def embed_sentence(sentence: str,
pooling_operation,
layers: str = '1',
use_scalar_mix: bool = False) -> Sentence:
embeddings = XLMEmbeddings(pretrained_model_name_or_path=xlm_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 = XLMEmbeddings(
model=xlm_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 _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_xlm(model_name):
return XLMEmbeddings(model_name)
def main():
argparser = argparse.ArgumentParser(
description="download embeddings for models")
argparser.add_argument("-bert",
"--bert",
action='store_true',
default=False,
help="bert embeddings (12 layers)")
argparser.add_argument("-roberta",
"--roberta",
action='store_true',
default=False,
help="roberta embeddings (12 layers)")
argparser.add_argument("-gpt2",
"--gpt2",
action='store_true',
default=False,
help="gpt2 embeddings (12 layers)")
argparser.add_argument("-xlm",
"--xlm",
action='store_true',
default=False,
help="xlm embeddings (24 layers)")
argparser.add_argument("-local",
"--local",
action='store_true',
default=False,
help="if local")
args = argparser.parse_args()
# verify arguments
if args.bert and args.roberta and args.xlm and args.gpt2:
print("select only one flag for model type from (bert, roberta, xlm)")
exit()
if not args.bert and not args.roberta and not args.xlm and not args.gpt2:
print("select at least flag for model type from (bert, roberta, xlm)")
exit()
if args.bert or args.roberta or args.gpt2:
num_layers = 12
if args.xlm:
num_layers = 24
# open sentences
file = open("cleaned_sentencesGLM.txt", "r").read().splitlines()
# specify model
print("uploading model...")
for layer in tqdm(range(num_layers)):
print(layer)
if args.bert:
embeddings = BertEmbeddings("bert-base-multilingual-cased",
layers="-{}".format(layer))
model_type = "bert"
elif args.roberta:
embeddings = RoBERTaEmbeddings("roberta-base",
layers="-{}".format(layer))
model_type = "roberta"
elif args.xlm:
embeddings = XLMEmbeddings("xlm-mlm-en-2048",
layers="-{}".format(layer))
model_type = "xlm"
elif args.gpt2:
embeddings = TransformerWordEmbeddings("gpt2",
layers="-{}".format(layer))
model_type = "gpt2"
else:
print("error on calling embeddings")
exit()
embed_matrix = get_embeddings(file, embeddings)
print("aggregating types...")
avg_sentence = process_sentence(embed_matrix, "avg")
max_sentence = process_sentence(embed_matrix, "max")
min_sentence = process_sentence(embed_matrix, "min")
last_sentence = process_sentence(embed_matrix, "last")
methods = ['avg', 'max', 'min', 'last']
mats = [avg_sentence, max_sentence, min_sentence, last_sentence]
bool_labels = [1] * len(file)
print("saving files...")
if args.local:
file_path = '../embeddings/{}/layer{}/'.format(model_type, layer)
else:
file_path = "/n/shieber_lab/Lab/users/cjou/embeddings/{}/layer{}/".format(
model_type, layer)
if not os.path.exists(file_path):
os.makedirs(file_path)
for i in range(len(methods)):
print("saving file: " + file_path + str(methods[i]) + ".p")
pickle.dump(mats[i], open(file_path + str(methods[i]) + ".p",
"wb"))
print("done.")
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]))