4995 lines
260 KiB
Plaintext
4995 lines
260 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": [],
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"toc_visible": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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},
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"accelerator": "GPU",
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"gpuClass": "standard"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"## connect to google drive (working on colab)"
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],
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"metadata": {
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"id": "G0ujnpy2tuBE"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from google.colab import drive\n",
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"drive.mount('/content/drive')"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Lwuh_S5pWY1j",
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"outputId": "27838dab-7be0-4447-883a-95559887c7c8"
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},
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Mounted at /content/drive\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "QNiUKMiqWLd0"
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"source": [
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"!mkdir moj7\n"
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],
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"metadata": {
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"id": "vlnrhRaEWNJF"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"%cd drive"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "539oWG3pXOAX",
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"outputId": "a9ae634d-d4a2-47dd-97d2-10c245c7c5d2"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/drive\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"%cd MyDrive"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "wgmiKs4BiAiT",
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"outputId": "fbbd0bc7-76bd-47bf-e38b-e051239e5ba7"
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},
|
|
"execution_count": null,
|
|
"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/drive/MyDrive\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"%cd moj7"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "jNdkji_hiAlt",
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"outputId": "962b875a-8d3f-433d-8d7b-dcd664ee1674"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/drive/MyDrive/moj7\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
|
|
"id": "P249ENeSiAqn"
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|
},
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|
"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"!pwd"
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],
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"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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|
"id": "IOHV3Iz4WNLc",
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"outputId": "f56a6ab7-73e6-4b03-824b-b5e749c8a82e"
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|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"/content/drive/MyDrive/moj7\n"
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]
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}
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]
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},
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|
{
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"cell_type": "code",
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|
"source": [],
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|
"metadata": {
|
|
"id": "2pLkeHY5Z9oT"
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|
},
|
|
"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Preprocess"
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],
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"metadata": {
|
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"id": "D7jhQfbttn9D"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import re"
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],
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"metadata": {
|
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"id": "_IPWOt2BZ_-q"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"train_file ='train/in.tsv.xz'\n",
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"test_file = 'test-A/in.tsv.xz'\n",
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"out_file = 'test-A/out.tsv'\n",
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"\n",
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"def preprocess(line):\n",
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" line = replace_endline(line)\n",
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" line = get_rid_of_header(line)\n",
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" return line\n",
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"\n",
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"def get_rid_of_header(line):\n",
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" line = line.split('\\t')[6:]\n",
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" return \"\".join(line)\n",
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" \n",
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"def replace_endline(line):\n",
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" line = re.sub(\"\\\\n|\\\\+\", \" \", line)\n",
|
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" return line"
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],
|
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"metadata": {
|
|
"id": "qDnZdPblWNNr"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": []
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|
},
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{
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"cell_type": "code",
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"source": [
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"from itertools import islice\n",
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"import regex as re\n",
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"import sys\n",
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"from torchtext.vocab import build_vocab_from_iterator\n",
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"import lzma\n",
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"import pickle\n",
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"\n",
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"\n",
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"\n",
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"def get_words_from_line(line):\n",
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" line = line.rstrip()\n",
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" yield '<s>'\n",
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" line = preprocess(line)\n",
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" for t in line.split(' '):\n",
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" yield t\n",
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" yield '</s>'\n",
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"\n",
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"\n",
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"def get_word_lines_from_file(file_name):\n",
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" n = 0\n",
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" with lzma.open(file_name, 'r') as fh:\n",
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" for line in fh:\n",
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" n+=1\n",
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" if n%1000==0:\n",
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" print(n)\n",
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" yield get_words_from_line(line.decode('utf-8'))\n",
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"#vocab_size = 20000\n",
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"vocab_size = 20000\n",
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"\n",
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"vocab = build_vocab_from_iterator(\n",
|
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" get_word_lines_from_file(train_file),\n",
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" max_tokens = vocab_size,\n",
|
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" specials = ['<unk>'])\n",
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"\n"
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],
|
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
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},
|
|
"id": "RVN0lKVZfwMe",
|
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"outputId": "305b03e4-f626-4560-a371-41bc5a0ea9c7"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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},
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{
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"cell_type": "code",
|
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"source": [
|
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"vocab['no']"
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],
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"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "b9vMOTlZxISl",
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"outputId": "a3a71b17-3fb2-4794-ae5d-a43d9af49b69"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
|
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"50"
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]
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},
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"metadata": {},
|
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"execution_count": 19
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}
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]
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},
|
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{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"with open('filename.pickle', 'wb') as handle:\n",
|
|
" pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)"
|
|
],
|
|
"metadata": {
|
|
"id": "6R9l6tuPxB_B"
|
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},
|
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"execution_count": null,
|
|
"outputs": []
|
|
},
|
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{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Create NN"
|
|
],
|
|
"metadata": {
|
|
"id": "Be25rS6Uvl4V"
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"from torch import nn\n",
|
|
"import torch\n",
|
|
"import pickle\n",
|
|
"# embed_size = 150\n",
|
|
"embed_size = 150\n",
|
|
"\n",
|
|
"class Bigram(nn.Module):\n",
|
|
" def __init__(self, vocabulary_size, embedding_size):\n",
|
|
" super(Bigram, self).__init__()\n",
|
|
" self.model = nn.Sequential(\n",
|
|
" nn.Embedding(vocabulary_size, embedding_size),\n",
|
|
" nn.Linear(embedding_size, vocabulary_size),\n",
|
|
" nn.Softmax()\n",
|
|
" )\n",
|
|
" def forward(self, x):\n",
|
|
" return self.model(x)\n",
|
|
"\n",
|
|
"model = Bigram(vocab_size, embed_size)\n",
|
|
"\n",
|
|
"vocab.set_default_index(vocab['<unk>'])\n",
|
|
"res = torch.tensor(vocab.forward(['order']))\n",
|
|
"print(res)\n"
|
|
],
|
|
"metadata": {
|
|
"id": "dGTOmcHwWNSi",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "88c30492-7a9a-4b96-9119-ecdf5865bb51"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"tensor([215])\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [],
|
|
"metadata": {
|
|
"id": "mWZ_jw-hxNXk"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"from torch.utils.data import IterableDataset\n",
|
|
"import itertools\n",
|
|
"\n",
|
|
"def look_ahead_iterator(gen):\n",
|
|
" prev = None\n",
|
|
" for item in gen:\n",
|
|
" if prev is not None:\n",
|
|
" yield (prev, item)\n",
|
|
" prev = item\n",
|
|
"\n",
|
|
"class Bigrams(IterableDataset):\n",
|
|
" def __init__(self, text_file, vocabulary_size):\n",
|
|
" self.vocab = build_vocab_from_iterator(\n",
|
|
" get_word_lines_from_file(text_file),\n",
|
|
" max_tokens = vocabulary_size,\n",
|
|
" specials = ['<unk>'])\n",
|
|
" self.vocab.set_default_index(self.vocab['<unk>'])\n",
|
|
" self.vocabulary_size = vocabulary_size\n",
|
|
" self.text_file = text_file\n",
|
|
"\n",
|
|
" def __iter__(self):\n",
|
|
" return look_ahead_iterator(\n",
|
|
" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
|
|
"\n",
|
|
"\n",
|
|
"train_dataset = Bigrams(train_file, vocab_size)"
|
|
],
|
|
"metadata": {
|
|
"id": "5CSigeomWNVT",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "4fb3b1ff-f91b-4799-fc5f-17bae10b94ec"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
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"1000\n",
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"2000\n",
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},
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{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"from torch.utils.data import DataLoader\n",
|
|
"\n",
|
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"next(iter(DataLoader(train_dataset, batch_size=5)))"
|
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],
|
|
"metadata": {
|
|
"id": "oYAZ772rWNX7",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
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},
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"outputId": "da3a811a-36cf-4d34-82d8-b1ce2447c232"
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},
|
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"execution_count": null,
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"outputs": [
|
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{
|
|
"output_type": "execute_result",
|
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"data": {
|
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"text/plain": [
|
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"[tensor([ 23, 191, 5791, 1, 112]),\n",
|
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" tensor([ 191, 5791, 1, 112, 159])]"
|
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]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 23
|
|
}
|
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]
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},
|
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{
|
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"cell_type": "markdown",
|
|
"source": [
|
|
"## Train"
|
|
],
|
|
"metadata": {
|
|
"id": "1H_dI372vrNh"
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
|
|
],
|
|
"metadata": {
|
|
"id": "N2u4Qmadgtdn"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"\n",
|
|
"model = Bigram(vocab_size, embed_size).to(device)\n",
|
|
"data = DataLoader(train_dataset, batch_size=1000)\n",
|
|
"optimizer = torch.optim.Adam(model.parameters())\n",
|
|
"criterion = torch.nn.NLLLoss()\n",
|
|
"## epochs=2\n",
|
|
"for i in range(2):\n",
|
|
" print('epoch: =', i)\n",
|
|
" model.train()\n",
|
|
" step = 0\n",
|
|
" for x, y in data:\n",
|
|
" x = x.to(device)\n",
|
|
" y = y.to(device)\n",
|
|
" optimizer.zero_grad()\n",
|
|
" ypredicted = model(x)\n",
|
|
" loss = criterion(torch.log(ypredicted), y)\n",
|
|
" if step % 100 == 0:\n",
|
|
" print(step, loss)\n",
|
|
" step += 1\n",
|
|
" loss.backward()\n",
|
|
" optimizer.step()\n",
|
|
" torch.save(model.state_dict(), 'model.bin') \n"
|
|
],
|
|
"metadata": {
|
|
"id": "OGk2tjbvWNag",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "868503a1-2849-40ff-e703-886fba094927"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"metadata": {
|
|
"tags": null
|
|
},
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"epoch: = 0\n"
|
|
]
|
|
},
|
|
{
|
|
"metadata": {
|
|
"tags": null
|
|
},
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
|
" input = module(input)\n"
|
|
]
|
|
},
|
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{
|
|
"output_type": "stream",
|
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"name": "stdout",
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"text": [
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"0 tensor(10.3037, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"100 tensor(8.7506, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"1000\n",
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"2000\n",
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"2800 tensor(5.0710, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"10000\n",
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"11000\n",
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"12000\n",
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"3400 tensor(5.3550, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"3500 tensor(5.4032, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"3600 tensor(5.1070, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"13000\n",
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"3700 tensor(5.4506, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"3900 tensor(5.4984, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"14000\n",
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"15000\n",
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"4300 tensor(5.5181, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"16000\n",
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"4500 tensor(5.5021, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"4700 tensor(5.4699, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"17000\n",
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"4800 tensor(5.2686, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5000 tensor(5.5061, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"18000\n",
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"5100 tensor(5.3180, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5300 tensor(5.3481, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"19000\n",
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"5400 tensor(5.2153, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"20000\n",
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"5600 tensor(5.3441, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5700 tensor(5.3958, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5800 tensor(5.8945, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"21000\n",
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"5900 tensor(5.5684, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6000 tensor(5.5715, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"22000\n",
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"6400 tensor(5.3024, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"23000\n",
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"6500 tensor(5.3010, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6600 tensor(6.0962, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6700 tensor(5.0961, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"24000\n",
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"6800 tensor(5.1091, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"25000\n",
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"7000 tensor(5.3128, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7100 tensor(5.3416, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"26000\n",
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"34000\n",
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"35000\n",
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"118500 tensor(5.0216, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118600 tensor(5.1426, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"425000\n",
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"118800 tensor(5.1688, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119000 tensor(4.7053, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119600 tensor(5.3674, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"430000\n",
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"120200 tensor(5.1214, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120400 tensor(5.3908, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120500 tensor(4.9955, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"epoch: = 1\n",
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"0 tensor(5.2093, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"100 tensor(5.3814, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"4000 tensor(4.8247, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"4300 tensor(5.1466, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"4500 tensor(5.0831, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"4800 tensor(5.0023, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5100 tensor(5.0103, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5600 tensor(4.9750, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5700 tensor(5.0814, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5900 tensor(5.2366, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6000 tensor(5.2223, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6100 tensor(4.9001, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6200 tensor(5.3510, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6300 tensor(5.2366, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6400 tensor(5.0379, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"23000\n",
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"6500 tensor(5.0265, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6600 tensor(5.5000, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6700 tensor(4.8587, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"24000\n",
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"6800 tensor(4.8712, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6900 tensor(5.1405, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"25000\n",
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"7000 tensor(5.0429, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7100 tensor(5.1420, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7200 tensor(5.2794, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"26000\n",
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"7300 tensor(5.2271, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7400 tensor(4.9754, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7500 tensor(5.3659, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7600 tensor(4.7902, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7700 tensor(5.2327, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7900 tensor(5.0152, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8000 tensor(5.1026, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"29000\n",
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"8200 tensor(5.0953, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8300 tensor(5.0372, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"30000\n",
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"8400 tensor(5.0211, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8500 tensor(4.7475, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8600 tensor(4.8443, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"31000\n",
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"8700 tensor(5.1471, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8800 tensor(5.1762, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8900 tensor(5.4800, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"32000\n",
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"9000 tensor(4.9010, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"9100 tensor(5.1490, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"33000\n",
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"9300 tensor(5.0652, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"34000\n",
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"9500 tensor(4.9114, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"9700 tensor(5.0307, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"9800 tensor(5.1592, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"9900 tensor(4.9091, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"10100 tensor(5.2265, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"10700 tensor(5.1943, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"11200 tensor(5.1724, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"11800 tensor(5.3687, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"12100 tensor(5.0138, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"106500 tensor(5.2242, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"106600 tensor(4.9223, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"106700 tensor(5.1467, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"382000\n",
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"106800 tensor(4.9459, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"106900 tensor(5.2412, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"383000\n",
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"107000 tensor(4.9030, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"107100 tensor(5.1487, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"107200 tensor(5.1303, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"384000\n",
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"107300 tensor(4.8918, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"107400 tensor(5.0686, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"107500 tensor(4.8140, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"385000\n",
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"107600 tensor(4.8693, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"107700 tensor(5.0879, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"107800 tensor(5.0939, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"386000\n",
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"107900 tensor(5.3087, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108000 tensor(5.0659, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108100 tensor(5.0273, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"387000\n",
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"108200 tensor(5.0357, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108300 tensor(5.0666, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"388000\n",
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"108400 tensor(5.1699, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108500 tensor(4.8493, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108600 tensor(5.0399, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"389000\n",
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"108700 tensor(4.5729, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108800 tensor(4.9686, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"108900 tensor(4.7163, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"390000\n",
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"109000 tensor(5.1307, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"109100 tensor(4.7140, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"109200 tensor(4.9282, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"391000\n",
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"109300 tensor(4.5945, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"109400 tensor(4.9045, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"109500 tensor(4.8345, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"392000\n",
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"109600 tensor(5.0347, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"109700 tensor(5.3972, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"393000\n",
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"109800 tensor(4.7386, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"109900 tensor(4.9312, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110000 tensor(5.0107, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"394000\n",
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"110100 tensor(4.9484, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110200 tensor(5.3088, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110300 tensor(5.1758, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"395000\n",
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"110400 tensor(4.6608, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110500 tensor(5.0811, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110600 tensor(4.9037, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"396000\n",
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"110700 tensor(4.8701, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110800 tensor(5.3046, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"110900 tensor(4.9689, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"397000\n",
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"111000 tensor(5.1637, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"111100 tensor(4.9131, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"398000\n",
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"111200 tensor(4.9308, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"111300 tensor(5.0290, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"111400 tensor(5.1249, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"399000\n",
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"111500 tensor(5.2130, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"111600 tensor(5.2382, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"111700 tensor(4.8168, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"400000\n",
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"111800 tensor(5.0276, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"111900 tensor(5.1121, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"112000 tensor(5.0463, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"401000\n",
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"112100 tensor(4.6612, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"112200 tensor(5.0632, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"402000\n",
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"112300 tensor(5.2173, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"112400 tensor(5.2271, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"112500 tensor(4.8618, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"403000\n",
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"112600 tensor(4.9399, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"112700 tensor(4.9514, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"112800 tensor(4.5700, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"404000\n",
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"112900 tensor(4.8508, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113000 tensor(5.0784, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113100 tensor(5.0205, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"405000\n",
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"113200 tensor(4.5998, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113300 tensor(4.9371, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113400 tensor(5.0241, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"406000\n",
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"113500 tensor(5.2931, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113600 tensor(5.2488, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"407000\n",
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"113700 tensor(4.8483, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113800 tensor(5.2471, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"113900 tensor(5.0391, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"408000\n",
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"114000 tensor(4.6978, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"114100 tensor(4.8170, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"114200 tensor(4.7943, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"409000\n",
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"114300 tensor(4.6678, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"114400 tensor(4.8687, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"114500 tensor(4.9471, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"410000\n",
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"114600 tensor(5.1608, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"114700 tensor(5.1240, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"114800 tensor(5.0586, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"411000\n",
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"114900 tensor(5.0095, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115000 tensor(4.9676, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"412000\n",
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"115100 tensor(5.0771, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115200 tensor(5.0396, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115300 tensor(5.0663, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"413000\n",
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"115400 tensor(5.2931, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115500 tensor(5.2901, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115600 tensor(5.0953, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"414000\n",
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"115700 tensor(4.9706, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115800 tensor(5.0768, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"115900 tensor(5.1668, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"415000\n",
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"116000 tensor(5.0913, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"116100 tensor(4.8294, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"116200 tensor(5.2189, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"416000\n",
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"116300 tensor(4.3633, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"116400 tensor(5.1168, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"417000\n",
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"116500 tensor(5.0554, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"116600 tensor(5.1477, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"116700 tensor(4.9884, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"418000\n",
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"116800 tensor(4.8381, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"116900 tensor(5.0709, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"117000 tensor(5.0518, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"419000\n",
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"117100 tensor(5.0967, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"117200 tensor(4.9430, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"117300 tensor(5.2903, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"420000\n",
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"117400 tensor(5.2733, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"117500 tensor(4.8548, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"421000\n",
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"117600 tensor(5.1768, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"117700 tensor(5.1013, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"117800 tensor(4.9804, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"422000\n",
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"117900 tensor(5.0671, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118000 tensor(5.1332, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118100 tensor(4.9436, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"423000\n",
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"118200 tensor(4.9859, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118300 tensor(4.9755, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118400 tensor(5.0902, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"424000\n",
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"118500 tensor(4.9845, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118600 tensor(5.1098, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118700 tensor(5.1192, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"425000\n",
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"118800 tensor(5.1445, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"118900 tensor(5.1731, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"426000\n",
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"119000 tensor(4.6613, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119100 tensor(4.9640, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119200 tensor(5.2998, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"427000\n",
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"119300 tensor(4.9427, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119400 tensor(4.9993, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119500 tensor(5.0117, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"428000\n",
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"119600 tensor(5.3324, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119700 tensor(4.9375, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"119800 tensor(5.0221, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"429000\n",
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"119900 tensor(4.7975, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120000 tensor(4.9253, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120100 tensor(4.9377, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"430000\n",
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"120200 tensor(5.0909, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120300 tensor(4.9946, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"431000\n",
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"120400 tensor(5.3612, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120500 tensor(4.9678, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"120600 tensor(5.2217, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"432000\n"
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]
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}
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]
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},
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{
|
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"cell_type": "markdown",
|
|
"source": [
|
|
"## Eval"
|
|
],
|
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"metadata": {
|
|
"id": "fnL8e_NkvwtY"
|
|
}
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"\n",
|
|
"model = Bigram(vocab_size, embed_size).to(device)\n",
|
|
"model.load_state_dict(torch.load('model.bin'))\n",
|
|
"model.eval()\n",
|
|
"\n",
|
|
"res = torch.tensor(vocab.forward(['for'])).to(device)\n",
|
|
"\n",
|
|
"out = model(res)\n",
|
|
"top = torch.topk(out[0], 10)\n",
|
|
"top_indices = top.indices.tolist()\n",
|
|
"top_probs = top.values.tolist()\n",
|
|
"top_words = vocab.lookup_tokens(top_indices)\n",
|
|
"list(zip(top_words, top_indices, top_probs))"
|
|
],
|
|
"metadata": {
|
|
"id": "7LykJxNEWNdJ",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "6b8f9a38-dbcd-4285-ff30-a45d0ee760bf"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stderr",
|
|
"text": [
|
|
"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
|
" input = module(input)\n"
|
|
]
|
|
},
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"[('<unk>', 0, 0.23156249523162842),\n",
|
|
" ('the', 1, 0.2045561522245407),\n",
|
|
" ('a', 5, 0.0636623203754425),\n",
|
|
" ('his', 20, 0.012841351330280304),\n",
|
|
" ('their', 40, 0.012044394388794899),\n",
|
|
" ('this', 28, 0.011758995242416859),\n",
|
|
" ('tho', 33, 0.010536346584558487),\n",
|
|
" ('some', 77, 0.008259670808911324),\n",
|
|
" ('any', 49, 0.007337945979088545),\n",
|
|
" ('an', 38, 0.007214350625872612)]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 26
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"vocab = train_dataset.vocab\n",
|
|
"res = torch.tensor(vocab.forward(['wait'])).to(device)\n",
|
|
"\n",
|
|
"out = model(res)\n",
|
|
"top = torch.topk(out[0], 20)\n",
|
|
"top_indices = top.indices.tolist()\n",
|
|
"top_probs = top.values.tolist()\n",
|
|
"top_words = vocab.lookup_tokens(top_indices)\n",
|
|
"list(zip(top_words, top_indices, top_probs))"
|
|
],
|
|
"metadata": {
|
|
"id": "wep6U9NYWNfx",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "a089a1e6-a4d3-49cb-d860-2c48a4d8f83b"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"[('until', 145, 0.17603398859500885),\n",
|
|
" ('for', 9, 0.16400693356990814),\n",
|
|
" ('<unk>', 0, 0.15191353857517242),\n",
|
|
" ('on', 15, 0.04624223709106445),\n",
|
|
" ('till', 677, 0.035729214549064636),\n",
|
|
" ('a', 5, 0.03367603197693825),\n",
|
|
" ('to', 4, 0.029361305758357048),\n",
|
|
" ('upon', 59, 0.01995147578418255),\n",
|
|
" ('and', 3, 0.01906605064868927),\n",
|
|
" ('in', 6, 0.013167516328394413),\n",
|
|
" ('at', 14, 0.011669990606606007),\n",
|
|
" ('the', 1, 0.010971800424158573),\n",
|
|
" ('of', 2, 0.005925077944993973),\n",
|
|
" ('with', 16, 0.0055325529538095),\n",
|
|
" ('In', 32, 0.004919056314975023),\n",
|
|
" ('until\\\\nthe', 5509, 0.004719363059848547),\n",
|
|
" ('tor', 532, 0.004647853318601847),\n",
|
|
" ('for\\\\nthe', 389, 0.004400868900120258),\n",
|
|
" ('two', 74, 0.0043497709557414055),\n",
|
|
" ('patiently', 14401, 0.004239553119987249)]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 28
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n",
|
|
"\n",
|
|
"embeddings = model.model[0].weight\n",
|
|
"\n",
|
|
"vec = embeddings[vocab['take']]\n",
|
|
"\n",
|
|
"similarities = cos(vec, embeddings)\n",
|
|
"\n",
|
|
"top = torch.topk(similarities, 10)\n",
|
|
"\n",
|
|
"top_indices = top.indices.tolist()\n",
|
|
"top_probs = top.values.tolist()\n",
|
|
"top_words = vocab.lookup_tokens(top_indices)\n",
|
|
"list(zip(top_words, top_indices, top_probs))"
|
|
],
|
|
"metadata": {
|
|
"id": "VCkcPiNCWNiZ",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "afcac703-ef72-45d3-b6af-cce72c8d9483"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"[('take', 152, 1.0000001192092896),\n",
|
|
" ('took', 248, 0.8024641275405884),\n",
|
|
" ('taking', 538, 0.775847852230072),\n",
|
|
" ('takes', 1108, 0.7307196855545044),\n",
|
|
" ('to\\\\ntake', 3165, 0.7301462888717651),\n",
|
|
" ('taken', 180, 0.6280043125152588),\n",
|
|
" ('will\\\\ntake', 11101, 0.6152595281600952),\n",
|
|
" ('tako', 6281, 0.5979241132736206),\n",
|
|
" ('have\\\\ntaken', 15483, 0.5244049429893494),\n",
|
|
" ('Take', 5203, 0.5183135867118835)]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 30
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"vocab = train_dataset.vocab\n",
|
|
"res = torch.tensor(vocab.forward(['take'])).to(device)\n",
|
|
"\n",
|
|
"out = model(res)\n",
|
|
"top = torch.topk(out[0], 20)\n",
|
|
"top_indices = top.indices.tolist()\n",
|
|
"top_probs = top.values.tolist()\n",
|
|
"top_words = vocab.lookup_tokens(top_indices)\n",
|
|
"list(zip(top_words, top_indices, top_probs))"
|
|
],
|
|
"metadata": {
|
|
"id": "km2U9fR4WNlI",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "ae1fd560-ac8f-480a-a961-f8eb0f265fac"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
"[('<unk>', 0, 0.18006379902362823),\n",
|
|
" ('the', 1, 0.11970410495996475),\n",
|
|
" ('a', 5, 0.07213426381349564),\n",
|
|
" ('care', 611, 0.027887443080544472),\n",
|
|
" ('up', 69, 0.027840441092848778),\n",
|
|
" ('it', 17, 0.023985734209418297),\n",
|
|
" ('place', 159, 0.020694952458143234),\n",
|
|
" ('advantage', 1458, 0.015635941177606583),\n",
|
|
" ('his', 20, 0.014868981204926968),\n",
|
|
" ('part', 131, 0.013506578281521797),\n",
|
|
" ('an', 38, 0.013118326663970947),\n",
|
|
" ('their', 40, 0.010849231854081154),\n",
|
|
" ('hold', 478, 0.010717789642512798),\n",
|
|
" ('them', 72, 0.010186631232500076),\n",
|
|
" ('to', 4, 0.009746687486767769),\n",
|
|
" ('this', 28, 0.009519988670945168),\n",
|
|
" ('any', 49, 0.009436620399355888),\n",
|
|
" ('her', 53, 0.008774512447416782),\n",
|
|
" ('him', 70, 0.008407332003116608),\n",
|
|
" ('all', 34, 0.007673078216612339)]"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 33
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### Create files for geval"
|
|
],
|
|
"metadata": {
|
|
"id": "GVDTtvY6v2pt"
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"def get_values(presc_word, model, vocab):\n",
|
|
" ixs = torch.tensor(vocab.forward([presc_word])).to(device)\n",
|
|
" out = model(ixs)\n",
|
|
" top = torch.topk(out[0], 20)\n",
|
|
" top_indices = top.indices.tolist()\n",
|
|
" top_probs = top.values.tolist()\n",
|
|
" top_words = vocab.lookup_tokens(top_indices)\n",
|
|
" return list(zip(top_words, top_probs))\n",
|
|
"\n",
|
|
"def last_word(text):\n",
|
|
" \"\"\"Return the last word of a string.\"\"\"\n",
|
|
" last_word = \"\"\n",
|
|
" for i in range(len(text)-1, -1, -1):\n",
|
|
" if text[i] == ' ':\n",
|
|
" return last_word[::-1]\n",
|
|
" else:\n",
|
|
" last_word += text[i]\n",
|
|
" return last_word[::-1]\n",
|
|
"\n",
|
|
"def first_word(text):\n",
|
|
" \"\"\"Return the first word of a string.\"\"\"\n",
|
|
" word = \"\"\n",
|
|
" for i in range(len(text)-1):\n",
|
|
" if text[i] == ' ':\n",
|
|
" return word\n",
|
|
" else:\n",
|
|
" word += text[i]\n",
|
|
" return word\n",
|
|
"\n",
|
|
"def sum_prob(dic):\n",
|
|
" probsum = sum(float(val) for key, val in dic.items())\n",
|
|
" probsum = probsum - float(dic.get('<unk>', 0))\n",
|
|
" if \"<unk>\" in dic.keys():\n",
|
|
" del dic['<unk>']\n",
|
|
" tab = [(key, val) for key, val in dic.items()]\n",
|
|
" tab.append(('<unk>', 1-probsum))\n",
|
|
" return tab\n",
|
|
"\n",
|
|
"def format_to(dic):\n",
|
|
" tab = sum_prob(dic)\n",
|
|
" result = ''\n",
|
|
" for element in tab[:-1]:\n",
|
|
" result+=str(element[0])+':'+str(element[1])+'\\t'\n",
|
|
" result+=':'+ str(tab[-1][1])+'\\n'\n",
|
|
" return result"
|
|
],
|
|
"metadata": {
|
|
"id": "rUqKisSfWNnp"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"source": [
|
|
"with lzma.open(test_file, 'rt') as file:\n",
|
|
" predict_words = []\n",
|
|
" results = []\n",
|
|
" for line in file:\n",
|
|
" split = line.split('\\t')[6:] \n",
|
|
" predict_words.append(last_word(split[0]))\n",
|
|
" \n",
|
|
" vocab = train_dataset.vocab\n",
|
|
" for presc_word in predict_words:\n",
|
|
" results.append(dict(get_values(presc_word, model ,vocab)))\n",
|
|
" \n",
|
|
" with open(out_file, 'w') as outfile:\n",
|
|
" for elem in results:\n",
|
|
" outfile.write(format_to(elem))\n",
|
|
"\n"
|
|
],
|
|
"metadata": {
|
|
"id": "cVpm-tRzWNqR"
|
|
},
|
|
"execution_count": null,
|
|
"outputs": []
|
|
}
|
|
]
|
|
} |