CONTEXT_SIZE = 2  # 2 words to the left, 2 to the right
raw_text = """We are about to study the idea of a computational process.
Computational processes are abstract beings that inhabit computers.
As they evolve, processes manipulate other abstract things called data.
The evolution of a process is directed by a pattern of rules
called a program. People create programs to direct processes. In effect,
we conjure the spirits of the computer with our spells.""".split()

# By deriving a set from `raw_text`, we deduplicate the array
vocab = set(raw_text)
vocab_size = len(vocab)

word_to_ix = {word: i for i, word in enumerate(vocab)}
data = []
for i in range(CONTEXT_SIZE, len(raw_text) - CONTEXT_SIZE):
    context = (
        [raw_text[i - j - 1] for j in range(CONTEXT_SIZE)]
        + [raw_text[i + j + 1] for j in range(CONTEXT_SIZE)]
    )
    target = raw_text[i]
    data.append((context, target))
print(data[:5])


class CBOW([nn.Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module "torch.nn.Module")):

    def __init__(self):
        pass

    def forward(self, inputs):
        pass

# Create your model and train. Here are some functions to help you make
# the data ready for use by your module.


def make_context_vector(context, word_to_ix):
    idxs = [word_to_ix[w] for w in context]
    return [torch.tensor](https://docs.pytorch.org/docs/stable/generated/torch.tensor.html#torch.tensor "torch.tensor")(idxs, dtype=[torch.long](https://docs.pytorch.org/docs/stable/tensor_attributes.html#torch.dtype "torch.dtype"))


make_context_vector(data[0][0], word_to_ix)  # example