how to convert a python list of lists to tensor using pytorch
I got a list that contains lists of different length. How can i transform it in a tensor in pytorch without using padding? Is it possible?
[[3, 5, 10, 11], [1, 5, 10]]


It depends on what you want to achieve with the data structure.
You can use torch.sparse, for example:
ll = [[3, 5, 10, 11], [1, 5, 10]]
n = len(ll)
m = max(len(l) for l in ll)

ids = [[], []]
values = []
for i, l in enumerate(ll):
    length = len(l)
    ids[0] += [i] * length  # rows
    ids[1] += list(range(length))  # cols
    values += l

t = torch.sparse_coo_tensor(ids, values, (n, m))

Otherwise, you can try with embedding techniques for corpus of documents, such as bag-of-words (though it will generate still some &quot;padding&quot;), tf-idf, etc.
bag-of-words with possible duplicates in inner lists
corpus = [[3, 5, 10, 11], [1, 5, 10]]
n = len(corpus)
m = max(max(inner) for inner in corpus)
t = torch.zeros(n, m)

for i, doc in enumerate(corpus):
    torch.bincount(corpus)

bag-of-words with distinct values in inner lists
corpus = [[3, 5, 10, 11], [1, 5, 10]]
n = len(corpus)
m = max(max(inner) for inner in corpus)

t = torch.zeros(n, m)
for i, doc in enumerate(corpus):
    t[i, doc] = 1

