Feature Extraction
Transformers
PyTorch
English
modernbert
genomics
rna
nucleotide
sequence-modeling
biology
bioinformatics
electra
Instructions to use FreakingPotato/RNAElectra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreakingPotato/RNAElectra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FreakingPotato/RNAElectra")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FreakingPotato/RNAElectra") model = AutoModel.from_pretrained("FreakingPotato/RNAElectra", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#3
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3664c4eeeda5e23731ef26cf50d8c3ef77d9c878a3f965154b16167f736daef8
|
| 3 |
+
size 369259168
|