73 lines
2.1 KiB
Plaintext
73 lines
2.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2dd04d34-25c0-470f-973d-1325ce0df797",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The history saving thread hit an unexpected error (OperationalError('disk I/O error')).History will not be written to the database.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/gscratch/scrubbed/mjilg/jupyter-notebook/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
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"from transformers import Trainer, TrainingArguments\n",
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"from torch.utils.data import Dataset\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
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"import torch\n",
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"import json"
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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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"id": "3504b633-4999-47d0-a6eb-ce7916206ced",
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"metadata": {},
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"outputs": [],
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"source": [
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"model_name = \"distilbert-base-uncased\"\n",
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"model = AutoModelForSequenceClassification.from_pretrained(model_name, \n",
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" num_labels=1) \n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.21"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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