DeNeutoy

import streamlit as st
import spacy
from spacy import displacy
import pandas as pd

from scispacy.umls_linking import UmlsEntityLinker
from scispacy.abbreviation import AbbreviationDetector


SPACY_MODEL_NAMES = ["en_core_sci_sm", "en_core_sci_md", "en_core_sci_lg"]

DeNeutoy

{"annotator_labels": ["neutral", "entailment", "neutral", "neutral", "neutral"], "captionID": "4705552913.jpg#2", "gold_label": "neutral", "pairID": "4705552913.jpg#2r1n", "sentence1": "Two women are embracing while holding to go packages.", "sentence1_binary_parse": "( ( Two women ) ( ( are ( embracing ( while ( holding ( to ( go packages ) ) ) ) ) ) . ) )", "sentence1_parse": "(ROOT (S (NP (CD Two) (NNS women)) (VP (VBP are) (VP (VBG embracing) (SBAR (IN while) (S (NP (VBG holding)) (VP (TO to) (VP (VB go) (NP (NNS packages)))))))) (. .)))", "sentence2": "The sisters are hugging goodbye while holding to go packages after just eating lunch.", "sentence2_binary_parse": "( ( The sisters ) ( ( are ( ( hugging goodbye ) ( while ( holding ( to ( ( go packages ) ( after ( just ( eating lunch ) ) ) ) ) ) ) ) ) . ) )", "sentence2_parse": "(ROOT (S (NP (DT The) (NNS sisters)) (VP (VBP are) (VP (VBG hugging) (NP (UH goodbye)) (PP (IN while) (S (VP (VBG holding) (S (VP (TO to) (VP (VB go) (NP (NNS packages)) (PP (IN af

DeNeutoy

name: CI

on:
  pull_request:
    branches:
    - master
  push:
    branches:
    - master

DeNeutoy

from mpmath import mp
mp.dps = 100

import torch

# Take some list of values that shrink to be really small in log space:
torch_lps = torch.log_softmax(-torch.arange(20.0, dtype=torch.float64), dim=0)

mpmath_lps = -torch.arange(20.0, dtype=torch.float64)
Z = sum([mp.exp(mp.mpf(mpmath_lps[i].item())) for i in range(len(mpmath_lps))])

DeNeutoy

import tensorflow as tf

def mix_models_scalar(linear_outputs, rnn_outputs, lstm_outputs):
    # Generate scalar valued weights to combine outputs of a
    # Linear RNN, vanilla RNN and LSTM. Each of the inputs to
    # this function is a list of outputs from the respective model.
    
    context_window_size = 20
    all_model_outputs = [linear_outputs, rnn_outputs, lstm_outputs]