

Input IDs should thereby consists of an array of integers, e.g. input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
The outputs of the following layers often consist of multi-dimensional float arrays and can look like this:
[[
 [-0.1465, -0.6501,  0.1993,  ,  0.1451,  0.3430,  0.6024],
 [-0.4417, -0.5920,  0.3450,  , -0.3062,  0.6182,  0.7132],
 [-0.5009, -0.7122,  0.4548,  , -0.3662,  0.6091,  0.7648],
 ,
 [-0.5613, -0.6332,  0.4324,  , -0.3792,  0.7372,  0.9288],
 [-0.5416, -0.6345,  0.4180,  , -0.3564,  0.6992,  0.9191],
 [-0.5334, -0.6403,  0.4271,  , -0.3339,  0.6533,  0.8694]]],
We expect that every model added to 🤗 Transformers passes a couple of integration tests, meaning that the original
model and the reimplemented version in 🤗 Transformers have to give the exact same output up to a precision of 0.001!
Since it is normal that the exact same model written in different libraries can give a slightly different output
depending on the library framework, we accept an error tolerance of 1e-3 (0.001). It is not enough if the model gives
nearly the same output, they have to be almost identical. Therefore, you will certainly compare the intermediate
outputs of the 🤗 Transformers version multiple times against the intermediate outputs of the original implementation of
brand_new_bert in which case an efficient debugging environment of the original repository is absolutely
important. Here is some advice to make your debugging environment as efficient as possible.