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: Fish hatch into larvae that are different from the adult form of species.
    sentences:
      - Fish hatch into larvae that are different from the adult form of?
      - amphibians hatch from eggs
      - >-
        A solenoid or coil wrapped around iron or certain other metals can form
        a(n) electromagnet.
  - source_sentence: >-
      About 200 countries and territories have reported coronavirus cases in
      2020 .
    sentences:
      - >-
        All-Time Olympic Games Medal Tally Analysis Home > Events > Olympics >
        Summer > Medal Tally > All-Time All-Time Olympic Games Medal Tally
        (Summer Olympics) Which country is the most successful at he Olympic
        Games? Here are the top ranked countries in terms of total medals won
        when all of the summer Games are considered (including the 2016 Rio
        Games). There are two tables presented, the first just lists the top
        countries based on the total medals won, the second table factors in how
        many Olympic Games the country appeared, averaging the total number of
        medals per Olympiad. A victory in a team sport is counted as one medal.
        The USA Has Won the Most Medals The US have clearly won the most gold
        medals and the most medals overall, more than doubling the next ranked
        country (these figures include medals won in Rio 2016). Second placed
        USSR had fewer appearances at the Olympics, and actually won more medals
        on average (see the 2nd table). The top 10 includes one country no
        longer in existence (the Soviet Union), so their medal totals will
        obviously not increase, however China is expected to continue a rapid
        rise up the ranks. With the addition of the 2016 data, China has moved
        up from 11th (in 2008) to 9th (2012) to 7th (2016). The country which
        has attended the most games without a medal is Monaco (20 Olympic
        Games), the country which has won the most medals without winning a gold
        medal is Malaysia (0 gold, 7 silver, 4 bronze). rank
      - >-
        An example of a reproductive behavior is salmon returning to their
        birthplace to lay their eggs
      - >-
        more than 664,000 cases of COVID-19 have been reported in over 190
        countries and territories , resulting in approximately 30,800 deaths .
  - source_sentence: >-
      The wave on a guitar string is transverse. the sound wave rattles a sheet
      of paper in a direction that shows the sound wave is what?
    sentences:
      - A Honda motorcycle parked in a grass driveway
      - >-
        In Panama tipping is a question of rewarding good service rather than an
        obligation. Restaurant bills don't include gratuities; adding 10% is
        customary. Bellhops and maids expect tips only in more expensive hotels,
        and $1–$2 per bag is the norm. You should also give a tip of up to $10
        per day to tour guides.
      - >-
        Figure 16.33 The wave on a guitar string is transverse. The sound wave
        rattles a sheet of paper in a direction that shows the sound wave is
        longitudinal.
  - source_sentence: The thermal production of a stove is generically used for
    sentences:
      - >-
        In total , 28 US victims were killed , while Viet Cong losses were
        killed 345 and a further 192 estimated killed .
      - a stove generates heat for cooking usually
      - >-
        A teenager has been charged over an incident in which a four-year-old
        girl was hurt when she was hit in the face by a brick thrown through a
        van window.
  - source_sentence: can sweet potatoes cause itching?
    sentences:
      - >-
        People with a true potato allergy may react immediately after touching,
        peeling, or eating potatoes. Symptoms may vary from person to person,
        but typical symptoms of a potato allergy include: rhinitis, including
        itchy or stinging eyes, a runny or stuffy nose, and sneezing.
      - riding a bike does not cause pollution
      - >-
        Dilation occurs when cell walls relax.. An aneurysm is a dilation, or
        bubble, that occurs in the wall of an artery. 
         an artery can be relaxed by dilation
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: 0.5415735956899457
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5432558343516091
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5451734598739164
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.5424313371117337
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5463232738462487
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.5433125789510533
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5415314946904102
            name: Pearson Dot
          - type: spearman_dot
            value: 0.543190094748625
            name: Spearman Dot
          - type: pearson_max
            value: 0.5463232738462487
            name: Pearson Max
          - type: spearman_max
            value: 0.5433125789510533
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allNLI dev
          type: allNLI-dev
        metrics:
          - type: cosine_accuracy
            value: 0.689453125
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.9186867475509644
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.5283018867924528
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8205500245094299
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.4144736842105263
            name: Cosine Precision
          - type: cosine_recall
            value: 0.7283236994219653
            name: Cosine Recall
          - type: cosine_ap
            value: 0.493528404484353
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.689453125
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 706.1145629882812
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.5283018867924528
            name: Dot F1
          - type: dot_f1_threshold
            value: 630.7012939453125
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.4144736842105263
            name: Dot Precision
          - type: dot_recall
            value: 0.7283236994219653
            name: Dot Recall
          - type: dot_ap
            value: 0.49366530598882574
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.69140625
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 251.75421142578125
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.5285412262156448
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 364.7342529296875
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.4166666666666667
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.7225433526011561
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.4930222958157673
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.689453125
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 11.180255889892578
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.5283018867924528
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 16.60896873474121
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.4144736842105263
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.7283236994219653
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.49351830756144216
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.69140625
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 706.1145629882812
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.5285412262156448
            name: Max F1
          - type: max_f1_threshold
            value: 630.7012939453125
            name: Max F1 Threshold
          - type: max_precision
            value: 0.4166666666666667
            name: Max Precision
          - type: max_recall
            value: 0.7283236994219653
            name: Max Recall
          - type: max_ap
            value: 0.49366530598882574
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Qnli dev
          type: Qnli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.677734375
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.7905216217041016
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.7005444646098005
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7738929986953735
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.6126984126984127
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8177966101694916
            name: Cosine Recall
          - type: cosine_ap
            value: 0.7092754004450792
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.677734375
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 607.5902099609375
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.7005444646098005
            name: Dot F1
          - type: dot_f1_threshold
            value: 594.82958984375
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.6126984126984127
            name: Dot Precision
          - type: dot_recall
            value: 0.8177966101694916
            name: Dot Recall
          - type: dot_ap
            value: 0.7093564593718997
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.677734375
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 403.15380859375
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.696113074204947
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 417.12298583984375
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5969696969696969
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8347457627118644
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.7081773921924046
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.677734375
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 17.944761276245117
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.7005444646098005
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 18.64351463317871
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.6126984126984127
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8177966101694916
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.7092822066603943
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.677734375
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 607.5902099609375
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.7005444646098005
            name: Max F1
          - type: max_f1_threshold
            value: 594.82958984375
            name: Max F1 Threshold
          - type: max_precision
            value: 0.6126984126984127
            name: Max Precision
          - type: max_recall
            value: 0.8347457627118644
            name: Max Recall
          - type: max_ap
            value: 0.7093564593718997
            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-toytest3-step1-checkpoints-tmp")
# Run inference
sentences = [
    'can sweet potatoes cause itching?',
    'People with a true potato allergy may react immediately after touching, peeling, or eating potatoes. Symptoms may vary from person to person, but typical symptoms of a potato allergy include: rhinitis, including itchy or stinging eyes, a runny or stuffy nose, and sneezing.',
    'riding a bike does not cause pollution',
]
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	0.5416
spearman_cosine	0.5433
pearson_manhattan	0.5452
spearman_manhattan	0.5424
pearson_euclidean	0.5463
spearman_euclidean	0.5433
pearson_dot	0.5415
spearman_dot	0.5432
pearson_max	0.5463
spearman_max	0.5433

Binary Classification

Dataset: allNLI-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6895
cosine_accuracy_threshold	0.9187
cosine_f1	0.5283
cosine_f1_threshold	0.8206
cosine_precision	0.4145
cosine_recall	0.7283
cosine_ap	0.4935
dot_accuracy	0.6895
dot_accuracy_threshold	706.1146
dot_f1	0.5283
dot_f1_threshold	630.7013
dot_precision	0.4145
dot_recall	0.7283
dot_ap	0.4937
manhattan_accuracy	0.6914
manhattan_accuracy_threshold	251.7542
manhattan_f1	0.5285
manhattan_f1_threshold	364.7343
manhattan_precision	0.4167
manhattan_recall	0.7225
manhattan_ap	0.493
euclidean_accuracy	0.6895
euclidean_accuracy_threshold	11.1803
euclidean_f1	0.5283
euclidean_f1_threshold	16.609
euclidean_precision	0.4145
euclidean_recall	0.7283
euclidean_ap	0.4935
max_accuracy	0.6914
max_accuracy_threshold	706.1146
max_f1	0.5285
max_f1_threshold	630.7013
max_precision	0.4167
max_recall	0.7283
max_ap	0.4937

Binary Classification

Dataset: Qnli-dev
Evaluated with BinaryClassificationEvaluator

Metric	Value
cosine_accuracy	0.6777
cosine_accuracy_threshold	0.7905
cosine_f1	0.7005
cosine_f1_threshold	0.7739
cosine_precision	0.6127
cosine_recall	0.8178
cosine_ap	0.7093
dot_accuracy	0.6777
dot_accuracy_threshold	607.5902
dot_f1	0.7005
dot_f1_threshold	594.8296
dot_precision	0.6127
dot_recall	0.8178
dot_ap	0.7094
manhattan_accuracy	0.6777
manhattan_accuracy_threshold	403.1538
manhattan_f1	0.6961
manhattan_f1_threshold	417.123
manhattan_precision	0.597
manhattan_recall	0.8347
manhattan_ap	0.7082
euclidean_accuracy	0.6777
euclidean_accuracy_threshold	17.9448
euclidean_f1	0.7005
euclidean_f1_threshold	18.6435
euclidean_precision	0.6127
euclidean_recall	0.8178
euclidean_ap	0.7093
max_accuracy	0.6777
max_accuracy_threshold	607.5902
max_f1	0.7005
max_f1_threshold	594.8296
max_precision	0.6127
max_recall	0.8347
max_ap	0.7094

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: 29.6 tokens
max: 369 tokens

min: 2 tokens
mean: 58.01 tokens
max: 437 tokens

	sentence1	sentence2
type	string	string
details	min: 4 tokens mean: 29.6 tokens max: 369 tokens	min: 2 tokens mean: 58.01 tokens max: 437 tokens

Samples:

sentence1	sentence2
`The song ‘Fashion for His Love’ by Lady Gaga is a tribute to which late fashion designer?`	Fashion Of His Love by Lady Gaga Songfacts Fashion Of His Love by Lady Gaga Songfacts Songfacts Gaga explained in a tweet that this track from her Born This Way Special Edition album is about the late Alexander McQueen. The fashion designer committed suicide by hanging on February 11, 2010 and Gaga was deeply affected by the tragic death of McQueen, who was a close personal friend. That same month, she performed at the 2010 Brit Awards wearing one of his couture creations and she also paid tribute to her late friend by setting the date on the prison security cameras in her Telephone video as the same day that McQueen's body was discovered in his London home.
`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`
`Helen Lederer is an English comedian .`	`Helen Lederer ( born 24 September 1954 ) is an English : //www.scotsman.com/news/now-or-never-1-1396369 comedian , writer and actress who emerged as part of the alternative comedy boom at the beginning of the 1980s .`

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: 29.01 tokens
max: 367 tokens

min: 2 tokens
mean: 56.14 tokens
max: 389 tokens

	sentence1	sentence2
type	string	string
details	min: 4 tokens mean: 29.01 tokens max: 367 tokens	min: 2 tokens mean: 56.14 tokens max: 389 tokens

Samples:

sentence1	sentence2
`What planet did the voyager 1 spacecraft visit in 1980?`	The Voyager 1 spacecraft visited Saturn in 1980. Voyager 2 followed in 1981. These probes sent back detailed pictures of Saturn, its rings, and some of its moons ( Figure below ). From the Voyager data, we learned what Saturn’s rings are made of. They are particles of water and ice with a little bit of dust. There are several gaps in the rings. These gaps were cleared out by moons within the rings. Gravity attracts dust and gas to the moon from the ring. This leaves a gap in the rings. Other gaps in the rings are caused by the competing forces of Saturn and its moons outside the rings.
`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.`
`Who had an 80s No 1 with Don't You Want Me?`	`The Human League - Don't You Want Me - YouTube The Human League - Don't You Want Me Want to watch this again later? Sign in to add this video to a playlist. Need to report the video? Sign in to report inappropriate content. Rating is available when the video has been rented. This feature is not available right now. Please try again later. Uploaded on Feb 27, 2009 Music video by The Human League performing Don't You Want Me (2003 Digital Remaster). Category`

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-toytest3-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-toytest3-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	10.4072	-	-	-	-
0.0020	2	11.0865	-	-	-	-
0.0030	3	9.5114	-	-	-	-
0.0039	4	9.9584	-	-	-	-
0.0049	5	10.068	-	-	-	-
0.0059	6	11.0224	-	-	-	-
0.0069	7	9.7703	-	-	-	-
0.0079	8	10.5005	-	-	-	-
0.0089	9	10.1987	-	-	-	-
0.0098	10	10.0277	-	-	-	-
0.0108	11	10.6965	-	-	-	-
0.0118	12	10.0609	-	-	-	-
0.0128	13	11.6214	-	-	-	-
0.0138	14	9.4053	-	-	-	-
0.0148	15	10.4014	-	-	-	-
0.0157	16	10.4119	-	-	-	-
0.0167	17	9.4658	-	-	-	-
0.0177	18	9.2169	-	-	-	-
0.0187	19	11.2337	-	-	-	-
0.0197	20	11.0572	-	-	-	-
0.0207	21	11.0452	-	-	-	-
0.0217	22	10.31	-	-	-	-
0.0226	23	9.1395	-	-	-	-
0.0236	24	8.4201	-	-	-	-
0.0246	25	8.6036	-	-	-	-
0.0256	26	11.7579	-	-	-	-
0.0266	27	10.1307	-	-	-	-
0.0276	28	9.2915	-	-	-	-
0.0285	29	9.0208	-	-	-	-
0.0295	30	8.6867	-	-	-	-
0.0305	31	8.0925	-	-	-	-
0.0315	32	8.6617	-	-	-	-
0.0325	33	8.3374	-	-	-	-
0.0335	34	7.8566	-	-	-	-
0.0344	35	9.0698	-	-	-	-
0.0354	36	7.7727	-	-	-	-
0.0364	37	7.6128	-	-	-	-
0.0374	38	7.8762	-	-	-	-
0.0384	39	7.5191	-	-	-	-
0.0394	40	7.5638	-	-	-	-
0.0404	41	7.1878	-	-	-	-
0.0413	42	6.8878	-	-	-	-
0.0423	43	7.5775	-	-	-	-
0.0433	44	7.1076	-	-	-	-
0.0443	45	6.5589	-	-	-	-
0.0453	46	7.4456	-	-	-	-
0.0463	47	6.8233	-	-	-	-
0.0472	48	6.7633	-	-	-	-
0.0482	49	6.6024	-	-	-	-
0.0492	50	6.2778	-	-	-	-
0.0502	51	6.1026	-	-	-	-
0.0512	52	6.632	-	-	-	-
0.0522	53	6.6962	-	-	-	-
0.0531	54	5.8514	-	-	-	-
0.0541	55	5.9951	-	-	-	-
0.0551	56	5.4554	-	-	-	-
0.0561	57	6.0147	-	-	-	-
0.0571	58	5.215	-	-	-	-
0.0581	59	6.4525	-	-	-	-
0.0591	60	5.4048	-	-	-	-
0.0600	61	5.0424	-	-	-	-
0.0610	62	6.2646	-	-	-	-
0.0620	63	5.0847	-	-	-	-
0.0630	64	5.4415	-	-	-	-
0.0640	65	5.2469	-	-	-	-
0.0650	66	5.1378	-	-	-	-
0.0659	67	5.1636	-	-	-	-
0.0669	68	5.5596	-	-	-	-
0.0679	69	4.9508	-	-	-	-
0.0689	70	5.2355	-	-	-	-
0.0699	71	4.7359	-	-	-	-
0.0709	72	4.8947	-	-	-	-
0.0719	73	4.6269	-	-	-	-
0.0728	74	4.6072	-	-	-	-
0.0738	75	4.9125	-	-	-	-
0.0748	76	4.5856	-	-	-	-
0.0758	77	4.7879	-	-	-	-
0.0768	78	4.5348	-	-	-	-
0.0778	79	4.3554	-	-	-	-
0.0787	80	4.2984	-	-	-	-
0.0797	81	4.5505	-	-	-	-
0.0807	82	4.5325	-	-	-	-
0.0817	83	4.2725	-	-	-	-
0.0827	84	4.3054	-	-	-	-
0.0837	85	4.5536	-	-	-	-
0.0846	86	4.0265	-	-	-	-
0.0856	87	4.7453	-	-	-	-
0.0866	88	4.071	-	-	-	-
0.0876	89	4.1582	-	-	-	-
0.0886	90	4.1131	-	-	-	-
0.0896	91	3.6582	-	-	-	-
0.0906	92	4.143	-	-	-	-
0.0915	93	4.2273	-	-	-	-
0.0925	94	3.9321	-	-	-	-
0.0935	95	4.2061	-	-	-	-
0.0945	96	4.1042	-	-	-	-
0.0955	97	3.9513	-	-	-	-
0.0965	98	3.8627	-	-	-	-
0.0974	99	4.3613	-	-	-	-
0.0984	100	3.8513	-	-	-	-
0.0994	101	3.5866	-	-	-	-
0.1004	102	3.5239	-	-	-	-
0.1014	103	3.5921	-	-	-	-
0.1024	104	3.5962	-	-	-	-
0.1033	105	4.0001	-	-	-	-
0.1043	106	4.1374	-	-	-	-
0.1053	107	3.9049	-	-	-	-
0.1063	108	3.2511	-	-	-	-
0.1073	109	3.2479	-	-	-	-
0.1083	110	3.6414	-	-	-	-
0.1093	111	3.6429	-	-	-	-
0.1102	112	3.423	-	-	-	-
0.1112	113	3.4967	-	-	-	-
0.1122	114	3.7649	-	-	-	-
0.1132	115	3.2845	-	-	-	-
0.1142	116	3.356	-	-	-	-
0.1152	117	3.2086	-	-	-	-
0.1161	118	3.5561	-	-	-	-
0.1171	119	3.7353	-	-	-	-
0.1181	120	3.403	-	-	-	-
0.1191	121	3.1009	-	-	-	-
0.1201	122	3.2139	-	-	-	-
0.1211	123	3.3339	-	-	-	-
0.1220	124	2.9464	-	-	-	-
0.1230	125	3.3366	-	-	-	-
0.1240	126	3.0618	-	-	-	-
0.125	127	3.0092	-	-	-	-
0.1260	128	2.7152	-	-	-	-
0.1270	129	2.9423	-	-	-	-
0.1280	130	2.6569	-	-	-	-
0.1289	131	2.8469	-	-	-	-
0.1299	132	2.9089	-	-	-	-
0.1309	133	2.5809	-	-	-	-
0.1319	134	2.6987	-	-	-	-
0.1329	135	3.2518	-	-	-	-
0.1339	136	2.9145	-	-	-	-
0.1348	137	2.4809	-	-	-	-
0.1358	138	2.8264	-	-	-	-
0.1368	139	2.5724	-	-	-	-
0.1378	140	2.6949	-	-	-	-
0.1388	141	2.6925	-	-	-	-
0.1398	142	2.9311	-	-	-	-
0.1407	143	2.5667	-	-	-	-
0.1417	144	3.2471	-	-	-	-
0.1427	145	2.2441	-	-	-	-
0.1437	146	2.75	-	-	-	-
0.1447	147	2.9669	-	-	-	-
0.1457	148	2.736	-	-	-	-
0.1467	149	3.104	-	-	-	-
0.1476	150	2.2175	-	-	-	-
0.1486	151	2.7415	-	-	-	-
0.1496	152	1.8707	-	-	-	-
0.1506	153	2.5961	2.2653	0.3116	0.4265	0.6462
0.1516	154	3.1149	-	-	-	-
0.1526	155	2.2976	-	-	-	-
0.1535	156	2.4436	-	-	-	-
0.1545	157	2.8826	-	-	-	-
0.1555	158	2.3664	-	-	-	-
0.1565	159	2.2485	-	-	-	-
0.1575	160	2.5167	-	-	-	-
0.1585	161	1.7183	-	-	-	-
0.1594	162	2.1003	-	-	-	-
0.1604	163	2.5785	-	-	-	-
0.1614	164	2.8789	-	-	-	-
0.1624	165	2.3425	-	-	-	-
0.1634	166	2.0966	-	-	-	-
0.1644	167	2.1126	-	-	-	-
0.1654	168	2.1824	-	-	-	-
0.1663	169	2.2009	-	-	-	-
0.1673	170	2.3796	-	-	-	-
0.1683	171	2.3096	-	-	-	-
0.1693	172	2.7897	-	-	-	-
0.1703	173	2.2097	-	-	-	-
0.1713	174	1.7508	-	-	-	-
0.1722	175	2.353	-	-	-	-
0.1732	176	2.4276	-	-	-	-
0.1742	177	2.1016	-	-	-	-
0.1752	178	1.8461	-	-	-	-
0.1762	179	1.8104	-	-	-	-
0.1772	180	2.6023	-	-	-	-
0.1781	181	2.5261	-	-	-	-
0.1791	182	2.1053	-	-	-	-
0.1801	183	1.9712	-	-	-	-
0.1811	184	2.4693	-	-	-	-
0.1821	185	2.1119	-	-	-	-
0.1831	186	2.4797	-	-	-	-
0.1841	187	2.1587	-	-	-	-
0.1850	188	1.9578	-	-	-	-
0.1860	189	2.1368	-	-	-	-
0.1870	190	2.4212	-	-	-	-
0.1880	191	1.9591	-	-	-	-
0.1890	192	1.5816	-	-	-	-
0.1900	193	1.4029	-	-	-	-
0.1909	194	1.9385	-	-	-	-
0.1919	195	1.5596	-	-	-	-
0.1929	196	1.6663	-	-	-	-
0.1939	197	2.0026	-	-	-	-
0.1949	198	2.0046	-	-	-	-
0.1959	199	1.5016	-	-	-	-
0.1969	200	2.184	-	-	-	-
0.1978	201	2.3442	-	-	-	-
0.1988	202	2.6981	-	-	-	-
0.1998	203	2.5481	-	-	-	-
0.2008	204	2.9798	-	-	-	-
0.2018	205	2.287	-	-	-	-
0.2028	206	1.9393	-	-	-	-
0.2037	207	2.892	-	-	-	-
0.2047	208	2.26	-	-	-	-
0.2057	209	2.5911	-	-	-	-
0.2067	210	2.1239	-	-	-	-
0.2077	211	2.0683	-	-	-	-
0.2087	212	1.768	-	-	-	-
0.2096	213	2.5468	-	-	-	-
0.2106	214	1.8956	-	-	-	-
0.2116	215	2.044	-	-	-	-
0.2126	216	1.5721	-	-	-	-
0.2136	217	1.6278	-	-	-	-
0.2146	218	1.7754	-	-	-	-
0.2156	219	1.8594	-	-	-	-
0.2165	220	1.8309	-	-	-	-
0.2175	221	2.0619	-	-	-	-
0.2185	222	2.3335	-	-	-	-
0.2195	223	2.023	-	-	-	-
0.2205	224	2.1975	-	-	-	-
0.2215	225	1.9228	-	-	-	-
0.2224	226	2.3565	-	-	-	-
0.2234	227	1.896	-	-	-	-
0.2244	228	2.0912	-	-	-	-
0.2254	229	2.7703	-	-	-	-
0.2264	230	1.6988	-	-	-	-
0.2274	231	2.0406	-	-	-	-
0.2283	232	1.9288	-	-	-	-
0.2293	233	2.0457	-	-	-	-
0.2303	234	1.7061	-	-	-	-
0.2313	235	1.6244	-	-	-	-
0.2323	236	2.0241	-	-	-	-
0.2333	237	1.567	-	-	-	-
0.2343	238	1.8084	-	-	-	-
0.2352	239	2.4363	-	-	-	-
0.2362	240	1.7532	-	-	-	-
0.2372	241	2.0797	-	-	-	-
0.2382	242	1.9562	-	-	-	-
0.2392	243	1.6751	-	-	-	-
0.2402	244	2.0265	-	-	-	-
0.2411	245	1.6065	-	-	-	-
0.2421	246	1.7439	-	-	-	-
0.2431	247	2.0237	-	-	-	-
0.2441	248	1.6128	-	-	-	-
0.2451	249	1.6581	-	-	-	-
0.2461	250	2.1538	-	-	-	-
0.2470	251	2.049	-	-	-	-
0.2480	252	1.2573	-	-	-	-
0.2490	253	1.5619	-	-	-	-
0.25	254	1.2611	-	-	-	-
0.2510	255	1.3443	-	-	-	-
0.2520	256	1.3436	-	-	-	-
0.2530	257	2.8117	-	-	-	-
0.2539	258	1.7563	-	-	-	-
0.2549	259	1.3148	-	-	-	-
0.2559	260	2.0278	-	-	-	-
0.2569	261	1.2403	-	-	-	-
0.2579	262	1.588	-	-	-	-
0.2589	263	2.0071	-	-	-	-
0.2598	264	1.5312	-	-	-	-
0.2608	265	1.8641	-	-	-	-
0.2618	266	1.2933	-	-	-	-
0.2628	267	1.6262	-	-	-	-
0.2638	268	1.721	-	-	-	-
0.2648	269	1.4713	-	-	-	-
0.2657	270	1.4625	-	-	-	-
0.2667	271	1.7254	-	-	-	-
0.2677	272	1.5108	-	-	-	-
0.2687	273	2.1126	-	-	-	-
0.2697	274	1.3967	-	-	-	-
0.2707	275	1.7067	-	-	-	-
0.2717	276	1.4847	-	-	-	-
0.2726	277	1.6515	-	-	-	-
0.2736	278	0.9367	-	-	-	-
0.2746	279	2.0267	-	-	-	-
0.2756	280	1.5023	-	-	-	-
0.2766	281	1.1248	-	-	-	-
0.2776	282	1.6224	-	-	-	-
0.2785	283	1.7969	-	-	-	-
0.2795	284	2.2498	-	-	-	-
0.2805	285	1.7477	-	-	-	-
0.2815	286	1.6261	-	-	-	-
0.2825	287	2.0911	-	-	-	-
0.2835	288	1.9519	-	-	-	-
0.2844	289	1.3132	-	-	-	-
0.2854	290	2.3292	-	-	-	-
0.2864	291	1.3781	-	-	-	-
0.2874	292	1.5753	-	-	-	-
0.2884	293	1.4158	-	-	-	-
0.2894	294	2.1661	-	-	-	-
0.2904	295	1.4928	-	-	-	-
0.2913	296	2.2825	-	-	-	-
0.2923	297	1.7261	-	-	-	-
0.2933	298	1.8635	-	-	-	-
0.2943	299	0.974	-	-	-	-
0.2953	300	1.53	-	-	-	-
0.2963	301	1.5985	-	-	-	-
0.2972	302	1.2169	-	-	-	-
0.2982	303	1.771	-	-	-	-
0.2992	304	1.4506	-	-	-	-
0.3002	305	1.9496	-	-	-	-
0.3012	306	1.2436	1.5213	0.4673	0.4808	0.6993
0.3022	307	2.2057	-	-	-	-
0.3031	308	1.6786	-	-	-	-
0.3041	309	1.748	-	-	-	-
0.3051	310	1.5541	-	-	-	-
0.3061	311	2.2968	-	-	-	-
0.3071	312	1.585	-	-	-	-
0.3081	313	1.8371	-	-	-	-
0.3091	314	1.1129	-	-	-	-
0.3100	315	1.5495	-	-	-	-
0.3110	316	1.4327	-	-	-	-
0.3120	317	1.4801	-	-	-	-
0.3130	318	1.7096	-	-	-	-
0.3140	319	1.6717	-	-	-	-
0.3150	320	1.7151	-	-	-	-
0.3159	321	1.7081	-	-	-	-
0.3169	322	1.431	-	-	-	-
0.3179	323	1.5734	-	-	-	-
0.3189	324	1.7307	-	-	-	-
0.3199	325	1.0644	-	-	-	-
0.3209	326	1.0651	-	-	-	-
0.3219	327	1.4805	-	-	-	-
0.3228	328	0.839	-	-	-	-
0.3238	329	1.1801	-	-	-	-
0.3248	330	1.36	-	-	-	-
0.3258	331	1.3371	-	-	-	-
0.3268	332	1.1707	-	-	-	-
0.3278	333	1.2572	-	-	-	-
0.3287	334	1.3537	-	-	-	-
0.3297	335	1.7096	-	-	-	-
0.3307	336	1.5137	-	-	-	-
0.3317	337	1.1989	-	-	-	-
0.3327	338	1.3559	-	-	-	-
0.3337	339	1.3643	-	-	-	-
0.3346	340	1.2283	-	-	-	-
0.3356	341	1.5829	-	-	-	-
0.3366	342	1.1866	-	-	-	-
0.3376	343	1.531	-	-	-	-
0.3386	344	1.5581	-	-	-	-
0.3396	345	1.5587	-	-	-	-
0.3406	346	1.1403	-	-	-	-
0.3415	347	1.9728	-	-	-	-
0.3425	348	1.0818	-	-	-	-
0.3435	349	1.2993	-	-	-	-
0.3445	350	1.7779	-	-	-	-
0.3455	351	1.319	-	-	-	-
0.3465	352	1.9236	-	-	-	-
0.3474	353	1.3085	-	-	-	-
0.3484	354	2.2049	-	-	-	-
0.3494	355	1.3697	-	-	-	-
0.3504	356	1.5367	-	-	-	-
0.3514	357	1.2516	-	-	-	-
0.3524	358	1.6497	-	-	-	-
0.3533	359	1.2457	-	-	-	-
0.3543	360	1.2733	-	-	-	-
0.3553	361	1.4768	-	-	-	-
0.3563	362	1.1363	-	-	-	-
0.3573	363	1.5731	-	-	-	-
0.3583	364	1.0821	-	-	-	-
0.3593	365	1.1563	-	-	-	-
0.3602	366	1.8843	-	-	-	-
0.3612	367	1.2239	-	-	-	-
0.3622	368	1.4411	-	-	-	-
0.3632	369	2.1003	-	-	-	-
0.3642	370	1.6558	-	-	-	-
0.3652	371	1.6502	-	-	-	-
0.3661	372	1.7204	-	-	-	-
0.3671	373	1.7422	-	-	-	-
0.3681	374	1.3859	-	-	-	-
0.3691	375	0.8876	-	-	-	-
0.3701	376	1.2399	-	-	-	-
0.3711	377	1.1039	-	-	-	-
0.3720	378	1.733	-	-	-	-
0.3730	379	1.6897	-	-	-	-
0.3740	380	2.0532	-	-	-	-
0.375	381	1.0156	-	-	-	-
0.3760	382	0.8888	-	-	-	-
0.3770	383	1.322	-	-	-	-
0.3780	384	1.6828	-	-	-	-
0.3789	385	1.1567	-	-	-	-
0.3799	386	1.6117	-	-	-	-
0.3809	387	1.1776	-	-	-	-
0.3819	388	1.641	-	-	-	-
0.3829	389	1.3454	-	-	-	-
0.3839	390	1.4292	-	-	-	-
0.3848	391	1.2256	-	-	-	-
0.3858	392	1.08	-	-	-	-
0.3868	393	0.7436	-	-	-	-
0.3878	394	1.4112	-	-	-	-
0.3888	395	0.8917	-	-	-	-
0.3898	396	0.9955	-	-	-	-
0.3907	397	1.2867	-	-	-	-
0.3917	398	1.0683	-	-	-	-
0.3927	399	0.9355	-	-	-	-
0.3937	400	1.1153	-	-	-	-
0.3947	401	1.1724	-	-	-	-
0.3957	402	1.4069	-	-	-	-
0.3967	403	1.2546	-	-	-	-
0.3976	404	2.2862	-	-	-	-
0.3986	405	1.2316	-	-	-	-
0.3996	406	1.7876	-	-	-	-
0.4006	407	0.6936	-	-	-	-
0.4016	408	1.3852	-	-	-	-
0.4026	409	1.9046	-	-	-	-
0.4035	410	1.4972	-	-	-	-
0.4045	411	0.5531	-	-	-	-
0.4055	412	1.3685	-	-	-	-
0.4065	413	1.1367	-	-	-	-
0.4075	414	1.1304	-	-	-	-
0.4085	415	1.5953	-	-	-	-
0.4094	416	2.0308	-	-	-	-
0.4104	417	1.7275	-	-	-	-
0.4114	418	0.9921	-	-	-	-
0.4124	419	1.3418	-	-	-	-
0.4134	420	1.108	-	-	-	-
0.4144	421	1.4359	-	-	-	-
0.4154	422	1.4537	-	-	-	-
0.4163	423	0.8416	-	-	-	-
0.4173	424	0.8904	-	-	-	-
0.4183	425	0.7937	-	-	-	-
0.4193	426	0.9105	-	-	-	-
0.4203	427	1.1661	-	-	-	-
0.4213	428	0.7751	-	-	-	-
0.4222	429	0.9039	-	-	-	-
0.4232	430	1.2651	-	-	-	-
0.4242	431	1.44	-	-	-	-
0.4252	432	0.9795	-	-	-	-
0.4262	433	2.1892	-	-	-	-
0.4272	434	1.214	-	-	-	-
0.4281	435	1.185	-	-	-	-
0.4291	436	1.2501	-	-	-	-
0.4301	437	1.6432	-	-	-	-
0.4311	438	1.0203	-	-	-	-
0.4321	439	1.5179	-	-	-	-
0.4331	440	1.1445	-	-	-	-
0.4341	441	1.3099	-	-	-	-
0.4350	442	0.8856	-	-	-	-
0.4360	443	0.5869	-	-	-	-
0.4370	444	1.6335	-	-	-	-
0.4380	445	1.4134	-	-	-	-
0.4390	446	1.0244	-	-	-	-
0.4400	447	1.103	-	-	-	-
0.4409	448	0.9848	-	-	-	-
0.4419	449	1.5089	-	-	-	-
0.4429	450	1.0422	-	-	-	-
0.4439	451	1.0462	-	-	-	-
0.4449	452	1.2857	-	-	-	-
0.4459	453	1.4132	-	-	-	-
0.4469	454	1.3061	-	-	-	-
0.4478	455	1.3977	-	-	-	-
0.4488	456	1.3557	-	-	-	-
0.4498	457	1.3595	-	-	-	-
0.4508	458	0.8647	-	-	-	-
0.4518	459	1.3905	1.2969	0.5433	0.4937	0.7094
0.4528	460	0.9467	-	-	-	-
0.4537	461	1.9372	-	-	-	-
0.4547	462	0.871	-	-	-	-
0.4557	463	1.2282	-	-	-	-
0.4567	464	1.3845	-	-	-	-
0.4577	465	1.2571	-	-	-	-
0.4587	466	1.2288	-	-	-	-
0.4596	467	1.1165	-	-	-	-
0.4606	468	1.8463	-	-	-	-
0.4616	469	0.9158	-	-	-	-
0.4626	470	0.8711	-	-	-	-
0.4636	471	1.4741	-	-	-	-
0.4646	472	0.914	-	-	-	-
0.4656	473	0.9435	-	-	-	-
0.4665	474	1.0876	-	-	-	-
0.4675	475	1.2365	-	-	-	-
0.4685	476	1.1237	-	-	-	-
0.4695	477	1.0097	-	-	-	-
0.4705	478	1.1548	-	-	-	-
0.4715	479	1.3203	-	-	-	-
0.4724	480	1.2533	-	-	-	-
0.4734	481	1.093	-	-	-	-
0.4744	482	1.2591	-	-	-	-
0.4754	483	0.6764	-	-	-	-
0.4764	484	0.8922	-	-	-	-
0.4774	485	0.8524	-	-	-	-
0.4783	486	1.2777	-	-	-	-
0.4793	487	1.1682	-	-	-	-
0.4803	488	0.8617	-	-	-	-
0.4813	489	1.0303	-	-	-	-
0.4823	490	0.9843	-	-	-	-
0.4833	491	1.2951	-	-	-	-
0.4843	492	1.7889	-	-	-	-
0.4852	493	1.118	-	-	-	-
0.4862	494	0.6772	-	-	-	-
0.4872	495	1.5058	-	-	-	-
0.4882	496	1.0068	-	-	-	-
0.4892	497	0.9024	-	-	-	-
0.4902	498	1.4816	-	-	-	-
0.4911	499	0.894	-	-	-	-
0.4921	500	1.1582	-	-	-	-
0.4931	501	1.4804	-	-	-	-
0.4941	502	1.2636	-	-	-	-
0.4951	503	1.0094	-	-	-	-
0.4961	504	0.7594	-	-	-	-
0.4970	505	1.2898	-	-	-	-
0.4980	506	1.3565	-	-	-	-
0.4990	507	1.0325	-	-	-	-
0.5	508	1.0519	-	-	-	-
0.5010	509	0.9802	-	-	-	-
0.5020	510	1.1117	-	-	-	-
0.5030	511	1.3585	-	-	-	-
0.5039	512	1.0381	-	-	-	-
0.5049	513	1.0171	-	-	-	-
0.5059	514	0.5678	-	-	-	-
0.5069	515	0.9347	-	-	-	-
0.5079	516	0.6305	-	-	-	-
0.5089	517	0.7072	-	-	-	-
0.5098	518	0.9746	-	-	-	-
0.5108	519	1.1782	-	-	-	-
0.5118	520	1.1354	-	-	-	-
0.5128	521	1.5752	-	-	-	-
0.5138	522	0.5952	-	-	-	-
0.5148	523	1.1171	-	-	-	-
0.5157	524	0.8234	-	-	-	-
0.5167	525	1.6701	-	-	-	-
0.5177	526	1.2111	-	-	-	-
0.5187	527	0.8299	-	-	-	-
0.5197	528	1.5734	-	-	-	-
0.5207	529	0.9172	-	-	-	-
0.5217	530	0.8025	-	-	-	-
0.5226	531	1.1499	-	-	-	-
0.5236	532	1.0328	-	-	-	-
0.5246	533	1.1305	-	-	-	-
0.5256	534	0.6715	-	-	-	-
0.5266	535	1.1361	-	-	-	-
0.5276	536	0.9132	-	-	-	-
0.5285	537	1.2195	-	-	-	-
0.5295	538	0.3731	-	-	-	-
0.5305	539	1.0005	-	-	-	-
0.5315	540	0.5519	-	-	-	-
0.5325	541	0.7529	-	-	-	-
0.5335	542	1.7004	-	-	-	-
0.5344	543	1.4667	-	-	-	-
0.5354	544	0.8349	-	-	-	-
0.5364	545	1.5575	-	-	-	-
0.5374	546	1.1703	-	-	-	-
0.5384	547	1.01	-	-	-	-
0.5394	548	1.1114	-	-	-	-
0.5404	549	0.516	-	-	-	-
0.5413	550	1.0422	-	-	-	-
0.5423	551	1.078	-	-	-	-
0.5433	552	1.0573	-	-	-	-
0.5443	553	0.9754	-	-	-	-
0.5453	554	0.9227	-	-	-	-
0.5463	555	1.5012	-	-	-	-
0.5472	556	1.0697	-	-	-	-
0.5482	557	1.4437	-	-	-	-
0.5492	558	1.0697	-	-	-	-
0.5502	559	0.8346	-	-	-	-
0.5512	560	0.6421	-	-	-	-
0.5522	561	0.6687	-	-	-	-
0.5531	562	0.982	-	-	-	-
0.5541	563	0.9299	-	-	-	-
0.5551	564	1.5852	-	-	-	-
0.5561	565	1.2132	-	-	-	-
0.5571	566	0.8426	-	-	-	-
0.5581	567	1.0496	-	-	-	-
0.5591	568	1.0436	-	-	-	-
0.5600	569	0.806	-	-	-	-
0.5610	570	0.6396	-	-	-	-
0.5620	571	1.6315	-	-	-	-
0.5630	572	1.3286	-	-	-	-
0.5640	573	0.7682	-	-	-	-
0.5650	574	0.7861	-	-	-	-
0.5659	575	1.0368	-	-	-	-
0.5669	576	1.1497	-	-	-	-
0.5679	577	0.9691	-	-	-	-
0.5689	578	0.7447	-	-	-	-
0.5699	579	1.3933	-	-	-	-
0.5709	580	1.0668	-	-	-	-
0.5719	581	0.6065	-	-	-	-
0.5728	582	0.9566	-	-	-	-
0.5738	583	0.7957	-	-	-	-
0.5748	584	1.0232	-	-	-	-
0.5758	585	1.4559	-	-	-	-
0.5768	586	0.8003	-	-	-	-
0.5778	587	0.9504	-	-	-	-
0.5787	588	1.5257	-	-	-	-
0.5797	589	0.5798	-	-	-	-
0.5807	590	0.8169	-	-	-	-
0.5817	591	1.1131	-	-	-	-
0.5827	592	1.2498	-	-	-	-
0.5837	593	0.8541	-	-	-	-
0.5846	594	1.0848	-	-	-	-
0.5856	595	0.8909	-	-	-	-
0.5866	596	0.7572	-	-	-	-
0.5876	597	1.3636	-	-	-	-
0.5886	598	0.8493	-	-	-	-
0.5896	599	0.9594	-	-	-	-
0.5906	600	1.1143	-	-	-	-
0.5915	601	0.7093	-	-	-	-
0.5925	602	1.0542	-	-	-	-
0.5935	603	1.0621	-	-	-	-
0.5945	604	0.6916	-	-	-	-
0.5955	605	1.0125	-	-	-	-
0.5965	606	0.8425	-	-	-	-
0.5974	607	1.2868	-	-	-	-
0.5984	608	1.3505	-	-	-	-
0.5994	609	1.2699	-	-	-	-
0.6004	610	1.1798	-	-	-	-

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}
}