Dungu .
- A Murder of Crows, A Parliament of Owls What do You Call a Group of Birds? Do
you know what a group of Ravens is called? What about a group of peacocks, snipe
or hummingbirds? Here is a list of Bird Collectives, terms that you can use to
describe a group of birds. Birds in general
- source_sentence: A person in a kitchen looking at the oven.
sentences:
- "staying warm has a positive impact on an animal 's survival. Furry animals grow\
\ thicker coats to keep warm in the winter. \n Furry animals grow thicker coats\
\ which has a positive impact on their survival. "
- A woman In the kitchen opening her oven.
- EE has apologised after a fault left some of its customers unable to use the internet
on their mobile devices.
- source_sentence: Air can be separated into several elements.
sentences:
- Which of the following substances can be separated into several elements?
- 'Funny Interesting Facts Humor Strange: Carl and the Passions changed band name
to what Carl and the Passions changed band name to what Beach Boys Carl and the
Passions - "So Tough" is the fifteenth studio album released by The Beach Boys
in 1972. In its initial release, it was the second disc of a two-album set with
Pet Sounds (which The Beach Boys were able to license from Capitol Records). Unfortunately,
due to the fact that Carl and the Passions - "So Tough" was a transitional album
that saw the departure of one member and the introduction of two new ones, making
it wildly inconsistent in terms of type of material present, it paled next to
their 1966 classic and was seen as something of a disappointment in its time of
release. The title of the album itself was a reference to an early band Carl Wilson
had been in as a teenager (some say a possible early name for the Beach Boys).
It was also the first album released under a new deal with Warner Bros. that allowed
the company to distribute all future Beach Boys product in foreign as well as
domestic markets.'
- Which statement correctly describes a relationship between two human body systems?
- source_sentence: What do outdoor plants require to survive?
sentences:
- "a plants require water for survival. If no rain or watering, the plant dies.\
\ \n Outdoor plants require rain to survive."
- (Vegan) soups are nutritious. In addition to them being easy to digest, most the
time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables,
and beans. Because the soup is full of those nutrients AND that it's easy to digest,
your body is able to absorb more of those nutrients into your system.
- If you do the math, there are 11,238,513 possible combinations of five white balls
(without order mattering). Multiply that by the 26 possible red balls, and you
get 292,201,338 possible Powerball number combinations. At $2 per ticket, you'd
need $584,402,676 to buy every single combination and guarantee a win.
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: 0.27561429926791464
name: Pearson Cosine
- type: spearman_cosine
value: 0.32606859471811517
name: Spearman Cosine
- type: pearson_manhattan
value: 0.3112396414398868
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.3379918226318111
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.29994864031298485
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.3260361203897462
name: Spearman Euclidean
- type: pearson_dot
value: 0.27336219058729005
name: Pearson Dot
- type: spearman_dot
value: 0.3235796341494495
name: Spearman Dot
- type: pearson_max
value: 0.3112396414398868
name: Pearson Max
- type: spearman_max
value: 0.3379918226318111
name: Spearman Max
- task:
type: binary-classification
name: Binary Classification
dataset:
name: allNLI dev
type: allNLI-dev
metrics:
- type: cosine_accuracy
value: 0.673828125
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.9788761138916016
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.5157894736842105
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8889895081520081
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.3702770780856423
name: Cosine Precision
- type: cosine_recall
value: 0.8497109826589595
name: Cosine Recall
- type: cosine_ap
value: 0.4327025118722887
name: Cosine Ap
- type: dot_accuracy
value: 0.673828125
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 751.3026733398438
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.5166959578207382
name: Dot F1
- type: dot_f1_threshold
value: 682.115234375
name: Dot F1 Threshold
- type: dot_precision
value: 0.3712121212121212
name: Dot Precision
- type: dot_recall
value: 0.8497109826589595
name: Dot Recall
- type: dot_ap
value: 0.43253813511417205
name: Dot Ap
- type: manhattan_accuracy
value: 0.671875
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 105.85862731933594
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.5217391304347826
name: Manhattan F1
- type: manhattan_f1_threshold
value: 241.55101013183594
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.38764044943820225
name: Manhattan Precision
- type: manhattan_recall
value: 0.7976878612716763
name: Manhattan Recall
- type: manhattan_ap
value: 0.4278948508381489
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.673828125
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 5.694375038146973
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.5157894736842105
name: Euclidean F1
- type: euclidean_f1_threshold
value: 13.050301551818848
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.3702770780856423
name: Euclidean Precision
- type: euclidean_recall
value: 0.8497109826589595
name: Euclidean Recall
- type: euclidean_ap
value: 0.4325438108928368
name: Euclidean Ap
- type: max_accuracy
value: 0.673828125
name: Max Accuracy
- type: max_accuracy_threshold
value: 751.3026733398438
name: Max Accuracy Threshold
- type: max_f1
value: 0.5217391304347826
name: Max F1
- type: max_f1_threshold
value: 682.115234375
name: Max F1 Threshold
- type: max_precision
value: 0.38764044943820225
name: Max Precision
- type: max_recall
value: 0.8497109826589595
name: Max Recall
- type: max_ap
value: 0.4327025118722887
name: Max Ap
- task:
type: binary-classification
name: Binary Classification
dataset:
name: Qnli dev
type: Qnli-dev
metrics:
- type: cosine_accuracy
value: 0.634765625
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.9121971130371094
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.6430868167202571
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8449763059616089
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5181347150259067
name: Cosine Precision
- type: cosine_recall
value: 0.847457627118644
name: Cosine Recall
- type: cosine_ap
value: 0.6377161139177543
name: Cosine Ap
- type: dot_accuracy
value: 0.63671875
name: Dot Accuracy
- type: dot_accuracy_threshold
value: 699.1280517578125
name: Dot Accuracy Threshold
- type: dot_f1
value: 0.6430868167202571
name: Dot F1
- type: dot_f1_threshold
value: 647.91845703125
name: Dot F1 Threshold
- type: dot_precision
value: 0.5181347150259067
name: Dot Precision
- type: dot_recall
value: 0.847457627118644
name: Dot Recall
- type: dot_ap
value: 0.6388138195772171
name: Dot Ap
- type: manhattan_accuracy
value: 0.642578125
name: Manhattan Accuracy
- type: manhattan_accuracy_threshold
value: 233.09597778320312
name: Manhattan Accuracy Threshold
- type: manhattan_f1
value: 0.6605783866057838
name: Manhattan F1
- type: manhattan_f1_threshold
value: 315.7362976074219
name: Manhattan F1 Threshold
- type: manhattan_precision
value: 0.5154394299287411
name: Manhattan Precision
- type: manhattan_recall
value: 0.9194915254237288
name: Manhattan Recall
- type: manhattan_ap
value: 0.6510660300493925
name: Manhattan Ap
- type: euclidean_accuracy
value: 0.634765625
name: Euclidean Accuracy
- type: euclidean_accuracy_threshold
value: 11.602351188659668
name: Euclidean Accuracy Threshold
- type: euclidean_f1
value: 0.6430868167202571
name: Euclidean F1
- type: euclidean_f1_threshold
value: 15.418830871582031
name: Euclidean F1 Threshold
- type: euclidean_precision
value: 0.5181347150259067
name: Euclidean Precision
- type: euclidean_recall
value: 0.847457627118644
name: Euclidean Recall
- type: euclidean_ap
value: 0.6377918821678507
name: Euclidean Ap
- type: max_accuracy
value: 0.642578125
name: Max Accuracy
- type: max_accuracy_threshold
value: 699.1280517578125
name: Max Accuracy Threshold
- type: max_f1
value: 0.6605783866057838
name: Max F1
- type: max_f1_threshold
value: 647.91845703125
name: Max F1 Threshold
- type: max_precision
value: 0.5181347150259067
name: Max Precision
- type: max_recall
value: 0.9194915254237288
name: Max Recall
- type: max_ap
value: 0.6510660300493925
name: Max Ap
---
# 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). 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
### 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): AdvancedWeightedPooling(
(alpha_dropout_layer): Dropout(p=0.01, inplace=False)
(gate_dropout_layer): Dropout(p=0.05, inplace=False)
(linear_cls_pj): Linear(in_features=768, out_features=768, bias=True)
(linear_cls_Qpj): Linear(in_features=768, out_features=768, bias=True)
(linear_mean_pj): Linear(in_features=768, out_features=768, bias=True)
(linear_attnOut): Linear(in_features=768, out_features=768, bias=True)
(mha): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(layernorm_output): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layernorm_weightedPooing): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layernorm_pjCls): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layernorm_pjMean): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layernorm_attnOut): LayerNorm((768,), eps=1e-05, elementwise_affine=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/DeBERTa3-s-CustomPoolin-toytest2-step1-checkpoints-tmp")
# Run inference
sentences = [
'What do outdoor plants require to survive?',
'a plants require water for survival. If no rain or watering, the plant dies. \n Outdoor plants require rain to survive.',
"(Vegan) soups are nutritious. In addition to them being easy to digest, most the time, soups are made from nutrient-dense ingredients like herbs, spices, vegetables, and beans. Because the soup is full of those nutrients AND that it's easy to digest, your body is able to absorb more of those nutrients into your system.",
]
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 | 0.2756 |
| **spearman_cosine** | **0.3261** |
| pearson_manhattan | 0.3112 |
| spearman_manhattan | 0.338 |
| pearson_euclidean | 0.2999 |
| spearman_euclidean | 0.326 |
| pearson_dot | 0.2734 |
| spearman_dot | 0.3236 |
| pearson_max | 0.3112 |
| spearman_max | 0.338 |
#### Binary Classification
* Dataset: `allNLI-dev`
* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.6738 |
| cosine_accuracy_threshold | 0.9789 |
| cosine_f1 | 0.5158 |
| cosine_f1_threshold | 0.889 |
| cosine_precision | 0.3703 |
| cosine_recall | 0.8497 |
| cosine_ap | 0.4327 |
| dot_accuracy | 0.6738 |
| dot_accuracy_threshold | 751.3027 |
| dot_f1 | 0.5167 |
| dot_f1_threshold | 682.1152 |
| dot_precision | 0.3712 |
| dot_recall | 0.8497 |
| dot_ap | 0.4325 |
| manhattan_accuracy | 0.6719 |
| manhattan_accuracy_threshold | 105.8586 |
| manhattan_f1 | 0.5217 |
| manhattan_f1_threshold | 241.551 |
| manhattan_precision | 0.3876 |
| manhattan_recall | 0.7977 |
| manhattan_ap | 0.4279 |
| euclidean_accuracy | 0.6738 |
| euclidean_accuracy_threshold | 5.6944 |
| euclidean_f1 | 0.5158 |
| euclidean_f1_threshold | 13.0503 |
| euclidean_precision | 0.3703 |
| euclidean_recall | 0.8497 |
| euclidean_ap | 0.4325 |
| max_accuracy | 0.6738 |
| max_accuracy_threshold | 751.3027 |
| max_f1 | 0.5217 |
| max_f1_threshold | 682.1152 |
| max_precision | 0.3876 |
| max_recall | 0.8497 |
| **max_ap** | **0.4327** |
#### Binary Classification
* Dataset: `Qnli-dev`
* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.6348 |
| cosine_accuracy_threshold | 0.9122 |
| cosine_f1 | 0.6431 |
| cosine_f1_threshold | 0.845 |
| cosine_precision | 0.5181 |
| cosine_recall | 0.8475 |
| cosine_ap | 0.6377 |
| dot_accuracy | 0.6367 |
| dot_accuracy_threshold | 699.1281 |
| dot_f1 | 0.6431 |
| dot_f1_threshold | 647.9185 |
| dot_precision | 0.5181 |
| dot_recall | 0.8475 |
| dot_ap | 0.6388 |
| manhattan_accuracy | 0.6426 |
| manhattan_accuracy_threshold | 233.096 |
| manhattan_f1 | 0.6606 |
| manhattan_f1_threshold | 315.7363 |
| manhattan_precision | 0.5154 |
| manhattan_recall | 0.9195 |
| manhattan_ap | 0.6511 |
| euclidean_accuracy | 0.6348 |
| euclidean_accuracy_threshold | 11.6024 |
| euclidean_f1 | 0.6431 |
| euclidean_f1_threshold | 15.4188 |
| euclidean_precision | 0.5181 |
| euclidean_recall | 0.8475 |
| euclidean_ap | 0.6378 |
| max_accuracy | 0.6426 |
| max_accuracy_threshold | 699.1281 |
| max_f1 | 0.6606 |
| max_f1_threshold | 647.9185 |
| max_precision | 0.5181 |
| max_recall | 0.9195 |
| **max_ap** | **0.6511** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 32,500 training samples
* Columns: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 4 tokens
- mean: 29.43 tokens
- max: 400 tokens
| - min: 2 tokens
- mean: 57.02 tokens
- max: 389 tokens
|
* Samples:
| sentence1 | sentence2 |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What is the chemical symbol for Silver? | Chemical Elements.com - Silver (Ag) Bentor, Yinon. Chemical Element.com - Silver. . For more information about citing online sources, please visit the MLA's Website . This page was created by Yinon Bentor. Use of this web site is restricted by this site's license agreement . Copyright © 1996-2012 Yinon Bentor. All Rights Reserved. |
| e. in solids the atoms are closely locked in position and can only vibrate, in liquids the atoms and molecules are more loosely connected and can collide with and move past one another, while in gases the atoms or molecules are free to move independently, colliding frequently. | Within a substance, atoms that collide frequently and move independently of one another are most likely in a gas |
| Keanu Neal was born in 1995 . | Keanu Neal ( born July 26 , 1995 ) is an American football safety for the Atlanta Falcons of the National Football League ( NFL ) . |
* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,664 evaluation samples
* Columns: sentence1 and sentence2
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 4 tokens
- mean: 28.9 tokens
- max: 348 tokens
| - min: 2 tokens
- mean: 57.31 tokens
- max: 450 tokens
|
* Samples:
| sentence1 | sentence2 |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Gene expression is regulated primarily at the what level? | Gene expression is regulated primarily at the transcriptional level. |
| Diffusion Diffusion is a process where atoms or molecules move from areas of high concentration to areas of low concentration. | Diffusion is the process in which a substance naturally moves from an area of higher to lower concentration. |
| In which James Bond film did Sean Connery wear the Bell Rocket Belt (Jet Pack)? | Jet Pack - James Bond Gadgets 125lbs Summary James Bond used the Jetpack in 1965's Thunderball, to escape from gunmen after killing a SPECTRE agent. The Jetpack In the 1965 movie Thunderball, James Bond (Sean Connery) uses Q's Jetpack to escape from two gunmen after killing Jacques Bouvar, SPECTRE Agent No. 6. It was also used in the Thunderball movie posters, being the "Look Up" part of the "Look Up! Look Down! Look Out!" tagline. The Jetpack returned in the 2002 movie Die Another Day, in the Q scene that showcased many other classic gadgets. The Jetpack is a very popular Bond gadget and is a favorite among many fans due to its originality and uniqueness. The Bell Rocket Belt The Jetpack is actually a Bell Rocket Belt, a fully functional rocket pack device. It was designed for use in the army, but was rejected because of its short flying time of 21-22 seconds. Powered by hydrogen peroxide, it could fly about 250m and reach a maximum altitude of 18m, going 55km/h. Despite its impracticality in the real world, the Jetpack made a spectacular debut in Thunderball. Although Sean Connery is seen in the takeoff and landings, the main flight was piloted by Gordon Yeager and Bill Suitor. |
* Loss: [GISTEmbedLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.025}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 256
- `lr_scheduler_type`: cosine_with_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
- `warmup_ratio`: 0.33
- `save_safetensors`: False
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest2-step1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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`: 32
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `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_min_lr
- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 3.3333333333333337e-06}
- `warmup_ratio`: 0.33
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: False
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-toytest2-step1-checkpoints-tmp
- `hub_strategy`: all_checkpoints
- `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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | Validation Loss | sts-test_spearman_cosine | allNLI-dev_max_ap | Qnli-dev_max_ap |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:-----------------:|:---------------:|
| 0.0010 | 1 | 18.7427 | - | - | - | - |
| 0.0020 | 2 | 11.6434 | - | - | - | - |
| 0.0030 | 3 | 7.4859 | - | - | - | - |
| 0.0039 | 4 | 7.3779 | - | - | - | - |
| 0.0049 | 5 | 17.5878 | - | - | - | - |
| 0.0059 | 6 | 8.4984 | - | - | - | - |
| 0.0069 | 7 | 8.375 | - | - | - | - |
| 0.0079 | 8 | 7.3241 | - | - | - | - |
| 0.0089 | 9 | 10.3081 | - | - | - | - |
| 0.0098 | 10 | 8.5363 | - | - | - | - |
| 0.0108 | 11 | 17.2241 | - | - | - | - |
| 0.0118 | 12 | 7.575 | - | - | - | - |
| 0.0128 | 13 | 9.1905 | - | - | - | - |
| 0.0138 | 14 | 11.7727 | - | - | - | - |
| 0.0148 | 15 | 9.5827 | - | - | - | - |
| 0.0157 | 16 | 7.4432 | - | - | - | - |
| 0.0167 | 17 | 7.1573 | - | - | - | - |
| 0.0177 | 18 | 19.8016 | - | - | - | - |
| 0.0187 | 19 | 19.5118 | - | - | - | - |
| 0.0197 | 20 | 7.9062 | - | - | - | - |
| 0.0207 | 21 | 8.6791 | - | - | - | - |
| 0.0217 | 22 | 7.7318 | - | - | - | - |
| 0.0226 | 23 | 7.9319 | - | - | - | - |
| 0.0236 | 24 | 7.192 | - | - | - | - |
| 0.0246 | 25 | 15.5799 | - | - | - | - |
| 0.0256 | 26 | 9.7859 | - | - | - | - |
| 0.0266 | 27 | 9.9259 | - | - | - | - |
| 0.0276 | 28 | 6.3076 | - | - | - | - |
| 0.0285 | 29 | 7.4471 | - | - | - | - |
| 0.0295 | 30 | 7.1246 | - | - | - | - |
| 0.0305 | 31 | 6.5505 | - | - | - | - |
| 0.0315 | 32 | 18.5194 | - | - | - | - |
| 0.0325 | 33 | 7.0747 | - | - | - | - |
| 0.0335 | 34 | 14.9456 | - | - | - | - |
| 0.0344 | 35 | 6.608 | - | - | - | - |
| 0.0354 | 36 | 8.4672 | - | - | - | - |
| 0.0364 | 37 | 6.8853 | - | - | - | - |
| 0.0374 | 38 | 13.6063 | - | - | - | - |
| 0.0384 | 39 | 7.2625 | - | - | - | - |
| 0.0394 | 40 | 6.2234 | - | - | - | - |
| 0.0404 | 41 | 14.9675 | - | - | - | - |
| 0.0413 | 42 | 6.6038 | - | - | - | - |
| 0.0423 | 43 | 13.1173 | - | - | - | - |
| 0.0433 | 44 | 16.6992 | - | - | - | - |
| 0.0443 | 45 | 6.4828 | - | - | - | - |
| 0.0453 | 46 | 5.9815 | - | - | - | - |
| 0.0463 | 47 | 6.1738 | - | - | - | - |
| 0.0472 | 48 | 7.134 | - | - | - | - |
| 0.0482 | 49 | 9.3933 | - | - | - | - |
| 0.0492 | 50 | 10.8085 | - | - | - | - |
| 0.0502 | 51 | 11.4172 | - | - | - | - |
| 0.0512 | 52 | 7.3397 | - | - | - | - |
| 0.0522 | 53 | 5.8851 | - | - | - | - |
| 0.0531 | 54 | 6.8105 | - | - | - | - |
| 0.0541 | 55 | 5.3637 | - | - | - | - |
| 0.0551 | 56 | 6.2628 | - | - | - | - |
| 0.0561 | 57 | 6.0039 | - | - | - | - |
| 0.0571 | 58 | 7.5859 | - | - | - | - |
| 0.0581 | 59 | 6.0802 | - | - | - | - |
| 0.0591 | 60 | 5.5822 | - | - | - | - |
| 0.0600 | 61 | 5.8773 | - | - | - | - |
| 0.0610 | 62 | 6.0814 | - | - | - | - |
| 0.0620 | 63 | 5.4483 | - | - | - | - |
| 0.0630 | 64 | 10.2506 | - | - | - | - |
| 0.0640 | 65 | 10.5976 | - | - | - | - |
| 0.0650 | 66 | 6.9942 | - | - | - | - |
| 0.0659 | 67 | 5.4813 | - | - | - | - |
| 0.0669 | 68 | 7.045 | - | - | - | - |
| 0.0679 | 69 | 5.8549 | - | - | - | - |
| 0.0689 | 70 | 8.8514 | - | - | - | - |
| 0.0699 | 71 | 5.2557 | - | - | - | - |
| 0.0709 | 72 | 5.1181 | - | - | - | - |
| 0.0719 | 73 | 5.5331 | - | - | - | - |
| 0.0728 | 74 | 5.5944 | - | - | - | - |
| 0.0738 | 75 | 4.6332 | - | - | - | - |
| 0.0748 | 76 | 4.9532 | - | - | - | - |
| 0.0758 | 77 | 5.055 | - | - | - | - |
| 0.0768 | 78 | 4.5005 | - | - | - | - |
| 0.0778 | 79 | 5.1997 | - | - | - | - |
| 0.0787 | 80 | 5.1479 | - | - | - | - |
| 0.0797 | 81 | 5.1777 | - | - | - | - |
| 0.0807 | 82 | 5.5565 | - | - | - | - |
| 0.0817 | 83 | 4.6999 | - | - | - | - |
| 0.0827 | 84 | 5.0681 | - | - | - | - |
| 0.0837 | 85 | 5.2208 | - | - | - | - |
| 0.0846 | 86 | 4.56 | - | - | - | - |
| 0.0856 | 87 | 4.6793 | - | - | - | - |
| 0.0866 | 88 | 4.4611 | - | - | - | - |
| 0.0876 | 89 | 9.623 | - | - | - | - |
| 0.0886 | 90 | 5.0316 | - | - | - | - |
| 0.0896 | 91 | 4.1771 | - | - | - | - |
| 0.0906 | 92 | 4.9652 | - | - | - | - |
| 0.0915 | 93 | 8.7432 | - | - | - | - |
| 0.0925 | 94 | 4.6234 | - | - | - | - |
| 0.0935 | 95 | 4.4016 | - | - | - | - |
| 0.0945 | 96 | 4.9903 | - | - | - | - |
| 0.0955 | 97 | 4.5606 | - | - | - | - |
| 0.0965 | 98 | 4.9534 | - | - | - | - |
| 0.0974 | 99 | 8.1838 | - | - | - | - |
| 0.0984 | 100 | 4.9736 | - | - | - | - |
| 0.0994 | 101 | 4.4733 | - | - | - | - |
| 0.1004 | 102 | 4.9725 | - | - | - | - |
| 0.1014 | 103 | 4.5861 | - | - | - | - |
| 0.1024 | 104 | 7.7634 | - | - | - | - |
| 0.1033 | 105 | 4.9915 | - | - | - | - |
| 0.1043 | 106 | 5.1391 | - | - | - | - |
| 0.1053 | 107 | 5.0157 | - | - | - | - |
| 0.1063 | 108 | 4.0982 | - | - | - | - |
| 0.1073 | 109 | 4.2178 | - | - | - | - |
| 0.1083 | 110 | 4.6193 | - | - | - | - |
| 0.1093 | 111 | 4.7638 | - | - | - | - |
| 0.1102 | 112 | 4.1207 | - | - | - | - |
| 0.1112 | 113 | 5.2034 | - | - | - | - |
| 0.1122 | 114 | 5.0693 | - | - | - | - |
| 0.1132 | 115 | 4.7895 | - | - | - | - |
| 0.1142 | 116 | 4.9486 | - | - | - | - |
| 0.1152 | 117 | 4.6552 | - | - | - | - |
| 0.1161 | 118 | 4.4555 | - | - | - | - |
| 0.1171 | 119 | 4.8977 | - | - | - | - |
| 0.1181 | 120 | 7.6836 | - | - | - | - |
| 0.1191 | 121 | 4.8106 | - | - | - | - |
| 0.1201 | 122 | 4.9958 | - | - | - | - |
| 0.1211 | 123 | 4.4585 | - | - | - | - |
| 0.1220 | 124 | 7.5559 | - | - | - | - |
| 0.1230 | 125 | 4.2636 | - | - | - | - |
| 0.1240 | 126 | 4.0436 | - | - | - | - |
| 0.125 | 127 | 4.7416 | - | - | - | - |
| 0.1260 | 128 | 4.2215 | - | - | - | - |
| 0.1270 | 129 | 6.3561 | - | - | - | - |
| 0.1280 | 130 | 6.2299 | - | - | - | - |
| 0.1289 | 131 | 4.3492 | - | - | - | - |
| 0.1299 | 132 | 4.0216 | - | - | - | - |
| 0.1309 | 133 | 6.963 | - | - | - | - |
| 0.1319 | 134 | 3.9474 | - | - | - | - |
| 0.1329 | 135 | 4.3437 | - | - | - | - |
| 0.1339 | 136 | 3.6267 | - | - | - | - |
| 0.1348 | 137 | 3.9896 | - | - | - | - |
| 0.1358 | 138 | 4.8156 | - | - | - | - |
| 0.1368 | 139 | 4.9751 | - | - | - | - |
| 0.1378 | 140 | 4.4144 | - | - | - | - |
| 0.1388 | 141 | 4.7213 | - | - | - | - |
| 0.1398 | 142 | 6.6081 | - | - | - | - |
| 0.1407 | 143 | 4.2929 | - | - | - | - |
| 0.1417 | 144 | 4.2537 | - | - | - | - |
| 0.1427 | 145 | 4.0647 | - | - | - | - |
| 0.1437 | 146 | 3.937 | - | - | - | - |
| 0.1447 | 147 | 5.6582 | - | - | - | - |
| 0.1457 | 148 | 4.2648 | - | - | - | - |
| 0.1467 | 149 | 4.4429 | - | - | - | - |
| 0.1476 | 150 | 3.6197 | - | - | - | - |
| 0.1486 | 151 | 3.7953 | - | - | - | - |
| 0.1496 | 152 | 3.8175 | - | - | - | - |
| 0.1506 | 153 | 4.5137 | 3.3210 | 0.1806 | 0.3919 | 0.5750 |
| 0.1516 | 154 | 4.3528 | - | - | - | - |
| 0.1526 | 155 | 3.6573 | - | - | - | - |
| 0.1535 | 156 | 3.5248 | - | - | - | - |
| 0.1545 | 157 | 3.9275 | - | - | - | - |
| 0.1555 | 158 | 7.1868 | - | - | - | - |
| 0.1565 | 159 | 3.6294 | - | - | - | - |
| 0.1575 | 160 | 3.6886 | - | - | - | - |
| 0.1585 | 161 | 3.1873 | - | - | - | - |
| 0.1594 | 162 | 6.1951 | - | - | - | - |
| 0.1604 | 163 | 3.9747 | - | - | - | - |
| 0.1614 | 164 | 7.004 | - | - | - | - |
| 0.1624 | 165 | 4.3221 | - | - | - | - |
| 0.1634 | 166 | 3.5963 | - | - | - | - |
| 0.1644 | 167 | 3.1988 | - | - | - | - |
| 0.1654 | 168 | 3.8236 | - | - | - | - |
| 0.1663 | 169 | 3.5063 | - | - | - | - |
| 0.1673 | 170 | 5.9843 | - | - | - | - |
| 0.1683 | 171 | 5.884 | - | - | - | - |
| 0.1693 | 172 | 4.1317 | - | - | - | - |
| 0.1703 | 173 | 3.9255 | - | - | - | - |
| 0.1713 | 174 | 4.1121 | - | - | - | - |
| 0.1722 | 175 | 3.7748 | - | - | - | - |
| 0.1732 | 176 | 5.1602 | - | - | - | - |
| 0.1742 | 177 | 4.8807 | - | - | - | - |
| 0.1752 | 178 | 3.4643 | - | - | - | - |
| 0.1762 | 179 | 3.4937 | - | - | - | - |
| 0.1772 | 180 | 5.2731 | - | - | - | - |
| 0.1781 | 181 | 4.6416 | - | - | - | - |
| 0.1791 | 182 | 3.5226 | - | - | - | - |
| 0.1801 | 183 | 4.7794 | - | - | - | - |
| 0.1811 | 184 | 3.8504 | - | - | - | - |
| 0.1821 | 185 | 3.5391 | - | - | - | - |
| 0.1831 | 186 | 4.0291 | - | - | - | - |
| 0.1841 | 187 | 3.5606 | - | - | - | - |
| 0.1850 | 188 | 3.8957 | - | - | - | - |
| 0.1860 | 189 | 4.3657 | - | - | - | - |
| 0.1870 | 190 | 5.0173 | - | - | - | - |
| 0.1880 | 191 | 4.3915 | - | - | - | - |
| 0.1890 | 192 | 3.4613 | - | - | - | - |
| 0.1900 | 193 | 3.2005 | - | - | - | - |
| 0.1909 | 194 | 3.3986 | - | - | - | - |
| 0.1919 | 195 | 3.7937 | - | - | - | - |
| 0.1929 | 196 | 3.8981 | - | - | - | - |
| 0.1939 | 197 | 3.7051 | - | - | - | - |
| 0.1949 | 198 | 3.8028 | - | - | - | - |
| 0.1959 | 199 | 3.3294 | - | - | - | - |
| 0.1969 | 200 | 4.1252 | - | - | - | - |
| 0.1978 | 201 | 4.2564 | - | - | - | - |
| 0.1988 | 202 | 3.8258 | - | - | - | - |
| 0.1998 | 203 | 3.1025 | - | - | - | - |
| 0.2008 | 204 | 3.5038 | - | - | - | - |
| 0.2018 | 205 | 3.6021 | - | - | - | - |
| 0.2028 | 206 | 3.7637 | - | - | - | - |
| 0.2037 | 207 | 3.2563 | - | - | - | - |
| 0.2047 | 208 | 3.9323 | - | - | - | - |
| 0.2057 | 209 | 3.489 | - | - | - | - |
| 0.2067 | 210 | 3.6549 | - | - | - | - |
| 0.2077 | 211 | 3.1609 | - | - | - | - |
| 0.2087 | 212 | 3.2467 | - | - | - | - |
| 0.2096 | 213 | 3.4514 | - | - | - | - |
| 0.2106 | 214 | 3.4945 | - | - | - | - |
| 0.2116 | 215 | 3.5932 | - | - | - | - |
| 0.2126 | 216 | 3.2289 | - | - | - | - |
| 0.2136 | 217 | 3.3279 | - | - | - | - |
| 0.2146 | 218 | 3.8141 | - | - | - | - |
| 0.2156 | 219 | 3.1171 | - | - | - | - |
| 0.2165 | 220 | 3.6287 | - | - | - | - |
| 0.2175 | 221 | 3.8517 | - | - | - | - |
| 0.2185 | 222 | 3.3836 | - | - | - | - |
| 0.2195 | 223 | 3.425 | - | - | - | - |
| 0.2205 | 224 | 3.6246 | - | - | - | - |
| 0.2215 | 225 | 3.5682 | - | - | - | - |
| 0.2224 | 226 | 3.3034 | - | - | - | - |
| 0.2234 | 227 | 3.9251 | - | - | - | - |
| 0.2244 | 228 | 3.146 | - | - | - | - |
| 0.2254 | 229 | 3.8859 | - | - | - | - |
| 0.2264 | 230 | 3.2977 | - | - | - | - |
| 0.2274 | 231 | 3.2664 | - | - | - | - |
| 0.2283 | 232 | 3.1275 | - | - | - | - |
| 0.2293 | 233 | 3.2408 | - | - | - | - |
| 0.2303 | 234 | 2.907 | - | - | - | - |
| 0.2313 | 235 | 2.9178 | - | - | - | - |
| 0.2323 | 236 | 3.324 | - | - | - | - |
| 0.2333 | 237 | 2.9172 | - | - | - | - |
| 0.2343 | 238 | 3.4324 | - | - | - | - |
| 0.2352 | 239 | 4.0563 | - | - | - | - |
| 0.2362 | 240 | 2.8736 | - | - | - | - |
| 0.2372 | 241 | 4.7174 | - | - | - | - |
| 0.2382 | 242 | 3.2025 | - | - | - | - |
| 0.2392 | 243 | 2.7835 | - | - | - | - |
| 0.2402 | 244 | 4.3158 | - | - | - | - |
| 0.2411 | 245 | 2.8619 | - | - | - | - |
| 0.2421 | 246 | 2.5156 | - | - | - | - |
| 0.2431 | 247 | 3.2144 | - | - | - | - |
| 0.2441 | 248 | 3.5927 | - | - | - | - |
| 0.2451 | 249 | 2.6059 | - | - | - | - |
| 0.2461 | 250 | 2.9758 | - | - | - | - |
| 0.2470 | 251 | 3.9214 | - | - | - | - |
| 0.2480 | 252 | 3.2892 | - | - | - | - |
| 0.2490 | 253 | 2.9503 | - | - | - | - |
| 0.25 | 254 | 2.5969 | - | - | - | - |
| 0.2510 | 255 | 2.9908 | - | - | - | - |
| 0.2520 | 256 | 2.8995 | - | - | - | - |
| 0.2530 | 257 | 3.124 | - | - | - | - |
| 0.2539 | 258 | 3.1197 | - | - | - | - |
| 0.2549 | 259 | 2.3073 | - | - | - | - |
| 0.2559 | 260 | 2.8441 | - | - | - | - |
| 0.2569 | 261 | 1.9788 | - | - | - | - |
| 0.2579 | 262 | 2.1442 | - | - | - | - |
| 0.2589 | 263 | 4.9015 | - | - | - | - |
| 0.2598 | 264 | 2.7866 | - | - | - | - |
| 0.2608 | 265 | 2.4588 | - | - | - | - |
| 0.2618 | 266 | 2.3909 | - | - | - | - |
| 0.2628 | 267 | 4.7394 | - | - | - | - |
| 0.2638 | 268 | 3.1581 | - | - | - | - |
| 0.2648 | 269 | 3.973 | - | - | - | - |
| 0.2657 | 270 | 4.1565 | - | - | - | - |
| 0.2667 | 271 | 2.5183 | - | - | - | - |
| 0.2677 | 272 | 3.614 | - | - | - | - |
| 0.2687 | 273 | 2.6858 | - | - | - | - |
| 0.2697 | 274 | 3.1182 | - | - | - | - |
| 0.2707 | 275 | 2.9628 | - | - | - | - |
| 0.2717 | 276 | 2.8376 | - | - | - | - |
| 0.2726 | 277 | 2.7858 | - | - | - | - |
| 0.2736 | 278 | 2.1037 | - | - | - | - |
| 0.2746 | 279 | 3.0436 | - | - | - | - |
| 0.2756 | 280 | 3.4125 | - | - | - | - |
| 0.2766 | 281 | 2.5027 | - | - | - | - |
| 0.2776 | 282 | 2.7922 | - | - | - | - |
| 0.2785 | 283 | 2.9762 | - | - | - | - |
| 0.2795 | 284 | 2.6458 | - | - | - | - |
| 0.2805 | 285 | 2.962 | - | - | - | - |
| 0.2815 | 286 | 2.5439 | - | - | - | - |
| 0.2825 | 287 | 2.8437 | - | - | - | - |
| 0.2835 | 288 | 3.2134 | - | - | - | - |
| 0.2844 | 289 | 2.5655 | - | - | - | - |
| 0.2854 | 290 | 2.9465 | - | - | - | - |
| 0.2864 | 291 | 2.4653 | - | - | - | - |
| 0.2874 | 292 | 3.1467 | - | - | - | - |
| 0.2884 | 293 | 2.6551 | - | - | - | - |
| 0.2894 | 294 | 2.5098 | - | - | - | - |
| 0.2904 | 295 | 2.5988 | - | - | - | - |
| 0.2913 | 296 | 3.778 | - | - | - | - |
| 0.2923 | 297 | 2.6257 | - | - | - | - |
| 0.2933 | 298 | 2.5142 | - | - | - | - |
| 0.2943 | 299 | 2.3182 | - | - | - | - |
| 0.2953 | 300 | 3.3505 | - | - | - | - |
| 0.2963 | 301 | 2.9615 | - | - | - | - |
| 0.2972 | 302 | 2.9136 | - | - | - | - |
| 0.2982 | 303 | 2.6192 | - | - | - | - |
| 0.2992 | 304 | 2.3255 | - | - | - | - |
| 0.3002 | 305 | 2.7168 | - | - | - | - |
| 0.3012 | 306 | 2.9137 | 2.4280 | 0.2507 | 0.4103 | 0.5948 |
| 0.3022 | 307 | 2.6681 | - | - | - | - |
| 0.3031 | 308 | 2.7219 | - | - | - | - |
| 0.3041 | 309 | 2.4057 | - | - | - | - |
| 0.3051 | 310 | 2.7402 | - | - | - | - |
| 0.3061 | 311 | 2.5512 | - | - | - | - |
| 0.3071 | 312 | 2.8553 | - | - | - | - |
| 0.3081 | 313 | 2.598 | - | - | - | - |
| 0.3091 | 314 | 2.6186 | - | - | - | - |
| 0.3100 | 315 | 2.3678 | - | - | - | - |
| 0.3110 | 316 | 2.886 | - | - | - | - |
| 0.3120 | 317 | 2.1738 | - | - | - | - |
| 0.3130 | 318 | 2.6619 | - | - | - | - |
| 0.3140 | 319 | 2.1818 | - | - | - | - |
| 0.3150 | 320 | 3.0407 | - | - | - | - |
| 0.3159 | 321 | 2.464 | - | - | - | - |
| 0.3169 | 322 | 2.7415 | - | - | - | - |
| 0.3179 | 323 | 2.7455 | - | - | - | - |
| 0.3189 | 324 | 2.4061 | - | - | - | - |
| 0.3199 | 325 | 2.0491 | - | - | - | - |
| 0.3209 | 326 | 3.3097 | - | - | - | - |
| 0.3219 | 327 | 2.3587 | - | - | - | - |
| 0.3228 | 328 | 1.9493 | - | - | - | - |
| 0.3238 | 329 | 2.5399 | - | - | - | - |
| 0.3248 | 330 | 2.3569 | - | - | - | - |
| 0.3258 | 331 | 1.9024 | - | - | - | - |
| 0.3268 | 332 | 2.3513 | - | - | - | - |
| 0.3278 | 333 | 2.2488 | - | - | - | - |
| 0.3287 | 334 | 1.9141 | - | - | - | - |
| 0.3297 | 335 | 2.7065 | - | - | - | - |
| 0.3307 | 336 | 2.139 | - | - | - | - |
| 0.3317 | 337 | 2.2345 | - | - | - | - |
| 0.3327 | 338 | 2.3612 | - | - | - | - |
| 0.3337 | 339 | 2.1413 | - | - | - | - |
| 0.3346 | 340 | 2.2214 | - | - | - | - |
| 0.3356 | 341 | 2.9006 | - | - | - | - |
| 0.3366 | 342 | 2.417 | - | - | - | - |
| 0.3376 | 343 | 2.2348 | - | - | - | - |
| 0.3386 | 344 | 2.4369 | - | - | - | - |
| 0.3396 | 345 | 2.7623 | - | - | - | - |
| 0.3406 | 346 | 2.6741 | - | - | - | - |
| 0.3415 | 347 | 3.0515 | - | - | - | - |
| 0.3425 | 348 | 2.4952 | - | - | - | - |
| 0.3435 | 349 | 2.1265 | - | - | - | - |
| 0.3445 | 350 | 2.0359 | - | - | - | - |
| 0.3455 | 351 | 3.107 | - | - | - | - |
| 0.3465 | 352 | 2.116 | - | - | - | - |
| 0.3474 | 353 | 2.1996 | - | - | - | - |
| 0.3484 | 354 | 2.9312 | - | - | - | - |
| 0.3494 | 355 | 2.2885 | - | - | - | - |
| 0.3504 | 356 | 3.0302 | - | - | - | - |
| 0.3514 | 357 | 2.2163 | - | - | - | - |
| 0.3524 | 358 | 2.8304 | - | - | - | - |
| 0.3533 | 359 | 2.2715 | - | - | - | - |
| 0.3543 | 360 | 2.3388 | - | - | - | - |
| 0.3553 | 361 | 2.2098 | - | - | - | - |
| 0.3563 | 362 | 2.0911 | - | - | - | - |
| 0.3573 | 363 | 2.3582 | - | - | - | - |
| 0.3583 | 364 | 1.8605 | - | - | - | - |
| 0.3593 | 365 | 2.2252 | - | - | - | - |
| 0.3602 | 366 | 2.2018 | - | - | - | - |
| 0.3612 | 367 | 2.1099 | - | - | - | - |
| 0.3622 | 368 | 2.1323 | - | - | - | - |
| 0.3632 | 369 | 2.4203 | - | - | - | - |
| 0.3642 | 370 | 2.7768 | - | - | - | - |
| 0.3652 | 371 | 2.3359 | - | - | - | - |
| 0.3661 | 372 | 2.3773 | - | - | - | - |
| 0.3671 | 373 | 2.4424 | - | - | - | - |
| 0.3681 | 374 | 1.9478 | - | - | - | - |
| 0.3691 | 375 | 1.6047 | - | - | - | - |
| 0.3701 | 376 | 1.7384 | - | - | - | - |
| 0.3711 | 377 | 2.1147 | - | - | - | - |
| 0.3720 | 378 | 1.8449 | - | - | - | - |
| 0.3730 | 379 | 2.6009 | - | - | - | - |
| 0.3740 | 380 | 2.4051 | - | - | - | - |
| 0.375 | 381 | 2.3035 | - | - | - | - |
| 0.3760 | 382 | 1.8955 | - | - | - | - |
| 0.3770 | 383 | 2.287 | - | - | - | - |
| 0.3780 | 384 | 1.9123 | - | - | - | - |
| 0.3789 | 385 | 1.9369 | - | - | - | - |
| 0.3799 | 386 | 2.1367 | - | - | - | - |
| 0.3809 | 387 | 1.9437 | - | - | - | - |
| 0.3819 | 388 | 2.3873 | - | - | - | - |
| 0.3829 | 389 | 1.7463 | - | - | - | - |
| 0.3839 | 390 | 2.8438 | - | - | - | - |
| 0.3848 | 391 | 2.4875 | - | - | - | - |
| 0.3858 | 392 | 2.0798 | - | - | - | - |
| 0.3868 | 393 | 2.2242 | - | - | - | - |
| 0.3878 | 394 | 1.8714 | - | - | - | - |
| 0.3888 | 395 | 1.5893 | - | - | - | - |
| 0.3898 | 396 | 1.5633 | - | - | - | - |
| 0.3907 | 397 | 1.8645 | - | - | - | - |
| 0.3917 | 398 | 1.8928 | - | - | - | - |
| 0.3927 | 399 | 1.3352 | - | - | - | - |
| 0.3937 | 400 | 3.3052 | - | - | - | - |
| 0.3947 | 401 | 1.6256 | - | - | - | - |
| 0.3957 | 402 | 1.8856 | - | - | - | - |
| 0.3967 | 403 | 1.8355 | - | - | - | - |
| 0.3976 | 404 | 1.8944 | - | - | - | - |
| 0.3986 | 405 | 1.7636 | - | - | - | - |
| 0.3996 | 406 | 2.8097 | - | - | - | - |
| 0.4006 | 407 | 1.9121 | - | - | - | - |
| 0.4016 | 408 | 1.9233 | - | - | - | - |
| 0.4026 | 409 | 1.543 | - | - | - | - |
| 0.4035 | 410 | 1.7207 | - | - | - | - |
| 0.4045 | 411 | 1.6344 | - | - | - | - |
| 0.4055 | 412 | 2.4177 | - | - | - | - |
| 0.4065 | 413 | 2.2995 | - | - | - | - |
| 0.4075 | 414 | 1.7681 | - | - | - | - |
| 0.4085 | 415 | 1.6562 | - | - | - | - |
| 0.4094 | 416 | 1.8896 | - | - | - | - |
| 0.4104 | 417 | 2.0671 | - | - | - | - |
| 0.4114 | 418 | 1.6097 | - | - | - | - |
| 0.4124 | 419 | 2.8126 | - | - | - | - |
| 0.4134 | 420 | 1.7028 | - | - | - | - |
| 0.4144 | 421 | 1.526 | - | - | - | - |
| 0.4154 | 422 | 2.5029 | - | - | - | - |
| 0.4163 | 423 | 1.7668 | - | - | - | - |
| 0.4173 | 424 | 1.9065 | - | - | - | - |
| 0.4183 | 425 | 1.6645 | - | - | - | - |
| 0.4193 | 426 | 1.8075 | - | - | - | - |
| 0.4203 | 427 | 1.872 | - | - | - | - |
| 0.4213 | 428 | 2.0487 | - | - | - | - |
| 0.4222 | 429 | 1.535 | - | - | - | - |
| 0.4232 | 430 | 1.8046 | - | - | - | - |
| 0.4242 | 431 | 2.2561 | - | - | - | - |
| 0.4252 | 432 | 2.0306 | - | - | - | - |
| 0.4262 | 433 | 2.1311 | - | - | - | - |
| 0.4272 | 434 | 2.3013 | - | - | - | - |
| 0.4281 | 435 | 1.6402 | - | - | - | - |
| 0.4291 | 436 | 1.9572 | - | - | - | - |
| 0.4301 | 437 | 1.6364 | - | - | - | - |
| 0.4311 | 438 | 1.446 | - | - | - | - |
| 0.4321 | 439 | 1.6009 | - | - | - | - |
| 0.4331 | 440 | 1.9469 | - | - | - | - |
| 0.4341 | 441 | 2.1951 | - | - | - | - |
| 0.4350 | 442 | 1.675 | - | - | - | - |
| 0.4360 | 443 | 1.4182 | - | - | - | - |
| 0.4370 | 444 | 2.2317 | - | - | - | - |
| 0.4380 | 445 | 2.1076 | - | - | - | - |
| 0.4390 | 446 | 1.6691 | - | - | - | - |
| 0.4400 | 447 | 1.6909 | - | - | - | - |
| 0.4409 | 448 | 3.1056 | - | - | - | - |
| 0.4419 | 449 | 1.4069 | - | - | - | - |
| 0.4429 | 450 | 2.1639 | - | - | - | - |
| 0.4439 | 451 | 1.5531 | - | - | - | - |
| 0.4449 | 452 | 2.1895 | - | - | - | - |
| 0.4459 | 453 | 1.9384 | - | - | - | - |
| 0.4469 | 454 | 1.7761 | - | - | - | - |
| 0.4478 | 455 | 2.8286 | - | - | - | - |
| 0.4488 | 456 | 2.4877 | - | - | - | - |
| 0.4498 | 457 | 1.7636 | - | - | - | - |
| 0.4508 | 458 | 1.1849 | - | - | - | - |
| 0.4518 | 459 | 1.8331 | 1.9854 | 0.3261 | 0.4327 | 0.6511 |
| 0.4528 | 460 | 2.0416 | - | - | - | - |
| 0.4537 | 461 | 2.1907 | - | - | - | - |
| 0.4547 | 462 | 1.7478 | - | - | - | - |
| 0.4557 | 463 | 1.9 | - | - | - | - |
| 0.4567 | 464 | 1.6876 | - | - | - | - |
| 0.4577 | 465 | 2.0035 | - | - | - | - |
| 0.4587 | 466 | 1.4127 | - | - | - | - |
| 0.4596 | 467 | 1.5593 | - | - | - | - |
| 0.4606 | 468 | 1.7 | - | - | - | - |
| 0.4616 | 469 | 1.5157 | - | - | - | - |
| 0.4626 | 470 | 1.6554 | - | - | - | - |
| 0.4636 | 471 | 1.7404 | - | - | - | - |
| 0.4646 | 472 | 2.1432 | - | - | - | - |
| 0.4656 | 473 | 1.7322 | - | - | - | - |
| 0.4665 | 474 | 1.7281 | - | - | - | - |
| 0.4675 | 475 | 1.5107 | - | - | - | - |
| 0.4685 | 476 | 1.779 | - | - | - | - |
| 0.4695 | 477 | 1.325 | - | - | - | - |
| 0.4705 | 478 | 1.073 | - | - | - | - |
| 0.4715 | 479 | 1.864 | - | - | - | - |
| 0.4724 | 480 | 2.3645 | - | - | - | - |
| 0.4734 | 481 | 1.181 | - | - | - | - |
| 0.4744 | 482 | 1.4562 | - | - | - | - |
| 0.4754 | 483 | 1.3105 | - | - | - | - |
| 0.4764 | 484 | 2.8012 | - | - | - | - |
| 0.4774 | 485 | 2.0114 | - | - | - | - |
| 0.4783 | 486 | 1.6307 | - | - | - | - |
| 0.4793 | 487 | 2.7733 | - | - | - | - |
| 0.4803 | 488 | 1.8211 | - | - | - | - |
| 0.4813 | 489 | 1.574 | - | - | - | - |
| 0.4823 | 490 | 1.9713 | - | - | - | - |
| 0.4833 | 491 | 1.2774 | - | - | - | - |
| 0.4843 | 492 | 2.58 | - | - | - | - |
| 0.4852 | 493 | 2.0594 | - | - | - | - |
| 0.4862 | 494 | 1.5857 | - | - | - | - |
| 0.4872 | 495 | 2.0028 | - | - | - | - |
| 0.4882 | 496 | 1.8863 | - | - | - | - |
| 0.4892 | 497 | 1.5171 | - | - | - | - |
| 0.4902 | 498 | 1.9355 | - | - | - | - |
| 0.4911 | 499 | 2.0675 | - | - | - | - |
| 0.4921 | 500 | 1.6017 | - | - | - | - |
| 0.4931 | 501 | 1.4089 | - | - | - | - |
| 0.4941 | 502 | 1.3836 | - | - | - | - |
| 0.4951 | 503 | 1.6033 | - | - | - | - |
| 0.4961 | 504 | 1.0891 | - | - | - | - |
| 0.4970 | 505 | 1.7119 | - | - | - | - |
| 0.4980 | 506 | 1.3685 | - | - | - | - |
| 0.4990 | 507 | 1.4252 | - | - | - | - |
| 0.5 | 508 | 1.5538 | - | - | - | - |
| 0.5010 | 509 | 1.7513 | - | - | - | - |
| 0.5020 | 510 | 1.1831 | - | - | - | - |
| 0.5030 | 511 | 1.7767 | - | - | - | - |
| 0.5039 | 512 | 1.4324 | - | - | - | - |
| 0.5049 | 513 | 2.1672 | - | - | - | - |
| 0.5059 | 514 | 1.6348 | - | - | - | - |
| 0.5069 | 515 | 1.7285 | - | - | - | - |
| 0.5079 | 516 | 2.0186 | - | - | - | - |
| 0.5089 | 517 | 1.382 | - | - | - | - |
| 0.5098 | 518 | 1.4509 | - | - | - | - |
| 0.5108 | 519 | 1.1043 | - | - | - | - |
| 0.5118 | 520 | 1.3322 | - | - | - | - |
| 0.5128 | 521 | 1.3267 | - | - | - | - |
| 0.5138 | 522 | 1.3639 | - | - | - | - |
| 0.5148 | 523 | 1.203 | - | - | - | - |
| 0.5157 | 524 | 1.8583 | - | - | - | - |
| 0.5167 | 525 | 2.267 | - | - | - | - |
| 0.5177 | 526 | 1.2935 | - | - | - | - |
| 0.5187 | 527 | 1.7431 | - | - | - | - |
| 0.5197 | 528 | 1.8484 | - | - | - | - |
| 0.5207 | 529 | 1.5626 | - | - | - | - |
| 0.5217 | 530 | 2.2645 | - | - | - | - |
| 0.5226 | 531 | 1.4313 | - | - | - | - |
| 0.5236 | 532 | 1.8204 | - | - | - | - |
| 0.5246 | 533 | 1.5659 | - | - | - | - |
| 0.5256 | 534 | 1.2689 | - | - | - | - |
| 0.5266 | 535 | 1.8193 | - | - | - | - |
| 0.5276 | 536 | 2.2902 | - | - | - | - |
| 0.5285 | 537 | 1.6936 | - | - | - | - |
| 0.5295 | 538 | 1.7305 | - | - | - | - |
| 0.5305 | 539 | 1.4449 | - | - | - | - |
| 0.5315 | 540 | 1.5594 | - | - | - | - |
| 0.5325 | 541 | 1.9678 | - | - | - | - |
| 0.5335 | 542 | 2.0327 | - | - | - | - |
| 0.5344 | 543 | 2.0456 | - | - | - | - |
| 0.5354 | 544 | 2.0452 | - | - | - | - |
| 0.5364 | 545 | 1.9435 | - | - | - | - |
| 0.5374 | 546 | 1.8963 | - | - | - | - |
| 0.5384 | 547 | 1.9536 | - | - | - | - |
| 0.5394 | 548 | 1.0665 | - | - | - | - |
| 0.5404 | 549 | 1.8067 | - | - | - | - |
| 0.5413 | 550 | 1.6227 | - | - | - | - |
| 0.5423 | 551 | 1.687 | - | - | - | - |
| 0.5433 | 552 | 1.5937 | - | - | - | - |
| 0.5443 | 553 | 0.9216 | - | - | - | - |
| 0.5453 | 554 | 1.3895 | - | - | - | - |
| 0.5463 | 555 | 1.7863 | - | - | - | - |
| 0.5472 | 556 | 1.2574 | - | - | - | - |
| 0.5482 | 557 | 2.108 | - | - | - | - |
| 0.5492 | 558 | 1.2782 | - | - | - | - |
| 0.5502 | 559 | 1.4959 | - | - | - | - |
| 0.5512 | 560 | 1.9191 | - | - | - | - |
| 0.5522 | 561 | 2.0049 | - | - | - | - |
| 0.5531 | 562 | 1.2511 | - | - | - | - |
| 0.5541 | 563 | 1.3912 | - | - | - | - |
| 0.5551 | 564 | 1.371 | - | - | - | - |
| 0.5561 | 565 | 1.6155 | - | - | - | - |
| 0.5571 | 566 | 1.4625 | - | - | - | - |
| 0.5581 | 567 | 0.86 | - | - | - | - |
| 0.5591 | 568 | 1.5753 | - | - | - | - |
| 0.5600 | 569 | 1.6126 | - | - | - | - |
| 0.5610 | 570 | 1.3171 | - | - | - | - |
| 0.5620 | 571 | 1.9378 | - | - | - | - |
| 0.5630 | 572 | 1.2736 | - | - | - | - |
| 0.5640 | 573 | 1.2368 | - | - | - | - |
| 0.5650 | 574 | 1.1005 | - | - | - | - |
| 0.5659 | 575 | 1.1765 | - | - | - | - |
| 0.5669 | 576 | 1.3557 | - | - | - | - |
| 0.5679 | 577 | 1.3224 | - | - | - | - |
| 0.5689 | 578 | 1.7914 | - | - | - | - |
| 0.5699 | 579 | 1.0633 | - | - | - | - |
| 0.5709 | 580 | 1.3624 | - | - | - | - |
| 0.5719 | 581 | 0.9804 | - | - | - | - |
| 0.5728 | 582 | 1.8246 | - | - | - | - |
| 0.5738 | 583 | 1.1806 | - | - | - | - |
| 0.5748 | 584 | 1.6243 | - | - | - | - |
| 0.5758 | 585 | 1.739 | - | - | - | - |
| 0.5768 | 586 | 1.2502 | - | - | - | - |
| 0.5778 | 587 | 1.6328 | - | - | - | - |
| 0.5787 | 588 | 1.3618 | - | - | - | - |
| 0.5797 | 589 | 1.1535 | - | - | - | - |
| 0.5807 | 590 | 1.2214 | - | - | - | - |
| 0.5817 | 591 | 1.4884 | - | - | - | - |
| 0.5827 | 592 | 1.4029 | - | - | - | - |
| 0.5837 | 593 | 1.0542 | - | - | - | - |
| 0.5846 | 594 | 1.5848 | - | - | - | - |
| 0.5856 | 595 | 1.405 | - | - | - | - |
| 0.5866 | 596 | 1.6281 | - | - | - | - |
| 0.5876 | 597 | 1.5228 | - | - | - | - |
| 0.5886 | 598 | 1.8192 | - | - | - | - |
| 0.5896 | 599 | 1.2403 | - | - | - | - |
| 0.5906 | 600 | 1.9368 | - | - | - | - |
| 0.5915 | 601 | 1.6623 | - | - | - | - |
| 0.5925 | 602 | 1.495 | - | - | - | - |
| 0.5935 | 603 | 1.7079 | - | - | - | - |
| 0.5945 | 604 | 1.0651 | - | - | - | - |
| 0.5955 | 605 | 1.2121 | - | - | - | - |
| 0.5965 | 606 | 1.5385 | - | - | - | - |
| 0.5974 | 607 | 1.1015 | - | - | - | - |
| 0.5984 | 608 | 1.7596 | - | - | - | - |
| 0.5994 | 609 | 1.5597 | - | - | - | - |
| 0.6004 | 610 | 1.3254 | - | - | - | - |