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")# 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
- Xet hash:
- 96ea15d28f06b31a4205a6fc999ca474fcfb6f6ada86577ae3bc802e60719b57
- Size of remote file:
- 357 MB
- SHA256:
- 13a55865f0a735539d2f9805cbec94807d94b1efbf7353426e367369c9e1fb4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.