# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("szd0709/MultiAgent4SyntheticBioNLP-models", device_map="auto")MultiAgent4SyntheticBioNLP experiment models
This repository archives Hugging Face models from multiple experiments and
datasets in the MultiAgent4SyntheticBioNLP project. Remote paths mirror the
project hierarchy beginning below experiments/, while local-only model or
models container directories are removed.
For example:
local: overall-results/phosphorylation/model/<run>/model/*
remote: overall-results/phosphorylation/<run>/*
Each run directory is directly loadable as a Transformers subfolder:
from transformers import AutoModel, AutoTokenizer
repo_id = "OWNER/REPOSITORY"
subfolder = "overall-results/phosphorylation/RUN_NAME"
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
model = AutoModel.from_pretrained(repo_id, subfolder=subfolder)
Each run contains only its final inference-model artifacts. Per-run
metrics.json, prepared-data/, prepared training CSVs, and checkpoints are
not uploaded.
Intended use and limitations
These are research artifacts for studying synthetic-data augmentation in biomedical NLP. They are not intended for clinical diagnosis or treatment decisions. Review the corresponding local experiment documentation, metrics, and data-selection manifest before reuse.
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="szd0709/MultiAgent4SyntheticBioNLP-models")