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: phase changes do not change
    sentences:
      - >-
        The major Atlantic slave trading nations, ordered by trade volume, were
        the Portuguese, the British, the Spanish, the French, the Dutch, and the
        Danish. Several had established outposts on the African coast where they
        purchased slaves from local African leaders.
      - >-
        phase changes do not change mass. Particles have mass, but mass is
        energy. 
         phase changes do not change  energy
      - >-
        According to the U.S. Census Bureau , the county is a total area of ,
        which has land and ( 0.2 % ) is water .
  - source_sentence: what jobs can you get with a bachelor degree in anthropology?
    sentences:
      - >-
        To determine the atomic weight of an element, you should add up protons
        and neutrons.
      - >-
        ['Paleontologist*', 'Archaeologist*', 'University Professor*', 'Market
        Research Analyst*', 'Primatologist.', 'Forensic Scientist*', 'Medical
        Anthropologist.', 'Museum Technician.']
      - >-
        The wingspan flies , the moth comes depending on the location from July
        to August .
  - source_sentence: Identify different forms of energy (e.g., light, sound, heat).
    sentences:
      - >-
        `` Irreplaceable '' '' remained on the chart for thirty weeks , and was
        certified double-platinum by the Recording Industry Association of
        America ( RIAA ) , denoting sales of two million downloads , and had
        sold over 3,139,000 paid digital downloads in the US as of October 2012
        , according to Nielsen SoundScan . ''
      - >-
        On Rotten Tomatoes , the film has a rating of 63 % , based on 87 reviews
        , with an average rating of 5.9/10 .
      - Heat, light, and sound are all different forms of energy.
  - source_sentence: what is so small it can only be seen with an electron microscope?
    sentences:
      - >-
        Viruses are so small that they can be seen only with an electron
        microscope.. Where most viruses are DNA, HIV is an RNA virus. 
         HIV is so small it can only be seen with an electron microscope
      - >-
        The development of modern lasers has opened many doors to both research
        and applications. A laser beam was used to measure the distance from the
        Earth to the moon. Lasers are important components of CD players. As the
        image above illustrates, lasers can provide precise focusing of beams to
        selectively destroy cancer cells in patients. The ability of a laser to
        focus precisely is due to high-quality crystals that help give rise to
        the laser beam. A variety of techniques are used to manufacture pure
        crystals for use in lasers.
      - >-
        Discussion for (a) This value is the net work done on the package. The
        person actually does more work than this, because friction opposes the
        motion. Friction does negative work and removes some of the energy the
        person expends and converts it to thermal energy. The net work equals
        the sum of the work done by each individual force. Strategy and Concept
        for (b) The forces acting on the package are gravity, the normal force,
        the force of friction, and the applied force. The normal force and force
        of gravity are each perpendicular to the displacement, and therefore do
        no work. Solution for (b) The applied force does work.
  - source_sentence: what aspects of your environment may relate to the epidemic of obesity
    sentences:
      - >-
        Jan Kromkamp ( born August 17 , 1980 in Makkinga , Netherlands ) is a
        Dutch footballer .
      - >-
        When chemicals in solution react, the proper way of writing the chemical
        formulas of the dissolved ionic compounds is in terms of the dissociated
        ions, not the complete ionic formula. A complete ionic equation is a
        chemical equation in which the dissolved ionic compounds are written as
        separated ions. Solubility rules are very useful in determining which
        ionic compounds are dissolved and which are not. For example, when
        NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to
        precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation
        includes NaCl, AgNO3, and NaNO3 written as separated ions:.
      - >-
        Genetic changes in human populations occur too slowly to be responsible
        for the obesity epidemic. Nevertheless, the variation in how people
        respond to the environment that promotes physical inactivity and intake
        of high-calorie foods suggests that genes do play a role in the
        development of obesity.
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: 0.6261543137722272
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6380566379755814
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.64545238834986
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.638675279413056
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.6433597379437007
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6380140347747367
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6259993945400585
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6378358352518761
            name: Spearman Dot
          - type: pearson_max
            value: 0.64545238834986
            name: Pearson Max
          - type: spearman_max
            value: 0.638675279413056
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allNLI dev
          type: allNLI-dev
        metrics:
          - type: cosine_accuracy
            value: 0.6796875
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.9540110230445862
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.5454545454545455
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8630210161209106
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.44244604316546765
            name: Cosine Precision
          - type: cosine_recall
            value: 0.7109826589595376
            name: Cosine Recall
          - type: cosine_ap
            value: 0.4867883737362577
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.6796875
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 733.4241943359375
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.5454545454545455
            name: Dot F1
          - type: dot_f1_threshold
            value: 663.4332885742188
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.44244604316546765
            name: Dot Precision
          - type: dot_recall
            value: 0.7109826589595376
            name: Dot Recall
          - type: dot_ap
            value: 0.4866316260057906
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.677734375
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 181.60472106933594
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.550537634408602
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 319.623046875
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.4383561643835616
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.7398843930635838
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.4882823795400136
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.6796875
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 8.408852577209473
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.5454545454545455
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 14.51208782196045
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.44244604316546765
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.7109826589595376
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.48671939529879626
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.6796875
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 733.4241943359375
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.550537634408602
            name: Max F1
          - type: max_f1_threshold
            value: 663.4332885742188
            name: Max F1 Threshold
          - type: max_precision
            value: 0.44244604316546765
            name: Max Precision
          - type: max_recall
            value: 0.7398843930635838
            name: Max Recall
          - type: max_ap
            value: 0.4882823795400136
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Qnli dev
          type: Qnli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.666015625
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8842042684555054
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.6793103448275861
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8030812740325928
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.5726744186046512
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8347457627118644
            name: Cosine Recall
          - type: cosine_ap
            value: 0.7052019976296955
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.66796875
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 679.375244140625
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.6794425087108014
            name: Dot F1
          - type: dot_f1_threshold
            value: 618.5501708984375
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5769230769230769
            name: Dot Precision
          - type: dot_recall
            value: 0.826271186440678
            name: Dot Recall
          - type: dot_ap
            value: 0.7054354866961744
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.669921875
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 288.81317138671875
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.6791862284820032
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 405.0858459472656
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5384615384615384
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.9194915254237288
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.7053588266129154
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.666015625
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 13.342350006103516
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.6793103448275861
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 17.398447036743164
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5726744186046512
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8347457627118644
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.7052194558904903
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.669921875
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 679.375244140625
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.6794425087108014
            name: Max F1
          - type: max_f1_threshold
            value: 618.5501708984375
            name: Max F1 Threshold
          - type: max_precision
            value: 0.5769230769230769
            name: Max Precision
          - type: max_recall
            value: 0.9194915254237288
            name: Max Recall
          - type: max_ap
            value: 0.7054354866961744
            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=0.05, inplace=False)
    (gate_dropout_layer): Dropout(p=0.0, inplace=False)
    (linear_cls_Qpj): 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_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-toytest4-step1-checkpoints-tmp")
# Run inference
sentences = [
    'what aspects of your environment may relate to the epidemic of obesity',
    'Genetic changes in human populations occur too slowly to be responsible for the obesity epidemic. Nevertheless, the variation in how people respond to the environment that promotes physical inactivity and intake of high-calorie foods suggests that genes do play a role in the development of obesity.',
    'When chemicals in solution react, the proper way of writing the chemical formulas of the dissolved ionic compounds is in terms of the dissociated ions, not the complete ionic formula. A complete ionic equation is a chemical equation in which the dissolved ionic compounds are written as separated ions. Solubility rules are very useful in determining which ionic compounds are dissolved and which are not. For example, when NaCl(aq) reacts with AgNO3(aq) in a double-replacement reaction to precipitate AgCl(s) and form NaNO3(aq), the complete ionic equation includes NaCl, AgNO3, and NaNO3 written as separated ions:.',
]
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	0.6262
spearman_cosine	0.6381
pearson_manhattan	0.6455
spearman_manhattan	0.6387
pearson_euclidean	0.6434
spearman_euclidean	0.638
pearson_dot	0.626
spearman_dot	0.6378
pearson_max	0.6455
spearman_max	0.6387

Binary Classification

Dataset: allNLI-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6797
cosine_accuracy_threshold	0.954
cosine_f1	0.5455
cosine_f1_threshold	0.863
cosine_precision	0.4424
cosine_recall	0.711
cosine_ap	0.4868
dot_accuracy	0.6797
dot_accuracy_threshold	733.4242
dot_f1	0.5455
dot_f1_threshold	663.4333
dot_precision	0.4424
dot_recall	0.711
dot_ap	0.4866
manhattan_accuracy	0.6777
manhattan_accuracy_threshold	181.6047
manhattan_f1	0.5505
manhattan_f1_threshold	319.623
manhattan_precision	0.4384
manhattan_recall	0.7399
manhattan_ap	0.4883
euclidean_accuracy	0.6797
euclidean_accuracy_threshold	8.4089
euclidean_f1	0.5455
euclidean_f1_threshold	14.5121
euclidean_precision	0.4424
euclidean_recall	0.711
euclidean_ap	0.4867
max_accuracy	0.6797
max_accuracy_threshold	733.4242
max_f1	0.5505
max_f1_threshold	663.4333
max_precision	0.4424
max_recall	0.7399
max_ap	0.4883

Binary Classification

Dataset: Qnli-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.666
cosine_accuracy_threshold	0.8842
cosine_f1	0.6793
cosine_f1_threshold	0.8031
cosine_precision	0.5727
cosine_recall	0.8347
cosine_ap	0.7052
dot_accuracy	0.668
dot_accuracy_threshold	679.3752
dot_f1	0.6794
dot_f1_threshold	618.5502
dot_precision	0.5769
dot_recall	0.8263
dot_ap	0.7054
manhattan_accuracy	0.6699
manhattan_accuracy_threshold	288.8132
manhattan_f1	0.6792
manhattan_f1_threshold	405.0858
manhattan_precision	0.5385
manhattan_recall	0.9195
manhattan_ap	0.7054
euclidean_accuracy	0.666
euclidean_accuracy_threshold	13.3424
euclidean_f1	0.6793
euclidean_f1_threshold	17.3984
euclidean_precision	0.5727
euclidean_recall	0.8347
euclidean_ap	0.7052
max_accuracy	0.6699
max_accuracy_threshold	679.3752
max_f1	0.6794
max_f1_threshold	618.5502
max_precision	0.5769
max_recall	0.9195
max_ap	0.7054

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: 29.39 tokens
max: 323 tokens

min: 2 tokens
mean: 54.42 tokens
max: 423 tokens

	sentence1	sentence2
type	string	string
details	min: 4 tokens mean: 29.39 tokens max: 323 tokens	min: 2 tokens mean: 54.42 tokens max: 423 tokens

Samples:

sentence1	sentence2
`In which London road is Harrod’s department store?`	`Harrods, Brompton Road, London`
`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`
`Joe Cole was unable to join West Bromwich Albion .`	`On 16th October Joe Cole took a long hard look at himself realising that he would never get the opportunity to join West Bromwich Albion and joined Coventry City instead.`

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-toytest4-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-toytest4-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	sts-test_spearman_cosine	allNLI-dev_max_ap	Qnli-dev_max_ap
0.0010	1	6.0688	-	-	-
0.0020	2	7.5576	-	-	-
0.0030	3	4.6849	-	-	-
0.0039	4	5.4503	-	-	-
0.0049	5	5.6057	-	-	-
0.0059	6	6.3049	-	-	-
0.0069	7	6.8336	-	-	-
0.0079	8	5.0777	-	-	-
0.0089	9	4.8358	-	-	-
0.0098	10	4.641	-	-	-
0.0108	11	4.828	-	-	-
0.0118	12	5.2269	-	-	-
0.0128	13	5.6772	-	-	-
0.0138	14	5.1422	-	-	-
0.0148	15	6.2469	-	-	-
0.0157	16	4.6802	-	-	-
0.0167	17	4.5492	-	-	-
0.0177	18	4.8062	-	-	-
0.0187	19	7.5141	-	-	-
0.0197	20	5.5202	-	-	-
0.0207	21	6.5025	-	-	-
0.0217	22	7.318	-	-	-
0.0226	23	4.6458	-	-	-
0.0236	24	4.6191	-	-	-
0.0246	25	4.3159	-	-	-
0.0256	26	6.3677	-	-	-
0.0266	27	5.6052	-	-	-
0.0276	28	4.196	-	-	-
0.0285	29	4.4802	-	-	-
0.0295	30	4.9193	-	-	-
0.0305	31	4.0996	-	-	-
0.0315	32	5.6307	-	-	-
0.0325	33	4.5745	-	-	-
0.0335	34	4.4514	-	-	-
0.0344	35	4.0617	-	-	-
0.0354	36	5.0298	-	-	-
0.0364	37	3.9815	-	-	-
0.0374	38	4.0871	-	-	-
0.0384	39	4.2378	-	-	-
0.0394	40	3.8226	-	-	-
0.0404	41	4.3519	-	-	-
0.0413	42	3.6345	-	-	-
0.0423	43	5.0829	-	-	-
0.0433	44	4.6701	-	-	-
0.0443	45	4.1371	-	-	-
0.0453	46	4.2418	-	-	-
0.0463	47	4.4766	-	-	-
0.0472	48	4.4797	-	-	-
0.0482	49	3.8471	-	-	-
0.0492	50	4.3194	-	-	-
0.0502	51	3.9426	-	-	-
0.0512	52	3.5333	-	-	-
0.0522	53	4.2426	-	-	-
0.0531	54	3.9816	-	-	-
0.0541	55	3.663	-	-	-
0.0551	56	3.9057	-	-	-
0.0561	57	4.0345	-	-	-
0.0571	58	3.5233	-	-	-
0.0581	59	3.7999	-	-	-
0.0591	60	3.1885	-	-	-
0.0600	61	3.6013	-	-	-
0.0610	62	3.392	-	-	-
0.0620	63	3.3814	-	-	-
0.0630	64	4.0428	-	-	-
0.0640	65	3.7825	-	-	-
0.0650	66	3.4181	-	-	-
0.0659	67	3.7793	-	-	-
0.0669	68	3.8344	-	-	-
0.0679	69	3.2165	-	-	-
0.0689	70	3.3811	-	-	-
0.0699	71	3.5984	-	-	-
0.0709	72	3.8583	-	-	-
0.0719	73	3.296	-	-	-
0.0728	74	2.7661	-	-	-
0.0738	75	2.9805	-	-	-
0.0748	76	2.566	-	-	-
0.0758	77	3.258	-	-	-
0.0768	78	3.3804	-	-	-
0.0778	79	2.8828	-	-	-
0.0787	80	3.1077	-	-	-
0.0797	81	2.9441	-	-	-
0.0807	82	2.9465	-	-	-
0.0817	83	2.7088	-	-	-
0.0827	84	2.9215	-	-	-
0.0837	85	3.4698	-	-	-
0.0846	86	2.2414	-	-	-
0.0856	87	3.1601	-	-	-
0.0866	88	2.7714	-	-	-
0.0876	89	3.0311	-	-	-
0.0886	90	3.0336	-	-	-
0.0896	91	1.9358	-	-	-
0.0906	92	2.6031	-	-	-
0.0915	93	2.7515	-	-	-
0.0925	94	2.8496	-	-	-
0.0935	95	1.8015	-	-	-
0.0945	96	2.8138	-	-	-
0.0955	97	2.0597	-	-	-
0.0965	98	2.1053	-	-	-
0.0974	99	2.6785	-	-	-
0.0984	100	2.588	-	-	-
0.0994	101	2.0099	-	-	-
0.1004	102	2.7947	-	-	-
0.1014	103	2.3274	-	-	-
0.1024	104	2.2545	-	-	-
0.1033	105	2.4575	-	-	-
0.1043	106	2.4413	-	-	-
0.1053	107	2.3185	-	-	-
0.1063	108	2.1577	-	-	-
0.1073	109	2.1278	-	-	-
0.1083	110	2.0967	-	-	-
0.1093	111	2.6142	-	-	-
0.1102	112	1.8553	-	-	-
0.1112	113	2.1523	-	-	-
0.1122	114	2.1726	-	-	-
0.1132	115	1.8564	-	-	-
0.1142	116	1.8413	-	-	-
0.1152	117	2.0441	-	-	-
0.1161	118	2.2159	-	-	-
0.1171	119	2.6779	-	-	-
0.1181	120	2.2976	-	-	-
0.1191	121	1.9407	-	-	-
0.1201	122	1.9019	-	-	-
0.1211	123	2.2149	-	-	-
0.1220	124	1.6823	-	-	-
0.1230	125	1.8402	-	-	-
0.1240	126	1.6914	-	-	-
0.125	127	2.1626	-	-	-
0.1260	128	1.6414	-	-	-
0.1270	129	2.2043	-	-	-
0.1280	130	1.9987	-	-	-
0.1289	131	1.8868	-	-	-
0.1299	132	1.8262	-	-	-
0.1309	133	2.0404	-	-	-
0.1319	134	1.9134	-	-	-
0.1329	135	2.3725	-	-	-
0.1339	136	1.4127	-	-	-
0.1348	137	1.6876	-	-	-
0.1358	138	1.8376	-	-	-
0.1368	139	1.6992	-	-	-
0.1378	140	1.5032	-	-	-
0.1388	141	2.0334	-	-	-
0.1398	142	2.3581	-	-	-
0.1407	143	1.4236	-	-	-
0.1417	144	2.202	-	-	-
0.1427	145	1.7654	-	-	-
0.1437	146	1.5748	-	-	-
0.1447	147	1.7996	-	-	-
0.1457	148	1.7517	-	-	-
0.1467	149	1.8933	-	-	-
0.1476	150	1.2836	-	-	-
0.1486	151	1.7145	-	-	-
0.1496	152	1.6499	-	-	-
0.1506	153	1.8273	0.4057	0.4389	0.6725
0.1516	154	2.2859	-	-	-
0.1526	155	1.0833	-	-	-
0.1535	156	1.6829	-	-	-
0.1545	157	2.1464	-	-	-
0.1555	158	1.745	-	-	-
0.1565	159	1.7319	-	-	-
0.1575	160	1.6968	-	-	-
0.1585	161	1.7401	-	-	-
0.1594	162	1.729	-	-	-
0.1604	163	2.0782	-	-	-
0.1614	164	2.6545	-	-	-
0.1624	165	1.4045	-	-	-
0.1634	166	1.2937	-	-	-
0.1644	167	1.1171	-	-	-
0.1654	168	1.3537	-	-	-
0.1663	169	1.7028	-	-	-
0.1673	170	1.4143	-	-	-
0.1683	171	1.8648	-	-	-
0.1693	172	1.6768	-	-	-
0.1703	173	1.9528	-	-	-
0.1713	174	1.1718	-	-	-
0.1722	175	1.8176	-	-	-
0.1732	176	0.8439	-	-	-
0.1742	177	1.5092	-	-	-
0.1752	178	1.1947	-	-	-
0.1762	179	1.6395	-	-	-
0.1772	180	1.4394	-	-	-
0.1781	181	1.7548	-	-	-
0.1791	182	1.1181	-	-	-
0.1801	183	1.0271	-	-	-
0.1811	184	2.3108	-	-	-
0.1821	185	2.1242	-	-	-
0.1831	186	1.9822	-	-	-
0.1841	187	2.3605	-	-	-
0.1850	188	1.5251	-	-	-
0.1860	189	1.2351	-	-	-
0.1870	190	1.5859	-	-	-
0.1880	191	1.8056	-	-	-
0.1890	192	1.349	-	-	-
0.1900	193	0.893	-	-	-
0.1909	194	1.5122	-	-	-
0.1919	195	1.3875	-	-	-
0.1929	196	1.29	-	-	-
0.1939	197	2.2931	-	-	-
0.1949	198	1.2663	-	-	-
0.1959	199	1.9712	-	-	-
0.1969	200	2.3307	-	-	-
0.1978	201	1.6544	-	-	-
0.1988	202	1.638	-	-	-
0.1998	203	1.3412	-	-	-
0.2008	204	1.4454	-	-	-
0.2018	205	1.5437	-	-	-
0.2028	206	1.4921	-	-	-
0.2037	207	1.4298	-	-	-
0.2047	208	1.6174	-	-	-
0.2057	209	1.4137	-	-	-
0.2067	210	1.5652	-	-	-
0.2077	211	1.1631	-	-	-
0.2087	212	1.2351	-	-	-
0.2096	213	1.7537	-	-	-
0.2106	214	1.3186	-	-	-
0.2116	215	1.2258	-	-	-
0.2126	216	0.7695	-	-	-
0.2136	217	1.2775	-	-	-
0.2146	218	1.6795	-	-	-
0.2156	219	1.2862	-	-	-
0.2165	220	1.1723	-	-	-
0.2175	221	1.3322	-	-	-
0.2185	222	1.7564	-	-	-
0.2195	223	1.1071	-	-	-
0.2205	224	1.2011	-	-	-
0.2215	225	1.2303	-	-	-
0.2224	226	1.212	-	-	-
0.2234	227	1.0117	-	-	-
0.2244	228	1.1907	-	-	-
0.2254	229	2.1293	-	-	-
0.2264	230	1.3063	-	-	-
0.2274	231	1.2841	-	-	-
0.2283	232	1.3778	-	-	-
0.2293	233	1.2242	-	-	-
0.2303	234	0.9227	-	-	-
0.2313	235	1.2221	-	-	-
0.2323	236	2.1041	-	-	-
0.2333	237	1.3341	-	-	-
0.2343	238	1.0876	-	-	-
0.2352	239	1.3328	-	-	-
0.2362	240	1.2958	-	-	-
0.2372	241	1.1522	-	-	-
0.2382	242	1.7942	-	-	-
0.2392	243	1.1325	-	-	-
0.2402	244	1.6466	-	-	-
0.2411	245	1.4608	-	-	-
0.2421	246	0.6375	-	-	-
0.2431	247	2.0177	-	-	-
0.2441	248	1.2069	-	-	-
0.2451	249	0.7639	-	-	-
0.2461	250	1.3465	-	-	-
0.2470	251	1.064	-	-	-
0.2480	252	1.3757	-	-	-
0.2490	253	1.612	-	-	-
0.25	254	0.7917	-	-	-
0.2510	255	1.5515	-	-	-
0.2520	256	0.799	-	-	-
0.2530	257	0.9882	-	-	-
0.2539	258	1.1814	-	-	-
0.2549	259	0.6394	-	-	-
0.2559	260	1.4756	-	-	-
0.2569	261	0.5338	-	-	-
0.2579	262	0.9779	-	-	-
0.2589	263	1.5307	-	-	-
0.2598	264	1.1213	-	-	-
0.2608	265	0.9482	-	-	-
0.2618	266	0.9599	-	-	-
0.2628	267	1.4455	-	-	-
0.2638	268	1.6496	-	-	-
0.2648	269	0.7402	-	-	-
0.2657	270	0.7835	-	-	-
0.2667	271	0.7821	-	-	-
0.2677	272	1.5422	-	-	-
0.2687	273	1.0995	-	-	-
0.2697	274	1.378	-	-	-
0.2707	275	1.3562	-	-	-
0.2717	276	0.7376	-	-	-
0.2726	277	1.1678	-	-	-
0.2736	278	1.2989	-	-	-
0.2746	279	1.9559	-	-	-
0.2756	280	1.1237	-	-	-
0.2766	281	0.952	-	-	-
0.2776	282	1.6629	-	-	-
0.2785	283	1.871	-	-	-
0.2795	284	1.5946	-	-	-
0.2805	285	1.4456	-	-	-
0.2815	286	1.4085	-	-	-
0.2825	287	1.1394	-	-	-
0.2835	288	1.0315	-	-	-
0.2844	289	1.488	-	-	-
0.2854	290	1.4006	-	-	-
0.2864	291	0.9237	-	-	-
0.2874	292	1.163	-	-	-
0.2884	293	1.7037	-	-	-
0.2894	294	0.8715	-	-	-
0.2904	295	1.2101	-	-	-
0.2913	296	1.1179	-	-	-
0.2923	297	1.3986	-	-	-
0.2933	298	1.7068	-	-	-
0.2943	299	0.8695	-	-	-
0.2953	300	1.3778	-	-	-
0.2963	301	1.2834	-	-	-
0.2972	302	0.8123	-	-	-
0.2982	303	1.6521	-	-	-
0.2992	304	1.1064	-	-	-
0.3002	305	0.9578	-	-	-
0.3012	306	0.9254	0.4888	0.4789	0.7040
0.3022	307	0.7541	-	-	-
0.3031	308	0.7324	-	-	-
0.3041	309	0.5974	-	-	-
0.3051	310	1.1481	-	-	-
0.3061	311	1.6179	-	-	-
0.3071	312	1.4641	-	-	-
0.3081	313	1.7185	-	-	-
0.3091	314	0.9328	-	-	-
0.3100	315	0.742	-	-	-
0.3110	316	1.4173	-	-	-
0.3120	317	0.7267	-	-	-
0.3130	318	0.9494	-	-	-
0.3140	319	1.5111	-	-	-
0.3150	320	1.6949	-	-	-
0.3159	321	1.7562	-	-	-
0.3169	322	1.2532	-	-	-
0.3179	323	1.1086	-	-	-
0.3189	324	0.7377	-	-	-
0.3199	325	1.085	-	-	-
0.3209	326	0.7767	-	-	-
0.3219	327	1.4441	-	-	-
0.3228	328	0.8146	-	-	-
0.3238	329	0.7403	-	-	-
0.3248	330	0.8476	-	-	-
0.3258	331	0.7323	-	-	-
0.3268	332	1.2241	-	-	-
0.3278	333	1.5065	-	-	-
0.3287	334	0.5259	-	-	-
0.3297	335	1.3103	-	-	-
0.3307	336	0.8655	-	-	-
0.3317	337	0.7575	-	-	-
0.3327	338	1.968	-	-	-
0.3337	339	1.317	-	-	-
0.3346	340	1.1972	-	-	-
0.3356	341	1.6323	-	-	-
0.3366	342	1.0469	-	-	-
0.3376	343	1.3349	-	-	-
0.3386	344	0.9544	-	-	-
0.3396	345	1.1894	-	-	-
0.3406	346	0.7717	-	-	-
0.3415	347	1.2563	-	-	-
0.3425	348	1.2437	-	-	-
0.3435	349	0.7806	-	-	-
0.3445	350	0.8303	-	-	-
0.3455	351	1.0926	-	-	-
0.3465	352	0.6654	-	-	-
0.3474	353	1.1087	-	-	-
0.3484	354	1.1525	-	-	-
0.3494	355	1.1127	-	-	-
0.3504	356	1.4267	-	-	-
0.3514	357	0.6148	-	-	-
0.3524	358	1.0123	-	-	-
0.3533	359	1.9682	-	-	-
0.3543	360	0.8487	-	-	-
0.3553	361	1.0412	-	-	-
0.3563	362	1.0902	-	-	-
0.3573	363	0.9606	-	-	-
0.3583	364	0.9206	-	-	-
0.3593	365	1.4727	-	-	-
0.3602	366	0.9379	-	-	-
0.3612	367	0.8387	-	-	-
0.3622	368	0.9692	-	-	-
0.3632	369	1.6298	-	-	-
0.3642	370	1.0882	-	-	-
0.3652	371	1.1558	-	-	-
0.3661	372	0.9546	-	-	-
0.3671	373	1.0124	-	-	-
0.3681	374	1.3916	-	-	-
0.3691	375	0.527	-	-	-
0.3701	376	0.6387	-	-	-
0.3711	377	1.1445	-	-	-
0.3720	378	1.3309	-	-	-
0.3730	379	1.5888	-	-	-
0.3740	380	1.4422	-	-	-
0.375	381	1.7044	-	-	-
0.3760	382	0.7913	-	-	-
0.3770	383	1.3241	-	-	-
0.3780	384	0.6473	-	-	-
0.3789	385	1.221	-	-	-
0.3799	386	0.7773	-	-	-
0.3809	387	1.054	-	-	-
0.3819	388	0.9862	-	-	-
0.3829	389	0.9684	-	-	-
0.3839	390	1.3244	-	-	-
0.3848	391	1.1787	-	-	-
0.3858	392	1.4698	-	-	-
0.3868	393	1.0961	-	-	-
0.3878	394	1.1364	-	-	-
0.3888	395	0.9368	-	-	-
0.3898	396	1.1731	-	-	-
0.3907	397	0.8686	-	-	-
0.3917	398	0.7481	-	-	-
0.3927	399	0.7261	-	-	-
0.3937	400	1.2062	-	-	-
0.3947	401	0.7462	-	-	-
0.3957	402	1.0318	-	-	-
0.3967	403	1.105	-	-	-
0.3976	404	1.009	-	-	-
0.3986	405	0.5941	-	-	-
0.3996	406	1.7972	-	-	-
0.4006	407	1.0544	-	-	-
0.4016	408	1.3912	-	-	-
0.4026	409	0.8305	-	-	-
0.4035	410	0.8688	-	-	-
0.4045	411	1.0069	-	-	-
0.4055	412	1.3141	-	-	-
0.4065	413	1.1042	-	-	-
0.4075	414	1.1011	-	-	-
0.4085	415	1.1192	-	-	-
0.4094	416	1.5957	-	-	-
0.4104	417	1.164	-	-	-
0.4114	418	0.6425	-	-	-
0.4124	419	0.6068	-	-	-
0.4134	420	0.9275	-	-	-
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0.8317	845	1.1881	-	-	-
0.8327	846	1.1807	-	-	-
0.8337	847	0.5896	-	-	-
0.8346	848	0.8483	-	-	-
0.8356	849	0.5409	-	-	-
0.8366	850	0.8286	-	-	-
0.8376	851	1.136	-	-	-
0.8386	852	0.819	-	-	-
0.8396	853	0.3029	-	-	-
0.8406	854	0.5538	-	-	-
0.8415	855	0.4572	-	-	-
0.8425	856	1.1516	-	-	-
0.8435	857	0.4768	-	-	-
0.8445	858	0.5357	-	-	-
0.8455	859	0.8879	-	-	-
0.8465	860	0.9121	-	-	-
0.8474	861	1.052	-	-	-
0.8484	862	0.8009	-	-	-
0.8494	863	0.2474	-	-	-
0.8504	864	0.8517	-	-	-
0.8514	865	0.5678	-	-	-
0.8524	866	0.4882	-	-	-
0.8533	867	0.638	-	-	-
0.8543	868	0.8012	-	-	-
0.8553	869	0.4172	-	-	-
0.8563	870	0.6727	-	-	-
0.8573	871	0.6383	-	-	-
0.8583	872	1.0728	-	-	-
0.8593	873	1.3336	-	-	-
0.8602	874	1.217	-	-	-
0.8612	875	0.8018	-	-	-
0.8622	876	1.2326	-	-	-
0.8632	877	1.0898	-	-	-
0.8642	878	0.7192	-	-	-
0.8652	879	0.7779	-	-	-
0.8661	880	0.9291	-	-	-
0.8671	881	0.6333	-	-	-
0.8681	882	0.9028	-	-	-
0.8691	883	0.9904	-	-	-
0.8701	884	0.5775	-	-	-
0.8711	885	0.9783	-	-	-
0.8720	886	1.111	-	-	-
0.8730	887	1.0398	-	-	-
0.8740	888	0.742	-	-	-
0.875	889	0.7606	-	-	-
0.8760	890	0.6941	-	-	-
0.8770	891	1.0219	-	-	-
0.8780	892	0.8949	-	-	-
0.8789	893	1.0013	-	-	-
0.8799	894	1.1122	-	-	-
0.8809	895	1.1711	-	-	-
0.8819	896	0.7457	-	-	-
0.8829	897	0.5431	-	-	-
0.8839	898	0.8159	-	-	-
0.8848	899	1.6812	-	-	-
0.8858	900	0.8163	-	-	-
0.8868	901	0.9315	-	-	-
0.8878	902	0.883	-	-	-
0.8888	903	0.8014	-	-	-
0.8898	904	1.4277	-	-	-
0.8907	905	0.7507	-	-	-
0.8917	906	0.9757	-	-	-
0.8927	907	0.6366	-	-	-
0.8937	908	0.7567	-	-	-
0.8947	909	0.7853	-	-	-
0.8957	910	0.7611	-	-	-
0.8967	911	0.8417	-	-	-
0.8976	912	0.5825	-	-	-
0.8986	913	1.0816	-	-	-
0.8996	914	1.1768	-	-	-
0.9006	915	0.4894	-	-	-
0.9016	916	0.8739	-	-	-
0.9026	917	1.0047	-	-	-
0.9035	918	0.7733	0.6115	0.5123	0.7069
0.9045	919	1.1274	-	-	-
0.9055	920	1.5414	-	-	-
0.9065	921	1.0839	-	-	-
0.9075	922	0.5004	-	-	-
0.9085	923	0.8282	-	-	-
0.9094	924	0.8041	-	-	-
0.9104	925	0.6129	-	-	-
0.9114	926	0.8697	-	-	-
0.9124	927	0.7138	-	-	-
0.9134	928	1.0387	-	-	-
0.9144	929	0.6024	-	-	-
0.9154	930	0.6641	-	-	-
0.9163	931	0.8028	-	-	-
0.9173	932	0.8097	-	-	-
0.9183	933	0.6162	-	-	-
0.9193	934	0.4264	-	-	-
0.9203	935	0.3983	-	-	-
0.9213	936	0.7519	-	-	-
0.9222	937	1.0682	-	-	-
0.9232	938	0.6805	-	-	-
0.9242	939	1.1305	-	-	-
0.9252	940	1.5805	-	-	-
0.9262	941	0.7031	-	-	-
0.9272	942	0.6847	-	-	-
0.9281	943	0.3777	-	-	-
0.9291	944	0.7438	-	-	-
0.9301	945	1.1754	-	-	-
0.9311	946	0.6602	-	-	-
0.9321	947	1.2893	-	-	-
0.9331	948	0.8315	-	-	-
0.9341	949	1.0961	-	-	-
0.9350	950	0.7657	-	-	-
0.9360	951	0.8915	-	-	-
0.9370	952	0.6784	-	-	-
0.9380	953	0.6772	-	-	-
0.9390	954	0.9102	-	-	-
0.9400	955	0.8083	-	-	-
0.9409	956	0.5966	-	-	-
0.9419	957	1.06	-	-	-
0.9429	958	1.0412	-	-	-
0.9439	959	0.9467	-	-	-
0.9449	960	0.847	-	-	-
0.9459	961	0.875	-	-	-
0.9469	962	0.8303	-	-	-
0.9478	963	0.8641	-	-	-
0.9488	964	0.6343	-	-	-
0.9498	965	0.7566	-	-	-
0.9508	966	0.9767	-	-	-
0.9518	967	0.7518	-	-	-
0.9528	968	0.5381	-	-	-
0.9537	969	0.9959	-	-	-
0.9547	970	0.8374	-	-	-
0.9557	971	0.5459	-	-	-
0.9567	972	0.6633	-	-	-
0.9577	973	0.9526	-	-	-
0.9587	974	0.8487	-	-	-
0.9596	975	0.6572	-	-	-
0.9606	976	0.8039	-	-	-
0.9616	977	0.6213	-	-	-
0.9626	978	0.5483	-	-	-
0.9636	979	0.516	-	-	-
0.9646	980	0.8891	-	-	-
0.9656	981	0.7904	-	-	-
0.9665	982	1.2282	-	-	-
0.9675	983	1.0609	-	-	-
0.9685	984	0.8063	-	-	-
0.9695	985	0.5294	-	-	-
0.9705	986	0.7394	-	-	-
0.9715	987	0.5749	-	-	-
0.9724	988	0.9125	-	-	-
0.9734	989	0.961	-	-	-
0.9744	990	0.5947	-	-	-
0.9754	991	0.6246	-	-	-
0.9764	992	0.6492	-	-	-
0.9774	993	1.0508	-	-	-
0.9783	994	0.7613	-	-	-
0.9793	995	0.706	-	-	-
0.9803	996	0.9458	-	-	-
0.9813	997	0.836	-	-	-
0.9823	998	0.7472	-	-	-
0.9833	999	0.9071	-	-	-
0.9843	1000	1.0935	-	-	-
0.9852	1001	0.5822	-	-	-
0.9862	1002	0.6596	-	-	-
0.9872	1003	0.5536	-	-	-
0.9882	1004	1.0741	-	-	-
0.9892	1005	0.4586	-	-	-
0.9902	1006	0.9633	-	-	-
0.9911	1007	0.8739	-	-	-
0.9921	1008	0.665	-	-	-
0.9931	1009	0.9486	-	-	-
0.9941	1010	0.5323	-	-	-
0.9951	1011	0.6006	-	-	-
0.9961	1012	0.4568	-	-	-
0.9970	1013	0.7544	-	-	-
0.9980	1014	1.2437	-	-	-
0.9990	1015	0.8985	-	-	-
1.0	1016	0.3179	-	-	-
1.0010	1017	1.2519	-	-	-
1.0020	1018	0.9775	-	-	-
1.0030	1019	0.8967	-	-	-
1.0039	1020	0.5918	-	-	-
1.0049	1021	0.5798	-	-	-
1.0059	1022	0.5934	-	-	-
1.0069	1023	0.6872	-	-	-
1.0079	1024	0.9535	-	-	-
1.0089	1025	0.4967	-	-	-
1.0098	1026	1.116	-	-	-
1.0108	1027	1.0446	-	-	-
1.0118	1028	0.5189	-	-	-
1.0128	1029	0.8272	-	-	-
1.0138	1030	0.4901	-	-	-
1.0148	1031	0.8926	-	-	-
1.0157	1032	0.5543	-	-	-
1.0167	1033	0.2579	-	-	-
1.0177	1034	0.865	-	-	-
1.0187	1035	1.0559	-	-	-
1.0197	1036	0.714	-	-	-
1.0207	1037	0.5185	-	-	-
1.0217	1038	1.3209	-	-	-
1.0226	1039	0.3696	-	-	-
1.0236	1040	0.4839	-	-	-
1.0246	1041	0.7694	-	-	-
1.0256	1042	1.3417	-	-	-
1.0266	1043	0.8292	-	-	-
1.0276	1044	0.3835	-	-	-
1.0285	1045	0.3755	-	-	-
1.0295	1046	0.6396	-	-	-
1.0305	1047	0.364	-	-	-
1.0315	1048	0.9232	-	-	-
1.0325	1049	0.6706	-	-	-
1.0335	1050	0.8329	-	-	-
1.0344	1051	0.4093	-	-	-
1.0354	1052	0.7251	-	-	-
1.0364	1053	0.9254	-	-	-
1.0374	1054	0.6365	-	-	-
1.0384	1055	0.7136	-	-	-
1.0394	1056	0.6278	-	-	-
1.0404	1057	0.6769	-	-	-
1.0413	1058	0.7704	-	-	-
1.0423	1059	0.7701	-	-	-
1.0433	1060	0.9639	-	-	-
1.0443	1061	0.7518	-	-	-
1.0453	1062	0.7474	-	-	-
1.0463	1063	1.2196	-	-	-
1.0472	1064	1.1476	-	-	-
1.0482	1065	0.7139	-	-	-
1.0492	1066	1.0979	-	-	-
1.0502	1067	0.7113	-	-	-
1.0512	1068	0.6796	-	-	-
1.0522	1069	1.1551	-	-	-
1.0531	1070	0.7947	-	-	-
1.0541	1071	0.6783	0.6381	0.4883	0.7054
1.0551	1072	0.7295	-	-	-
1.0561	1073	0.4529	-	-	-
1.0571	1074	0.2625	-	-	-
1.0581	1075	1.2557	-	-	-
1.0591	1076	0.4826	-	-	-
1.0600	1077	1.2309	-	-	-
1.0610	1078	0.4545	-	-	-
1.0620	1079	0.6658	-	-	-
1.0630	1080	0.8651	-	-	-
1.0640	1081	1.0702	-	-	-
1.0650	1082	1.0225	-	-	-
1.0659	1083	0.6952	-	-	-
1.0669	1084	1.0918	-	-	-
1.0679	1085	0.5222	-	-	-
1.0689	1086	1.1311	-	-	-
1.0699	1087	0.6464	-	-	-
1.0709	1088	0.801	-	-	-
1.0719	1089	0.3743	-	-	-
1.0728	1090	0.6204	-	-	-
1.0738	1091	0.5369	-	-	-
1.0748	1092	0.6895	-	-	-
1.0758	1093	1.2127	-	-	-
1.0768	1094	0.8397	-	-	-
1.0778	1095	0.3694	-	-	-
1.0787	1096	0.7576	-	-	-
1.0797	1097	0.9494	-	-	-
1.0807	1098	0.8477	-	-	-
1.0817	1099	0.4869	-	-	-
1.0827	1100	0.6753	-	-	-
1.0837	1101	0.7351	-	-	-
1.0846	1102	0.8257	-	-	-
1.0856	1103	1.144	-	-	-
1.0866	1104	0.5526	-	-	-
1.0876	1105	0.6686	-	-	-
1.0886	1106	0.7683	-	-	-
1.0896	1107	0.3337	-	-	-
1.0906	1108	0.8686	-	-	-
1.0915	1109	0.66	-	-	-
1.0925	1110	0.9329	-	-	-
1.0935	1111	0.5129	-	-	-
1.0945	1112	0.8907	-	-	-
1.0955	1113	0.6305	-	-	-
1.0965	1114	0.3746	-	-	-
1.0974	1115	0.6791	-	-	-
1.0984	1116	1.4172	-	-	-
1.0994	1117	0.5184	-	-	-
1.1004	1118	0.4803	-	-	-
1.1014	1119	0.647	-	-	-
1.1024	1120	0.3867	-	-	-
1.1033	1121	0.4965	-	-	-
1.1043	1122	0.9508	-	-	-
1.1053	1123	0.709	-	-	-
1.1063	1124	0.9625	-	-	-
1.1073	1125	0.7724	-	-	-
1.1083	1126	0.4318	-	-	-
1.1093	1127	0.6653	-	-	-
1.1102	1128	0.781	-	-	-
1.1112	1129	0.6985	-	-	-
1.1122	1130	1.2949	-	-	-
1.1132	1131	0.6845	-	-	-
1.1142	1132	0.7022	-	-	-
1.1152	1133	0.8645	-	-	-
1.1161	1134	0.5331	-	-	-
1.1171	1135	0.5636	-	-	-
1.1181	1136	0.4511	-	-	-
1.1191	1137	0.605	-	-	-
1.1201	1138	0.6367	-	-	-
1.1211	1139	1.1988	-	-	-
1.1220	1140	0.5973	-	-	-
1.1230	1141	0.8703	-	-	-
1.1240	1142	0.48	-	-	-
1.125	1143	0.8903	-	-	-
1.1260	1144	0.6978	-	-	-
1.1270	1145	0.8008	-	-	-
1.1280	1146	0.9583	-	-	-
1.1289	1147	0.5823	-	-	-
1.1299	1148	0.974	-	-	-
1.1309	1149	1.1149	-	-	-
1.1319	1150	0.5558	-	-	-
1.1329	1151	0.9106	-	-	-
1.1339	1152	0.671	-	-	-
1.1348	1153	0.9424	-	-	-
1.1358	1154	0.6605	-	-	-
1.1368	1155	0.536	-	-	-
1.1378	1156	0.38	-	-	-
1.1388	1157	0.6255	-	-	-
1.1398	1158	1.435	-	-	-
1.1407	1159	0.5903	-	-	-
1.1417	1160	0.9517	-	-	-
1.1427	1161	0.4636	-	-	-
1.1437	1162	0.3721	-	-	-
1.1447	1163	0.6832	-	-	-
1.1457	1164	0.5893	-	-	-
1.1467	1165	0.6226	-	-	-
1.1476	1166	0.5715	-	-	-
1.1486	1167	1.0958	-	-	-
1.1496	1168	0.689	-	-	-
1.1506	1169	0.6443	-	-	-
1.1516	1170	0.9278	-	-	-
1.1526	1171	0.7009	-	-	-
1.1535	1172	0.6254	-	-	-
1.1545	1173	0.7537	-	-	-
1.1555	1174	0.7559	-	-	-
1.1565	1175	0.8148	-	-	-
1.1575	1176	0.2994	-	-	-
1.1585	1177	0.2148	-	-	-
1.1594	1178	1.0492	-	-	-
1.1604	1179	0.5191	-	-	-
1.1614	1180	1.3508	-	-	-
1.1624	1181	1.0396	-	-	-
1.1634	1182	0.5003	-	-	-
1.1644	1183	0.7736	-	-	-
1.1654	1184	0.3263	-	-	-
1.1663	1185	1.2059	-	-	-
1.1673	1186	1.3514	-	-	-
1.1683	1187	0.9086	-	-	-
1.1693	1188	0.7245	-	-	-
1.1703	1189	0.717	-	-	-
1.1713	1190	0.6741	-	-	-
1.1722	1191	1.2695	-	-	-
1.1732	1192	0.406	-	-	-
1.1742	1193	0.5408	-	-	-
1.1752	1194	0.4256	-	-	-
1.1762	1195	0.4827	-	-	-
1.1772	1196	0.5621	-	-	-
1.1781	1197	0.6734	-	-	-
1.1791	1198	0.4685	-	-	-
1.1801	1199	0.859	-	-	-
1.1811	1200	1.4388	-	-	-
1.1821	1201	1.1846	-	-	-
1.1831	1202	0.6824	-	-	-
1.1841	1203	1.2064	-	-	-
1.1850	1204	0.5668	-	-	-
1.1860	1205	0.5286	-	-	-
1.1870	1206	0.6316	-	-	-
1.1880	1207	0.8011	-	-	-
1.1890	1208	0.66	-	-	-
1.1900	1209	0.3735	-	-	-
1.1909	1210	0.7461	-	-	-
1.1919	1211	0.5508	-	-	-
1.1929	1212	0.6379	-	-	-
1.1939	1213	0.5204	-	-	-
1.1949	1214	0.7522	-	-	-
1.1959	1215	0.9415	-	-	-
1.1969	1216	0.6351	-	-	-
1.1978	1217	0.262	-	-	-
1.1988	1218	0.9048	-	-	-
1.1998	1219	0.6562	-	-	-
1.2008	1220	0.4502	-	-	-

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}
}