BamiBERT-ViMedNER

Base model

This model is fine-tuned from Qualcomm-AI-Research/BamiBERT for Vietnamese biomedical Named Entity Recognition (NER).

Dataset

This model was fine-tuned on the ViMedNER dataset.

Repository: https://github.com/tdtrinh11/ViMedNer

Training Hyperparameters

The model was fine-tuned from Qualcomm-AI-Research/BamiBERT for Vietnamese biomedical Named Entity Recognition (NER) using the following training configuration.

Evaluation

The model was evaluated on the ViMedNER test set using the seqeval evaluation metrics for named entity recognition.

Overall Performance

Per-Entity Performance

Aggregate Metrics

Note: Evaluation was performed using the seqeval library on the official ViMedNER test split. The model achieves an overall micro F1-score of 68.6% on the ViMedNER test set. Performance is strongest on the ten_benh (Disease) entity type with an F1-score of 0.81, while nguyen_nhan_benh (Cause) remains the most challenging category due to its relatively small number of training examples and semantic diversity.

Quick Start

Install dependencies

pip install transformers torch

Load the Model

from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification

MODEL_NAME = "cbc-528a/BamiBERT-ViMedNER"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME)

Example Inference

import torch
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification

MODEL_NAME = "cbc-528a/BamiBERT-ViMedNER"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME)

sentence = "Bệnh nhân bị ung thư gan và được điều trị bằng Sorafenib."

words = sentence.split()

inputs = tokenizer(
    words,
    is_split_into_words=True,
    return_tensors="pt"
)

with torch.no_grad():
    outputs = model(**inputs)

predictions = outputs.logits.argmax(dim=-1)[0]

word_ids = inputs.word_ids()

id2label = model.config.id2label

previous_word = None

for pred, word_idx in zip(predictions, word_ids):

    if word_idx is None or word_idx == previous_word:
        continue

    previous_word = word_idx

    print(
        words[word_idx],
        id2label[pred.item()]
    )

Sample Output

Bệnh        O
nhân        O
bị          O
ung         B-ten_benh
thư         I-ten_benh
gan         I-ten_benh
và          O
được        O
điều        O
trị         O
bằng        O
Sorafenib   B-bien_phap_dieu_tri
.           O

License

This model is a fine-tuned derivative of Qualcomm-AI-Research/BamiBERT.

Accordingly, use of this model is subject to:

• BSD 3-Clause Clear License
• Qualcomm Responsible AI License

Please refer to the original BamiBERT repository for the complete license terms.

Acknowledgements & Citation

This model is fine-tuned based on the BamiBERT architecture. I would like to express my gratitude to the original authors for their foundational work.

BibTeX:

@article{BamiBERT,
  title    = {{BamiBERT: A New BERT-based Language Model for Vietnamese}},
  author   = {Dat Quoc Nguyen and Thinh Pham and Chi Tran and Linh The Nguyen},
  journal  = {arXiv preprint},
  volume   = {arXiv:2607.02259},
  year     = {2026}
}