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: 0.27561429926791464
            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 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	0.2756
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

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

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

	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

	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	-	-	-	-
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	-	-	-	-

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}
}