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import requests | |
import time | |
import os | |
import sys | |
import openai | |
import tiktoken | |
from termcolor import colored | |
openai.api_key = open(os.path.expanduser('~/.openai')).read().strip() |
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"""Script for fine-tuning Pegasus | |
Example usage: | |
# use XSum dataset as example, with first 1000 docs as training data | |
from datasets import load_dataset | |
dataset = load_dataset("xsum") | |
train_texts, train_labels = dataset['train']['document'][:1000], dataset['train']['summary'][:1000] | |
# use Pegasus Large model as base for fine-tuning | |
model_name = 'google/pegasus-large' | |
train_dataset, _, _, tokenizer = prepare_data(model_name, train_texts, train_labels) |
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import argparse | |
import itertools | |
import numpy as np | |
import operator | |
import os | |
import pickle | |
import spacy | |
import scispacy | |
import time |
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import pandas as pd | |
import re | |
import spacy | |
import neuralcoref | |
nlp = spacy.load('en_core_web_lg') | |
neuralcoref.add_to_pipe(nlp) | |
def get_entity_pairs(text, coref=True): |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
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1. create tools.policy file: | |
grant { | |
permission java.security.AllPermission; | |
}; | |
2. run to start jstatd: | |
jstatd -J-Djava.security.policy=tools.policy |