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
fix: incorrect condition control flow
#261
by tpob - opened
- geneformer/pretrainer.py +1 -1
geneformer/pretrainer.py
CHANGED
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@@ -381,7 +381,7 @@ class GeneformerPreCollator(SpecialTokensMixin):
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| 381 |
return_tensors = "tf" if return_tensors is None else return_tensors
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| 382 |
elif is_torch_available() and _is_torch(first_element):
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| 383 |
return_tensors = "pt" if return_tensors is None else return_tensors
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| 384 |
-
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| 385 |
return_tensors = "np" if return_tensors is None else return_tensors
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| 386 |
else:
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| 387 |
raise ValueError(
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| 381 |
return_tensors = "tf" if return_tensors is None else return_tensors
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| 382 |
elif is_torch_available() and _is_torch(first_element):
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| 383 |
return_tensors = "pt" if return_tensors is None else return_tensors
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| 384 |
+
elif isinstance(first_element, np.ndarray):
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| 385 |
return_tensors = "np" if return_tensors is None else return_tensors
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| 386 |
else:
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| 387 |
raise ValueError(
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