import ctypes import multiprocessing as mp from itertools import combinations import pandas as pd import numpy as np from scipy.stats import pearsonr from tqdm import tqdm df = None def init_df(shared_arr, r, c, columns): global df a = np.frombuffer(shared_arr.get_obj()).res...
train_embeddings = embed_sentence_pairs(trainloader, tokenizer, model, disable_tqdm=True) val_matched_embeddings = embed_sentence_pairs(valmatchedloader, tokenizer, model, disable_tqdm=True) val_mismatched_embeddings = embed_sentence_pairs(valmismatchedloader, tokenizer, model, disable_tqdm=True)Copy ...
It supports conventional loops, e.g., by using tqdm_range instead of range, and it supports Pandas by providing progress_map and progress_apply operations on dataframes.3 Our accompanying notebooks on GitHub use these operations, but we stick to plain Pandas here in the book. Blueprints for ...
The key idea behind graph representation learning is to learn a mapping function that embeds nodes, or entire (sub)graphs (from non-euclidean), as points in low-dimensional vector space (to embedding space). The aim is to optimize this mapping so that nodes which...
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# Progress bar for loopsfrom tqdm import tqdm# JSON handlingimport json# Zstandard compression libraryimport zstandard as zstd# Tokenization library for large language modelsimport tiktoken# Math operations (used for advanced math functions)import math Preparing the Training Data Our training datase...