Hugging Face's logo

bobox
/
DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp

PyTorch
TensorBoard
deberta-v2
Model card Files Metrics Community
DeBERTa3-s-CustomPoolin-toytest4-step1-checkpoints-tmp / checkpoint-610 /README.md
bobox's picture
bobox
Training in progress, step 610, checkpoint
3acf097 verified
preview code
|
raw
history blame
97.9 kB
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: 0.5377382226514003
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5410237309359288
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5464293120330461
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.5401021234588343
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.5469897917607747
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.5409800984560722
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.5376496659087263
            name: Pearson Dot
          - type: spearman_dot
            value: 0.5408811086658744
            name: Spearman Dot
          - type: pearson_max
            value: 0.5469897917607747
            name: Pearson Max
          - type: spearman_max
            value: 0.5410237309359288
            name: Spearman Max
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: allNLI dev
          type: allNLI-dev
        metrics:
          - type: cosine_accuracy
            value: 0.68359375
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.9088386297225952
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.5350553505535056
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.8140230178833008
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.39295392953929537
            name: Cosine Precision
          - type: cosine_recall
            value: 0.838150289017341
            name: Cosine Recall
          - type: cosine_ap
            value: 0.48873606015680937
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.68359375
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 699.0950927734375
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.5350553505535056
            name: Dot F1
          - type: dot_f1_threshold
            value: 625.3240356445312
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.39295392953929537
            name: Dot Precision
          - type: dot_recall
            value: 0.838150289017341
            name: Dot Recall
          - type: dot_ap
            value: 0.48885724782911755
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.68359375
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 256.45477294921875
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.5396145610278372
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 339.225830078125
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.42857142857142855
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.7283236994219653
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.4920209563997524
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.68359375
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 11.834823608398438
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.5350553505535056
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 16.90357780456543
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.39295392953929537
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.838150289017341
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.4887203371983184
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.68359375
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 699.0950927734375
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.5396145610278372
            name: Max F1
          - type: max_f1_threshold
            value: 625.3240356445312
            name: Max F1 Threshold
          - type: max_precision
            value: 0.42857142857142855
            name: Max Precision
          - type: max_recall
            value: 0.838150289017341
            name: Max Recall
          - type: max_ap
            value: 0.4920209563997524
            name: Max Ap
      - task:
          type: binary-classification
          name: Binary Classification
        dataset:
          name: Qnli dev
          type: Qnli-dev
        metrics:
          - type: cosine_accuracy
            value: 0.693359375
            name: Cosine Accuracy
          - type: cosine_accuracy_threshold
            value: 0.8319265842437744
            name: Cosine Accuracy Threshold
          - type: cosine_f1
            value: 0.685337726523888
            name: Cosine F1
          - type: cosine_f1_threshold
            value: 0.74552983045578
            name: Cosine F1 Threshold
          - type: cosine_precision
            value: 0.5606469002695418
            name: Cosine Precision
          - type: cosine_recall
            value: 0.8813559322033898
            name: Cosine Recall
          - type: cosine_ap
            value: 0.6873625888187367
            name: Cosine Ap
          - type: dot_accuracy
            value: 0.693359375
            name: Dot Accuracy
          - type: dot_accuracy_threshold
            value: 639.0776977539062
            name: Dot Accuracy Threshold
          - type: dot_f1
            value: 0.685337726523888
            name: Dot F1
          - type: dot_f1_threshold
            value: 572.7136840820312
            name: Dot F1 Threshold
          - type: dot_precision
            value: 0.5606469002695418
            name: Dot Precision
          - type: dot_recall
            value: 0.8813559322033898
            name: Dot Recall
          - type: dot_ap
            value: 0.6878718449643791
            name: Dot Ap
          - type: manhattan_accuracy
            value: 0.69921875
            name: Manhattan Accuracy
          - type: manhattan_accuracy_threshold
            value: 362.1485900878906
            name: Manhattan Accuracy Threshold
          - type: manhattan_f1
            value: 0.6857142857142857
            name: Manhattan F1
          - type: manhattan_f1_threshold
            value: 430.38519287109375
            name: Manhattan F1 Threshold
          - type: manhattan_precision
            value: 0.5682451253481894
            name: Manhattan Precision
          - type: manhattan_recall
            value: 0.864406779661017
            name: Manhattan Recall
          - type: manhattan_ap
            value: 0.6874910715870401
            name: Manhattan Ap
          - type: euclidean_accuracy
            value: 0.693359375
            name: Euclidean Accuracy
          - type: euclidean_accuracy_threshold
            value: 16.06937026977539
            name: Euclidean Accuracy Threshold
          - type: euclidean_f1
            value: 0.685337726523888
            name: Euclidean F1
          - type: euclidean_f1_threshold
            value: 19.772865295410156
            name: Euclidean F1 Threshold
          - type: euclidean_precision
            value: 0.5606469002695418
            name: Euclidean Precision
          - type: euclidean_recall
            value: 0.8813559322033898
            name: Euclidean Recall
          - type: euclidean_ap
            value: 0.6873686687008952
            name: Euclidean Ap
          - type: max_accuracy
            value: 0.69921875
            name: Max Accuracy
          - type: max_accuracy_threshold
            value: 639.0776977539062
            name: Max Accuracy Threshold
          - type: max_f1
            value: 0.6857142857142857
            name: Max F1
          - type: max_f1_threshold
            value: 572.7136840820312
            name: Max F1 Threshold
          - type: max_precision
            value: 0.5682451253481894
            name: Max Precision
          - type: max_recall
            value: 0.8813559322033898
            name: Max Recall
          - type: max_ap
            value: 0.6878718449643791
            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=0.05, 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

Metric Value
pearson_cosine 0.5377
spearman_cosine 0.541
pearson_manhattan 0.5464
spearman_manhattan 0.5401
pearson_euclidean 0.547
spearman_euclidean 0.541
pearson_dot 0.5376
spearman_dot 0.5409
pearson_max 0.547
spearman_max 0.541

Binary Classification

Metric Value
cosine_accuracy 0.6836
cosine_accuracy_threshold 0.9088
cosine_f1 0.5351
cosine_f1_threshold 0.814
cosine_precision 0.393
cosine_recall 0.8382
cosine_ap 0.4887
dot_accuracy 0.6836
dot_accuracy_threshold 699.0951
dot_f1 0.5351
dot_f1_threshold 625.324
dot_precision 0.393
dot_recall 0.8382
dot_ap 0.4889
manhattan_accuracy 0.6836
manhattan_accuracy_threshold 256.4548
manhattan_f1 0.5396
manhattan_f1_threshold 339.2258
manhattan_precision 0.4286
manhattan_recall 0.7283
manhattan_ap 0.492
euclidean_accuracy 0.6836
euclidean_accuracy_threshold 11.8348
euclidean_f1 0.5351
euclidean_f1_threshold 16.9036
euclidean_precision 0.393
euclidean_recall 0.8382
euclidean_ap 0.4887
max_accuracy 0.6836
max_accuracy_threshold 699.0951
max_f1 0.5396
max_f1_threshold 625.324
max_precision 0.4286
max_recall 0.8382
max_ap 0.492

Binary Classification

Metric Value
cosine_accuracy 0.6934
cosine_accuracy_threshold 0.8319
cosine_f1 0.6853
cosine_f1_threshold 0.7455
cosine_precision 0.5606
cosine_recall 0.8814
cosine_ap 0.6874
dot_accuracy 0.6934
dot_accuracy_threshold 639.0777
dot_f1 0.6853
dot_f1_threshold 572.7137
dot_precision 0.5606
dot_recall 0.8814
dot_ap 0.6879
manhattan_accuracy 0.6992
manhattan_accuracy_threshold 362.1486
manhattan_f1 0.6857
manhattan_f1_threshold 430.3852
manhattan_precision 0.5682
manhattan_recall 0.8644
manhattan_ap 0.6875
euclidean_accuracy 0.6934
euclidean_accuracy_threshold 16.0694
euclidean_f1 0.6853
euclidean_f1_threshold 19.7729
euclidean_precision 0.5606
euclidean_recall 0.8814
euclidean_ap 0.6874
max_accuracy 0.6992
max_accuracy_threshold 639.0777
max_f1 0.6857
max_f1_threshold 572.7137
max_precision 0.5682
max_recall 0.8814
max_ap 0.6879

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
  • 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 - - -
0.4144 421 0.8836 - - -
0.4154 422 1.2115 - - -
0.4163 423 0.8367 - - -
0.4173 424 1.0595 - - -
0.4183 425 0.826 - - -
0.4193 426 0.707 - - -
0.4203 427 0.6235 - - -
0.4213 428 0.7719 - - -
0.4222 429 1.0862 - - -
0.4232 430 0.9311 - - -
0.4242 431 1.2339 - - -
0.4252 432 0.9891 - - -
0.4262 433 1.8443 - - -
0.4272 434 1.1799 - - -
0.4281 435 0.759 - - -
0.4291 436 1.1002 - - -
0.4301 437 0.9141 - - -
0.4311 438 0.5467 - - -
0.4321 439 0.7476 - - -
0.4331 440 1.14 - - -
0.4341 441 1.1504 - - -
0.4350 442 1.26 - - -
0.4360 443 1.0311 - - -
0.4370 444 1.0646 - - -
0.4380 445 0.8687 - - -
0.4390 446 0.6839 - - -
0.4400 447 1.1376 - - -
0.4409 448 0.9759 - - -
0.4419 449 0.7971 - - -
0.4429 450 0.9708 - - -
0.4439 451 0.8217 - - -
0.4449 452 1.3728 - - -
0.4459 453 0.9119 - - -
0.4469 454 1.012 - - -
0.4478 455 1.3738 - - -
0.4488 456 0.8219 - - -
0.4498 457 1.2558 - - -
0.4508 458 0.6247 - - -
0.4518 459 0.7295 0.5410 0.4920 0.6879
0.4528 460 0.8154 - - -
0.4537 461 1.1392 - - -
0.4547 462 0.8618 - - -
0.4557 463 0.9669 - - -
0.4567 464 0.8804 - - -
0.4577 465 0.8479 - - -
0.4587 466 0.6296 - - -
0.4596 467 0.8449 - - -
0.4606 468 0.9772 - - -
0.4616 469 0.6424 - - -
0.4626 470 0.9169 - - -
0.4636 471 0.7599 - - -
0.4646 472 0.8943 - - -
0.4656 473 0.9475 - - -
0.4665 474 1.4518 - - -
0.4675 475 1.274 - - -
0.4685 476 0.7306 - - -
0.4695 477 0.9238 - - -
0.4705 478 0.6593 - - -
0.4715 479 1.0183 - - -
0.4724 480 1.2577 - - -
0.4734 481 0.8738 - - -
0.4744 482 1.1416 - - -
0.4754 483 0.7135 - - -
0.4764 484 1.2587 - - -
0.4774 485 0.8823 - - -
0.4783 486 0.8423 - - -
0.4793 487 0.7704 - - -
0.4803 488 0.7049 - - -
0.4813 489 1.1893 - - -
0.4823 490 1.3985 - - -
0.4833 491 1.3567 - - -
0.4843 492 1.2573 - - -
0.4852 493 0.7671 - - -
0.4862 494 0.5425 - - -
0.4872 495 0.9372 - - -
0.4882 496 0.799 - - -
0.4892 497 0.9548 - - -
0.4902 498 1.0855 - - -
0.4911 499 1.0465 - - -
0.4921 500 1.1004 - - -
0.4931 501 0.6392 - - -
0.4941 502 0.7102 - - -
0.4951 503 1.3242 - - -
0.4961 504 0.6861 - - -
0.4970 505 0.9291 - - -
0.4980 506 0.8592 - - -
0.4990 507 0.9462 - - -
0.5 508 1.0167 - - -
0.5010 509 1.0118 - - -
0.5020 510 0.6741 - - -
0.5030 511 1.4578 - - -
0.5039 512 1.2959 - - -
0.5049 513 0.8533 - - -
0.5059 514 0.6685 - - -
0.5069 515 1.1556 - - -
0.5079 516 0.8177 - - -
0.5089 517 0.6296 - - -
0.5098 518 0.8407 - - -
0.5108 519 0.6987 - - -
0.5118 520 0.9888 - - -
0.5128 521 0.8938 - - -
0.5138 522 0.582 - - -
0.5148 523 0.6596 - - -
0.5157 524 0.6029 - - -
0.5167 525 0.9806 - - -
0.5177 526 0.9463 - - -
0.5187 527 0.7088 - - -
0.5197 528 0.7525 - - -
0.5207 529 0.7625 - - -
0.5217 530 0.8271 - - -
0.5226 531 0.6129 - - -
0.5236 532 1.1563 - - -
0.5246 533 0.8131 - - -
0.5256 534 0.5363 - - -
0.5266 535 0.8819 - - -
0.5276 536 0.9772 - - -
0.5285 537 1.2102 - - -
0.5295 538 1.1234 - - -
0.5305 539 1.1857 - - -
0.5315 540 0.7873 - - -
0.5325 541 0.5034 - - -
0.5335 542 1.3305 - - -
0.5344 543 1.1727 - - -
0.5354 544 1.2825 - - -
0.5364 545 1.0446 - - -
0.5374 546 0.9838 - - -
0.5384 547 1.2194 - - -
0.5394 548 0.7709 - - -
0.5404 549 0.748 - - -
0.5413 550 1.0948 - - -
0.5423 551 0.915 - - -
0.5433 552 1.537 - - -
0.5443 553 0.3239 - - -
0.5453 554 0.9592 - - -
0.5463 555 0.7737 - - -
0.5472 556 0.613 - - -
0.5482 557 1.3646 - - -
0.5492 558 0.6659 - - -
0.5502 559 0.5207 - - -
0.5512 560 0.9467 - - -
0.5522 561 0.5692 - - -
0.5531 562 1.5855 - - -
0.5541 563 0.8855 - - -
0.5551 564 1.1829 - - -
0.5561 565 0.978 - - -
0.5571 566 1.1818 - - -
0.5581 567 0.701 - - -
0.5591 568 1.0226 - - -
0.5600 569 0.5937 - - -
0.5610 570 0.8095 - - -
0.5620 571 1.174 - - -
0.5630 572 0.96 - - -
0.5640 573 0.8339 - - -
0.5650 574 0.717 - - -
0.5659 575 0.5938 - - -
0.5669 576 0.6501 - - -
0.5679 577 0.7003 - - -
0.5689 578 0.5525 - - -
0.5699 579 0.7003 - - -
0.5709 580 1.059 - - -
0.5719 581 0.8625 - - -
0.5728 582 0.5862 - - -
0.5738 583 0.9162 - - -
0.5748 584 0.926 - - -
0.5758 585 1.2729 - - -
0.5768 586 0.8935 - - -
0.5778 587 0.541 - - -
0.5787 588 1.1455 - - -
0.5797 589 0.7306 - - -
0.5807 590 0.9088 - - -
0.5817 591 0.9166 - - -
0.5827 592 0.8679 - - -
0.5837 593 0.9329 - - -
0.5846 594 1.1201 - - -
0.5856 595 0.6418 - - -
0.5866 596 1.145 - - -
0.5876 597 1.4041 - - -
0.5886 598 0.6954 - - -
0.5896 599 0.4567 - - -
0.5906 600 1.1305 - - -
0.5915 601 0.8077 - - -
0.5925 602 0.6143 - - -
0.5935 603 1.3139 - - -
0.5945 604 0.7694 - - -
0.5955 605 0.9622 - - -
0.5965 606 0.91 - - -
0.5974 607 1.3125 - - -
0.5984 608 1.0153 - - -
0.5994 609 0.8468 - - -
0.6004 610 1.1026 - - -

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}
}