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pankajrajdeo
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182500_bioformer_8L

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:9358675
loss:TripletLoss
text-embeddings-inference
Inference Endpoints
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SentenceTransformer

This is a sentence-transformers model trained. 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 tokens
  • Similarity Function: Cosine Similarity

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/182500_bioformer_8L")
# Run inference
sentences = [
    'vägtrafikolyckor',
    'accidente vial',
    'trimeresurus andersoni',
]
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

Unnamed Dataset

  • Size: 9,358,675 training samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 6 tokens
    • mean: 12.84 tokens
    • max: 23 tokens
    • min: 3 tokens
    • mean: 15.45 tokens
    • max: 187 tokens
    • min: 3 tokens
    • mean: 14.75 tokens
    • max: 91 tokens
  • Samples:
    anchor positive negative
    (131)i-makroaggregerat albumin macroagrégats d'albumine humaine marquée à l'iode 131 1-acylglycerophosphorylinositol
    (131)i-makroaggregerat albumin albumin, radio-iodinated serum allo-aromadendrane-10alpha,14-diol
    (131)i-makroaggregerat albumin serum albumin, radio iodinated acquired zygomatic hyperplasia
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Evaluation Dataset

Unnamed Dataset

  • Size: 820,102 evaluation samples
  • Columns: anchor, positive, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor positive negative
    type string string string
    details
    • min: 3 tokens
    • mean: 10.54 tokens
    • max: 20 tokens
    • min: 3 tokens
    • mean: 13.21 tokens
    • max: 183 tokens
    • min: 3 tokens
    • mean: 14.98 tokens
    • max: 322 tokens
  • Samples:
    anchor positive negative
    15-ketosteryloleathydrolase steroid esterase, lipoidal glutamic acid-lysine-tyrosine terpolymer
    15-ketosteryloleathydrolase hydrolase, cholesterol ester unionicola parvipora
    15-ketosteryloleathydrolase acylhydrolase, sterol ester mayamaea fossalis var. fossalis
  • Loss: TripletLoss with these parameters:
    {
    "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
    "triplet_margin": 5
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 128
  • learning_rate: 2e-05
  • num_train_epochs: 10
  • warmup_ratio: 0.1
  • fp16: True
  • load_best_model_at_end: 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: 10
  • max_steps: -1
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • 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: 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: 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: 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
  • use_liger_kernel: 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
0.0137 1000 2.6865 -
0.0274 2000 1.4053 -
0.0410 3000 0.9222 -
0.0547 4000 0.7162 -
0.0684 5000 0.6036 -
0.0821 6000 0.5245 -
0.0957 7000 0.4665 -
0.1094 8000 0.4215 -
0.1231 9000 0.3931 -
0.1368 10000 0.3661 -
0.1504 11000 0.348 -
0.1641 12000 0.3241 -
0.1778 13000 0.3108 -
0.1915 14000 0.2943 -
0.2052 15000 0.2817 -
0.2188 16000 0.2653 -
0.2325 17000 0.2562 -
0.2462 18000 0.2529 -
0.2599 19000 0.2438 -
0.2735 20000 0.2359 -
0.2872 21000 0.2237 -
0.3009 22000 0.2207 -
0.3146 23000 0.2143 -
0.3283 24000 0.2141 -
0.3419 25000 0.2024 -
0.3556 26000 0.196 -
0.3693 27000 0.1951 -
0.3830 28000 0.19 -
0.3966 29000 0.1864 -
0.4103 30000 0.1866 -
0.4240 31000 0.1797 -
0.4377 32000 0.1805 -
0.4513 33000 0.1681 -
0.4650 34000 0.1712 -
0.4787 35000 0.1698 -
0.4924 36000 0.1619 -
0.4992 36500 - 0.1407
0.5061 37000 0.1652 -
0.5197 38000 0.1622 -
0.5334 39000 0.1603 -
0.5471 40000 0.1518 -
0.5608 41000 0.1488 -
0.5744 42000 0.1531 -
0.5881 43000 0.1472 -
0.6018 44000 0.1454 -
0.6155 45000 0.1473 -
0.6291 46000 0.1411 -
0.6428 47000 0.1389 -
0.6565 48000 0.1375 -
0.6702 49000 0.1393 -
0.6839 50000 0.1366 -
0.6975 51000 0.134 -
0.7112 52000 0.1331 -
0.7249 53000 0.1323 -
0.7386 54000 0.1309 -
0.7522 55000 0.1254 -
0.7659 56000 0.1298 -
0.7796 57000 0.1244 -
0.7933 58000 0.1254 -
0.8069 59000 0.1205 -
0.8206 60000 0.1213 -
0.8343 61000 0.1226 -
0.8480 62000 0.1187 -
0.8617 63000 0.1158 -
0.8753 64000 0.1171 -
0.8890 65000 0.1137 -
0.9027 66000 0.1172 -
0.9164 67000 0.1169 -
0.9300 68000 0.1137 -
0.9437 69000 0.1145 -
0.9574 70000 0.1127 -
0.9711 71000 0.1126 -
0.9848 72000 0.1126 -
0.9984 73000 0.1078 0.0997
1.0121 74000 0.0999 -
1.0258 75000 0.1001 -
1.0395 76000 0.0962 -
1.0531 77000 0.0984 -
1.0668 78000 0.0982 -
1.0805 79000 0.098 -
1.0942 80000 0.0964 -
1.1078 81000 0.0964 -
1.1215 82000 0.0949 -
1.1352 83000 0.0929 -
1.1489 84000 0.0914 -
1.1626 85000 0.0918 -
1.1762 86000 0.0916 -
1.1899 87000 0.0891 -
1.2036 88000 0.0921 -
1.2173 89000 0.0925 -
1.2309 90000 0.091 -
1.2446 91000 0.0875 -
1.2583 92000 0.0898 -
1.2720 93000 0.0856 -
1.2856 94000 0.0866 -
1.2993 95000 0.0843 -
1.3130 96000 0.0848 -
1.3267 97000 0.0872 -
1.3404 98000 0.0853 -
1.3540 99000 0.0898 -
1.3677 100000 0.0831 -
1.3814 101000 0.0819 -
1.3951 102000 0.0842 -
1.4087 103000 0.083 -
1.4224 104000 0.0824 -
1.4361 105000 0.0802 -
1.4498 106000 0.0834 -
1.4634 107000 0.0833 -
1.4771 108000 0.0815 -
1.4908 109000 0.079 -
1.4976 109500 - 0.0820
1.5045 110000 0.0809 -
1.5182 111000 0.0784 -
1.5318 112000 0.0767 -
1.5455 113000 0.0782 -
1.5592 114000 0.0799 -
1.5729 115000 0.0787 -
1.5865 116000 0.0798 -
1.6002 117000 0.0821 -
1.6139 118000 0.0771 -
1.6276 119000 0.0758 -
1.6413 120000 0.0789 -
1.6549 121000 0.0777 -
1.6686 122000 0.0755 -
1.6823 123000 0.0774 -
1.6960 124000 0.0748 -
1.7096 125000 0.077 -
1.7233 126000 0.0755 -
1.7370 127000 0.0749 -
1.7507 128000 0.0718 -
1.7643 129000 0.0753 -
1.7780 130000 0.0728 -
1.7917 131000 0.0704 -
1.8054 132000 0.0719 -
1.8191 133000 0.0711 -
1.8327 134000 0.0713 -
1.8464 135000 0.0695 -
1.8601 136000 0.0716 -
1.8738 137000 0.0691 -
1.8874 138000 0.0692 -
1.9011 139000 0.0744 -
1.9148 140000 0.0726 -
1.9285 141000 0.0682 -
1.9421 142000 0.0695 -
1.9558 143000 0.0723 -
1.9695 144000 0.0711 -
1.9832 145000 0.0692 -
1.9969 146000 0.0694 0.0704
2.0105 147000 0.0572 -
2.0242 148000 0.0545 -
2.0379 149000 0.0549 -
2.0516 150000 0.0552 -
2.0652 151000 0.0551 -
2.0789 152000 0.0559 -
2.0926 153000 0.0582 -
2.1063 154000 0.0587 -
2.1199 155000 0.0529 -
2.1336 156000 0.059 -
2.1473 157000 0.0534 -
2.1610 158000 0.0547 -
2.1747 159000 0.0543 -
2.1883 160000 0.0558 -
2.2020 161000 0.0548 -
2.2157 162000 0.0534 -
2.2294 163000 0.0548 -
2.2430 164000 0.0546 -
2.2567 165000 0.053 -
2.2704 166000 0.0557 -
2.2841 167000 0.0541 -
2.2978 168000 0.0527 -
2.3114 169000 0.0542 -
2.3251 170000 0.0529 -
2.3388 171000 0.0554 -
2.3525 172000 0.054 -
2.3661 173000 0.0506 -
2.3798 174000 0.054 -
2.3935 175000 0.0525 -
2.4072 176000 0.0542 -
2.4208 177000 0.0546 -
2.4345 178000 0.0516 -
2.4482 179000 0.053 -
2.4619 180000 0.0542 -
2.4756 181000 0.0538 -
2.4892 182000 0.0536 -
2.4961 182500 - 0.0655

Framework Versions

  • Python: 3.9.16
  • Sentence Transformers: 3.1.1
  • Transformers: 4.45.2
  • PyTorch: 2.4.1+cu121
  • Accelerate: 1.0.0
  • Datasets: 3.0.1
  • Tokenizers: 0.20.0

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",
}

TripletLoss 

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
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