# Reproducible Results?

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**Category:** 🤗Transformers\
**Created:** [March 3, 2024, 5:54pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Freproducible-results%2F75717 "2024-03-03T17:54:13Z")\
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**Page:** 1

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**Author:** ![QiyaoWei](https://avatars.discourse-cdn.com/v4/letter/q/8797f3/32.png) [@QiyaoWei](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FQiyaoWei)%5C
**Post date:** [March 3, 2024, 5:54pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Freproducible-results%2F75717%2F1 "2024-03-03T17:54:13Z")

</div>

I have a general question about reproducible results in Huggingface. My experiments usually work quite well, yielding the same results across multiple runs. However, I notice that when I am extending a model class, the results are no longer the same even though the setting should be. For example, say I am extending some Huggingface model like this

```auto
class MyModel(HuggingfaceModel):
def __init__ (self, *args):
super(). __init__ (*args)

# Want to have some extra operation here
if torch.rand() < 0:
#do something

```

For context, the number “0” might be a hyperparameter that I want to vary. Even when I evaluate `torch.rand() < 0`, which shouldn’t evaluate true anyways and therefore should not change the code compared to the original model, I notice that my training results still look different. Is this expected, or am I seeding something incorrectly here (note that I am not explicitly doing any seeding so I am leaving everything to be done in the HuggingfaceModel)?