paradetox-finetune.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f662c1c0",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2024-01-07T05:25:25.870472Z",
     "iopub.status.busy": "2024-01-07T05:25:25.869579Z",
     "iopub.status.idle": "2024-01-07T05:26:53.151508Z",
     "shell.execute_reply": "2024-01-07T05:26:53.150563Z"
    },
    "papermill": {
     "duration": 87.291458,
     "end_time": "2024-01-07T05:26:53.153797",
     "exception": false,
     "start_time": "2024-01-07T05:25:25.862339",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gdown\r\n",
      "  Downloading gdown-4.7.1-py3-none-any.whl (15 kB)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from gdown) (3.12.2)\r\n",
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      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\r\n",
      "Installing collected packages: gdown\r\n",
      "Successfully installed gdown-4.7.1\r\n",
      "Collecting transformers==4.10.0\r\n",
      "  Downloading transformers-4.10.0-py3-none-any.whl (2.8 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m34.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (3.12.2)\r\n",
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      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (1.24.3)\r\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (21.3)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (6.0.1)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (2023.8.8)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (2.31.0)\r\n",
      "Collecting sacremoses (from transformers==4.10.0)\r\n",
      "  Obtaining dependency information for sacremoses from https://files.pythonhosted.org/packages/0b/f0/89ee2bc9da434bd78464f288fdb346bc2932f2ee80a90b2a4bbbac262c74/sacremoses-0.1.1-py3-none-any.whl.metadata\r\n",
      "  Downloading sacremoses-0.1.1-py3-none-any.whl.metadata (8.3 kB)\r\n",
      "Collecting tokenizers<0.11,>=0.10.1 (from transformers==4.10.0)\r\n",
      "  Downloading tokenizers-0.10.3.tar.gz (212 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.7/212.7 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.10.0) (4.66.1)\r\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.0.12->transformers==4.10.0) (2023.12.2)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.0.12->transformers==4.10.0) (4.5.0)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->transformers==4.10.0) (3.0.9)\r\n",
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      "Requirement already satisfied: click in /opt/conda/lib/python3.10/site-packages (from sacremoses->transformers==4.10.0) (8.1.7)\r\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.10/site-packages (from sacremoses->transformers==4.10.0) (1.3.2)\r\n",
      "Downloading sacremoses-0.1.1-py3-none-any.whl (897 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m897.5/897.5 kB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hBuilding wheels for collected packages: tokenizers\r\n",
      "  Building wheel for tokenizers (pyproject.toml) ... \u001b[?25l-\b \berror\r\n",
      "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\r\n",
      "  \r\n",
      "  \u001b[31m×\u001b[0m \u001b[32mBuilding wheel for tokenizers \u001b[0m\u001b[1;32m(\u001b[0m\u001b[32mpyproject.toml\u001b[0m\u001b[1;32m)\u001b[0m did not run successfully.\r\n",
      "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\r\n",
      "  \u001b[31m╰─>\u001b[0m \u001b[31m[51 lines of output]\u001b[0m\r\n",
      "  \u001b[31m   \u001b[0m running bdist_wheel\r\n",
      "  \u001b[31m   \u001b[0m running build\r\n",
      "  \u001b[31m   \u001b[0m running build_py\r\n",
      "  \u001b[31m   \u001b[0m creating build\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/models\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/models/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/models\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/decoders\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/decoders/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/decoders\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/normalizers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/normalizers/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/normalizers\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/pre_tokenizers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/pre_tokenizers/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/pre_tokenizers\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/processors\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/processors/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/processors\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/trainers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/trainers/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/trainers\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/base_tokenizer.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/byte_level_bpe.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/sentencepiece_bpe.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/char_level_bpe.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/bert_wordpiece.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/implementations/sentencepiece_unigram.py -> build/lib.linux-x86_64-cpython-310/tokenizers/implementations\r\n",
      "  \u001b[31m   \u001b[0m creating build/lib.linux-x86_64-cpython-310/tokenizers/tools\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/tools/__init__.py -> build/lib.linux-x86_64-cpython-310/tokenizers/tools\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/tools/visualizer.py -> build/lib.linux-x86_64-cpython-310/tokenizers/tools\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/models/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers/models\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/decoders/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers/decoders\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/normalizers/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers/normalizers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/pre_tokenizers/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers/pre_tokenizers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/processors/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers/processors\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/trainers/__init__.pyi -> build/lib.linux-x86_64-cpython-310/tokenizers/trainers\r\n",
      "  \u001b[31m   \u001b[0m copying py_src/tokenizers/tools/visualizer-styles.css -> build/lib.linux-x86_64-cpython-310/tokenizers/tools\r\n",
      "  \u001b[31m   \u001b[0m running build_ext\r\n",
      "  \u001b[31m   \u001b[0m running build_rust\r\n",
      "  \u001b[31m   \u001b[0m error: can't find Rust compiler\r\n",
      "  \u001b[31m   \u001b[0m \r\n",
      "  \u001b[31m   \u001b[0m If you are using an outdated pip version, it is possible a prebuilt wheel is available for this package but pip is not able to install from it. Installing from the wheel would avoid the need for a Rust compiler.\r\n",
      "  \u001b[31m   \u001b[0m \r\n",
      "  \u001b[31m   \u001b[0m To update pip, run:\r\n",
      "  \u001b[31m   \u001b[0m \r\n",
      "  \u001b[31m   \u001b[0m     pip install --upgrade pip\r\n",
      "  \u001b[31m   \u001b[0m \r\n",
      "  \u001b[31m   \u001b[0m and then retry package installation.\r\n",
      "  \u001b[31m   \u001b[0m \r\n",
      "  \u001b[31m   \u001b[0m If you did intend to build this package from source, try installing a Rust compiler from your system package manager and ensure it is on the PATH during installation. Alternatively, rustup (available at https://rustup.rs) is the recommended way to download and update the Rust compiler toolchain.\r\n",
      "  \u001b[31m   \u001b[0m \u001b[31m[end of output]\u001b[0m\r\n",
      "  \r\n",
      "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\r\n",
      "\u001b[31m  ERROR: Failed building wheel for tokenizers\u001b[0m\u001b[31m\r\n",
      "\u001b[0m\u001b[?25hFailed to build tokenizers\r\n",
      "\u001b[31mERROR: Could not build wheels for tokenizers, which is required to install pyproject.toml-based projects\u001b[0m\u001b[31m\r\n",
      "\u001b[0mCollecting flash-attn\r\n",
      "  Downloading flash_attn-2.4.2.tar.gz (2.4 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m33.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from flash-attn) (2.0.0)\r\n",
      "Collecting einops (from flash-attn)\r\n",
      "  Obtaining dependency information for einops from https://files.pythonhosted.org/packages/29/0b/2d1c0ebfd092e25935b86509a9a817159212d82aa43d7fb07eca4eeff2c2/einops-0.7.0-py3-none-any.whl.metadata\r\n",
      "  Downloading einops-0.7.0-py3-none-any.whl.metadata (13 kB)\r\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from flash-attn) (21.3)\r\n",
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      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->flash-attn) (3.12.2)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch->flash-attn) (4.5.0)\r\n",
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      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->flash-attn) (3.1.2)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->flash-attn) (2.1.3)\r\n",
      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->flash-attn) (1.3.0)\r\n",
      "Downloading einops-0.7.0-py3-none-any.whl (44 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hBuilding wheels for collected packages: flash-attn\r\n",
      "  Building wheel for flash-attn (setup.py) ... \u001b[?25l-\b \b\\\b \b|\b \bdone\r\n",
      "\u001b[?25h  Created wheel for flash-attn: filename=flash_attn-2.4.2-cp310-cp310-linux_x86_64.whl size=113935202 sha256=738d0ba133f067ea30a5aa8d85e35f83d4e65467b13693e2d04bf86312f78990\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/9d/cf/7f/d14555553b5b30698dae0a4159fdd058157e7021cec565ecaa\r\n",
      "Successfully built flash-attn\r\n",
      "Installing collected packages: einops, flash-attn\r\n",
      "Successfully installed einops-0.7.0 flash-attn-2.4.2\r\n",
      "/bin/bash: line 1: 1.23.0: No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "!pip install gdown\n",
    "!pip install -q -U bitsandbytes peft accelerate optimum\n",
    "!pip install transformers==4.10.0\n",
    "!pip install flash-attn --no-build-isolation\n",
    "\n",
    "!pip install torch>=1.7.0,!=1.8.0\n",
    "!pip install numpy <1.23.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd527c79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:26:53.173468Z",
     "iopub.status.busy": "2024-01-07T05:26:53.173156Z",
     "iopub.status.idle": "2024-01-07T05:27:09.178594Z",
     "shell.execute_reply": "2024-01-07T05:27:09.177594Z"
    },
    "papermill": {
     "duration": 16.018132,
     "end_time": "2024-01-07T05:27:09.181106",
     "exception": false,
     "start_time": "2024-01-07T05:26:53.162974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEVICE: cuda\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import gdown\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "from peft import get_peft_config, get_peft_model, get_peft_model_state_dict, LoraConfig, TaskType\n",
    "\n",
    "import transformers\n",
    "from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Model \n",
    "from transformers import DataCollatorForSeq2Seq, get_scheduler, BitsAndBytesConfig\n",
    "from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer, EarlyStoppingCallback, IntervalStrategy\n",
    "from datasets import load_dataset, Dataset, DatasetDict, load_metric\n",
    "\n",
    "import os, sys\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "np.object = object\n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "if device==\"cuda\":\n",
    "    torch.cuda.empty_cache()\n",
    "    torch.cuda.set_per_process_memory_fraction(0.7)\n",
    "print(\"DEVICE:\", device)\n",
    "\n",
    "# set w&b secrets as environment variable\n",
    "user_secrets = UserSecretsClient()\n",
    "wandb_key = user_secrets.get_secret(\"w&b\")\n",
    "huggingface_key = user_secrets.get_secret(\"huggingface-write\")\n",
    "os.environ[\"WANDB_API_KEY\"] = wandb_key\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "446ada2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:09.201052Z",
     "iopub.status.busy": "2024-01-07T05:27:09.200385Z",
     "iopub.status.idle": "2024-01-07T05:27:09.608341Z",
     "shell.execute_reply": "2024-01-07T05:27:09.607425Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://huggingface.co/datasets/s-nlp/paradetox/raw/main/train.tsv\n",
      "To: /kaggle/temp/train.tsv\n",
      "100%|██████████| 2.04M/2.04M [00:00<00:00, 8.26MB/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/kaggle/temp/train.tsv'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://huggingface.co/datasets/s-nlp/paradetox/raw/main/train.tsv'\n",
    "destination_path = \"/kaggle/temp/\"\n",
    "\n",
    "if os.path.exists(destination_path) != True:\n",
    "        os.mkdir(destination_path)\n",
    "        \n",
    "filename = url.split(\"/\")[-1]\n",
    "outpath = os.path.join(destination_path, filename)\n",
    "gdown.download(url=url, output=outpath, quiet=False, fuzzy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af5c4b3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:09.630785Z",
     "iopub.status.busy": "2024-01-07T05:27:09.630444Z",
     "iopub.status.idle": "2024-01-07T05:27:09.708470Z",
     "shell.execute_reply": "2024-01-07T05:27:09.707548Z"
    },
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     "duration": 0.09061,
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     "exception": false,
     "start_time": "2024-01-07T05:27:09.619938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19744 entries, 0 to 19743\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   toxic    19744 non-null  object\n",
      " 1   neutral  19744 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 308.6+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>toxic</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>he had steel balls too !</td>\n",
       "      <td>he was brave too!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dude should have been taken to api , he would ...</td>\n",
       "      <td>It would have been good if he went to api. He ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>im not gonna sell the fucking picture , i just...</td>\n",
       "      <td>I'm not gonna sell the picture, i just want to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>the garbage that is being created by cnn and o...</td>\n",
       "      <td>the news that is being created by cnn and othe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>the reason they dont exist is because neither ...</td>\n",
       "      <td>The reason they don't exist is because neither...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               toxic  \\\n",
       "0                           he had steel balls too !   \n",
       "1  dude should have been taken to api , he would ...   \n",
       "2  im not gonna sell the fucking picture , i just...   \n",
       "3  the garbage that is being created by cnn and o...   \n",
       "4  the reason they dont exist is because neither ...   \n",
       "\n",
       "                                             neutral  \n",
       "0                                  he was brave too!  \n",
       "1  It would have been good if he went to api. He ...  \n",
       "2  I'm not gonna sell the picture, i just want to...  \n",
       "3  the news that is being created by cnn and othe...  \n",
       "4  The reason they don't exist is because neither...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(outpath, sep='\\t')\n",
    "# rename columns\n",
    "df = df.rename(columns={\"en_toxic_comment\":\"toxic\", \"en_neutral_comment\":\"neutral\"})\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7827dd71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:09.731695Z",
     "iopub.status.busy": "2024-01-07T05:27:09.731404Z",
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     "shell.execute_reply": "2024-01-07T05:27:09.752454Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# to huggingface dataset type\n",
    "dataset = Dataset.from_pandas(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffa3d340",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:09.776336Z",
     "iopub.status.busy": "2024-01-07T05:27:09.776020Z",
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     "shell.execute_reply": "2024-01-07T05:27:09.779565Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "MAX_INLENGTH = 50\n",
    "MAX_OUTLENGTH = 50\n",
    "LEARNING_RATE = 5e-5\n",
    "BATCH_SIZE = 4\n",
    "EPOCHS = 1\n",
    "MODEL_NAME = \"Ribin/t5-base_detoxParaphraser\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66d7f061",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2024-01-07T05:27:09.791932",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e047011076944109ebfaf4ea3d92113",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/21.0k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_minor": 0
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      "text/plain": [
       "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "added_tokens.json:   0%|          | 0.00/2.61k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "version_minor": 0
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      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb66ac3e7b5e4af4a868d217f50bdee8",
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      },
      "text/plain": [
       "config.json:   0%|          | 0.00/1.53k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8bd81ec45f6442ebb9423e6200b544f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/142 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#Flash-Attention-2\n",
    "# https://huggingface.co/blog/4bit-transformers-bitsandbytes\n",
    "# load model in 4-bit\n",
    "quantization_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_use_double_quant=True,\n",
    "    bnb_4bit_compute_dtype=torch.float16\n",
    ")\n",
    "\n",
    "tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME, model_max_length=MAX_INLENGTH)\n",
    "tokenizer.add_special_tokens({'pad_token': '[PAD]'}) \n",
    "\n",
    "model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME,\n",
    "                                                   device_map='auto',\n",
    "#                                                    quantization_config=quantization_config,\n",
    "#                                                    attn_implementation=\"flash_attention_2\",\n",
    "#                                                    torch_dtype=torch.float16,\n",
    "                                                   return_dict=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bba64e81",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:28.361918Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def preprocessing(samples):\n",
    "    toxic = samples['toxic']\n",
    "    neutral = samples['neutral']\n",
    "    \n",
    "    prefix_toxic = \"Toxic version: \"\n",
    "    prefix_nontoxic = \"Non-toxic version: \"\n",
    "    \n",
    "    inputs = [prefix_toxic + text for text in toxic]\n",
    "    outputs = [prefix_nontoxic + text for text in neutral]\n",
    "    encodings = tokenizer(text=inputs, \n",
    "            text_target=outputs,\n",
    "            padding=\"max_length\",\n",
    "            max_length=MAX_INLENGTH,\n",
    "            truncation= True)\n",
    "    return encodings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68df7e17",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3b042430665246b4a4c3a9d96baae785",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'train': ['input_ids', 'attention_mask', 'labels'],\n",
       " 'test': ['input_ids', 'attention_mask', 'labels']}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_dataset = dataset.map(preprocessing, batched=True, remove_columns=dataset.column_names)\n",
    "\n",
    "tokenized_dataset = tokenized_dataset.train_test_split(train_size=0.90, seed=12)\n",
    "tokenized_dataset.set_format(\"pt\")\n",
    "tokenized_dataset.column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a0eee7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:34.785416Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "label_pad_token_id = -100\n",
    "data_collator = DataCollatorForSeq2Seq(\n",
    "    tokenizer,\n",
    "    model=model,\n",
    "    label_pad_token_id=label_pad_token_id,\n",
    "    pad_to_multiple_of=8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86594b61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:34.815522Z",
     "iopub.status.busy": "2024-01-07T05:27:34.815234Z",
     "iopub.status.idle": "2024-01-07T05:27:34.820541Z",
     "shell.execute_reply": "2024-01-07T05:27:34.819833Z"
    },
    "papermill": {
     "duration": 0.019509,
     "end_time": "2024-01-07T05:27:34.822455",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def test(model, tokenizer, toxic_text, device):\n",
    "    model = model\n",
    "    batch = tokenizer([toxic_text], return_tensors=\"pt\").to(device)\n",
    "    with torch.no_grad():\n",
    "        outputs = model.generate(input_ids=batch['input_ids'], max_new_tokens=MAX_OUTLENGTH)\n",
    "    non_toxic_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
    "    print(non_toxic_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f24e365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-07T05:27:34.846057Z",
     "iopub.status.busy": "2024-01-07T05:27:34.845804Z",
     "iopub.status.idle": "2024-01-07T05:27:37.062466Z",
     "shell.execute_reply": "2024-01-07T05:27:37.061367Z"
    },
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     "duration": 2.230656,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<pad> Non-toxic version: Who are you?']\n"
     ]
    }
   ],
   "source": [
    "toxic_text = \"Toxic version: who the fuck are you?\"\n",
    "test(model, tokenizer, toxic_text, \"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "331c2fb0",
   "metadata": {
    "execution": {
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       "Downloading builder script:   0%|          | 0.00/2.48k [00:00<?, ?B/s]"
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   "source": [
    "# Load the BLEU metric\n",
    "bleu_metric = load_metric(\"bleu\")\n",
    "\n",
    "def compute_metrics(eval_preds):\n",
    "    preds, labels = eval_preds\n",
    "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
    "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
    "\n",
    "    # Some simple post-processing\n",
    "    decoded_preds = [pred.strip() for pred in decoded_preds]\n",
    "    decoded_labels = [[label.strip()] for label in decoded_labels]\n",
    "\n",
    "    # Compute BLEU score\n",
    "    result = bleu_metric.compute(predictions=decoded_preds, references=decoded_labels)\n",
    "    result = {\"bleu\": result[\"score\"]}\n",
    "\n",
    "    return result"
   ]
  },
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   "execution_count": 14,
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   "source": [
    "output_dir = \"/kaggle/working/detox\"\n",
    "\n",
    "training_args = Seq2SeqTrainingArguments(\n",
    "        output_dir=output_dir,\n",
    "        learning_rate=LEARNING_RATE,\n",
    "        num_train_epochs=EPOCHS,\n",
    "        logging_dir=f\"{output_dir}/logs\",\n",
    "        evaluation_strategy=\"steps\",  # Evaluate every specified number of steps\n",
    "        eval_steps=15,  # Specify the number of steps between evaluations\n",
    "        logging_strategy=\"steps\",\n",
    "        logging_steps=15,\n",
    "        save_strategy=\"steps\",\n",
    "        save_steps=100,\n",
    "        warmup_steps=100,\n",
    "        per_device_train_batch_size=12,\n",
    "        per_device_eval_batch_size=6,\n",
    "        gradient_accumulation_steps=12,\n",
    "        eval_accumulation_steps=12,\n",
    "        weight_decay=0.01,\n",
    "        fp16=True, \n",
    "#         metric_for_best_model='f1',\n",
    "#         load_best_model_at_end=True\n",
    "        )\n",
    "\n",
    "trainer = Seq2SeqTrainer(\n",
    "        model=model,\n",
    "        args=training_args,\n",
    "        data_collator=data_collator,\n",
    "        train_dataset=tokenized_dataset['train'],\n",
    "        eval_dataset=tokenized_dataset['test'],\n",
    "#         callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],\n",
    "#         compute_metrics=compute_metrics\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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    },
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mribinbaby\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20240107_052740-8d8abo5o\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mcerulean-wood-4\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/ribinbaby/huggingface\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/ribinbaby/huggingface/runs/8d8abo5o\u001b[0m\n"
     ]
    },
    {
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       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='123' max='123' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [123/123 07:01, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.119000</td>\n",
       "      <td>0.862605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1.111700</td>\n",
       "      <td>0.851181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>1.086900</td>\n",
       "      <td>0.833959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>1.049900</td>\n",
       "      <td>0.814320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>1.006000</td>\n",
       "      <td>0.787291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>0.953000</td>\n",
       "      <td>0.763292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>0.933600</td>\n",
       "      <td>0.734475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>120</td>\n",
       "      <td>0.900900</td>\n",
       "      <td>0.723049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
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       "      \n",
       "      <progress value='330' max='330' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [330/330 00:13]\n",
       "    </div>\n",
       "    "
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "{'eval_loss': 0.7226091027259827,\n",
       " 'eval_runtime': 13.9749,\n",
       " 'eval_samples_per_second': 141.325,\n",
       " 'eval_steps_per_second': 23.614,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()\n",
    "result = trainer.evaluate()\n",
    "result"
   ]
  },
  {
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   "execution_count": 16,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Ribin/t5-base_detoxParaphraser\n"
     ]
    },
    {
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      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
      ]
     },
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     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/Ribin/t5-base_detoxParaphraser/commit/e7327b15b49648b482851c3519a042f2e6497226', commit_message='Upload tokenizer', commit_description='', oid='e7327b15b49648b482851c3519a042f2e6497226', pr_url=None, pr_revision=None, pr_num=None)"
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     "execution_count": 16,
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   ],
   "source": [
    "model_id = MODEL_NAME\n",
    "print(model_id)\n",
    "model.push_to_hub(model_id, token=huggingface_key)\n",
    "tokenizer.push_to_hub(model_id, token=huggingface_key)"
   ]
  },
  {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "END\n"
     ]
    }
   ],
   "source": [
    "print(\"END\")"
   ]
  }
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