This experimental model was fine-tuned on structured drug-product records from the official openFDA National Drug Code Directory. Training text includes brand name, active ingredients, strength, dosage form, and route.

The openFDA source data is generally available under CC0/public-domain terms; the base model and software dependencies retain their own terms. This model is for research and catalog-matching experiments. It must not be used for diagnosis, prescribing, dosage decisions, interaction checks, or automatic medicine substitution.

Held-out evaluation:

nearest-neighbour exact-product accuracy: 0.6585 -> 0.8171
validation triplet accuracy: 0.8995 -> 0.9416
normalized separation margin: 2.5155 -> 2.8190

This is a sentence-transformers model finetuned from FremyCompany/BioLORD-2023 on the pairs and triplets datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: FremyCompany/BioLORD-2023
Maximum Sequence Length: 32 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text
Training Datasets:
- pairs
- triplets

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'MPNetModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'mean', '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'Brand: smart care island mango hand sanitizer. Ingredients: alcohol. Strength: 70 ml/100ml. Form: spray. Route: topical.',
    'Brand: ashleybelle moisturizing hand sanitizer cranberry sage. Ingredients: alcohol. Strength: 70 ml/100ml. Form: spray. Route: topical.',
    'Brand: walgreens dandruff itchy dry scalp defense anti-dandruff. Ingredients: pyrithione zinc. Strength: 10 mg/ml. Form: shampoo. Route: topical.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8526, 0.4529],
#         [0.8526, 1.0000, 0.4206],
#         [0.4529, 0.4206, 1.0000]])

Evaluation

Metrics

Composition

Dataset: comp
Evaluated with [object Object].CompositionEvaluator

Metric	Value
same	0.9165
diff	0.5142
margin	0.4023
margin_norm	2.819
nn_accuracy	0.8171

Triplet

Dataset: val
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9416

Training Details

Training Datasets

pairs

Dataset: pairs
Size: 13,331 training samples
Columns: anchor and positive
Approximate statistics based on the first 100 samples:
anchor positive
type string string
modality text text
details
min: 27 tokens
mean: 31.86 tokens
max: 32 tokens
min: 28 tokens
mean: 31.86 tokens
max: 32 tokens

Samples:

anchor	positive
`Brand: gps topical anesthetic. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: primo topical anesthetic. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`
`Brand: bencocaine topical anesthetic. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: tiger supply inc topical anesthetic. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`
`Brand: safco sensicaine ultra topical anesthetic gel. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: advance topical anesthetic gel. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`

Loss: CachedGISTEmbedLoss with these parameters:
json
1{ 2 "guide": "SentenceTransformer(None)", 3 "temperature": 0.01, 4 "mini_batch_size": 64, 5 "mini_batch_num_tokens": null, 6 "margin_strategy": "absolute", 7 "margin": 0.0, 8 "contrast_anchors": true, 9 "contrast_positives": true, 10 "gather_across_devices": false 11}

triplets

Dataset: triplets
Size: 27,325 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 100 samples:

	anchor	positive	negative
type	string	string	string
modality	text	text	text
details	min: 27 tokens mean: 31.8 tokens max: 32 tokens	min: 28 tokens mean: 31.88 tokens max: 32 tokens	min: 28 tokens mean: 31.61 tokens max: 32 tokens

Samples:

anchor	positive	negative
`Brand: candee caine topical anesthetic. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: advance topical anesthetic gel. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: 7 select oral pain maximum strength relief. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: topical.`
`Brand: kolorz topical anesthetic blue rasberry. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: kolorz topical anesthetic triple mint. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: lubelife climax control delay. Ingredients: benzocaine. Strength: 7.5 g/100ml. Form: spray. Route: topical.`
`Brand: quala topical anesthetic gel. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: kolorz topical anesthetic cotton candy. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental.`	`Brand: hurricaine topical anesthetic. Ingredients: benzocaine. Strength: 200 mg/g. Form: gel. Route: dental \| periodontal.`

Loss: CachedGISTEmbedLoss with these parameters:
json
1{ 2 "guide": "SentenceTransformer(None)", 3 "temperature": 0.01, 4 "mini_batch_size": 64, 5 "mini_batch_num_tokens": null, 6 "margin_strategy": "absolute", 7 "margin": 0.0, 8 "contrast_anchors": true, 9 "contrast_positives": true, 10 "gather_across_devices": false 11}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 512
num_train_epochs: 1.0
learning_rate: 1e-05
warmup_steps: 0.1
bf16: True
load_best_model_at_end: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

per_device_train_batch_size: 512
num_train_epochs: 1.0
max_steps: -1
learning_rate: 1e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: True
fp16: False
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 8
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: None
fsdp_config: None
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	comp_nn_accuracy	val_cosine_accuracy
-1	-1	-	0.6585	0.8995
0.6173	50	0.6714	-	-
1.0	81	-	0.8171	0.9416

The bold row denotes the saved checkpoint.

Training Time

Training: 1.9 minutes
Evaluation: 0.8 seconds
Total: 1.9 minutes

Framework Versions

Python: 3.12.10
Sentence Transformers: 5.7.0
Transformers: 5.14.1
PyTorch: 2.11.0+cu128
Accelerate: 1.14.0
Datasets: 5.0.1
Tokenizers: 0.22.2

Additional Resources

Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

Aditya109/us-openfda-drug-embed-v1

SentenceTransformer based on FremyCompany/BioLORD-2023

U.S. openFDA Drug Product Research Model