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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: 0.5663924244809233
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5774005992806329
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.579538083337237
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.5777711397249536
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5785108788501522
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.5773859208668966
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5664900588871681
            name: Pearson Dot
          - type: spearman_dot
            value: 0.577422795906283
            name: Spearman Dot
          - type: pearson_max
            value: 0.579538083337237
            name: Pearson Max
          - type: spearman_max
            value: 0.5777711397249536
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allNLI dev
          type: allNLI-dev
        metrics:
          - type: cosine_accuracy
            value: 0.69921875
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.9163634777069092
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.5397727272727272
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8912649154663086
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.5307262569832403
            name: Cosine Precision
          - type: cosine_recall
            value: 0.5491329479768786
            name: Cosine Recall
          - type: cosine_ap
            value: 0.5203950730799954
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.69921875
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 704.9437255859375
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.5397727272727272
            name: Dot F1
          - type: dot_f1_threshold
            value: 685.6298217773438
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5307262569832403
            name: Dot Precision
          - type: dot_recall
            value: 0.5491329479768786
            name: Dot Recall
          - type: dot_ap
            value: 0.5204314509187654
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.69921875
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 250.848388671875
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.538888888888889
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 287.8966064453125
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5187165775401069
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.5606936416184971
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.5196278189093784
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.69921875
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 11.343721389770508
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.5397727272727272
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 12.934247970581055
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5307262569832403
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.5491329479768786
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.5204045464403957
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.69921875
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 704.9437255859375
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.5397727272727272
            name: Max F1
          - type: max_f1_threshold
            value: 685.6298217773438
            name: Max F1 Threshold
          - type: max_precision
            value: 0.5307262569832403
            name: Max Precision
          - type: max_recall
            value: 0.5606936416184971
            name: Max Recall
          - type: max_ap
            value: 0.5204314509187654
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Qnli dev
          type: Qnli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.689453125
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8274233937263489
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.6847826086956521
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.7854544520378113
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.5981012658227848
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8008474576271186
            name: Cosine Recall
          - type: cosine_ap
            value: 0.7122261593973184
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.6875
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 636.4744262695312
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.6847826086956521
            name: Dot F1
          - type: dot_f1_threshold
            value: 604.311767578125
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5981012658227848
            name: Dot Precision
          - type: dot_recall
            value: 0.8008474576271186
            name: Dot Recall
          - type: dot_ap
            value: 0.7120508993892436
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.685546875
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 363.32275390625
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.6798561151079136
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 403.3307800292969
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.590625
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.8008474576271186
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.7106099609248304
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.689453125
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 16.29575538635254
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.6847826086956521
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 18.169567108154297
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5981012658227848
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8008474576271186
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.7122614787233053
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.689453125
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 636.4744262695312
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.6847826086956521
            name: Max F1
          - type: max_f1_threshold
            value: 604.311767578125
            name: Max F1 Threshold
          - type: max_precision
            value: 0.5981012658227848
            name: Max Precision
          - type: max_recall
            value: 0.8008474576271186
            name: Max Recall
          - type: max_ap
            value: 0.7122614787233053
            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

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

Metric Value
pearson_cosine 0.5664
spearman_cosine 0.5774
pearson_manhattan 0.5795
spearman_manhattan 0.5778
pearson_euclidean 0.5785
spearman_euclidean 0.5774
pearson_dot 0.5665
spearman_dot 0.5774
pearson_max 0.5795
spearman_max 0.5778

Binary Classification

Metric Value
cosine_accuracy 0.6992
cosine_accuracy_threshold 0.9164
cosine_f1 0.5398
cosine_f1_threshold 0.8913
cosine_precision 0.5307
cosine_recall 0.5491
cosine_ap 0.5204
dot_accuracy 0.6992
dot_accuracy_threshold 704.9437
dot_f1 0.5398
dot_f1_threshold 685.6298
dot_precision 0.5307
dot_recall 0.5491
dot_ap 0.5204
manhattan_accuracy 0.6992
manhattan_accuracy_threshold 250.8484
manhattan_f1 0.5389
manhattan_f1_threshold 287.8966
manhattan_precision 0.5187
manhattan_recall 0.5607
manhattan_ap 0.5196
euclidean_accuracy 0.6992
euclidean_accuracy_threshold 11.3437
euclidean_f1 0.5398
euclidean_f1_threshold 12.9342
euclidean_precision 0.5307
euclidean_recall 0.5491
euclidean_ap 0.5204
max_accuracy 0.6992
max_accuracy_threshold 704.9437
max_f1 0.5398
max_f1_threshold 685.6298
max_precision 0.5307
max_recall 0.5607
max_ap 0.5204

Binary Classification

Metric Value
cosine_accuracy 0.6895
cosine_accuracy_threshold 0.8274
cosine_f1 0.6848
cosine_f1_threshold 0.7855
cosine_precision 0.5981
cosine_recall 0.8008
cosine_ap 0.7122
dot_accuracy 0.6875
dot_accuracy_threshold 636.4744
dot_f1 0.6848
dot_f1_threshold 604.3118
dot_precision 0.5981
dot_recall 0.8008
dot_ap 0.7121
manhattan_accuracy 0.6855
manhattan_accuracy_threshold 363.3228
manhattan_f1 0.6799
manhattan_f1_threshold 403.3308
manhattan_precision 0.5906
manhattan_recall 0.8008
manhattan_ap 0.7106
euclidean_accuracy 0.6895
euclidean_accuracy_threshold 16.2958
euclidean_f1 0.6848
euclidean_f1_threshold 18.1696
euclidean_precision 0.5981
euclidean_recall 0.8008
euclidean_ap 0.7123
max_accuracy 0.6895
max_accuracy_threshold 636.4744
max_f1 0.6848
max_f1_threshold 604.3118
max_precision 0.5981
max_recall 0.8008
max_ap 0.7123

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
  • 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
  • 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 - - - -
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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}
}