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Open-weight model · Token classification
by OpenMed OpenMed/OpenMed-NER-DNADetect-SuperMedical-125M
Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for biomedical entity recognition - proteins, dna…
What it takes to serve OpenMed-NER-DNADetect-SuperMedical-125M (124M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
By OpenMed, published under apache-2.0, revision 2c0f34199a82.
Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types
This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.
This model can identify and classify the following biomedical entities:
B-DNAB-RNAB-cell_lineB-cell_typeB-protein11 files, 253.5 MB in total. The weights are 1 file totalling 248.2 MB in safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 248.2 MB | 5608c42e45c4 |
| config.json | Configuration | 1.1 KB | — |
| special_tokens_map.json | Configuration | 280 B | — |
| test_results.json | Configuration | 196 B | — |
| README.md | Documentation | 11.8 KB | — |
| openmed_vs_sota_grouped_bars.png | Other | 497.0 KB | 626b37d9b20c |
| .gitattributes | Repository | 1.6 KB | — |
| merges.txt | Tokenizer | 456.3 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 1.2 KB | — |
| vocab.json | Tokenizer | 798.3 KB | — |
Released by OpenMed through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 248.2 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (124M parameters) plus a working margin. A long context needs more.
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Yes. OpenMed-NER-DNADetect-SuperMedical-125M is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
514 tokens, from the maximum position embeddings in its published configuration.
This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 10 - Transformers 4.37.2 - Pytorch 2.2.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1
Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the ncbi dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications. This…
This is a SpanMarker model trained on the Acronym Identification dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script. Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span-marker-bert-base-acronyms. You can finetune this model on your own dataset. - learningrate: 5e-05 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 2 Carbon emissions were measured using CodeCarbon.
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