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from pytorch_transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
import os
import wget
import numpy as np
from fairseq.models.roberta import RobertaModel
from joblib import load
pretrained = os.path.join(os.path.dirname(__file__), "pretrained")
ROBERTA_STS_PATH = pretrained + '/roBERTa_STS'
ROBERTA_MNLI_PATH = pretrained + '/roBERTa_MNLI'
AGGREGATOR_DIR = pretrained + '/aggregators/'
AGGREGATOR_2015_2016 = \
pretrained + '/aggregators/nn_2015_2016_6_dim' \
'.joblib'
AGGREGATOR_2015_2017 = \
pretrained + '/aggregators/nn_2015_2017_6_dim' \
'.joblib'
AGGREGATOR_2015_2016_8_dim = \
pretrained + '/aggregators/nn_2015_2016_8_dim' \
'.joblib'
AGGREGATOR_2015_2017_8_dim = \
pretrained + '/aggregators/nn_2015_2017_8_dim' \
'.joblib'
ROBERTA_STS_URL = "https://nubia-nn.s3.amazonaws.com/" \
"neural-feature-extractors/checkpoint_best.pt"
ROBERTA_MNLI_URL = "https://nubia-nn.s3.amazonaws.com/" \
"neural-feature-extractors/model_mnli.pt"
AGGREGATOR_2015_2016_URL = "https://nubia-nn.s3.amazonaws.com/" \
"aggregators/nn_2015_2016_6_dim.joblib"
AGGREGATOR_2015_2017_URL = "https://nubia-nn.s3.amazonaws.com/" \
"aggregators/nn_2015_2017_6_dim.joblib"
AGGREGATOR_2015_2016_8_dim_URL = "https://nubia-nn.s3.amazonaws.com/" \
"aggregators/nn_2015_2016_8_dim.joblib"
AGGREGATOR_2015_2017_8_dim_URL = "https://nubia-nn.s3.amazonaws.com/" \
"aggregators/nn_2015_2017_8_dim.joblib"
class Nubia:
def __init__(self):
if not os.path.exists(AGGREGATOR_DIR):
os.makedirs(AGGREGATOR_DIR)
if not os.path.isfile(AGGREGATOR_2015_2016):
print("Downloading aggregators from s3...")
wget.download(AGGREGATOR_2015_2016_URL,
AGGREGATOR_2015_2016)
if not os.path.isfile(AGGREGATOR_2015_2017):
print("\nDownloading aggregators from s3...")
wget.download(AGGREGATOR_2015_2017_URL,
AGGREGATOR_2015_2017)
if not os.path.isfile(AGGREGATOR_2015_2016_8_dim):
print("\nDownloading aggregators from s3...")
wget.download(AGGREGATOR_2015_2016_8_dim_URL,
AGGREGATOR_2015_2016_8_dim)
if not os.path.isfile(AGGREGATOR_2015_2017_8_dim):
print("\nDownloading aggregators from s3...")
wget.download(AGGREGATOR_2015_2017_8_dim_URL,
AGGREGATOR_2015_2017_8_dim)
if not os.path.isfile(ROBERTA_STS_PATH + '/checkpoint_best.pt'):
print("\nDownloading ROBERTA STS model from s3...")
wget.download(ROBERTA_STS_URL, ROBERTA_STS_PATH +
'/checkpoint_best.pt')
if not os.path.isfile(ROBERTA_MNLI_PATH + '/model_mnli.pt'):
print("\nDownloading ROBERTA MNLI model from s3...")
wget.download(ROBERTA_MNLI_URL, ROBERTA_MNLI_PATH +
'/model_mnli.pt')
print('\n')
self.roberta_STS = RobertaModel.from_pretrained(
checkpoint_file='checkpoint_best.pt',
model_name_or_path=ROBERTA_STS_PATH)
self.roberta_STS.eval()
self.roberta_MNLI = RobertaModel.from_pretrained(
checkpoint_file='model_mnli.pt',
model_name_or_path=ROBERTA_MNLI_PATH)
self.roberta_MNLI.eval()
self.tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
self.gpt_model = GPT2LMHeadModel.from_pretrained('gpt2')
self.agg_one = load(AGGREGATOR_2015_2016)
self.agg_two = load(AGGREGATOR_2015_2017)
self.agg_one_8_dim = load(AGGREGATOR_2015_2016_8_dim)
self.agg_two_8_dim = load(AGGREGATOR_2015_2017_8_dim)
@staticmethod
def _download_progress_bar(current, total, width=80):
print("Downloading: %d%% [%d / %d] bytes" % (
current / total * 100, current, total))
def _roberta_tokenizer(self, ref, hyp):
tokens = self.roberta_STS.encode(ref, hyp)
return tokens
def _roberta_similarity(self, tokens):
if len(tokens) > 512:
tokens = tokens[:512]
features = self.roberta_STS.extract_features(tokens)
predicted_semantic_distance = 5.0 * \
self.roberta_STS.model.classification_heads['sentence_classification_head'](features)
return predicted_semantic_distance
def _roberta_mnli_all_values(self, tokens):
if len(tokens) > 512:
tokens = tokens[:512]
prediction = self.roberta_MNLI.predict('mnli', tokens)[0].\
cpu().detach().numpy()
return prediction
def _gpt_score(self, text):
tokenize_input = self.tokenizer.tokenize(text)
tensor_input = torch.tensor([[self.tokenizer. eos_token_id] +
self.tokenizer.convert_tokens_to_ids(
tokenize_input)])
with torch.no_grad():
outputs = self.gpt_model(tensor_input, labels=tensor_input)
loss, logits = outputs[:2]
return loss
def nubia(self, ref, hyp, get_features=False, six_dim=False,
aggregator="agg_two", gpt_ref=None):
tokens = self._roberta_tokenizer(ref, hyp)
sim = float(self._roberta_similarity(tokens)[0])
mnli_zero, mnli_one, mnli_two = self._roberta_mnli_all_values(tokens)
if not gpt_ref:
gpt_ref = self._gpt_score(ref)
gpt_hyp = self._gpt_score(hyp)
else:
gpt_hyp = gpt_ref
len_ref = len(ref.split(" "))
len_hyp = len(hyp.split(" "))
mnli_friendly = torch.nn.functional.softmax(
torch.tensor([mnli_zero, mnli_one, mnli_two]), dim=0).tolist()
neural_features_6_dim = np.array(
[float(sim), float(mnli_zero), float(mnli_one), float(mnli_two),
float(gpt_ref), float(gpt_hyp)]) # 6 Neural Features
neural_features_8_dim = np.array(
[float(sim), float(mnli_zero), float(mnli_one), float(mnli_two),
float(gpt_ref), float(gpt_hyp), float(len_ref),
float(len_hyp)]) # 8 Neural Features
if aggregator == "agg_one":
if six_dim:
nubia_metric = float(self.agg_one.predict(
neural_features_6_dim.reshape(1, -1))[0])
else:
nubia_metric = float(self.agg_one_8_dim.predict(
neural_features_8_dim.reshape(1, -1))[0])
else:
if six_dim:
nubia_metric = float(self.agg_two.predict(
neural_features_6_dim.reshape(1, -1))[0])
else:
nubia_metric = float(self.agg_two_8_dim.predict(
neural_features_8_dim.reshape(1, -1))[0])
if get_features:
return {"nubia_score": nubia_metric, "features": {
"semantic_relation": min(5.0, sim),
"contradiction": mnli_friendly[0]*100,
"irrelevancy": mnli_friendly[1]*100,
"logical_agreement": mnli_friendly[2]*100,
"grammar_ref": gpt_ref.item(),
"grammar_hyp": gpt_hyp.item(),
}
}, gpt_ref
return nubia_metric, gpt_ref
def score(self, ref, hyp, verbose=False, get_features=False,
six_dim=False, aggregator="agg_two"):
if not ref or not hyp:
if get_features:
return {"nubia_score": 0, "features": {
"semantic_relation": 0,
"contradiction": 0,
"irrelevancy": 0,
"logical_agreement": 0,
"grammar_ref": 0,
"grammar_hyp": 0,
}
}
return 0
nubia, gpt_ref = self.nubia(ref, hyp, get_features=True, six_dim=six_dim,
aggregator=aggregator)
self_similarity, _ = self.nubia(ref, ref,
get_features=False, six_dim=six_dim,
aggregator=aggregator, gpt_ref=gpt_ref)
amplitude = abs(self_similarity) + 1
difference = self_similarity - nubia["nubia_score"]
calibrated = 1.0 - (float(difference) / float(amplitude))
if calibrated > 0:
calibrated = min(1.0, calibrated)
else:
calibrated = max(0.0, calibrated)
if verbose:
print("Semantic relation: " +
str(min(5.0, nubia["features"]["semantic_relation"])) +
'/5.0')
print("Percent chance of contradiction: " +
str(nubia["features"]["contradiction"]) + "%")
print("Percent chance of irrelevancy or new information: " +
str(nubia["features"]["irrelevancy"]) + "%")
print("Percent chance of logical agreement: " +
str(nubia["features"]["logical_agreement"]) + "%\n")
print("NUBIA score: " + str(calibrated) + "/1.0")
nubia["nubia_score"] = calibrated
if get_features:
return nubia
return calibrated
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