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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ctheodoris/Geneformer") model = AutoModelForMaskedLM.from_pretrained("ctheodoris/Geneformer", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Christina Theodoris commited on
Commit ·
79788b6
1
Parent(s): cf0d7d4
update type of null_dict_list in docstring
Browse files
geneformer/in_silico_perturber_stats.py
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@@ -19,7 +19,7 @@ Geneformer in silico perturber stats generator.
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**Description:**
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| Input data is raw in silico perturbation results in the form of dictionaries outputted by ``in_silico_perturber``.
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"""
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@@ -864,8 +864,8 @@ class InSilicoPerturberStats:
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| Path to directory where perturbation data will be saved as .csv
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output_prefix : str
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| Prefix for output .csv
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null_dict_list: dict
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| List of loaded null
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**Outputs:**
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**Description:**
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| Aggregates data or calculates stats for in silico perturbations based on type of statistics specified in InSilicoPerturberStats.
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| Input data is raw in silico perturbation results in the form of dictionaries outputted by ``in_silico_perturber``.
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"""
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| Path to directory where perturbation data will be saved as .csv
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output_prefix : str
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| Prefix for output .csv
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null_dict_list: list[dict]
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| List of loaded null distribution dictionary if more than one comparison vs. the null is to be performed
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**Outputs:**
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