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pankajrajdeo
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Bioformer-8L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:33870508
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Inference Endpoints
Model card Files Community

SentenceTransformer

This is a sentence-transformers model trained on the parquet dataset. It maps sentences & paragraphs to a 512-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
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 512 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • parquet

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 512, '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("pankajrajdeo/Bioformer-8L-UMLS-Pubmed_PMC-Forward_TCE-Epoch-3")
# Run inference
sentences = [
    'De Garengeot Hernia, an acute appendicitis in the right femoral hernia canal, and successful management with transabdominal closure and appendectomy: a case Report.',
    'Incarceration of the appendix within a femoral hernia is a rare condition of abdominal wall hernia about 0.1 to 0.5% in reported femoral hernia. We report a case of a 56-year-old female whose appendix was trapped in the right femoral canal. There are few reports in the literature on entrapment of the appendix within a femoral hernia. The management of this condition includes antibiotics, drainage appendectomy, hernioplasty and mesh repair.',
    "With the increasing population worldwide more wastewater is created by human activities and discharged into the waterbodies. This is causing the contamination of aquatic bodies, thus disturbing the marine ecosystems. The rising population is also posing a challenge to meet the demands of fresh drinking water in the water-scarce regions of the world, where drinking water is made available to people by desalination process. The fouling of composite membranes remains a major challenge in water desalination. In this innovative study, we present a novel probabilistic approach to analyse and anticipate the predominant fouling mechanisms in the filtration process. Our establishment of a robust theoretical framework hinges upon the utilization of both the geometric law and the Hermia model, elucidating the concept of resistance in series (RIS). By manipulating the transmembrane pressure, we demonstrate effective management of permeate flux rate and overall product quality. Our investigations reveal a decrease in permeate flux in three distinct phases over time, with the final stage marked by a significant reduction due to the accumulation of a denser cake layer. Additionally, an increase in transmembrane pressure leads to a correlative rise in permeate flux, while also exerting negative effects such as membrane ruptures. Our study highlights the minimal immediate impact of the intermediate blocking mechanism (n = 1) on permeate flux, necessitating continuous monitoring for potential long-term effects. Additionally, we note a reduced membrane selectivity across all three fouling types (n = 0, n = 1.5, n = 2). Ultimately, our findings indicate that the membrane undergoes complete fouling with a probability of P = 0.9 in the presence of all three fouling mechanisms. This situation renders the membrane unable to produce water at its previous flow rate, resulting in a significant reduction in the desalination plant's productivity. I have demonstrated that higher pressure values notably correlate with increased permeate flux across all four membrane types. This correlation highlights the significant role of TMP in enhancing the production rate of purified water or desired substances through membrane filtration systems. Our innovative approach opens new perspectives for water desalination management and optimization, providing crucial insights into fouling mechanisms and proposing potential strategies to address associated challenges.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

parquet

  • Dataset: parquet
  • Size: 33,870,508 training samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 1000 samples:
    anchor positive
    type string string
    details
    • min: 5 tokens
    • mean: 36.24 tokens
    • max: 106 tokens
    • min: 30 tokens
    • mean: 282.68 tokens
    • max: 512 tokens
  • Samples:
    anchor positive
    How TO OBTAIN THE BRAIN OF THE CAT. How to obtain the Brain of the Cat, (Wilder).-Correction: Page 158, second column, line 7, "grains," should be "grams;" page 159, near middle of 2nd column, "successily," should be "successively;" page 161, the number of Flower's paper is 3.
    ADDRESS OF COL. GARRICK MALLERY, U. S. ARMY. It may be conceded that after man had all his present faculties, he did not choose between the adoption of voice and gesture, and never with those faculties, was in a state where the one was used, to the absolute exclusion of the other. The epoch, however, to which our speculations relate is that in which he had not reached the present symmetric development of his intellect and of his bodily organs, and the inquiry is: Which mode of communication was earliest adopted to his single wants and informed intelligence? With the voice he could imitate distinictively but few sounds of nature, while with gesture he could exhibit actions, motions, positions, forms, dimensions, directions and distances, with their derivations and analogues. It would seem from this unequal division of capacity that oral speech remained rudimentary long after gesture had become an efficient mode of communication. With due allowance for all purely imitative sounds, and for the spontaneous action of vocal organs unde...
    DOLBEAR ON THE NATURE AND CONSTITUTION OF MATTER. Mr. Dopp desires to make the following correction in his paper in the last issue: "In my article on page 200 of "Science", the expression and should have been and being the velocity of light.
  • Loss: MultipleNegativesRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

parquet

  • Dataset: parquet
  • Size: 33,870,508 evaluation samples
  • Columns: anchor and positive
  • Approximate statistics based on the first 1000 samples:
    anchor positive
    type string string
    details
    • min: 6 tokens
    • mean: 24.64 tokens
    • max: 64 tokens
    • min: 9 tokens
    • mean: 279.56 tokens
    • max: 512 tokens
  • Samples:
    anchor positive
    Noticing education campaigns or public health messages about vaping among youth in the United States, Canada and England from 2018 to 2022. Public health campaigns have the potential to correct vaping misperceptions. However, campaigns highlighting vaping harms to youth may increase misperceptions that vaping is equally/more harmful than smoking. Vaping campaigns have been implemented in the United States and Canada since 2018 and in England since 2017 but with differing focus: youth vaping prevention. Over half of youth reported noticing vaping campaigns, and noticing increased from August 2018 to February 2020. Consistent with implementation of youth vaping prevention campaigns in the United States and Canada, most youth reported noticing vaping campaigns/messages, and most were perceived to negatively portray vaping.
    Comprehensive performance evaluation of six bioaerosol samplers based on an aerosol wind tunnel. Choosing a suitable bioaerosol sampler for atmospheric microbial monitoring has been a challenge to researchers interested in environmental microbiology, especially during a pandemic. However, a comprehensive and integrated evaluation method to fully assess bioaerosol sampler performance is still lacking. Herein, we constructed a customized wind tunnel operated at 2-20 km/h wind speed to systematically and efficiently evaluate the performance of six frequently used samplers, where various aerosols, including Arizona test dust, bacterial spores, gram-positive and gram-negative bacteria, phages, and viruses, were generated. After 10 or 60 min of sampling, the physical and biological sampling efficiency and short or long-term sampling capabilities were determined by performing aerodynamic particle size analysis, live microbial culturing, and a qPCR assay. The results showed that AGI-30 and BioSampler impingers have good physical and biological sampling efficiencies for short-term sampling...
    The occurrence, sources, and health risks of substituted polycyclic aromatic hydrocarbons (SPAHs) cannot be ignored. Similar to parent polycyclic aromatic hydrocarbons (PPAHs), substituted PAHs (SPAHs) are prevalent in the environment and harmful to humans. However, they have not received much attention. This study investigated the occurrence, distribution, and sources of 10 PPAHs and 15 SPAHs in soil, water, and indoor and outdoor PM2.5 and dust in high-exposure areas (EAH) near industrial parks and low-exposure areas (EAL) far from industrial parks. PAH pollution in all media was more severe in the EAH than in the EAL. All SPAHs were detected in this study, with alkylated and oxygenated PAHs being predominant. Additionally, 3-OH-BaP and 1-OH-Pyr were detected in all dust samples in this study, and 6-N-Chr, a compound with carcinogenicity 10 times higher than that of BaP, was detected at high levels in all tap water samples. According to the indoor-outdoor ratio, PAHs in indoor PM2.5 in the EAH mainly originated from indoor pollution sources; however, those in the EAL were simultaneously affected by...
  • 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: 128
  • learning_rate: 2e-05
  • max_steps: 754146
  • log_level: info
  • fp16: True
  • dataloader_num_workers: 16
  • load_best_model_at_end: True
  • resume_from_checkpoint: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: steps
  • prediction_loss_only: True
  • per_device_train_batch_size: 128
  • 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: 2e-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: 754146
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.0
  • warmup_steps: 0
  • log_level: info
  • 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: 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: 16
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • 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: True
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: None
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • 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
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss Validation Loss
0.0000 1 4.0337 -
0.0040 1000 1.0731 -
0.0080 2000 0.2694 -
0.0119 3000 0.204 -
0.0159 4000 0.2225 -
0.0199 5000 0.1825 -
0.0239 6000 0.1539 -
0.0278 7000 0.1408 -
0.0318 8000 0.1364 -
0.0358 9000 0.1276 -
0.0398 10000 0.1185 -
0.0438 11000 0.1028 -
0.0477 12000 0.1086 -
0.0517 13000 0.1172 -
0.0557 14000 0.1129 -
0.0597 15000 0.1063 -
0.0636 16000 0.1774 -
0.0676 17000 0.0872 -
0.0716 18000 0.1404 -
0.0756 19000 0.0798 -
0.0796 20000 0.1644 -
0.0835 21000 0.0848 -
0.0875 22000 0.1551 -
0.0915 23000 0.0884 -
0.0955 24000 0.0923 -
0.0994 25000 0.0754 -
0.1034 26000 0.1395 -
0.1074 27000 0.0813 -
0.1114 28000 0.1574 -
0.1154 29000 0.09 -
0.1193 30000 0.1319 -
0.1233 31000 0.116 -
0.1273 32000 0.0806 -
0.1313 33000 0.2095 -
0.1353 34000 0.0716 -
0.1392 35000 0.0909 -
0.1432 36000 0.1333 -
0.1472 37000 0.067 -
0.1512 38000 0.1183 -
0.1551 39000 0.0739 -
0.1591 40000 0.0662 -
0.1631 41000 0.1371 -
0.1671 42000 0.0913 -
0.1711 43000 0.0867 -
0.1750 44000 0.1184 -
0.1790 45000 0.0913 -
0.1830 46000 0.0857 -
0.1870 47000 0.1223 -
0.1909 48000 0.0731 -
0.1949 49000 0.1028 -
0.1989 50000 0.1107 -
0.2029 51000 0.0726 -
0.2069 52000 0.076 -
0.2108 53000 0.0923 -
0.2148 54000 0.0896 -
0.2188 55000 0.0755 -
0.2228 56000 0.0627 -
0.2267 57000 0.0837 -
0.2307 58000 0.0732 -
0.2347 59000 0.0655 -
0.2387 60000 0.0653 -
0.2427 61000 0.0845 -
0.2466 62000 0.0568 -
0.2506 63000 0.0534 -
0.2546 64000 0.0723 -
0.2586 65000 0.0873 -
0.2625 66000 0.0615 -
0.2665 67000 0.0598 -
0.2705 68000 0.0573 -
0.2745 69000 0.062 -
0.2785 70000 0.1152 -
0.2824 71000 0.0945 -
0.2864 72000 0.0853 -
0.2904 73000 0.0457 -
0.2944 74000 0.0604 -
0.2983 75000 0.1008 -
0.3023 76000 0.0564 -
0.3063 77000 0.1009 -
0.3103 78000 0.0531 -
0.3143 79000 0.0966 -
0.3182 80000 0.0991 -
0.3222 81000 0.0617 -
0.3262 82000 0.0685 -
0.3302 83000 0.0895 -
0.3342 84000 0.0424 -
0.3381 85000 0.0646 -
0.3421 86000 0.0796 -
0.3461 87000 0.0599 -
0.3501 88000 0.1033 -
0.3540 89000 0.0475 -
0.3580 90000 0.0366 -
0.3620 91000 0.0402 -
0.3660 92000 0.0587 -
0.3700 93000 0.0661 -
0.3739 94000 0.077 -
0.3779 95000 0.0906 -
0.3819 96000 0.05 -
0.3859 97000 0.0505 -
0.3898 98000 0.0413 -
0.3938 99000 0.038 -
0.3978 100000 0.0478 -
0.4018 101000 0.073 -
0.4058 102000 0.0527 -
0.4097 103000 0.0351 -
0.4137 104000 0.0377 -
0.4177 105000 0.0347 -
0.4217 106000 0.0431 -
0.4256 107000 0.0613 -
0.4296 108000 0.0825 -
0.4336 109000 0.0546 -
0.4376 110000 0.0335 -
0.4416 111000 0.0232 -
0.4455 112000 0.0525 -
0.4495 113000 0.0473 -
0.4535 114000 0.0342 -
0.4575 115000 0.0346 -
0.4614 116000 0.0279 -
0.4654 117000 0.034 -
0.4694 118000 0.0778 -
0.4734 119000 0.0788 -
0.4774 120000 0.0703 -
0.4813 121000 0.0708 -
0.4853 122000 0.0393 -
0.4893 123000 0.037 -
0.4933 124000 0.0426 -
0.4972 125000 0.0335 -
0.5012 126000 0.0317 -
0.5052 127000 0.0406 -
0.5092 128000 0.0302 -
0.5132 129000 0.0284 -
0.5171 130000 0.0416 -
0.5211 131000 0.065 -
0.5251 132000 0.0402 -
0.5291 133000 0.0348 -
0.5331 134000 0.033 -
0.5370 135000 0.0485 -
0.5410 136000 0.0364 -
0.5450 137000 0.0686 -
0.5490 138000 0.0648 -
0.5529 139000 0.0652 -
0.5569 140000 0.0626 -
0.5609 141000 0.0684 -
0.5649 142000 0.0482 -
0.5689 143000 0.0517 -
0.5728 144000 0.0389 -
0.5768 145000 0.0435 -
0.5808 146000 0.044 -
0.5848 147000 0.03 -
0.5887 148000 0.0254 -
0.5927 149000 0.0268 -
0.5967 150000 0.0409 -
0.6007 151000 0.0401 -
0.6047 152000 0.0317 -
0.6086 153000 0.0309 -
0.6126 154000 0.0389 -
0.6166 155000 0.0368 -
0.6206 156000 0.0434 -
0.6245 157000 0.0469 -
0.6285 158000 0.0734 -
0.6325 159000 0.0544 -
0.6365 160000 0.0498 -
0.6405 161000 0.0256 -
0.6444 162000 0.0302 -
0.6484 163000 0.0398 -
0.6524 164000 0.0657 -
0.6564 165000 0.0548 -
0.6603 166000 0.0638 -
0.6643 167000 0.0288 -
0.6683 168000 0.0273 -
0.6723 169000 0.0323 -
0.6763 170000 0.045 -
0.6802 171000 0.0416 -
0.6842 172000 0.0281 -
0.6882 173000 0.0554 -
0.6922 174000 0.0435 -
0.6961 175000 0.0375 -
0.7001 176000 0.0354 -
0.7041 177000 0.038 -
0.7081 178000 0.0319 -
0.7121 179000 0.0329 -
0.7160 180000 0.0492 -
0.7200 181000 0.0545 -
0.7240 182000 0.051 -
0.7280 183000 0.045 -
0.7320 184000 0.0342 -
0.7359 185000 0.0237 -
0.7399 186000 0.0369 -
0.7439 187000 0.0437 -
0.7479 188000 0.0467 -
0.7518 189000 0.0424 -
0.7558 190000 0.0458 -
0.7598 191000 0.0434 -
0.7638 192000 0.0471 -
0.7678 193000 0.0404 -
0.7717 194000 0.0373 -
0.7757 195000 0.0254 -
0.7797 196000 0.0235 -
0.7837 197000 0.0212 -
0.7876 198000 0.0236 -
0.7916 199000 0.0226 -
0.7956 200000 0.0208 -
0.7996 201000 0.0276 -
0.8036 202000 0.0235 -
0.8075 203000 0.0358 -
0.8115 204000 0.0451 -
0.8155 205000 0.0442 -
0.8195 206000 0.0411 -
0.8234 207000 0.0447 -
0.8274 208000 0.0427 -
0.8314 209000 0.0259 -
0.8354 210000 0.0219 -
0.8394 211000 0.0253 -
0.8433 212000 0.0253 -
0.8473 213000 0.0242 -
0.8513 214000 0.0251 -
0.8553 215000 0.0223 -
0.8592 216000 0.0253 -
0.8632 217000 0.024 -
0.8672 218000 0.0249 -
0.8712 219000 0.0234 -
0.8752 220000 0.0232 -
0.8791 221000 0.0231 -
0.8831 222000 0.0264 -
0.8871 223000 0.051 -
0.8911 224000 0.0436 -
0.8950 225000 0.0429 -
0.8990 226000 0.0406 -
0.9030 227000 0.0363 -
0.9070 228000 0.0316 -
0.9110 229000 0.023 -
0.9149 230000 0.0239 -
0.9189 231000 0.0225 -
0.9229 232000 0.0223 -
0.9269 233000 0.0225 -
0.9309 234000 0.0223 -
0.9348 235000 0.0191 -
0.9388 236000 0.0202 -
0.9428 237000 0.0149 -
0.9468 238000 0.0215 -
0.9507 239000 0.012 -
0.9547 240000 0.0141 -
0.9587 241000 0.0152 -
0.9627 242000 0.0311 -
0.9667 243000 0.0344 -
0.9706 244000 0.0209 -
0.9746 245000 0.0145 -
0.9786 246000 0.0128 -
0.9826 247000 0.0124 -
0.9865 248000 0.0123 -
0.9905 249000 0.0141 -
0.9945 250000 0.0115 -
0.9985 251000 0.0131 -
1.0000 251382 - 0.0022
1.0025 252000 0.3338 -
1.0064 253000 0.1293 -
1.0104 254000 0.0893 -
1.0144 255000 0.0811 -
1.0184 256000 0.0863 -
1.0223 257000 0.0872 -
1.0263 258000 0.0693 -
1.0303 259000 0.0643 -
1.0343 260000 0.0638 -
1.0383 261000 0.0511 -
1.0422 262000 0.0554 -
1.0462 263000 0.0643 -
1.0502 264000 0.0492 -
1.0542 265000 0.0583 -
1.0581 266000 0.0738 -
1.0621 267000 0.079 -
1.0661 268000 0.0823 -
1.0701 269000 0.0673 -
1.0741 270000 0.059 -
1.0780 271000 0.0946 -
1.0820 272000 0.0494 -
1.0860 273000 0.0958 -
1.0900 274000 0.0561 -
1.0939 275000 0.0517 -
1.0979 276000 0.0502 -
1.1019 277000 0.0874 -
1.1059 278000 0.0463 -
1.1099 279000 0.0472 -
1.1138 280000 0.1038 -
1.1178 281000 0.0542 -
1.1218 282000 0.1036 -
1.1258 283000 0.05 -
1.1298 284000 0.1052 -
1.1337 285000 0.073 -
1.1377 286000 0.041 -
1.1417 287000 0.0981 -
1.1457 288000 0.0415 -
1.1496 289000 0.0561 -
1.1536 290000 0.0612 -
1.1576 291000 0.0426 -
1.1616 292000 0.0711 -
1.1656 293000 0.0703 -
1.1695 294000 0.0508 -
1.1735 295000 0.0756 -
1.1775 296000 0.053 -
1.1815 297000 0.0581 -
1.1854 298000 0.0793 -
1.1894 299000 0.0476 -
1.1934 300000 0.0601 -
1.1974 301000 0.0713 -
1.2014 302000 0.0501 -
1.2053 303000 0.0457 -
1.2093 304000 0.0471 -
1.2133 305000 0.0682 -
1.2173 306000 0.0544 -
1.2212 307000 0.0359 -
1.2252 308000 0.047 -
1.2292 309000 0.0527 -
1.2332 310000 0.0437 -
1.2372 311000 0.0384 -
1.2411 312000 0.0508 -
1.2451 313000 0.0383 -
1.2491 314000 0.0376 -
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3.0000 754146 - 0.0013

Framework Versions

  • Python: 3.11.11
  • Sentence Transformers: 3.4.1
  • Transformers: 4.48.2
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.3.0
  • Datasets: 3.2.0
  • Tokenizers: 0.21.0

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}
}
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