PyTree implements tree-structured neural networks in PyTorch. The package provides highly generic implementations as well as efficient batching methods.
To install please run:
pip install git+https://github.com/AntoineSimoulin/pytreeData must be in the str format as detailed in the example below:
from pytree.data import prepare_input_from_constituency_tree
parse_tree_example = '(TOP (S (NP (_ I)) (VP (_ saw) (NP (_ Sarah)) (PP (_ with) (NP (_ a) (_ telescope)))) (_ .)))'
input_test, head_idx_test, head_idx_r_test, head_idx_l_test = prepare_input_from_constituency_tree(parse_tree_example)
print(input_test)
# ['[CLS]', 'I', 'saw', 'Sarah', 'with', 'a', 'telescope', '.', '[S]', '[S]', '[VP]', '[VP]', '[PP]', '[NP]']
print(head_idx_test)
# [0, 9, 11, 11, 12, 13, 13, 8, 0, 8, 9, 10, 10, 12]
print(head_idx_r_test)
# [0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0]
print(head_idx_l_test)
# [0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1]Data must be in the conll-X format as detailed in the example below:
from pytree.data import prepare_input_from_dependency_tree
parse_tree_example = """1 I _ _ _ _ 2 nsubj _ _
2 saw _ _ _ _ 0 root _ _
3 Sarah _ _ _ _ 2 dobj _ _
4 with _ _ _ _ 2 prep _ _
5 a _ _ _ _ 6 det _ _
6 telescope _ _ _ _ 4 pobj _ _
7 . _ _ _ _ 2 punct _ _
"""
input_test, head_idx_test = prepare_input_from_dependency_tree(parse_tree_example)
print(input_test)
# ['[CLS]', 'I', 'saw', 'Sarah', 'with', 'a', 'telescope', '.']
print(head_idx_test)
# [0, 2, 0, 2, 2, 6, 4, 2]You may encode the word with GloVe emebddings :
from pytree.data.glove_tokenizer import GloveTokenizer
glove_tokenizer = GloveTokenizer(glove_file_path='./glove.6B.300d.txt', vocab_size=10000)
input_test = glove_tokenizer.convert_tokens_to_ids(input_test)
print(input_test)
# [1, 1, 824, 1, 19, 9, 1, 4]Then prepare the data:
from pytree.data.utils import build_tree_ids_n_ary
tree_ids_test, tree_ids_test_r, tree_ids_test_l = build_tree_ids_n_ary(head_idx_test, head_idx_r_test, head_idx_l_test)
inputs = {'input_ids': torch.tensor(input_test).unsqueeze(0),
'tree_ids': torch.tensor(tree_ids_test).unsqueeze(0),
'tree_ids_r': torch.tensor(tree_ids_test_r).unsqueeze(0),
'tree_ids_l': torch.tensor(tree_ids_test_l).unsqueeze(0)}And apply the model:
from pytree.models import NaryConfig, NaryTree
config = NaryConfig()
tree_encoder = NaryTree(config)
(h, c), h_root = tree_encoder(inputs)
print(h)
# tensor([[[ 0.0000, 0.0000, 0.0000, ..., 0.0000, 0.0000, 0.0000],
# [ 0.0113, -0.0066, 0.0089, ..., 0.0064, 0.0076, -0.0048],
# [ 0.0110, -0.0073, 0.0110, ..., 0.0070, 0.0046, -0.0049],
# ...,
# [ 0.0254, -0.0138, 0.0224, ..., 0.0131, 0.0148, -0.0143],
# [ 0.0346, -0.0172, 0.0281, ..., 0.0140, 0.0198, -0.0267],
# [ 0.0247, -0.0126, 0.0201, ..., 0.0116, 0.0162, -0.0184]]],
# grad_fn=<SWhereBackward>)
print(h_root.shape)
# torch.Size([150])We also provide a full demonstration with the SICK dataset and batched processing in the examples folder.