pohanchi

#/usr/bin/zsh
#test_task = ['<annoyance>', '<pride>', '<joy>', '<sadness>', '<approval>', '<confusion>']

# echo "container ID = ${curid}"
# echo "task name = ${1},"
# echo "mode= ${2},"
# echo "agent= ${3},"
# echo "prompt = ${4},"
# echo "bot = ${5},"
# echo "type = ${6},"

pohanchi

for emo in annoyance pride joy sadness approval confusion;
do
    for human_pt in {0..1};
    do
        echo "=================================================================="
        echo "$emo-for-$human_pt-th human-pt"
        source ~/.zshrc; tasker "human$emo-$human_pt" "bash run_emo_h.sh $emo dgpt $human_pt" 
        sleep 20
        echo "=================================================================="
    done

pohanchi

#put it in your remote server
screen -ls | grep  longjob | awk ' { print $1 }'

pohanchi

import torch
from torch import nn


class SelfAttentionPooling(nn.Module):
    """
    Implementation of SelfAttentionPooling 
    Original Paper: Self-Attention Encoding and Pooling for Speaker Recognition
    https://arxiv.org/pdf/2008.01077v1.pdf
    """

pohanchi

"""
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)