Text Classification
Transformers
Safetensors
English
bert
single-cell
perturbation-prediction
crispr
crispra
geneformer
vcbench
foundation-model
benchmark
norman-2019
bert-classifier
Eval Results (legacy)
Instructions to use appliedscientific/vcbench-geneformer-perturbation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appliedscientific/vcbench-geneformer-perturbation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="appliedscientific/vcbench-geneformer-perturbation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("appliedscientific/vcbench-geneformer-perturbation") model = AutoModelForSequenceClassification.from_pretrained("appliedscientific/vcbench-geneformer-perturbation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- efd28ca76d4963b9d87a6d3d86d75d738643b07c5cc1f5c6a472fd070f5383c7
- Size of remote file:
- 5.97 kB
- SHA256:
- 5d443287c8811d6434e9e494f8eced6fd749726824f33c0f442dbf16d36da750
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