def _find_model_function(self):
embedding_dim = self.configuration['embed_size']
embedding_matrix_filepath = self.base_data_dir + 'embedding_matrix'
if os.path.exists(embedding_matrix_filepath):
embedding_matrix = super()._load_object(embedding_matrix_filepath)
else:
embedding_filepath = self.configuration['embedding_filepath']
embedding_matrix = embedding._read_embeddings_from_text_file(embedding_filepath, embedding_dim,
self.vocab, namespace='tokens')
super()._save_object(embedding_matrix_filepath, embedding_matrix)
embedding_matrix = embedding_matrix.to(self.configuration['device'])
token_embedding = Embedding(num_embeddings=self.vocab.get_vocab_size(namespace='tokens'),
embedding_dim=embedding_dim, padding_index=0, vocab_namespace='tokens',
trainable=self._is_train_token_embeddings(), weight=embedding_matrix)
# the embedder maps the input tokens to the appropriate embedding matrix
word_embedder: TextFieldEmbedder = BasicTextFieldEmbedder({"tokens": token_embedding})
position_embedding = Embedding(num_embeddings=self.vocab.get_vocab_size(namespace='position'),
embedding_dim=self._get_position_embeddings_dim(), padding_index=0)
position_embedder: TextFieldEmbedder = BasicTextFieldEmbedder({"position": position_embedding},
# we'll be ignoring masks so we'll need to set this to True
allow_unmatched_keys=True)
model_function = self._find_model_function_pure()
model = model_function(
word_embedder,
position_embedder,
self.distinct_polarities,
self.vocab,
self.configuration,
)
self._print_args(model)
model = model.to(self.configuration['device'])
return model
def _find_model_function(self):
embedding_dim = self.configuration['embed_size']
embedding_matrix_filepath = self.base_data_dir + 'embedding_matrix'
if os.path.exists(embedding_matrix_filepath):
embedding_matrix = super()._load_object(embedding_matrix_filepath)
else:
embedding_filepath = self.configuration['embedding_filepath']
embedding_matrix = embedding._read_embeddings_from_text_file(
embedding_filepath,
embedding_dim,
self.vocab,
namespace='tokens')
super()._save_object(embedding_matrix_filepath, embedding_matrix)
token_embedding = Embedding(
num_embeddings=self.vocab.get_vocab_size(namespace='tokens'),
embedding_dim=embedding_dim,
padding_index=0,
vocab_namespace='tokens',
trainable=False,
weight=embedding_matrix)
# the embedder maps the input tokens to the appropriate embedding matrix
word_embedder: TextFieldEmbedder = BasicTextFieldEmbedder(
{"tokens": token_embedding})
position_embedding = Embedding(
num_embeddings=self.vocab.get_vocab_size(namespace='position'),
embedding_dim=25,
padding_index=0)
position_embedder: TextFieldEmbedder = BasicTextFieldEmbedder(
{"position": position_embedding},
# we'll be ignoring masks so we'll need to set this to True
allow_unmatched_keys=True)
# bert_embedder = PretrainedBertEmbedder(
# pretrained_model=self.bert_file_path,
# top_layer_only=True, # conserve memory
# requires_grad=True
# )
# bert_word_embedder: TextFieldEmbedder = BasicTextFieldEmbedder({"bert": bert_embedder},
# # we'll be ignoring masks so we'll need to set this to True
# allow_unmatched_keys=True)
bert_word_embedder = self._get_bert_word_embedder()
model = pytorch_models.AsMilSimultaneouslyBert(
word_embedder,
position_embedder,
self.distinct_categories,
self.distinct_polarities,
self.vocab,
self.configuration,
bert_word_embedder=bert_word_embedder)
self._print_args(model)
model = model.to(self.configuration['device'])
return model
def load_glove_embeddings(vocab):
"""
Loads pre-trained GloVe embeddings.
Returns
-------
TextFieldEmbedder
"""
embedding_matrix = _read_embeddings_from_text_file(file_uri="https://s3-us-west-2.amazonaws.com/allennlp/datasets/glove/glove.840B.300d.txt.gz",
embedding_dim=300,
vocab=vocab)
print("Pre-trained Glove loaded:", embedding_matrix.size())
token_embedding = Embedding(num_embeddings=vocab.get_vocab_size('tokens'),
embedding_dim=300,
weight=embedding_matrix)
word_embeddings: TextFieldEmbedder = BasicTextFieldEmbedder({"tokens": token_embedding})
return word_embeddings
discourse_validation_dataset = discourse_reader.read(
cached_path(DISCOURSE_VALIDATION_PATH))
vocab = Vocabulary.from_instances(claim_train_dataset + \
claim_validation_dataset + \
discourse_train_dataset + \
discourse_validation_dataset)
discourse_dict = {
'RESULTS': 0,
'METHODS': 1,
'CONCLUSIONS': 2,
'BACKGROUND': 3,
'OBJECTIVE': 4
}
claim_dict = {'0': 0, '1': 1}
embedding_matrix = _read_embeddings_from_text_file(
file_uri=PUBMED_PRETRAINED_FILE,
embedding_dim=EMBEDDING_DIM,
vocab=vocab)
token_embedding = Embedding(num_embeddings=vocab.get_vocab_size('tokens'),
embedding_dim=EMBEDDING_DIM,
weight=embedding_matrix)
word_embeddings = BasicTextFieldEmbedder({"tokens": token_embedding})
sentence_encoder = PytorchSeq2VecWrapper(
torch.nn.LSTM(EMBEDDING_DIM,
HIDDEN_DIM,
batch_first=True,
bidirectional=True))
model = DiscourseClaimCrfClassifier(
vocab,
word_embeddings,
sentence_encoder,
)
