# Dao Minh: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/daominhwysi
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Models

Model · Fill mask

### [mmbert-small-vi-exam-seq-labeling](https://savrn.com/models/mmbert-small-vi-exam-seq-labeling)

[Dao Minh](https://savrn.com/model-publishers/daominhwysi)

This model is a fine-tuned version of jhu-clsp/mmBERT-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 4 - distributedtype: multi-GPU - numdevices: 2 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - totalevalbatchsize: 8 - lrschedulertype: cosine - lrschedulerwarmupsteps: 0.1 - numepochs: 4 - mixedprecisiontraining: Native AMP - Transformers 5.0.0 - Pytorch 2.10.0+cu128 - Datasets 5.0.0 - Tokenizers 0.22.2

Open weights mit 308M parameters 8,192 tokens transformers

[View model](https://savrn.com/models/mmbert-small-vi-exam-seq-labeling)

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- [All model publishers](https://savrn.com/model-publishers)
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## Source

- Listed from their public repositories, read 2026-09-25.
- [Hugging Face profile](https://huggingface.co/daominhwysi)
- [How the hub is built](https://savrn.com/model-hub/methodology)