NeuML / biomedbert-hash-nano

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Model's Last Updated: December 23 2025

Introduction of biomedbert-hash-nano

Model Details of biomedbert-hash-nano

BiomedBERT Hash Nano

This is a 970K parameter BERT encoder-only model trained on data from PubMed . The raw data was transformed using PaperETL with the results stored as a local dataset via the Hugging Face Datasets library .

biomedbert-hash-nano is built with the BERT Hash architecture as described in the following links.

Usage

biomedbert-hash-nano can be loaded using Hugging Face Transformers as follows. Note that given that this is a custom architecture, trust_remote_code needs to be set.

from transformers import AutoModel

model = AutoModel.from_pretrained("neuml/biomedbert-hash-nano", trust_remote_code=True)

The model is intended to be further fine-tuned for a specific task such as Text Classification, Entity Extraction, Sentence Embeddings and so on.

Evaluation Results

This Medical Abstracts Text Classification Dataset was used to evaluate the model's performance. A handful of biomedical models and general models were selected for comparison.

Metrics were generated using Hugging Face's standard run_glue script as shown below.

python run_glue.py --model_name_or_path neuml/biomedbert-hash-nano --dataset-name medclassify --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 32 --learning_rate 1e-4 --num_train_epochs 4 --output_dir outputs --trust-remote-code True

Note: The original dataset was saved locally as medclassify the the condition_label column renamed to label to work more easily with the glue script

Model Parameters Accuracy Loss
biomedbert-hash-nano 0.969M 0.6195 0.9464
bert-hash-nano 0.969M 0.5045 1.2192
bert-base-uncased 110M 0.6118 0.9712
biomedbert-base 110M 0.6195 0.9037
ModernBERT-base 149M 0.5672 1.1079
BioClinical-ModernBERT-base 149M 0.5679 1.0915

As we can see, this model performs very well against models much larger in size. This dataset is a challenging one!

Runs of NeuML biomedbert-hash-nano on huggingface.co

23
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More Information About biomedbert-hash-nano huggingface.co Model

More biomedbert-hash-nano license Visit here:

https://choosealicense.com/licenses/apache-2.0

biomedbert-hash-nano huggingface.co

biomedbert-hash-nano huggingface.co is an AI model on huggingface.co that provides biomedbert-hash-nano's model effect (), which can be used instantly with this NeuML biomedbert-hash-nano model. huggingface.co supports a free trial of the biomedbert-hash-nano model, and also provides paid use of the biomedbert-hash-nano. Support call biomedbert-hash-nano model through api, including Node.js, Python, http.

biomedbert-hash-nano huggingface.co Url

https://huggingface.co/NeuML/biomedbert-hash-nano

NeuML biomedbert-hash-nano online free

biomedbert-hash-nano huggingface.co is an online trial and call api platform, which integrates biomedbert-hash-nano's modeling effects, including api services, and provides a free online trial of biomedbert-hash-nano, you can try biomedbert-hash-nano online for free by clicking the link below.

NeuML biomedbert-hash-nano online free url in huggingface.co:

https://huggingface.co/NeuML/biomedbert-hash-nano

biomedbert-hash-nano install

biomedbert-hash-nano is an open source model from GitHub that offers a free installation service, and any user can find biomedbert-hash-nano on GitHub to install. At the same time, huggingface.co provides the effect of biomedbert-hash-nano install, users can directly use biomedbert-hash-nano installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

biomedbert-hash-nano install url in huggingface.co:

https://huggingface.co/NeuML/biomedbert-hash-nano

Url of biomedbert-hash-nano

biomedbert-hash-nano huggingface.co Url

Provider of biomedbert-hash-nano huggingface.co

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