README.md

metadata

base_model: sentence-transformers/stsb-distilbert-base
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:622302
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      Does fTO Genotype interact with Improvement in Aerobic Fitness on Body
      Weight Loss During Lifestyle Intervention?
    sentences:
      - >-
        The study population count 46 550 male workers, 1670 (3.6%) of whom
        incurred at least one work-related injury requiring admission to
        hospital within a period of 5 years following hearing tests conducted
        between 1987 and 2005. The noise exposure and hearing loss-related data
        were gathered during occupational noise-induced hearing loss (NIHL)
        screening. The hospital data were used to identify all members of the
        study population who were admitted, and the reason for admission.
        Finally, access to the death-related data made it possible to identify
        participants who died during the course of the study. Cox proportional
        hazards model taking into account hearing status, noise levels, age and
        cumulative duration of noise exposure at the time of the hearing test
        established the risk of work-related injuries leading to admission to
        hospital.
      - >-
        Carriers of a hereditary mutation in BRCA are at high risk for breast
        and ovarian cancer. The first person from a family known to carry the
        mutation, the index person, has to share genetic information with
        relatives. This study is aimed at determining the number of relatives
        tested for a BRCA mutation, and the exploration of facilitating and
        debilitating factors in the transmission of genetic information from
        index patient to relatives.
      - >-
        Not every participant responds with a comparable body weight loss to
        lifestyle intervention, despite the same compliance. Genetic factors may
        explain parts of this difference. Variation in fat mass and
        obesity-associated gene (FTO) is the strongest common genetic
        determinant of body weight. The aim of the present study was to evaluate
        the impact of FTO genotype differences in the link between improvement
        of fitness and reduction of body weight during a lifestyle intervention.
  - source_sentence: >-
      Is family history of exceptional longevity associated with lower serum
      uric acid levels in Ashkenazi Jews?
    sentences:
      - To evaluate the effect of fasting on gastric emptying in mice.
      - >-
        To test whether lower serum uric acid (UA) levels are associated with
        longevity independent of renal function.
      - >-
        Inducible NOS mRNA expression was significantly lower in CF patients
        with and without bacterial infection than in healthy children (0.22 and
        0.23 v 0.76; p=0.002 and p=0.01, respectively). Low levels of iNOS gene
        expression were accompanied by low levels of iNOS protein expression as
        detected by Western blot analysis.
  - source_sentence: >-
      Do hepatocellular carcinomas compromise quantitative tests of liver
      function?
    sentences:
      - >-
        MEPE had no effect on glomerular filtration rate or single-nephron
        filtration rate, but it increased phosphate excretion significantly. In
        animals infused with vehicle alone (time controls), no significant
        change was seen in either the proximal tubular fluid:plasma phosphate
        concentration ratio (TF/P(Pi)) or the fraction of filtered phosphate
        reaching the late proximal convoluted tubule (FD(Pi)); whereas in rats
        infused with MEPE, TF/P(Pi) increased from 0.49 ± 0.07 to 0.68 ± 0.04 (n
        = 22; P = 0.01) and FD(Pi) increased from 0.20 ± 0.03 to 0.33 ± 0.03 (n
        = 22; P < 0.01).
      - >-
        Hepatocellular carcinoma, which usually develops in cirrhotic livers, is
        one of the most frequent cancers worldwide. If and how far hepatoma
        growth influences liver function is unclear. Therefore, we compared a
        broad panel of quantitative tests of liver function in cirrhotic
        patients with and without hepatocellular carcinoma.
      - >-
        A study was undertaken to measure cough frequency in children with
        stable asthma using a validated monitoring device, and to assess the
        correlation between cough frequency and the degree and type of airway
        inflammation.
  - source_sentence: >-
      Does hand-assisted laparoscopic digestive surgery provide safety and
      tactile sensation for malignancy or obesity?
    sentences:
      - >-
        In human aortic endothelial cells (HAECs) exposed to high glucose and
        aortas of diabetic mice, activation of p66(Shc) by protein kinase C βII
        (PKCβII) persisted after returning to normoglycemia. Persistent p66(Shc)
        upregulation and mitochondrial translocation were associated with
        continued reactive oxygen species (ROS) production, reduced nitric oxide
        bioavailability, and apoptosis. We show that p66(Shc) gene
        overexpression was epigenetically regulated by promoter CpG
        hypomethylation and general control nonderepressible 5-induced histone 3
        acetylation. Furthermore, p66(Shc)-derived ROS production maintained
        PKCβII upregulation and PKCβII-dependent inhibitory phosphorylation of
        endothelial nitric oxide synthase at Thr-495, leading to a detrimental
        vicious cycle despite restoration of normoglycemia. Moreover, p66(Shc)
        activation accounted for the persistent elevation of the advanced
        glycated end product precursor methylglyoxal. In vitro and in vivo gene
        silencing of p66(Shc), performed at the time of glucose normalization,
        blunted ROS production, restored endothelium-dependent vasorelaxation,
        and attenuated apoptosis by limiting cytochrome c release, caspase 3
        activity, and cleavage of poly (ADP-ribose) polymerase.
      - >-
        Recently, 13 of our patients underwent hand-assisted advanced
        laparoscopic surgery using this device. In this series, we had two cases
        of gastrectomy, two cases of gastric bypass for morbid obesity, two
        Whipple cases for periampullary tumor, and seven cases of bowel
        resection. On the basis of this series, we were able to assess the
        utility of this device.
      - >-
        Healthy men and women (n = 13; age: 48 +/- 17 y) were studied on 2
        occasions: after > or = 48 h with no exercise and 17 h after a 60-min
        bout of endurance exercise. During each trial, brachial artery flow
        mediated dilation (FMD) was used to assess endothelial function before
        and after the ingestion of a candy bar and soft drink. Glucose, insulin,
        and thiobarbituric acid-reactive substances (TBARS), a marker of
        oxidative stress, were measured in blood obtained during each FMD
        measurement. The insulin sensitivity index was calculated from the
        glucose and insulin data.
  - source_sentence: >-
      Do correlations between plasma-neuropeptides and temperament dimensions
      differ between suicidal patients and healthy controls?
    sentences:
      - >-
        Decreased plasma levels of plasma-neuropeptide Y (NPY) and
        plasma-corticotropin releasing hormone (CRH), and increased levels of
        plasma delta-sleep inducing peptide (DSIP) in suicide attempters with
        mood disorders have previously been observed. This study was performed
        in order to further understand the clinical relevance of these findings.
      - >-
        Brain death was induced in Wistar rats by intracranial balloon
        inflation. Pulmonary capillary leak was estimated using radioiodinated
        albumin. Development of pulmonary edema was assessed by measurement of
        wet and dry lung weights. Cell surface expression of CD11b/CD18 by
        neutrophils was determined using flow cytometry. Enzyme-linked
        immunosorbent assays were used to measure the levels of TNFalpha,
        IL-1beta, CINC-1, and CINC-3 in serum and bronchoalveolar lavage.
        Quantitative reverse-transcription polymerase chain reaction was used to
        determine the expression of cytokine mRNA (IL-1beta, CINC-1 and CINC-3)
        in lung tissue.
      - >-
        Seven hundred fifty patients entered the study. One hundred sixty-eight
        patients (22.4%) presented with a total of 193 extracutaneous
        manifestations, as follows: articular (47.2%), neurologic (17.1%),
        vascular (9.3%), ocular (8.3%), gastrointestinal (6.2%), respiratory
        (2.6%), cardiac (1%), and renal (1%). Other autoimmune conditions were
        present in 7.3% of patients. Neurologic involvement consisted of
        epilepsy, central nervous system vasculitis, peripheral neuropathy,
        vascular malformations, headache, and neuroimaging abnormalities. Ocular
        manifestations were episcleritis, uveitis, xerophthalmia, glaucoma, and
        papilledema. In more than one-fourth of these children, articular,
        neurologic, and ocular involvements were unrelated to the site of skin
        lesions. Raynaud's phenomenon was reported in 16 patients. Respiratory
        involvement consisted essentially of restrictive lung disease.
        Gastrointestinal involvement was reported in 12 patients and consisted
        exclusively of gastroesophageal reflux. Thirty patients (4%) had
        multiple extracutaneous features, but systemic sclerosis (SSc) developed
        in only 1 patient. In patients with extracutaneous involvement, the
        prevalence of antinuclear antibodies and rheumatoid factor was
        significantly higher than that among patients with only skin
        involvement. However, Scl-70 and anticentromere, markers of SSc, were
        not significantly increased.
model-index:
  - name: SentenceTransformer based on sentence-transformers/stsb-distilbert-base
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: med eval dev
          type: med-eval-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.9825
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.998
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9985
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9985
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9825
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.8438333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.5588
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.29309999999999997
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3413382936507936
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8453946428571428
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9191847222222223
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9578416666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9461928701093355
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9899583333333333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9168772609607218
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.9705
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.9955
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9985
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.999
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.9705
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.8141666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.546
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.28995
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.3365662698412698
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8156482142857142
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.8994174603174604
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9480904761904763
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.9297315742366127
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.9828083333333333
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.8926507948277561
            name: Dot Map@100

SentenceTransformer based on sentence-transformers/stsb-distilbert-base

This is a sentence-transformers model finetuned from sentence-transformers/stsb-distilbert-base. 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: sentence-transformers/stsb-distilbert-base
• Maximum Sequence Length: 128 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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': 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("alpha-brain/pubmed-stsb-distilbert-base-mnrl")
# Run inference
sentences = [
    'Do correlations between plasma-neuropeptides and temperament dimensions differ between suicidal patients and healthy controls?',
    'Decreased plasma levels of plasma-neuropeptide Y (NPY) and plasma-corticotropin releasing hormone (CRH), and increased levels of plasma delta-sleep inducing peptide (DSIP) in suicide attempters with mood disorders have previously been observed. This study was performed in order to further understand the clinical relevance of these findings.',
    "Seven hundred fifty patients entered the study. One hundred sixty-eight patients (22.4%) presented with a total of 193 extracutaneous manifestations, as follows: articular (47.2%), neurologic (17.1%), vascular (9.3%), ocular (8.3%), gastrointestinal (6.2%), respiratory (2.6%), cardiac (1%), and renal (1%). Other autoimmune conditions were present in 7.3% of patients. Neurologic involvement consisted of epilepsy, central nervous system vasculitis, peripheral neuropathy, vascular malformations, headache, and neuroimaging abnormalities. Ocular manifestations were episcleritis, uveitis, xerophthalmia, glaucoma, and papilledema. In more than one-fourth of these children, articular, neurologic, and ocular involvements were unrelated to the site of skin lesions. Raynaud's phenomenon was reported in 16 patients. Respiratory involvement consisted essentially of restrictive lung disease. Gastrointestinal involvement was reported in 12 patients and consisted exclusively of gastroesophageal reflux. Thirty patients (4%) had multiple extracutaneous features, but systemic sclerosis (SSc) developed in only 1 patient. In patients with extracutaneous involvement, the prevalence of antinuclear antibodies and rheumatoid factor was significantly higher than that among patients with only skin involvement. However, Scl-70 and anticentromere, markers of SSc, were not significantly increased.",
]
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

Information Retrieval

• Dataset: med-eval-dev
• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.9825
cosine_accuracy@3	0.998
cosine_accuracy@5	0.9985
cosine_accuracy@10	0.9985
cosine_precision@1	0.9825
cosine_precision@3	0.8438
cosine_precision@5	0.5588
cosine_precision@10	0.2931
cosine_recall@1	0.3413
cosine_recall@3	0.8454
cosine_recall@5	0.9192
cosine_recall@10	0.9578
cosine_ndcg@10	0.9462
cosine_mrr@10	0.99
cosine_map@100	0.9169
dot_accuracy@1	0.9705
dot_accuracy@3	0.9955
dot_accuracy@5	0.9985
dot_accuracy@10	0.999
dot_precision@1	0.9705
dot_precision@3	0.8142
dot_precision@5	0.546
dot_precision@10	0.2899
dot_recall@1	0.3366
dot_recall@3	0.8156
dot_recall@5	0.8994
dot_recall@10	0.9481
dot_ndcg@10	0.9297
dot_mrr@10	0.9828
dot_map@100	0.8927

Training Details

Training Dataset

Unnamed Dataset

• Size: 622,302 training samples
• Columns: question and contexts
• Approximate statistics based on the first 1000 samples:

  	question	contexts
  type	string	string
  details	min: 9 tokens mean: 27.35 tokens max: 60 tokens	min: 5 tokens mean: 88.52 tokens max: 128 tokens

• Samples:

  question	contexts
  Does low-level human equivalent gestational lead exposure produce sex-specific motor and coordination abnormalities and late-onset obesity in year-old mice?	Low-level developmental lead exposure is linked to cognitive and neurological disorders in children. However, the long-term effects of gestational lead exposure (GLE) have received little attention.
  Does insulin in combination with selenium inhibit HG/Pal-induced cardiomyocyte apoptosis by Cbl-b regulating p38MAPK/CBP/Ku70 pathway?	In this study, we investigated whether insulin and selenium in combination (In/Se) suppresses cardiomyocyte apoptosis and whether this protection is mediated by Cbl-b regulating p38MAPK/CBP/Ku70 pathway.
  Does arthroscopic subacromial decompression result in normal shoulder function after two years in less than 50 % of patients?	The aim of this study was to evaluate the outcome two years after arthroscopic subacromial decompression using the Western Ontario Rotator-Cuff (WORC) index and a diagram-based questionnaire to self-assess active shoulder range of motion (ROM).

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 32,753 evaluation samples
• Columns: question and contexts
• Approximate statistics based on the first 1000 samples:

  	question	contexts
  type	string	string
  details	min: 11 tokens mean: 27.52 tokens max: 56 tokens	min: 3 tokens mean: 88.59 tokens max: 128 tokens

• Samples:

  question	contexts
  Does [ Chemical components from essential oil of Pandanus amaryllifolius leave ]?	The essential oil of Pandanus amaryllifolius leaves was analyzed by gas chromatography-mass spectrum, and the relative content of each component was determined by area normalization method.
  Is elevated C-reactive protein associated with the tumor depth of invasion but not with disease recurrence in stage II and III colorectal cancer?	We previously demonstrated that elevated serum C-reactive protein (CRP) level is associated with depth of tumor invasion in operable colorectal cancer. There is also increasing evidence to show that raised CRP concentration is associated with poor survival in patients with colorectal cancer. The purpose of this study was to investigate the correlation between preoperative CRP concentrations and short-term disease recurrence in cases with stage II and III colorectal cancer.
  Do neuropeptide Y and peptide YY protect from weight loss caused by Bacille Calmette-Guérin in mice?	Deletion of PYY and NPY aggravated the BCG-induced loss of body weight, which was most pronounced in NPY-/-;PYY-/- mice (maximum loss: 15%). The weight loss in NPY-/-;PYY-/- mice did not normalize during the 2 week observation period. BCG suppressed the circadian pattern of locomotion, exploration and food intake. However, these changes took a different time course than the prolonged weight loss caused by BCG in NPY-/-;PYY-/- mice. The effect of BCG to increase circulating IL-6 (measured 16 days post-treatment) remained unaltered by knockout of PYY, NPY or NPY plus PYY.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 64
• num_train_epochs: 1

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 8
• 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: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• 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: False
• 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: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• 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: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	loss	med-eval-dev_cosine_map@100
0	0	-	-	0.3328
0.0103	100	0.7953	-	-
0.0206	200	0.5536	-	-
0.0257	250	-	0.1041	0.7474
0.0309	300	0.4755	-	-
0.0411	400	0.4464	-	-
0.0514	500	0.3986	0.0761	0.7786
0.0617	600	0.357	-	-
0.0720	700	0.3519	-	-
0.0771	750	-	0.0685	0.8029
0.0823	800	0.3197	-	-
0.0926	900	0.3247	-	-
0.1028	1000	0.3048	0.0549	0.8108
0.1131	1100	0.2904	-	-
0.1234	1200	0.281	-	-
0.1285	1250	-	0.0503	0.8181
0.1337	1300	0.2673	-	-
0.1440	1400	0.2645	-	-
0.1543	1500	0.2511	0.0457	0.8332
0.1645	1600	0.2541	-	-
0.1748	1700	0.2614	-	-
0.1800	1750	-	0.0401	0.8380
0.1851	1800	0.2263	-	-
0.1954	1900	0.2466	-	-
0.2057	2000	0.2297	0.0365	0.8421
0.2160	2100	0.2225	-	-
0.2262	2200	0.212	-	-
0.2314	2250	-	0.0344	0.8563
0.2365	2300	0.2257	-	-
0.2468	2400	0.1953	-	-
0.2571	2500	0.1961	0.0348	0.8578
0.2674	2600	0.1888	-	-
0.2777	2700	0.2039	-	-
0.2828	2750	-	0.0319	0.8610
0.2879	2800	0.1939	-	-
0.2982	2900	0.202	-	-
0.3085	3000	0.1915	0.0292	0.8678
0.3188	3100	0.1987	-	-
0.3291	3200	0.1877	-	-
0.3342	3250	-	0.0275	0.8701
0.3394	3300	0.1874	-	-
0.3497	3400	0.1689	-	-
0.3599	3500	0.169	0.0281	0.8789
0.3702	3600	0.1631	-	-
0.3805	3700	0.1611	-	-
0.3856	3750	-	0.0263	0.8814
0.3908	3800	0.1764	-	-
0.4011	3900	0.1796	-	-
0.4114	4000	0.1729	0.0249	0.8805
0.4216	4100	0.1551	-	-
0.4319	4200	0.1543	-	-
0.4371	4250	-	0.0241	0.8867
0.4422	4300	0.1549	-	-
0.4525	4400	0.1432	-	-
0.4628	4500	0.1592	0.0219	0.8835
0.4731	4600	0.1517	-	-
0.4833	4700	0.1463	-	-
0.4885	4750	-	0.0228	0.8928
0.4936	4800	0.1525	-	-
0.5039	4900	0.1426	-	-
0.5142	5000	0.1524	0.0209	0.8903
0.5245	5100	0.1443	-	-
0.5348	5200	0.1468	-	-
0.5399	5250	-	0.0212	0.8948
0.5450	5300	0.151	-	-
0.5553	5400	0.1443	-	-
0.5656	5500	0.1438	0.0212	0.8982
0.5759	5600	0.1409	-	-
0.5862	5700	0.1346	-	-
0.5913	5750	-	0.0207	0.8983
0.5965	5800	0.1315	-	-
0.6067	5900	0.1425	-	-
0.6170	6000	0.136	0.0188	0.8970
0.6273	6100	0.1426	-	-
0.6376	6200	0.1353	-	-
0.6427	6250	-	0.0185	0.8969
0.6479	6300	0.1269	-	-
0.6582	6400	0.1159	-	-
0.6684	6500	0.1311	0.0184	0.9028
0.6787	6600	0.1179	-	-
0.6890	6700	0.115	-	-
0.6942	6750	-	0.0184	0.9046
0.6993	6800	0.1254	-	-
0.7096	6900	0.1233	-	-
0.7199	7000	0.122	0.0174	0.9042
0.7302	7100	0.1238	-	-
0.7404	7200	0.1257	-	-
0.7456	7250	-	0.0175	0.9074
0.7507	7300	0.1222	-	-
0.7610	7400	0.1194	-	-
0.7713	7500	0.1284	0.0166	0.9080
0.7816	7600	0.1147	-	-
0.7919	7700	0.1182	-	-
0.7970	7750	-	0.0170	0.9116
0.8021	7800	0.1157	-	-
0.8124	7900	0.1299	-	-
0.8227	8000	0.114	0.0163	0.9105
0.8330	8100	0.1141	-	-
0.8433	8200	0.1195	-	-
0.8484	8250	-	0.0160	0.9112
0.8536	8300	0.1073	-	-
0.8638	8400	0.1044	-	-
0.8741	8500	0.1083	0.0160	0.9153
0.8844	8600	0.1103	-	-
0.8947	8700	0.1145	-	-
0.8998	8750	-	0.0154	0.9133
0.9050	8800	0.1083	-	-
0.9153	8900	0.1205	-	-
0.9255	9000	0.1124	0.0153	0.9162
0.9358	9100	0.1067	-	-
0.9461	9200	0.116	-	-
0.9513	9250	-	0.0152	0.9171
0.9564	9300	0.1126	-	-
0.9667	9400	0.1075	-	-
0.9770	9500	0.1128	0.0149	0.9169
0.9872	9600	0.1143	-	-
0.9975	9700	0.1175	-	-

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.1.1
• Transformers: 4.44.2
• PyTorch: 2.4.0
• Accelerate: 0.34.2
• Datasets: 3.0.0
• 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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}