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drug-name-SBERT

dekshitha-k / drug-name-SBERT

Run on huggingface.co
huggingface.co
Total runs: 14
24-hour runs: 0
7-day runs: -7
30-day runs: -6
Model's Last Updated: November 18 2024
sentence-similarity

Introduction of drug-name-SBERT

Model Details of drug-name-SBERT

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2 . It maps sentences & paragraphs to a 384-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 Sources
Full Model Architecture 
SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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})
  (2): Normalize()
)
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("dekshitha-k/drug-name-SBERT")
# Run inference
sentences = [
    "['ampace at 5 mg/50 mg tabcap']",
    "[' Atecard M 5 mg/50 mg Tabcap']",
    "[' Laxnova Plus Syrup', ' Lacofin P Syrup', ' Softway Plus Syrup']",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Training Details
Training Dataset
Unnamed Dataset
  • Size: 173,752 training samples
  • Columns: sentence_0 , sentence_1 , and label
  • Approximate statistics based on the first 1000 samples:
    sentence_0 sentence_1 label
    type string string int
    details
    • min: 4 tokens
    • mean: 29.93 tokens
    • max: 56 tokens
    • min: 9 tokens
    • mean: 29.71 tokens
    • max: 59 tokens
    • 1: 100.00%
  • Samples:
    sentence_0 sentence_1 label
    ['unipod 50mg tablet dt', ' Xop 50mg Tablet DT', ' Grandcef 50mg Tablet DT'] [' C Pod 50mg Tablet DT', ' Codimol 50mg Tablet DT', ' Cepime O 50mg Tablet DT'] 1
    ['lenvima 10mg capsule', ' Lenvamed 10mg Capsule', ' Bdfoie 10mg Capsule'] [' Lenced 10mg Capsule', ' Lentykine 10 Capsule', ' Lenvatol 10mg Capsule'] 1
    ['brecold 5mg/325mg/5mg tablet', ' Snowzy Cold Tablet', ' Pcp Forte 5mg/325mg/5mg Tablet'] [' Ioncold-P 5mg/325mg/5mg Tablet', ' Candicold 5mg/325mg/5mg Tablet', ' Colvaxy Tablet'] 1
  • Loss: CosineSimilarityLoss with these parameters: 
    {
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}
Training Hyperparameters
Non-Default Hyperparameters
  • per_device_train_batch_size : 16
  • per_device_eval_batch_size : 16
  • num_train_epochs : 1
  • multi_dataset_batch_sampler : round_robin
All Hyperparameters
Click to expand
  • overwrite_output_dir : False
  • do_predict : False
  • eval_strategy : no
  • prediction_loss_only : True
  • per_device_train_batch_size : 16
  • per_device_eval_batch_size : 16
  • 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
  • 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
  • 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 : round_robin
Training Logs
Epoch Step Training Loss
0.0460 500 0.0384
0.0921 1000 0.0045
0.1381 1500 0.0009
0.1842 2000 0.0003
0.2302 2500 0.0002
0.2762 3000 0.0001
0.3223 3500 0.0001
0.3683 4000 0.0001
0.4144 4500 0.0
0.4604 5000 0.0
0.5064 5500 0.0
0.5525 6000 0.0
0.5985 6500 0.0
0.6446 7000 0.0
0.6906 7500 0.0
0.7366 8000 0.0
0.7827 8500 0.0
0.8287 9000 0.0
0.8748 9500 0.0
0.9208 10000 0.0
0.9669 10500 0.0
Framework Versions
  • Python: 3.10.12
  • Sentence Transformers: 3.3.0
  • Transformers: 4.46.2
  • PyTorch: 2.5.1+cu121
  • Accelerate: 1.1.1
  • Datasets: 3.1.0
  • Tokenizers: 0.20.3
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",
}

Runs of dekshitha-k drug-name-SBERT on huggingface.co

14
Total runs
0
24-hour runs
0
3-day runs
-7
7-day runs
-6
30-day runs

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dekshitha-k
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