Text Classification
Transformers
English
bert
biology
genomics
dna
variant-effect-prediction
dnabert
deepvregulome
transcription-factors
histone-modifications
ENCODE
chip-seq
regulatory-variants
cancer-genomics
glioblastoma
noncoding-variants
fine-tuned
sequence-classification
Eval Results (legacy)
Instructions to use duttaprat/DeepVRegulome with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duttaprat/DeepVRegulome with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="duttaprat/DeepVRegulome")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("duttaprat/DeepVRegulome") model = AutoModelForSequenceClassification.from_pretrained("duttaprat/DeepVRegulome", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload models/CTCFL/pytorch_model.bin with huggingface_hub
Browse files
models/CTCFL/pytorch_model.bin
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