checkpoint-1220/README.md

metadata

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. 
         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. 
         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: 0.509950648911252
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5372664597807683
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5423369169040173
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.5495945820201323
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5284216887479147
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.5375220789858359
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5045563506807493
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5314762283231191
            name: Spearman Dot
          - type: pearson_max
            value: 0.5423369169040173
            name: Pearson Max
          - type: spearman_max
            value: 0.5495945820201323
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allNLI dev
          type: allNLI-dev
        metrics:
          - type: cosine_accuracy
            value: 0.69140625
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.9270304441452026
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.5363984674329503
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8337951898574829
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.40114613180515757
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8092485549132948
            name: Cosine Recall
          - type: cosine_ap
            value: 0.49228397783239236
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.69140625
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 709.9082641601562
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.5384615384615384
            name: Dot F1
          - type: dot_f1_threshold
            value: 638.5916748046875
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.4034582132564842
            name: Dot Precision
          - type: dot_recall
            value: 0.8092485549132948
            name: Dot Recall
          - type: dot_ap
            value: 0.4925267288107693
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.689453125
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 214.40182495117188
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.5391621129326047
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 346.060302734375
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.39361702127659576
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8554913294797688
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.49086550398875856
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.69140625
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 10.571220397949219
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.5363984674329503
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 15.952140808105469
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.40114613180515757
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8092485549132948
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.4923798851447917
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.69140625
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 709.9082641601562
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.5391621129326047
            name: Max F1
          - type: max_f1_threshold
            value: 638.5916748046875
            name: Max F1 Threshold
          - type: max_precision
            value: 0.4034582132564842
            name: Max Precision
          - type: max_recall
            value: 0.8554913294797688
            name: Max Recall
          - type: max_ap
            value: 0.4925267288107693
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Qnli dev
          type: Qnli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.6640625
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8653759956359863
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.6816608996539792
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7897687554359436
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.5760233918128655
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8347457627118644
            name: Cosine Recall
          - type: cosine_ap
            value: 0.6846632153971953
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.66015625
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 657.5789794921875
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.6814310051107325
            name: Dot F1
          - type: dot_f1_threshold
            value: 598.9862670898438
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5698005698005698
            name: Dot Precision
          - type: dot_recall
            value: 0.847457627118644
            name: Dot Recall
          - type: dot_ap
            value: 0.6832739733257174
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.6796875
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 294.7474670410156
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.6860068259385665
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 376.3381652832031
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5742857142857143
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8516949152542372
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.6961691800504137
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.6640625
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 14.331533432006836
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.6804123711340205
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 17.989498138427734
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5722543352601156
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8389830508474576
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.6849916947033118
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.6796875
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 657.5789794921875
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.6860068259385665
            name: Max F1
          - type: max_f1_threshold
            value: 598.9862670898438
            name: Max F1 Threshold
          - type: max_precision
            value: 0.5760233918128655
            name: Max Precision
          - type: max_recall
            value: 0.8516949152542372
            name: Max Recall
          - type: max_ap
            value: 0.6961691800504137
            name: Max Ap

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from 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
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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

Metric	Value
pearson_cosine	0.51
spearman_cosine	0.5373
pearson_manhattan	0.5423
spearman_manhattan	0.5496
pearson_euclidean	0.5284
spearman_euclidean	0.5375
pearson_dot	0.5046
spearman_dot	0.5315
pearson_max	0.5423
spearman_max	0.5496

Binary Classification

• Dataset: allNLI-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6914
cosine_accuracy_threshold	0.927
cosine_f1	0.5364
cosine_f1_threshold	0.8338
cosine_precision	0.4011
cosine_recall	0.8092
cosine_ap	0.4923
dot_accuracy	0.6914
dot_accuracy_threshold	709.9083
dot_f1	0.5385
dot_f1_threshold	638.5917
dot_precision	0.4035
dot_recall	0.8092
dot_ap	0.4925
manhattan_accuracy	0.6895
manhattan_accuracy_threshold	214.4018
manhattan_f1	0.5392
manhattan_f1_threshold	346.0603
manhattan_precision	0.3936
manhattan_recall	0.8555
manhattan_ap	0.4909
euclidean_accuracy	0.6914
euclidean_accuracy_threshold	10.5712
euclidean_f1	0.5364
euclidean_f1_threshold	15.9521
euclidean_precision	0.4011
euclidean_recall	0.8092
euclidean_ap	0.4924
max_accuracy	0.6914
max_accuracy_threshold	709.9083
max_f1	0.5392
max_f1_threshold	638.5917
max_precision	0.4035
max_recall	0.8555
max_ap	0.4925

Binary Classification

• Dataset: Qnli-dev
• Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6641
cosine_accuracy_threshold	0.8654
cosine_f1	0.6817
cosine_f1_threshold	0.7898
cosine_precision	0.576
cosine_recall	0.8347
cosine_ap	0.6847
dot_accuracy	0.6602
dot_accuracy_threshold	657.579
dot_f1	0.6814
dot_f1_threshold	598.9863
dot_precision	0.5698
dot_recall	0.8475
dot_ap	0.6833
manhattan_accuracy	0.6797
manhattan_accuracy_threshold	294.7475
manhattan_f1	0.686
manhattan_f1_threshold	376.3382
manhattan_precision	0.5743
manhattan_recall	0.8517
manhattan_ap	0.6962
euclidean_accuracy	0.6641
euclidean_accuracy_threshold	14.3315
euclidean_f1	0.6804
euclidean_f1_threshold	17.9895
euclidean_precision	0.5723
euclidean_recall	0.839
euclidean_ap	0.685
max_accuracy	0.6797
max_accuracy_threshold	657.579
max_f1	0.686
max_f1_threshold	598.9863
max_precision	0.576
max_recall	0.8517
max_ap	0.6962

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. http://www.chemicalelements.com/elements/ag.html. 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 with these parameters:

  {'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 with these parameters:

  {'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

Training Logs

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	-	-	-	-
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0.3819	388	2.3873	-	-	-	-
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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	-	-	-	-
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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	-	-	-	-
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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	-	-	-	-
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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	-	-	-	-
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0.4567	464	1.6876	-	-	-	-
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0.4596	467	1.5593	-	-	-	-
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0.4616	469	1.5157	-	-	-	-
0.4626	470	1.6554	-	-	-	-
0.4636	471	1.7404	-	-	-	-
0.4646	472	2.1432	-	-	-	-
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0.4675	475	1.5107	-	-	-	-
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0.4705	478	1.073	-	-	-	-
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0.4724	480	2.3645	-	-	-	-
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0.4754	483	1.3105	-	-	-	-
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0.4774	485	2.0114	-	-	-	-
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0.4793	487	2.7733	-	-	-	-
0.4803	488	1.8211	-	-	-	-
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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	-	-	-	-
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0.4951	503	1.6033	-	-	-	-
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0.5030	511	1.7767	-	-	-	-
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0.5217	530	2.2645	-	-	-	-
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0.5236	532	1.8204	-	-	-	-
0.5246	533	1.5659	-	-	-	-
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0.5266	535	1.8193	-	-	-	-
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0.5335	542	2.0327	-	-	-	-
0.5344	543	2.0456	-	-	-	-
0.5354	544	2.0452	-	-	-	-
0.5364	545	1.9435	-	-	-	-
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0.5384	547	1.9536	-	-	-	-
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0.5404	549	1.8067	-	-	-	-
0.5413	550	1.6227	-	-	-	-
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0.5433	552	1.5937	-	-	-	-
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0.5522	561	2.0049	-	-	-	-
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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	-	-	-	-
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0.5669	576	1.3557	-	-	-	-
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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	-	-	-	-
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0.5817	591	1.4884	-	-	-	-
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0.5846	594	1.5848	-	-	-	-
0.5856	595	1.405	-	-	-	-
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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	-	-	-	-
0.6014	611	1.3595	-	-	-	-
0.6024	612	1.0326	1.6315	0.3597	0.4478	0.6598
0.6033	613	1.4822	-	-	-	-
0.6043	614	0.9997	-	-	-	-
0.6053	615	1.4946	-	-	-	-
0.6063	616	1.3491	-	-	-	-
0.6073	617	1.2118	-	-	-	-
0.6083	618	1.6948	-	-	-	-
0.6093	619	2.0457	-	-	-	-
0.6102	620	1.2724	-	-	-	-
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0.6122	622	1.6639	-	-	-	-
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0.6142	624	1.2424	-	-	-	-
0.6152	625	0.8214	-	-	-	-
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0.6171	627	1.6008	-	-	-	-
0.6181	628	1.3225	-	-	-	-
0.6191	629	1.4452	-	-	-	-
0.6201	630	1.7723	-	-	-	-
0.6211	631	1.035	-	-	-	-
0.6220	632	1.6132	-	-	-	-
0.6230	633	1.4167	-	-	-	-
0.6240	634	1.1764	-	-	-	-
0.625	635	1.0702	-	-	-	-
0.6260	636	1.3096	-	-	-	-
0.6270	637	1.4174	-	-	-	-
0.6280	638	1.8742	-	-	-	-
0.6289	639	1.3372	-	-	-	-
0.6299	640	1.4453	-	-	-	-
0.6309	641	0.6792	-	-	-	-
0.6319	642	1.2363	-	-	-	-
0.6329	643	1.2037	-	-	-	-
0.6339	644	1.105	-	-	-	-
0.6348	645	1.8319	-	-	-	-
0.6358	646	1.4261	-	-	-	-
0.6368	647	1.0431	-	-	-	-
0.6378	648	1.2132	-	-	-	-
0.6388	649	1.1464	-	-	-	-
0.6398	650	1.3076	-	-	-	-
0.6407	651	0.9162	-	-	-	-
0.6417	652	1.6739	-	-	-	-
0.6427	653	1.3627	-	-	-	-
0.6437	654	1.084	-	-	-	-
0.6447	655	0.8349	-	-	-	-
0.6457	656	1.5575	-	-	-	-
0.6467	657	0.7503	-	-	-	-
0.6476	658	1.5593	-	-	-	-
0.6486	659	1.1788	-	-	-	-
0.6496	660	1.5119	-	-	-	-
0.6506	661	1.1793	-	-	-	-
0.6516	662	1.1577	-	-	-	-
0.6526	663	1.4391	-	-	-	-
0.6535	664	1.2291	-	-	-	-
0.6545	665	1.3118	-	-	-	-
0.6555	666	1.1358	-	-	-	-
0.6565	667	1.814	-	-	-	-
0.6575	668	1.038	-	-	-	-
0.6585	669	1.2344	-	-	-	-
0.6594	670	1.1064	-	-	-	-
0.6604	671	1.8745	-	-	-	-
0.6614	672	1.8775	-	-	-	-
0.6624	673	1.2582	-	-	-	-
0.6634	674	1.3454	-	-	-	-
0.6644	675	1.2374	-	-	-	-
0.6654	676	0.7227	-	-	-	-
0.6663	677	1.2036	-	-	-	-
0.6673	678	1.5248	-	-	-	-
0.6683	679	1.0584	-	-	-	-
0.6693	680	0.7455	-	-	-	-
0.6703	681	1.0616	-	-	-	-
0.6713	682	1.1377	-	-	-	-
0.6722	683	1.7022	-	-	-	-
0.6732	684	1.4008	-	-	-	-
0.6742	685	1.4894	-	-	-	-
0.6752	686	1.5464	-	-	-	-
0.6762	687	1.5018	-	-	-	-
0.6772	688	1.5803	-	-	-	-
0.6781	689	1.5654	-	-	-	-
0.6791	690	1.2537	-	-	-	-
0.6801	691	1.4901	-	-	-	-
0.6811	692	0.9597	-	-	-	-
0.6821	693	1.5515	-	-	-	-
0.6831	694	1.1314	-	-	-	-
0.6841	695	1.2442	-	-	-	-
0.6850	696	1.5542	-	-	-	-
0.6860	697	0.7617	-	-	-	-
0.6870	698	1.4626	-	-	-	-
0.6880	699	0.6546	-	-	-	-
0.6890	700	1.1726	-	-	-	-
0.6900	701	0.7522	-	-	-	-
0.6909	702	1.6203	-	-	-	-
0.6919	703	1.0822	-	-	-	-
0.6929	704	0.9652	-	-	-	-
0.6939	705	0.9884	-	-	-	-
0.6949	706	1.3876	-	-	-	-
0.6959	707	1.1726	-	-	-	-
0.6969	708	1.1426	-	-	-	-
0.6978	709	1.2303	-	-	-	-
0.6988	710	1.3438	-	-	-	-
0.6998	711	1.3823	-	-	-	-
0.7008	712	1.5166	-	-	-	-
0.7018	713	1.4148	-	-	-	-
0.7028	714	0.9564	-	-	-	-
0.7037	715	1.1031	-	-	-	-
0.7047	716	1.6628	-	-	-	-
0.7057	717	1.3693	-	-	-	-
0.7067	718	1.0247	-	-	-	-
0.7077	719	0.7778	-	-	-	-
0.7087	720	1.3158	-	-	-	-
0.7096	721	1.5329	-	-	-	-
0.7106	722	1.2845	-	-	-	-
0.7116	723	0.7895	-	-	-	-
0.7126	724	0.9098	-	-	-	-
0.7136	725	0.7648	-	-	-	-
0.7146	726	1.0951	-	-	-	-
0.7156	727	1.1351	-	-	-	-
0.7165	728	1.0514	-	-	-	-
0.7175	729	1.2328	-	-	-	-
0.7185	730	1.0664	-	-	-	-
0.7195	731	1.1058	-	-	-	-
0.7205	732	0.6698	-	-	-	-
0.7215	733	1.1438	-	-	-	-
0.7224	734	1.0473	-	-	-	-
0.7234	735	1.5307	-	-	-	-
0.7244	736	1.2537	-	-	-	-
0.7254	737	0.7006	-	-	-	-
0.7264	738	0.8487	-	-	-	-
0.7274	739	1.193	-	-	-	-
0.7283	740	0.9711	-	-	-	-
0.7293	741	0.5139	-	-	-	-
0.7303	742	1.1185	-	-	-	-
0.7313	743	0.9982	-	-	-	-
0.7323	744	0.7987	-	-	-	-
0.7333	745	1.161	-	-	-	-
0.7343	746	0.9832	-	-	-	-
0.7352	747	1.6041	-	-	-	-
0.7362	748	1.1084	-	-	-	-
0.7372	749	1.1264	-	-	-	-
0.7382	750	0.6759	-	-	-	-
0.7392	751	1.5241	-	-	-	-
0.7402	752	1.6181	-	-	-	-
0.7411	753	1.1862	-	-	-	-
0.7421	754	1.0147	-	-	-	-
0.7431	755	1.0435	-	-	-	-
0.7441	756	0.9175	-	-	-	-
0.7451	757	1.5608	-	-	-	-
0.7461	758	1.7027	-	-	-	-
0.7470	759	0.9282	-	-	-	-
0.7480	760	0.8072	-	-	-	-
0.7490	761	1.13	-	-	-	-
0.75	762	1.153	-	-	-	-
0.7510	763	1.7723	-	-	-	-
0.7520	764	0.9833	-	-	-	-
0.7530	765	1.1766	1.3821	0.4583	0.4713	0.6741
0.7539	766	1.2351	-	-	-	-
0.7549	767	0.9521	-	-	-	-
0.7559	768	1.1981	-	-	-	-
0.7569	769	0.8031	-	-	-	-
0.7579	770	1.2502	-	-	-	-
0.7589	771	0.9313	-	-	-	-
0.7598	772	0.8738	-	-	-	-
0.7608	773	1.0316	-	-	-	-
0.7618	774	1.0679	-	-	-	-
0.7628	775	1.0308	-	-	-	-
0.7638	776	1.0663	-	-	-	-
0.7648	777	1.3565	-	-	-	-
0.7657	778	0.9103	-	-	-	-
0.7667	779	1.477	-	-	-	-
0.7677	780	1.2526	-	-	-	-
0.7687	781	0.8647	-	-	-	-
0.7697	782	0.8498	-	-	-	-
0.7707	783	1.5257	-	-	-	-
0.7717	784	0.5493	-	-	-	-
0.7726	785	1.2321	-	-	-	-
0.7736	786	1.4306	-	-	-	-
0.7746	787	1.0428	-	-	-	-
0.7756	788	1.3553	-	-	-	-
0.7766	789	1.1102	-	-	-	-
0.7776	790	0.9225	-	-	-	-
0.7785	791	1.2585	-	-	-	-
0.7795	792	1.1335	-	-	-	-
0.7805	793	1.2781	-	-	-	-
0.7815	794	1.0265	-	-	-	-
0.7825	795	1.1846	-	-	-	-
0.7835	796	1.2123	-	-	-	-
0.7844	797	1.0861	-	-	-	-
0.7854	798	1.1929	-	-	-	-
0.7864	799	0.8202	-	-	-	-
0.7874	800	1.0745	-	-	-	-
0.7884	801	0.9162	-	-	-	-
0.7894	802	1.2077	-	-	-	-
0.7904	803	1.432	-	-	-	-
0.7913	804	0.9384	-	-	-	-
0.7923	805	1.4686	-	-	-	-
0.7933	806	0.8565	-	-	-	-
0.7943	807	1.1208	-	-	-	-
0.7953	808	1.006	-	-	-	-
0.7963	809	0.71	-	-	-	-
0.7972	810	1.0219	-	-	-	-
0.7982	811	1.0836	-	-	-	-
0.7992	812	1.1318	-	-	-	-
0.8002	813	1.5411	-	-	-	-
0.8012	814	1.0063	-	-	-	-
0.8022	815	1.277	-	-	-	-
0.8031	816	1.0547	-	-	-	-
0.8041	817	0.7084	-	-	-	-
0.8051	818	1.2581	-	-	-	-
0.8061	819	0.7857	-	-	-	-
0.8071	820	0.7219	-	-	-	-
0.8081	821	1.2658	-	-	-	-
0.8091	822	1.4563	-	-	-	-
0.8100	823	0.9098	-	-	-	-
0.8110	824	0.8606	-	-	-	-
0.8120	825	1.1116	-	-	-	-
0.8130	826	0.952	-	-	-	-
0.8140	827	0.6831	-	-	-	-
0.8150	828	0.8267	-	-	-	-
0.8159	829	0.934	-	-	-	-
0.8169	830	1.7555	-	-	-	-
0.8179	831	1.0109	-	-	-	-
0.8189	832	1.4509	-	-	-	-
0.8199	833	0.6548	-	-	-	-
0.8209	834	0.7777	-	-	-	-
0.8219	835	0.8929	-	-	-	-
0.8228	836	1.282	-	-	-	-
0.8238	837	0.6726	-	-	-	-
0.8248	838	1.0643	-	-	-	-
0.8258	839	1.01	-	-	-	-
0.8268	840	1.0631	-	-	-	-
0.8278	841	1.092	-	-	-	-
0.8287	842	1.0219	-	-	-	-
0.8297	843	1.0437	-	-	-	-
0.8307	844	0.8789	-	-	-	-
0.8317	845	1.5409	-	-	-	-
0.8327	846	1.365	-	-	-	-
0.8337	847	1.0567	-	-	-	-
0.8346	848	0.59	-	-	-	-
0.8356	849	0.839	-	-	-	-
0.8366	850	0.727	-	-	-	-
0.8376	851	0.8681	-	-	-	-
0.8386	852	1.5186	-	-	-	-
0.8396	853	0.8923	-	-	-	-
0.8406	854	0.8248	-	-	-	-
0.8415	855	0.9884	-	-	-	-
0.8425	856	1.257	-	-	-	-
0.8435	857	0.8841	-	-	-	-
0.8445	858	1.232	-	-	-	-
0.8455	859	1.2214	-	-	-	-
0.8465	860	1.3332	-	-	-	-
0.8474	861	0.8272	-	-	-	-
0.8484	862	1.0901	-	-	-	-
0.8494	863	1.2892	-	-	-	-
0.8504	864	0.8817	-	-	-	-
0.8514	865	0.944	-	-	-	-
0.8524	866	0.6212	-	-	-	-
0.8533	867	1.4346	-	-	-	-
0.8543	868	0.7204	-	-	-	-
0.8553	869	1.4009	-	-	-	-
0.8563	870	1.1522	-	-	-	-
0.8573	871	0.9558	-	-	-	-
0.8583	872	0.8871	-	-	-	-
0.8593	873	1.0025	-	-	-	-
0.8602	874	0.8617	-	-	-	-
0.8612	875	1.1982	-	-	-	-
0.8622	876	1.2484	-	-	-	-
0.8632	877	1.0181	-	-	-	-
0.8642	878	0.8535	-	-	-	-
0.8652	879	1.144	-	-	-	-
0.8661	880	1.1927	-	-	-	-
0.8671	881	0.7245	-	-	-	-
0.8681	882	1.0118	-	-	-	-
0.8691	883	1.4399	-	-	-	-
0.8701	884	1.0666	-	-	-	-
0.8711	885	1.1947	-	-	-	-
0.8720	886	0.8908	-	-	-	-
0.8730	887	1.394	-	-	-	-
0.8740	888	0.802	-	-	-	-
0.875	889	0.9879	-	-	-	-
0.8760	890	0.9895	-	-	-	-
0.8770	891	1.0927	-	-	-	-
0.8780	892	1.4376	-	-	-	-
0.8789	893	1.1509	-	-	-	-
0.8799	894	0.7944	-	-	-	-
0.8809	895	1.4574	-	-	-	-
0.8819	896	1.2278	-	-	-	-
0.8829	897	1.1974	-	-	-	-
0.8839	898	1.0711	-	-	-	-
0.8848	899	1.3942	-	-	-	-
0.8858	900	1.1718	-	-	-	-
0.8868	901	1.1274	-	-	-	-
0.8878	902	0.9954	-	-	-	-
0.8888	903	1.0155	-	-	-	-
0.8898	904	1.4032	-	-	-	-
0.8907	905	1.4135	-	-	-	-
0.8917	906	1.1893	-	-	-	-
0.8927	907	0.7243	-	-	-	-
0.8937	908	1.1674	-	-	-	-
0.8947	909	1.3777	-	-	-	-
0.8957	910	1.0281	-	-	-	-
0.8967	911	1.2869	-	-	-	-
0.8976	912	1.0125	-	-	-	-
0.8986	913	1.0047	-	-	-	-
0.8996	914	0.8839	-	-	-	-
0.9006	915	1.4279	-	-	-	-
0.9016	916	1.053	-	-	-	-
0.9026	917	0.7841	-	-	-	-
0.9035	918	0.7738	1.2464	0.5249	0.4860	0.6758
0.9045	919	0.8998	-	-	-	-
0.9055	920	1.7733	-	-	-	-
0.9065	921	0.8032	-	-	-	-
0.9075	922	0.6311	-	-	-	-
0.9085	923	0.9534	-	-	-	-
0.9094	924	1.2538	-	-	-	-
0.9104	925	0.5411	-	-	-	-
0.9114	926	0.797	-	-	-	-
0.9124	927	1.2115	-	-	-	-
0.9134	928	0.8412	-	-	-	-
0.9144	929	0.7325	-	-	-	-
0.9154	930	1.6631	-	-	-	-
0.9163	931	0.8573	-	-	-	-
0.9173	932	0.8194	-	-	-	-
0.9183	933	1.1627	-	-	-	-
0.9193	934	1.3729	-	-	-	-
0.9203	935	1.0679	-	-	-	-
0.9213	936	1.2123	-	-	-	-
0.9222	937	0.9217	-	-	-	-
0.9232	938	0.6959	-	-	-	-
0.9242	939	0.9306	-	-	-	-
0.9252	940	1.2815	-	-	-	-
0.9262	941	1.1188	-	-	-	-
0.9272	942	0.8454	-	-	-	-
0.9281	943	0.7369	-	-	-	-
0.9291	944	1.2244	-	-	-	-
0.9301	945	1.1402	-	-	-	-
0.9311	946	0.4213	-	-	-	-
0.9321	947	1.0047	-	-	-	-
0.9331	948	1.1353	-	-	-	-
0.9341	949	1.2862	-	-	-	-
0.9350	950	0.6358	-	-	-	-
0.9360	951	0.676	-	-	-	-
0.9370	952	1.1628	-	-	-	-
0.9380	953	0.861	-	-	-	-
0.9390	954	0.7121	-	-	-	-
0.9400	955	0.6625	-	-	-	-
0.9409	956	0.9343	-	-	-	-
0.9419	957	0.8103	-	-	-	-
0.9429	958	1.047	-	-	-	-
0.9439	959	0.6455	-	-	-	-
0.9449	960	1.0978	-	-	-	-
0.9459	961	0.9522	-	-	-	-
0.9469	962	0.9151	-	-	-	-
0.9478	963	1.2515	-	-	-	-
0.9488	964	1.0809	-	-	-	-
0.9498	965	0.6971	-	-	-	-
0.9508	966	1.4086	-	-	-	-
0.9518	967	1.3102	-	-	-	-
0.9528	968	1.5008	-	-	-	-
0.9537	969	0.9449	-	-	-	-
0.9547	970	1.0705	-	-	-	-
0.9557	971	0.7788	-	-	-	-
0.9567	972	1.0007	-	-	-	-
0.9577	973	0.9345	-	-	-	-
0.9587	974	1.4525	-	-	-	-
0.9596	975	0.5892	-	-	-	-
0.9606	976	0.927	-	-	-	-
0.9616	977	0.7473	-	-	-	-
0.9626	978	0.8173	-	-	-	-
0.9636	979	0.6888	-	-	-	-
0.9646	980	0.8059	-	-	-	-
0.9656	981	0.9087	-	-	-	-
0.9665	982	1.5197	-	-	-	-
0.9675	983	0.6625	-	-	-	-
0.9685	984	1.0372	-	-	-	-
0.9695	985	0.91	-	-	-	-
0.9705	986	0.7797	-	-	-	-
0.9715	987	0.838	-	-	-	-
0.9724	988	0.9568	-	-	-	-
0.9734	989	1.4704	-	-	-	-
0.9744	990	0.8209	-	-	-	-
0.9754	991	0.86	-	-	-	-
0.9764	992	0.7749	-	-	-	-
0.9774	993	0.7225	-	-	-	-
0.9783	994	0.8488	-	-	-	-
0.9793	995	1.4838	-	-	-	-
0.9803	996	1.3282	-	-	-	-
0.9813	997	0.9905	-	-	-	-
0.9823	998	1.7902	-	-	-	-
0.9833	999	0.7411	-	-	-	-
0.9843	1000	1.2985	-	-	-	-
0.9852	1001	0.9858	-	-	-	-
0.9862	1002	0.8951	-	-	-	-
0.9872	1003	0.5526	-	-	-	-
0.9882	1004	1.466	-	-	-	-
0.9892	1005	0.5767	-	-	-	-
0.9902	1006	0.8946	-	-	-	-
0.9911	1007	1.0825	-	-	-	-
0.9921	1008	1.0152	-	-	-	-
0.9931	1009	0.7586	-	-	-	-
0.9941	1010	1.3353	-	-	-	-
0.9951	1011	0.9555	-	-	-	-
0.9961	1012	1.0585	-	-	-	-
0.9970	1013	0.6774	-	-	-	-
0.9980	1014	1.5326	-	-	-	-
0.9990	1015	1.0034	-	-	-	-
1.0	1016	0.8925	-	-	-	-
1.0010	1017	1.1286	-	-	-	-
1.0020	1018	0.9836	-	-	-	-
1.0030	1019	0.6727	-	-	-	-
1.0039	1020	0.8101	-	-	-	-
1.0049	1021	0.7747	-	-	-	-
1.0059	1022	0.6931	-	-	-	-
1.0069	1023	1.2261	-	-	-	-
1.0079	1024	0.7315	-	-	-	-
1.0089	1025	0.61	-	-	-	-
1.0098	1026	0.5402	-	-	-	-
1.0108	1027	1.3353	-	-	-	-
1.0118	1028	0.9483	-	-	-	-
1.0128	1029	0.9226	-	-	-	-
1.0138	1030	0.6536	-	-	-	-
1.0148	1031	0.7343	-	-	-	-
1.0157	1032	0.7647	-	-	-	-
1.0167	1033	0.7541	-	-	-	-
1.0177	1034	1.1746	-	-	-	-
1.0187	1035	1.3935	-	-	-	-
1.0197	1036	1.5224	-	-	-	-
1.0207	1037	0.9067	-	-	-	-
1.0217	1038	0.7184	-	-	-	-
1.0226	1039	0.5991	-	-	-	-
1.0236	1040	0.8841	-	-	-	-
1.0246	1041	0.7474	-	-	-	-
1.0256	1042	0.7444	-	-	-	-
1.0266	1043	1.4098	-	-	-	-
1.0276	1044	0.6033	-	-	-	-
1.0285	1045	0.93	-	-	-	-
1.0295	1046	0.7308	-	-	-	-
1.0305	1047	0.7339	-	-	-	-
1.0315	1048	1.3459	-	-	-	-
1.0325	1049	0.6866	-	-	-	-
1.0335	1050	0.8323	-	-	-	-
1.0344	1051	0.5301	-	-	-	-
1.0354	1052	1.1091	-	-	-	-
1.0364	1053	0.7301	-	-	-	-
1.0374	1054	1.1041	-	-	-	-
1.0384	1055	0.433	-	-	-	-
1.0394	1056	0.8808	-	-	-	-
1.0404	1057	0.5836	-	-	-	-
1.0413	1058	0.6459	-	-	-	-
1.0423	1059	0.4647	-	-	-	-
1.0433	1060	1.4988	-	-	-	-
1.0443	1061	1.2506	-	-	-	-
1.0453	1062	0.5674	-	-	-	-
1.0463	1063	0.8192	-	-	-	-
1.0472	1064	1.443	-	-	-	-
1.0482	1065	0.4771	-	-	-	-
1.0492	1066	0.5886	-	-	-	-
1.0502	1067	0.9119	-	-	-	-
1.0512	1068	0.7875	-	-	-	-
1.0522	1069	0.7041	-	-	-	-
1.0531	1070	0.5232	-	-	-	-
1.0541	1071	0.57	1.1571	0.5373	0.4925	0.6962
1.0551	1072	0.8948	-	-	-	-
1.0561	1073	1.0059	-	-	-	-
1.0571	1074	0.9796	-	-	-	-
1.0581	1075	0.9632	-	-	-	-
1.0591	1076	1.1254	-	-	-	-
1.0600	1077	0.8915	-	-	-	-
1.0610	1078	0.5213	-	-	-	-
1.0620	1079	0.6755	-	-	-	-
1.0630	1080	0.6665	-	-	-	-
1.0640	1081	0.729	-	-	-	-
1.0650	1082	0.9047	-	-	-	-
1.0659	1083	1.0514	-	-	-	-
1.0669	1084	1.6729	-	-	-	-
1.0679	1085	0.8698	-	-	-	-
1.0689	1086	1.1209	-	-	-	-
1.0699	1087	0.9425	-	-	-	-
1.0709	1088	0.7151	-	-	-	-
1.0719	1089	0.7336	-	-	-	-
1.0728	1090	1.0971	-	-	-	-
1.0738	1091	0.5334	-	-	-	-
1.0748	1092	1.0171	-	-	-	-
1.0758	1093	1.0153	-	-	-	-
1.0768	1094	0.6662	-	-	-	-
1.0778	1095	0.5451	-	-	-	-
1.0787	1096	0.7743	-	-	-	-
1.0797	1097	0.9187	-	-	-	-
1.0807	1098	1.1919	-	-	-	-
1.0817	1099	0.9405	-	-	-	-
1.0827	1100	0.5361	-	-	-	-
1.0837	1101	1.233	-	-	-	-
1.0846	1102	0.8222	-	-	-	-
1.0856	1103	0.906	-	-	-	-
1.0866	1104	0.62	-	-	-	-
1.0876	1105	0.887	-	-	-	-
1.0886	1106	0.6135	-	-	-	-
1.0896	1107	0.2279	-	-	-	-
1.0906	1108	1.0091	-	-	-	-
1.0915	1109	1.2457	-	-	-	-
1.0925	1110	0.6233	-	-	-	-
1.0935	1111	0.6004	-	-	-	-
1.0945	1112	1.0716	-	-	-	-
1.0955	1113	0.5854	-	-	-	-
1.0965	1114	0.5708	-	-	-	-
1.0974	1115	1.0223	-	-	-	-
1.0984	1116	0.6434	-	-	-	-
1.0994	1117	0.363	-	-	-	-
1.1004	1118	1.2779	-	-	-	-
1.1014	1119	0.8207	-	-	-	-
1.1024	1120	0.6738	-	-	-	-
1.1033	1121	1.0475	-	-	-	-
1.1043	1122	0.9189	-	-	-	-
1.1053	1123	1.0991	-	-	-	-
1.1063	1124	0.7348	-	-	-	-
1.1073	1125	0.7178	-	-	-	-
1.1083	1126	0.893	-	-	-	-
1.1093	1127	0.7549	-	-	-	-
1.1102	1128	1.4074	-	-	-	-
1.1112	1129	0.8321	-	-	-	-
1.1122	1130	1.0289	-	-	-	-
1.1132	1131	0.8296	-	-	-	-
1.1142	1132	0.7587	-	-	-	-
1.1152	1133	0.6198	-	-	-	-
1.1161	1134	0.7474	-	-	-	-
1.1171	1135	0.719	-	-	-	-
1.1181	1136	0.517	-	-	-	-
1.1191	1137	0.4452	-	-	-	-
1.1201	1138	1.1683	-	-	-	-
1.1211	1139	0.7705	-	-	-	-
1.1220	1140	0.7873	-	-	-	-
1.1230	1141	0.7939	-	-	-	-
1.1240	1142	0.6513	-	-	-	-
1.125	1143	1.4787	-	-	-	-
1.1260	1144	0.7314	-	-	-	-
1.1270	1145	0.4927	-	-	-	-
1.1280	1146	0.9718	-	-	-	-
1.1289	1147	0.6738	-	-	-	-
1.1299	1148	1.2356	-	-	-	-
1.1309	1149	0.902	-	-	-	-
1.1319	1150	0.5957	-	-	-	-
1.1329	1151	1.1595	-	-	-	-
1.1339	1152	0.9146	-	-	-	-
1.1348	1153	0.5402	-	-	-	-
1.1358	1154	0.6177	-	-	-	-
1.1368	1155	0.6705	-	-	-	-
1.1378	1156	0.603	-	-	-	-
1.1388	1157	0.733	-	-	-	-
1.1398	1158	1.1947	-	-	-	-
1.1407	1159	0.6454	-	-	-	-
1.1417	1160	1.0318	-	-	-	-
1.1427	1161	1.0791	-	-	-	-
1.1437	1162	0.928	-	-	-	-
1.1447	1163	0.5488	-	-	-	-
1.1457	1164	0.7937	-	-	-	-
1.1467	1165	0.6262	-	-	-	-
1.1476	1166	0.6925	-	-	-	-
1.1486	1167	0.6949	-	-	-	-
1.1496	1168	0.2552	-	-	-	-
1.1506	1169	0.7877	-	-	-	-
1.1516	1170	0.7581	-	-	-	-
1.1526	1171	0.4293	-	-	-	-
1.1535	1172	0.3826	-	-	-	-
1.1545	1173	0.9302	-	-	-	-
1.1555	1174	0.9708	-	-	-	-
1.1565	1175	0.4477	-	-	-	-
1.1575	1176	0.7183	-	-	-	-
1.1585	1177	0.6087	-	-	-	-
1.1594	1178	0.8324	-	-	-	-
1.1604	1179	0.3906	-	-	-	-
1.1614	1180	1.5957	-	-	-	-
1.1624	1181	1.2033	-	-	-	-
1.1634	1182	0.473	-	-	-	-
1.1644	1183	0.6048	-	-	-	-
1.1654	1184	0.861	-	-	-	-
1.1663	1185	0.9065	-	-	-	-
1.1673	1186	0.6253	-	-	-	-
1.1683	1187	1.227	-	-	-	-
1.1693	1188	0.7943	-	-	-	-
1.1703	1189	0.6892	-	-	-	-
1.1713	1190	1.0856	-	-	-	-
1.1722	1191	0.7547	-	-	-	-
1.1732	1192	0.4079	-	-	-	-
1.1742	1193	0.6927	-	-	-	-
1.1752	1194	1.0321	-	-	-	-
1.1762	1195	1.1502	-	-	-	-
1.1772	1196	0.9344	-	-	-	-
1.1781	1197	1.0466	-	-	-	-
1.1791	1198	0.6004	-	-	-	-
1.1801	1199	0.7064	-	-	-	-
1.1811	1200	0.659	-	-	-	-
1.1821	1201	0.6445	-	-	-	-
1.1831	1202	1.1254	-	-	-	-
1.1841	1203	0.7727	-	-	-	-
1.1850	1204	0.8912	-	-	-	-
1.1860	1205	1.3996	-	-	-	-
1.1870	1206	0.6232	-	-	-	-
1.1880	1207	1.2074	-	-	-	-
1.1890	1208	0.7199	-	-	-	-
1.1900	1209	0.5826	-	-	-	-
1.1909	1210	0.7618	-	-	-	-
1.1919	1211	1.1995	-	-	-	-
1.1929	1212	0.6375	-	-	-	-
1.1939	1213	0.5623	-	-	-	-
1.1949	1214	0.7756	-	-	-	-
1.1959	1215	0.8011	-	-	-	-
1.1969	1216	0.4404	-	-	-	-
1.1978	1217	0.8401	-	-	-	-
1.1988	1218	0.8971	-	-	-	-
1.1998	1219	1.0111	-	-	-	-
1.2008	1220	0.8536	-	-	-	-

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.2.1
• Transformers: 4.44.2
• PyTorch: 2.5.0+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.2
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

GISTEmbedLoss

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}