Retrieving original data from PyTorch nn.Embedding
I'm passing a dataframe with 5 categories (ex. car, bus, ...) into nn.Embedding.
When I do embedding.parameters(), I can see that there are 5tensors but how do I know which index corresponds to the original input (ex. car, bus, ...)?

You can't as tensors are unnamed (only dimensions can be named, see PyTorch's Named Tensors).
You have to keep the names in separate data container, for example (4 categories here):
import pandas as pd
import torch

df = pd.DataFrame(
    {
        &quot;bus&quot;: [1.0, 2, 3, 4, 5],
        &quot;car&quot;: [6.0, 7, 8, 9, 10],
        &quot;bike&quot;: [11.0, 12, 13, 14, 15],
        &quot;train&quot;: [16.0, 17, 18, 19, 20],
    }
)

df_data = df.to_numpy().T
df_names = list(df)

embedding = torch.nn.Embedding(df_data.shape[0], df_data.shape[1])
embedding.weight.data = torch.from_numpy(df_data)

Now you can simply use it with any index you want:
index = 1
embedding(torch.tensor(index)), df_names[index]

This would give you (tensor[6, 7, 8, 9, 10], &quot;car&quot;) so the data and respective column name.
