def embed_sentence(sentence: str,
pooling_operation,
layers: str = '1',
use_scalar_mix: bool = False) -> Sentence:
embeddings = XLNetEmbeddings(pretrained_model_name_or_path=xlnet_model,
layers=layers,
pooling_operation=pooling_operation,
use_scalar_mix=use_scalar_mix)
flair_sentence = Sentence(sentence)
embeddings.embed(flair_sentence)
return flair_sentence
def embed_sentence(
sentence: str,
pooling_operation,
layers: str = "1",
use_scalar_mix: bool = False,
) -> Sentence:
embeddings = XLNetEmbeddings(
model=xlnet_model,
layers=layers,
pooling_operation=pooling_operation,
use_scalar_mix=use_scalar_mix,
)
flair_sentence = Sentence(sentence)
embeddings.embed(flair_sentence)
return flair_sentence
def __init__(self, *embeddings: str):
print("May need a couple moments to instantiate...")
self.embedding_stack = []
# Load correct Embeddings module
for model_name_or_path in embeddings:
if "bert" in model_name_or_path and "roberta" not in model_name_or_path:
self.embedding_stack.append(BertEmbeddings(model_name_or_path))
elif "roberta" in model_name_or_path:
self.embedding_stack.append(
RoBERTaEmbeddings(model_name_or_path))
elif "gpt2" in model_name_or_path:
self.embedding_stack.append(
OpenAIGPT2Embeddings(model_name_or_path))
elif "xlnet" in model_name_or_path:
self.embedding_stack.append(
XLNetEmbeddings(model_name_or_path))
elif "xlm" in model_name_or_path:
self.embedding_stack.append(XLMEmbeddings(model_name_or_path))
elif ("flair" in model_name_or_path
or model_name_or_path in FLAIR_PRETRAINED_MODEL_NAMES):
self.embedding_stack.append(
FlairEmbeddings(model_name_or_path))
else:
try:
self.embedding_stack.append(
WordEmbeddings(model_name_or_path))
except ValueError:
raise ValueError(
f"Embeddings not found for the model key: {model_name_or_path}, check documentation or custom model path to verify specified model"
)
assert len(self.embedding_stack) != 0
self.stacked_embeddings = StackedEmbeddings(
embeddings=self.embedding_stack)
def __init__(self, *embeddings: str):
print("May need a couple moments to instantiate...")
self.embedding_stack = []
# Load correct Embeddings module
for model_name_or_path in embeddings:
if "bert" in model_name_or_path and "roberta" not in model_name_or_path:
self.embedding_stack.append(BertEmbeddings(model_name_or_path))
elif "roberta" in model_name_or_path:
self.embedding_stack.append(
RoBERTaEmbeddings(model_name_or_path))
elif "gpt2" in model_name_or_path:
self.embedding_stack.append(
OpenAIGPT2Embeddings(model_name_or_path))
elif "xlnet" in model_name_or_path:
self.embedding_stack.append(
XLNetEmbeddings(model_name_or_path))
elif "xlm" in model_name_or_path:
self.embedding_stack.append(XLMEmbeddings(model_name_or_path))
elif ("flair" in model_name_or_path
or model_name_or_path in FLAIR_PRETRAINED_MODEL_NAMES):
self.embedding_stack.append(
FlairEmbeddings(model_name_or_path))
else:
print(
f"Corresponding flair embedding module not found for {model_name_or_path}"
)
assert len(self.embedding_stack) != 0
self.stacked_embeddings = StackedEmbeddings(
embeddings=self.embedding_stack)
def other_embeddings(embd):
sess = tf.InteractiveSession()
train_data_list = []
test_data_list = []
val_data_list = []
if embd == 'glove':
print('Starting Glove Embedding...')
glove_embedding = WordEmbeddings('glove')
document_embeddings = DocumentPoolEmbeddings(
embeddings=[glove_embedding])
elif embd == 'xlnet':
print('Starting XLNet Embedding...')
xlnet_embedding = XLNetEmbeddings('xlnet-large-cased')
document_embeddings = DocumentPoolEmbeddings(
embeddings=[xlnet_embedding])
elif embd == 'fasttext':
print('Starting Fasttext Embedding...')
fasttext_embedding = WordEmbeddings('en')
document_embeddings = DocumentPoolEmbeddings(
embeddings=[fasttext_embedding])
elif embd == 'elmo':
print('Starting ELMo Embedding...')
elmo_embedding = ELMoEmbeddings()
document_embeddings = DocumentPoolEmbeddings(
embeddings=[elmo_embedding])
else:
# init Flair embeddings
flair_forward_embedding = FlairEmbeddings('multi-forward')
flair_backward_embedding = FlairEmbeddings('multi-backward')
glove_embedding = WordEmbeddings('glove')
# now create the DocumentPoolEmbeddings object that combines all embeddings
document_embeddings = DocumentPoolEmbeddings(embeddings=[
glove_embedding, flair_forward_embedding, flair_backward_embedding
])
print('Train embedding Started...')
for text in final_train['text'].tolist():
text = Sentence(text)
document_embeddings.embed(text)
emb = text.get_embedding().detach().numpy()
emb = tf.constant(emb).eval()
train_data_list.append(emb)
print('Embedded Train data!!')
print('Test embedding Started...')
for text in final_test['text'].tolist():
text = Sentence(text)
document_embeddings.embed(text)
emb = text.get_embedding().detach().numpy()
emb = tf.constant(emb).eval()
test_data_list.append(emb)
print('Embedded Test data!!')
for text in final_val['text'].tolist():
text = Sentence(text)
document_embeddings.embed(text)
emb = text.get_embedding().detach().numpy()
emb = tf.constant(emb).eval()
val_data_list.append(emb)
print('Embedded Test data!!')
return train_data_list, test_data_list, val_data_list
def _get_stacked_embeddings(self) -> StackedEmbeddings:
layers = ",".join(str(layer) for layer in self.experiment.layers)
pooling_operation = self.experiment.pooling_operation
token_embeddings = []
for embedding in self.experiment.embeddings:
if embedding.startswith("roberta"):
token_embeddings.append(
RoBERTaEmbeddings(
pretrained_model_name_or_path=embedding,
pooling_operation=pooling_operation,
layers=layers,
use_scalar_mix=self.experiment.use_scalar_mix,
))
elif (embedding.startswith("bert")
or embedding.startswith("distilbert")
or embedding.startswith("spanbert")):
token_embeddings.append(
BertEmbeddings(
bert_model_or_path=embedding,
pooling_operation=pooling_operation,
layers=layers,
use_scalar_mix=self.experiment.use_scalar_mix,
))
elif embedding.startswith("elmo"):
model_name = embedding.split("-")[-1]
token_embeddings.append(ELMoEmbeddings(model=model_name))
elif embedding.startswith("gpt2"):
token_embeddings.append(
OpenAIGPT2Embeddings(
pretrained_model_name_or_path=embedding,
pooling_operation=pooling_operation,
layers=layers,
use_scalar_mix=self.experiment.use_scalar_mix,
))
elif embedding.startswith("xlm"):
token_embeddings.append(
XLMEmbeddings(
pretrained_model_name_or_path=embedding,
pooling_operation=pooling_operation,
layers=layers,
use_scalar_mix=self.experiment.use_scalar_mix,
))
elif embedding.startswith("xlnet"):
token_embeddings.append(
XLNetEmbeddings(
pretrained_model_name_or_path=embedding,
pooling_operation=pooling_operation,
layers=layers,
use_scalar_mix=self.experiment.use_scalar_mix,
))
return StackedEmbeddings(embeddings=token_embeddings)
def embed_text(
self,
text: Union[List[Sentence], Sentence, List[str], str],
model_name_or_path: str = "bert-base-cased",
) -> List[Sentence]:
""" Produces embeddings for text
* **text** - Text input, it can be a string or any of Flair's `Sentence` input formats
* **model_name_or_path** - The hosted model name key or model path
**return** - A list of Flair's `Sentence`s
"""
# Convert into sentences
if isinstance(text, str):
sentences = Sentence(text)
elif isinstance(text, list) and all(isinstance(t, str) for t in text):
sentences = [Sentence(t) for t in text]
else:
sentences = text
# Load correct Embeddings module
if not self.models[model_name_or_path]:
if "bert" in model_name_or_path and "roberta" not in model_name_or_path:
self.models[model_name_or_path] = BertEmbeddings(
model_name_or_path)
elif "roberta" in model_name_or_path:
self.models[model_name_or_path] = RoBERTaEmbeddings(
model_name_or_path)
elif "gpt2" in model_name_or_path:
self.models[model_name_or_path] = OpenAIGPT2Embeddings(
model_name_or_path)
elif "xlnet" in model_name_or_path:
self.models[model_name_or_path] = XLNetEmbeddings(
model_name_or_path)
elif "xlm" in model_name_or_path:
self.models[model_name_or_path] = XLMEmbeddings(
model_name_or_path)
elif ("flair" in model_name_or_path
or model_name_or_path in FLAIR_PRETRAINED_MODEL_NAMES):
self.models[model_name_or_path] = FlairEmbeddings(
model_name_or_path)
else:
try:
self.models[model_name_or_path] = WordEmbeddings(
model_name_or_path)
except ValueError:
raise ValueError(
f"Embeddings not found for the model key: {model_name_or_path}, check documentation or custom model path to verify specified model"
)
return Sentence("")
embedding = self.models[model_name_or_path]
return embedding.embed(sentences)
def __init__(
self,
*embeddings: str,
methods: List[str] = ["rnn", "pool"],
configs: Dict = {
"pool_configs": {
"fine_tune_mode": "linear",
"pooling": "mean"
},
"rnn_configs": {
"hidden_size": 512,
"rnn_layers": 1,
"reproject_words": True,
"reproject_words_dimension": 256,
"bidirectional": False,
"dropout": 0.5,
"word_dropout": 0.0,
"locked_dropout": 0.0,
"rnn_type": "GRU",
"fine_tune": True,
},
},
):
print("May need a couple moments to instantiate...")
self.embedding_stack = []
# Check methods
for m in methods:
assert m in self.__class__.__allowed_methods
# Set configs for pooling and rnn parameters
for k, v in configs.items():
assert k in self.__class__.__allowed_configs
setattr(self, k, v)
# Load correct Embeddings module
for model_name_or_path in embeddings:
if "bert" in model_name_or_path and "roberta" not in model_name_or_path:
self.embedding_stack.append(BertEmbeddings(model_name_or_path))
elif "roberta" in model_name_or_path:
self.embedding_stack.append(
RoBERTaEmbeddings(model_name_or_path))
elif "gpt2" in model_name_or_path:
self.embedding_stack.append(
OpenAIGPT2Embeddings(model_name_or_path))
elif "xlnet" in model_name_or_path:
self.embedding_stack.append(
XLNetEmbeddings(model_name_or_path))
elif "xlm" in model_name_or_path:
self.embedding_stack.append(XLMEmbeddings(model_name_or_path))
elif ("flair" in model_name_or_path
or model_name_or_path in FLAIR_PRETRAINED_MODEL_NAMES):
self.embedding_stack.append(
FlairEmbeddings(model_name_or_path))
else:
try:
self.embedding_stack.append(
WordEmbeddings(model_name_or_path))
except ValueError:
raise ValueError(
f"Embeddings not found for the model key: {model_name_or_path}, check documentation or custom model path to verify specified model"
)
assert len(self.embedding_stack) != 0
if "pool" in methods:
self.pool_embeddings = DocumentPoolEmbeddings(
self.embedding_stack, **self.pool_configs)
print("Pooled embedding loaded")
if "rnn" in methods:
self.rnn_embeddings = DocumentRNNEmbeddings(
self.embedding_stack, **self.rnn_configs)
print("RNN embeddings loaded")
def get_xlnet(model_name):
return XLNetEmbeddings(model_name)
return {token.text: token.embedding.numpy() for token in sentence}
if __name__ == "__main__":
from test_textsim import *
from flair.embeddings import XLMRobertaEmbeddings, BertEmbeddings, XLNetEmbeddings, XLMEmbeddings, RoBERTaEmbeddings
measures = {}
SAME, DIFF = load_data("./data/test_STS2017en-en.txt")
MODELS = {
"xlmr": XLMRobertaEmbeddings(),
"bert": BertEmbeddings(),
"xlnet": XLNetEmbeddings(),
"xlm": XLMEmbeddings(),
"roberta": RoBERTaEmbeddings(),
}
for model in MODELS:
print(model)
results = run_experiment(SAME,
DIFF,
lambda x: flair_embed_dict(x, MODELS[model]),
wmdistance,
inverse=True)
measures['{}-wmdist'.format(model)] = results
print(score(results[0], results[1]))
'BertLS': [
BertEmbeddings(bert_model_or_path='bert-large-uncased',
layers="0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,"
"15,16,17,18,19,20,21,22,23,24",
use_scalar_mix=True)
],
"RoBERTa": [RoBERTaEmbeddings('roberta-base')],
"RoBERTaL": [RoBERTaEmbeddings('roberta-large')],
"RoBERTaLS": [
RoBERTaEmbeddings(pretrained_model_name_or_path="roberta-large",
layers="0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,"
"15,16,17,18,19,20,21,22,23,24",
use_scalar_mix=True)
],
"XLNet":
[XLNetEmbeddings(pretrained_model_name_or_path="xlnet-large-cased")],
"XLNetS": [
XLNetEmbeddings(pretrained_model_name_or_path="xlnet-large-cased",
layers="0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,"
"15,16,17,18,19,20,21,22,23,24",
use_scalar_mix=True)
],
"DistilBert": [BertEmbeddings('distilbert-base-uncased')]
}
# Choose the tested embeddings
word_embeddings = model_selector[model_type]
# create an RNN
document_embeddings = DocumentRNNEmbeddings(word_embeddings,
hidden_size=256,
if ARGS.restore:
ensemble_tagger = EnsembleTagger.load(model_path + "final-model.pt")
else:
elmo_tagger = SequenceTagger(hidden_size=256,
embeddings=ELMoEmbeddings('small'),
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
bert_tagger = SequenceTagger(hidden_size=256,
embeddings=BertEmbeddings(),
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
xlnet_tagger = SequenceTagger(hidden_size=256,
embeddings=XLNetEmbeddings(),
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
flair_tagger = SequenceTagger(hidden_size=256,
embeddings=StackedEmbeddings([
FlairEmbeddings('news-forward'),
FlairEmbeddings('news-backward')
]),
tag_dictionary=tag_dictionary,
tag_type=tag_type,
use_crf=True)
models = []
if ARGS.model == "be":
models = [bert_tagger, elmo_tagger]
elif ARGS.model == "bf":
END = '\033[0m'
granularity_level = "Sent" #"Word" "Sent" "Paragraph"
dynamic = False ##Controls if we highlight the more important words more or not
graph = False ###ONLY WORKS WITH "Word" granularity_level
word_doc = True
html = True
word_window_size = 10 ##This is really doubled since it's Bi-directional. Only matters for word level granularity
highlight_color_intensity = 175 # try values between 25 and 200
doc_embeddings = []
scores = []
stacked_embeddings = DocumentPoolEmbeddings([
#WordEmbeddings('en'),
XLNetEmbeddings('xlnet-large-cased', use_scalar_mix='True')
#WordEmbeddings('glove'),
#WordEmbeddings('extvec'),#ELMoEmbeddings('original'),
#BertEmbeddings('bert-base-cased'),
#FlairEmbeddings('news-forward-fast'),
#FlairEmbeddings('news-backward-fast'),
#OpenAIGPTEmbeddings()
#TransformerXLEmbeddings()
]) #, mode='max')
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--card_file_path",
"-c",
type=str,
nlp = StanfordCoreNLP('http://localhost:9000')
properties = {'annotators': 'ssplit', 'outputFormat': 'json'}
#Getting user arguments
mode, number, _set, load, iteration, cuda_option, save_path, log_file, architecture, embedding_type, loss_mode, learning_rate, score_mode, max_pool, args = parse_arguments(
)
#define the embeddings
if embedding_type == 1:
selected_embedding = BertEmbeddings()
embed_dim = 3072
elif embedding_type == 2:
selected_embedding = FlairEmbeddings("news-forward")
embed_dim = 2048
elif embedding_type == 3:
selected_embedding = XLNetEmbeddings()
embed_dim = 2048
bert = BertEmbeddings()
flair = FlairEmbeddings("news-forward")
# Load the pretrained model of resnet
resnet = models.resnet101(pretrained=True)
# Use the model object to select the desired layer
modules = list(resnet.children())[:-1]
resnet = nn.Sequential(*modules)
resnet.eval()
#define the transformers for the picture
scaler = transforms.Scale((224, 224))
