Instructions to use ctheodoris/Geneformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctheodoris/Geneformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ctheodoris/Geneformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ctheodoris/Geneformer") model = AutoModelForMaskedLM.from_pretrained("ctheodoris/Geneformer") - Inference
- Notebooks
- Google Colab
- Kaggle
"save_model_without_heads" is redundant
#385
by madhavanvenkatesh - opened
perturber and emb-extractor works even when provided pytorch_model.bin with the classification weights/biases (no need to remove classification heads to do downstream tasks:
newly initialized: ['cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight'] treated correctly with BertForMaskedLM
ctheodoris changed pull request status to merged