prune_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd48bc06-2c45-4226-b18a-802795f18cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from datasets import load_dataset, load_metric\n",
    "from transformers import AutoTokenizer, AutoModelForQuestionAnswering, TrainingArguments, Trainer\n",
    "from torch.utils.data import Dataset\n",
    "import logging\n",
    "\n",
    "from datasets import load_dataset\n",
    "\n",
    "raw_datasets  = load_dataset(\"glue\", 'sst2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0af2d704-b278-45aa-88eb-d442a299e322",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kowsher/miniconda3/envs/LD/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForMaskedLM, AutoConfig\n",
    "#from roberta import RobertaForSequenceClassification\n",
    "\n",
    "\n",
    "model_name = \"FacebookAI/roberta-base\"\n",
    "\n",
    "#config.num_labels=2\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d7f7b9f8-fb0c-472c-984c-4e7403b0485d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
    "\n",
    "col_to_delete = ['question1','sentence2']\n",
    "\n",
    "def preprocessing_function(examples):\n",
    "    return tokenizer(examples['sentence'], truncation=True, max_length=128)\n",
    "    \n",
    "tokenized_datasets = raw_datasets.map(preprocessing_function, batched=True)\n",
    "\n",
    "\n",
    "# llama_tokenized_datasets = llama_tokenized_datasets.rename_column(\"target\", \"label\")\n",
    "tokenized_datasets.set_format(\"torch\")\n",
    "\n",
    "# Data collator for padding a batch of examples to the maximum length seen in the batch\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09b64106-306a-49dc-b917-0013b04c1b3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d3e1e1e-a176-476d-a09b-ffef8c304fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import RobertaForSequenceClassification\n",
    "from transformers.activations import ACT2FN\n",
    "import random\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55cda082-cc14-45b6-8750-fb5671598e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a49a4e8f-0039-410e-ab82-5dfb55617f66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at FacebookAI/roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import leader\n",
    "from tqdm.auto import tqdm    \n",
    "model = RobertaForSequenceClassification.from_pretrained(model_name, num_labels=2)\n",
    "\n",
    "# Example text input\n",
    "input_text = tokenized_datasets['train']['sentence'][0:200]\n",
    "'''\n",
    "input_text = []\n",
    "for i in tqdm(range(len(tokenized_datasets['train']['premise'][0:200]))):\n",
    "    st = tokenized_datasets['train']['premise'][i] + tokenized_datasets['train']['hypothesis'][i]\n",
    "    input_text.append(st)\n",
    "    '''\n",
    "input_ids = tokenizer(input_text, return_tensors='pt', padding=True, truncation=True)['input_ids']\n",
    "\n",
    "\n",
    "# Get the embedding of the input text\n",
    "with torch.no_grad():\n",
    "    embeddings = model.roberta.embeddings(input_ids)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae66fc3a-c382-4f33-a167-72d32d16ef22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PEFT with the custom row method to replace layers\n",
    "leader.PEFT_prunig(model, method='column', rank=3, input_data=embeddings, descending=False)\n",
    "#targets=['key', 'value', 'dense', 'query'])\n",
    "# method = 'row', 'column', 'random'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20873d5b-6578-4315-a6cd-cdfb5ffffb97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import evaluate\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "\n",
    "\n",
    "    logits, labels = eval_pred # eval_pred is the tuple of predictions and labels returned by the model\n",
    "    predictions = np.argmax(logits, axis=-1)\n",
    "    \n",
    "    precision = metrics.precision_score(labels, predictions, average=\"macro\")\n",
    "    recall = metrics.recall_score(labels, predictions, average=\"macro\")\n",
    "    f1 = metrics.f1_score(labels, predictions, average=\"macro\")\n",
    "    accuracy = metrics.accuracy_score(labels, predictions)\n",
    "    \n",
    "    return {\"precision\": precision, \"recall\": recall, \"f1-score\": f1, 'accuracy': accuracy}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e5b54f3-7558-4f8f-bd0d-053e50495741",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kowsher/miniconda3/envs/LD/lib/python3.10/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "\n",
    "import time\n",
    "from transformers import Trainer, TrainingArguments\n",
    "training_args = TrainingArguments(\n",
    "    output_dir='dir',\n",
    "    learning_rate=1e-3,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=3,\n",
    "    weight_decay=0.00,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    save_strategy=\"steps\",\n",
    "    save_total_limit=2,\n",
    "    save_steps=10000000,\n",
    "    logging_steps=100,\n",
    "   \n",
    "    load_best_model_at_end=True,\n",
    "    lr_scheduler_type=\"cosine\",  # You can choose from 'linear', 'cosine', 'cosine_with_restarts', 'polynomial', etc.\n",
    "    warmup_steps=100,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_datasets[\"train\"],\n",
    "    eval_dataset=tokenized_datasets[\"validation\"],\n",
    "\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2227cdf-7c5d-4dfd-894b-a2e64ebc0366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='12630' max='12630' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [12630/12630 10:30, Epoch 3/3]\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",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>0.670500</td>\n",
       "      <td>0.397452</td>\n",
       "      <td>0.848336</td>\n",
       "      <td>0.840469</td>\n",
       "      <td>0.840595</td>\n",
       "      <td>0.841743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>0.398600</td>\n",
       "      <td>0.266502</td>\n",
       "      <td>0.898116</td>\n",
       "      <td>0.892365</td>\n",
       "      <td>0.892824</td>\n",
       "      <td>0.893349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>0.313300</td>\n",
       "      <td>0.257506</td>\n",
       "      <td>0.900864</td>\n",
       "      <td>0.894618</td>\n",
       "      <td>0.895095</td>\n",
       "      <td>0.895642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>0.325600</td>\n",
       "      <td>0.233832</td>\n",
       "      <td>0.912963</td>\n",
       "      <td>0.913067</td>\n",
       "      <td>0.912842</td>\n",
       "      <td>0.912844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>0.340400</td>\n",
       "      <td>0.237777</td>\n",
       "      <td>0.908839</td>\n",
       "      <td>0.900164</td>\n",
       "      <td>0.900699</td>\n",
       "      <td>0.901376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>0.311500</td>\n",
       "      <td>0.291823</td>\n",
       "      <td>0.894566</td>\n",
       "      <td>0.883935</td>\n",
       "      <td>0.884348</td>\n",
       "      <td>0.885321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>0.290800</td>\n",
       "      <td>0.203637</td>\n",
       "      <td>0.925564</td>\n",
       "      <td>0.925328</td>\n",
       "      <td>0.925415</td>\n",
       "      <td>0.925459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>0.304700</td>\n",
       "      <td>0.208579</td>\n",
       "      <td>0.923351</td>\n",
       "      <td>0.922992</td>\n",
       "      <td>0.923112</td>\n",
       "      <td>0.923165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>0.313800</td>\n",
       "      <td>0.200333</td>\n",
       "      <td>0.930239</td>\n",
       "      <td>0.929875</td>\n",
       "      <td>0.929997</td>\n",
       "      <td>0.930046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.288900</td>\n",
       "      <td>0.225876</td>\n",
       "      <td>0.926005</td>\n",
       "      <td>0.925160</td>\n",
       "      <td>0.925376</td>\n",
       "      <td>0.925459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1100</td>\n",
       "      <td>0.279600</td>\n",
       "      <td>0.193487</td>\n",
       "      <td>0.928854</td>\n",
       "      <td>0.928917</td>\n",
       "      <td>0.928881</td>\n",
       "      <td>0.928899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1200</td>\n",
       "      <td>0.265300</td>\n",
       "      <td>0.217701</td>\n",
       "      <td>0.917403</td>\n",
       "      <td>0.917403</td>\n",
       "      <td>0.917403</td>\n",
       "      <td>0.917431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1300</td>\n",
       "      <td>0.282400</td>\n",
       "      <td>0.215811</td>\n",
       "      <td>0.922069</td>\n",
       "      <td>0.922203</td>\n",
       "      <td>0.922015</td>\n",
       "      <td>0.922018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1400</td>\n",
       "      <td>0.246500</td>\n",
       "      <td>0.307129</td>\n",
       "      <td>0.910274</td>\n",
       "      <td>0.901290</td>\n",
       "      <td>0.901835</td>\n",
       "      <td>0.902523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1500</td>\n",
       "      <td>0.241700</td>\n",
       "      <td>0.229578</td>\n",
       "      <td>0.919863</td>\n",
       "      <td>0.919571</td>\n",
       "      <td>0.919674</td>\n",
       "      <td>0.919725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1600</td>\n",
       "      <td>0.245200</td>\n",
       "      <td>0.235090</td>\n",
       "      <td>0.923174</td>\n",
       "      <td>0.921571</td>\n",
       "      <td>0.921885</td>\n",
       "      <td>0.922018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1700</td>\n",
       "      <td>0.275300</td>\n",
       "      <td>0.244886</td>\n",
       "      <td>0.923439</td>\n",
       "      <td>0.920192</td>\n",
       "      <td>0.920641</td>\n",
       "      <td>0.920872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1800</td>\n",
       "      <td>0.261000</td>\n",
       "      <td>0.202482</td>\n",
       "      <td>0.931933</td>\n",
       "      <td>0.931633</td>\n",
       "      <td>0.931189</td>\n",
       "      <td>0.931193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1900</td>\n",
       "      <td>0.267200</td>\n",
       "      <td>0.206191</td>\n",
       "      <td>0.932363</td>\n",
       "      <td>0.932506</td>\n",
       "      <td>0.932335</td>\n",
       "      <td>0.932339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.244300</td>\n",
       "      <td>0.231254</td>\n",
       "      <td>0.916265</td>\n",
       "      <td>0.914688</td>\n",
       "      <td>0.914993</td>\n",
       "      <td>0.915138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2100</td>\n",
       "      <td>0.265800</td>\n",
       "      <td>0.222717</td>\n",
       "      <td>0.926581</td>\n",
       "      <td>0.926581</td>\n",
       "      <td>0.926581</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2200</td>\n",
       "      <td>0.225800</td>\n",
       "      <td>0.211002</td>\n",
       "      <td>0.926736</td>\n",
       "      <td>0.924992</td>\n",
       "      <td>0.925324</td>\n",
       "      <td>0.925459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2300</td>\n",
       "      <td>0.223500</td>\n",
       "      <td>0.219162</td>\n",
       "      <td>0.932291</td>\n",
       "      <td>0.932380</td>\n",
       "      <td>0.932324</td>\n",
       "      <td>0.932339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2400</td>\n",
       "      <td>0.251400</td>\n",
       "      <td>0.212780</td>\n",
       "      <td>0.928239</td>\n",
       "      <td>0.926076</td>\n",
       "      <td>0.926451</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.247540</td>\n",
       "      <td>0.929378</td>\n",
       "      <td>0.925907</td>\n",
       "      <td>0.926382</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2600</td>\n",
       "      <td>0.243200</td>\n",
       "      <td>0.212155</td>\n",
       "      <td>0.923188</td>\n",
       "      <td>0.923329</td>\n",
       "      <td>0.923160</td>\n",
       "      <td>0.923165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2700</td>\n",
       "      <td>0.241800</td>\n",
       "      <td>0.231066</td>\n",
       "      <td>0.929785</td>\n",
       "      <td>0.927160</td>\n",
       "      <td>0.927576</td>\n",
       "      <td>0.927752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2800</td>\n",
       "      <td>0.225100</td>\n",
       "      <td>0.222543</td>\n",
       "      <td>0.923756</td>\n",
       "      <td>0.920150</td>\n",
       "      <td>0.920621</td>\n",
       "      <td>0.920872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2900</td>\n",
       "      <td>0.242500</td>\n",
       "      <td>0.198204</td>\n",
       "      <td>0.930208</td>\n",
       "      <td>0.930296</td>\n",
       "      <td>0.930045</td>\n",
       "      <td>0.930046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.245200</td>\n",
       "      <td>0.215597</td>\n",
       "      <td>0.927927</td>\n",
       "      <td>0.927170</td>\n",
       "      <td>0.926592</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3100</td>\n",
       "      <td>0.234500</td>\n",
       "      <td>0.245526</td>\n",
       "      <td>0.921530</td>\n",
       "      <td>0.916561</td>\n",
       "      <td>0.917089</td>\n",
       "      <td>0.917431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3200</td>\n",
       "      <td>0.242500</td>\n",
       "      <td>0.216739</td>\n",
       "      <td>0.924168</td>\n",
       "      <td>0.923665</td>\n",
       "      <td>0.923157</td>\n",
       "      <td>0.923165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3300</td>\n",
       "      <td>0.234900</td>\n",
       "      <td>0.204166</td>\n",
       "      <td>0.933238</td>\n",
       "      <td>0.931959</td>\n",
       "      <td>0.932242</td>\n",
       "      <td>0.932339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3400</td>\n",
       "      <td>0.244200</td>\n",
       "      <td>0.194605</td>\n",
       "      <td>0.939312</td>\n",
       "      <td>0.936295</td>\n",
       "      <td>0.936758</td>\n",
       "      <td>0.936927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>0.231200</td>\n",
       "      <td>0.219849</td>\n",
       "      <td>0.931160</td>\n",
       "      <td>0.931296</td>\n",
       "      <td>0.931184</td>\n",
       "      <td>0.931193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3600</td>\n",
       "      <td>0.233700</td>\n",
       "      <td>0.200463</td>\n",
       "      <td>0.936598</td>\n",
       "      <td>0.935421</td>\n",
       "      <td>0.935693</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3700</td>\n",
       "      <td>0.230800</td>\n",
       "      <td>0.195723</td>\n",
       "      <td>0.936030</td>\n",
       "      <td>0.935590</td>\n",
       "      <td>0.935731</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3800</td>\n",
       "      <td>0.230300</td>\n",
       "      <td>0.242275</td>\n",
       "      <td>0.927742</td>\n",
       "      <td>0.923529</td>\n",
       "      <td>0.924042</td>\n",
       "      <td>0.924312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3900</td>\n",
       "      <td>0.209600</td>\n",
       "      <td>0.200922</td>\n",
       "      <td>0.936993</td>\n",
       "      <td>0.935337</td>\n",
       "      <td>0.935670</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>0.225800</td>\n",
       "      <td>0.203628</td>\n",
       "      <td>0.935157</td>\n",
       "      <td>0.932959</td>\n",
       "      <td>0.933346</td>\n",
       "      <td>0.933486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4100</td>\n",
       "      <td>0.237900</td>\n",
       "      <td>0.182772</td>\n",
       "      <td>0.930225</td>\n",
       "      <td>0.929465</td>\n",
       "      <td>0.928886</td>\n",
       "      <td>0.928899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4200</td>\n",
       "      <td>0.201400</td>\n",
       "      <td>0.218563</td>\n",
       "      <td>0.931575</td>\n",
       "      <td>0.929538</td>\n",
       "      <td>0.929906</td>\n",
       "      <td>0.930046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4300</td>\n",
       "      <td>0.224500</td>\n",
       "      <td>0.192747</td>\n",
       "      <td>0.930009</td>\n",
       "      <td>0.930043</td>\n",
       "      <td>0.930025</td>\n",
       "      <td>0.930046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4400</td>\n",
       "      <td>0.241900</td>\n",
       "      <td>0.200142</td>\n",
       "      <td>0.929477</td>\n",
       "      <td>0.929296</td>\n",
       "      <td>0.928898</td>\n",
       "      <td>0.928899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>0.209700</td>\n",
       "      <td>0.219543</td>\n",
       "      <td>0.927579</td>\n",
       "      <td>0.926202</td>\n",
       "      <td>0.926494</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4600</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.212584</td>\n",
       "      <td>0.928851</td>\n",
       "      <td>0.928959</td>\n",
       "      <td>0.928886</td>\n",
       "      <td>0.928899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4700</td>\n",
       "      <td>0.196500</td>\n",
       "      <td>0.198373</td>\n",
       "      <td>0.929252</td>\n",
       "      <td>0.928665</td>\n",
       "      <td>0.928836</td>\n",
       "      <td>0.928899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4800</td>\n",
       "      <td>0.219700</td>\n",
       "      <td>0.200184</td>\n",
       "      <td>0.932536</td>\n",
       "      <td>0.932169</td>\n",
       "      <td>0.932292</td>\n",
       "      <td>0.932339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4900</td>\n",
       "      <td>0.195200</td>\n",
       "      <td>0.222677</td>\n",
       "      <td>0.926965</td>\n",
       "      <td>0.924949</td>\n",
       "      <td>0.925309</td>\n",
       "      <td>0.925459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>0.204100</td>\n",
       "      <td>0.206620</td>\n",
       "      <td>0.927781</td>\n",
       "      <td>0.926160</td>\n",
       "      <td>0.926480</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5100</td>\n",
       "      <td>0.200900</td>\n",
       "      <td>0.203823</td>\n",
       "      <td>0.926560</td>\n",
       "      <td>0.926623</td>\n",
       "      <td>0.926587</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5200</td>\n",
       "      <td>0.202800</td>\n",
       "      <td>0.218447</td>\n",
       "      <td>0.927477</td>\n",
       "      <td>0.924865</td>\n",
       "      <td>0.925277</td>\n",
       "      <td>0.925459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5300</td>\n",
       "      <td>0.210800</td>\n",
       "      <td>0.206859</td>\n",
       "      <td>0.935901</td>\n",
       "      <td>0.936011</td>\n",
       "      <td>0.935778</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5400</td>\n",
       "      <td>0.200700</td>\n",
       "      <td>0.201881</td>\n",
       "      <td>0.933238</td>\n",
       "      <td>0.931959</td>\n",
       "      <td>0.932242</td>\n",
       "      <td>0.932339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>0.206400</td>\n",
       "      <td>0.223332</td>\n",
       "      <td>0.934132</td>\n",
       "      <td>0.931790</td>\n",
       "      <td>0.932190</td>\n",
       "      <td>0.932339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5600</td>\n",
       "      <td>0.217000</td>\n",
       "      <td>0.191810</td>\n",
       "      <td>0.938026</td>\n",
       "      <td>0.938137</td>\n",
       "      <td>0.938062</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5700</td>\n",
       "      <td>0.182700</td>\n",
       "      <td>0.233816</td>\n",
       "      <td>0.930429</td>\n",
       "      <td>0.925781</td>\n",
       "      <td>0.926323</td>\n",
       "      <td>0.926606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5800</td>\n",
       "      <td>0.209000</td>\n",
       "      <td>0.200236</td>\n",
       "      <td>0.937840</td>\n",
       "      <td>0.936548</td>\n",
       "      <td>0.936836</td>\n",
       "      <td>0.936927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5900</td>\n",
       "      <td>0.230400</td>\n",
       "      <td>0.185329</td>\n",
       "      <td>0.943774</td>\n",
       "      <td>0.943809</td>\n",
       "      <td>0.943791</td>\n",
       "      <td>0.943807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>0.192800</td>\n",
       "      <td>0.188361</td>\n",
       "      <td>0.940334</td>\n",
       "      <td>0.940473</td>\n",
       "      <td>0.940359</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6100</td>\n",
       "      <td>0.225500</td>\n",
       "      <td>0.177519</td>\n",
       "      <td>0.939218</td>\n",
       "      <td>0.939179</td>\n",
       "      <td>0.939197</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6200</td>\n",
       "      <td>0.175400</td>\n",
       "      <td>0.218090</td>\n",
       "      <td>0.934746</td>\n",
       "      <td>0.934506</td>\n",
       "      <td>0.934595</td>\n",
       "      <td>0.934633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6300</td>\n",
       "      <td>0.207400</td>\n",
       "      <td>0.186193</td>\n",
       "      <td>0.938125</td>\n",
       "      <td>0.938263</td>\n",
       "      <td>0.938070</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6400</td>\n",
       "      <td>0.211800</td>\n",
       "      <td>0.185268</td>\n",
       "      <td>0.940334</td>\n",
       "      <td>0.940473</td>\n",
       "      <td>0.940359</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6500</td>\n",
       "      <td>0.205200</td>\n",
       "      <td>0.191800</td>\n",
       "      <td>0.938073</td>\n",
       "      <td>0.938221</td>\n",
       "      <td>0.938068</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6600</td>\n",
       "      <td>0.173400</td>\n",
       "      <td>0.199313</td>\n",
       "      <td>0.934746</td>\n",
       "      <td>0.934506</td>\n",
       "      <td>0.934595</td>\n",
       "      <td>0.934633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6700</td>\n",
       "      <td>0.203300</td>\n",
       "      <td>0.192558</td>\n",
       "      <td>0.940624</td>\n",
       "      <td>0.940178</td>\n",
       "      <td>0.940322</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6800</td>\n",
       "      <td>0.207200</td>\n",
       "      <td>0.173382</td>\n",
       "      <td>0.946102</td>\n",
       "      <td>0.946062</td>\n",
       "      <td>0.946080</td>\n",
       "      <td>0.946101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6900</td>\n",
       "      <td>0.203300</td>\n",
       "      <td>0.179869</td>\n",
       "      <td>0.940334</td>\n",
       "      <td>0.940473</td>\n",
       "      <td>0.940359</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>0.190400</td>\n",
       "      <td>0.195637</td>\n",
       "      <td>0.935736</td>\n",
       "      <td>0.935800</td>\n",
       "      <td>0.935763</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7100</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.223857</td>\n",
       "      <td>0.929477</td>\n",
       "      <td>0.929296</td>\n",
       "      <td>0.928898</td>\n",
       "      <td>0.928899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7200</td>\n",
       "      <td>0.181700</td>\n",
       "      <td>0.210042</td>\n",
       "      <td>0.944396</td>\n",
       "      <td>0.943515</td>\n",
       "      <td>0.943745</td>\n",
       "      <td>0.943807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7300</td>\n",
       "      <td>0.181700</td>\n",
       "      <td>0.222648</td>\n",
       "      <td>0.936598</td>\n",
       "      <td>0.935421</td>\n",
       "      <td>0.935693</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7400</td>\n",
       "      <td>0.181200</td>\n",
       "      <td>0.193857</td>\n",
       "      <td>0.938093</td>\n",
       "      <td>0.938010</td>\n",
       "      <td>0.938047</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>0.168300</td>\n",
       "      <td>0.203070</td>\n",
       "      <td>0.935935</td>\n",
       "      <td>0.935632</td>\n",
       "      <td>0.935739</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7600</td>\n",
       "      <td>0.195500</td>\n",
       "      <td>0.194796</td>\n",
       "      <td>0.941467</td>\n",
       "      <td>0.941557</td>\n",
       "      <td>0.941501</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7700</td>\n",
       "      <td>0.212900</td>\n",
       "      <td>0.190557</td>\n",
       "      <td>0.938041</td>\n",
       "      <td>0.938179</td>\n",
       "      <td>0.938065</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7800</td>\n",
       "      <td>0.184800</td>\n",
       "      <td>0.196597</td>\n",
       "      <td>0.940320</td>\n",
       "      <td>0.940431</td>\n",
       "      <td>0.940356</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7900</td>\n",
       "      <td>0.191700</td>\n",
       "      <td>0.199780</td>\n",
       "      <td>0.939268</td>\n",
       "      <td>0.939137</td>\n",
       "      <td>0.939191</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>0.193700</td>\n",
       "      <td>0.188713</td>\n",
       "      <td>0.938125</td>\n",
       "      <td>0.938263</td>\n",
       "      <td>0.938070</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8100</td>\n",
       "      <td>0.210400</td>\n",
       "      <td>0.181782</td>\n",
       "      <td>0.942683</td>\n",
       "      <td>0.942599</td>\n",
       "      <td>0.942636</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8200</td>\n",
       "      <td>0.177500</td>\n",
       "      <td>0.194454</td>\n",
       "      <td>0.936030</td>\n",
       "      <td>0.935590</td>\n",
       "      <td>0.935731</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8300</td>\n",
       "      <td>0.185800</td>\n",
       "      <td>0.191254</td>\n",
       "      <td>0.939531</td>\n",
       "      <td>0.939010</td>\n",
       "      <td>0.939170</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8400</td>\n",
       "      <td>0.186700</td>\n",
       "      <td>0.190845</td>\n",
       "      <td>0.943499</td>\n",
       "      <td>0.942304</td>\n",
       "      <td>0.942583</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8500</td>\n",
       "      <td>0.175700</td>\n",
       "      <td>0.186965</td>\n",
       "      <td>0.939201</td>\n",
       "      <td>0.939347</td>\n",
       "      <td>0.939214</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8600</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.182330</td>\n",
       "      <td>0.939178</td>\n",
       "      <td>0.939305</td>\n",
       "      <td>0.939211</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8700</td>\n",
       "      <td>0.158100</td>\n",
       "      <td>0.188431</td>\n",
       "      <td>0.938327</td>\n",
       "      <td>0.937884</td>\n",
       "      <td>0.938026</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8800</td>\n",
       "      <td>0.188600</td>\n",
       "      <td>0.182805</td>\n",
       "      <td>0.939425</td>\n",
       "      <td>0.939052</td>\n",
       "      <td>0.939178</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8900</td>\n",
       "      <td>0.148700</td>\n",
       "      <td>0.207702</td>\n",
       "      <td>0.940559</td>\n",
       "      <td>0.938758</td>\n",
       "      <td>0.939111</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>0.173500</td>\n",
       "      <td>0.183485</td>\n",
       "      <td>0.938041</td>\n",
       "      <td>0.938179</td>\n",
       "      <td>0.938065</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9100</td>\n",
       "      <td>0.175600</td>\n",
       "      <td>0.190487</td>\n",
       "      <td>0.942683</td>\n",
       "      <td>0.942599</td>\n",
       "      <td>0.942636</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9200</td>\n",
       "      <td>0.178400</td>\n",
       "      <td>0.180961</td>\n",
       "      <td>0.938041</td>\n",
       "      <td>0.938179</td>\n",
       "      <td>0.938065</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9300</td>\n",
       "      <td>0.177500</td>\n",
       "      <td>0.184425</td>\n",
       "      <td>0.943807</td>\n",
       "      <td>0.943767</td>\n",
       "      <td>0.943786</td>\n",
       "      <td>0.943807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9400</td>\n",
       "      <td>0.159700</td>\n",
       "      <td>0.189062</td>\n",
       "      <td>0.939799</td>\n",
       "      <td>0.938926</td>\n",
       "      <td>0.939153</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9500</td>\n",
       "      <td>0.149400</td>\n",
       "      <td>0.200227</td>\n",
       "      <td>0.938898</td>\n",
       "      <td>0.937716</td>\n",
       "      <td>0.937990</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9600</td>\n",
       "      <td>0.184600</td>\n",
       "      <td>0.183066</td>\n",
       "      <td>0.942641</td>\n",
       "      <td>0.942641</td>\n",
       "      <td>0.942641</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9700</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.185179</td>\n",
       "      <td>0.938442</td>\n",
       "      <td>0.937842</td>\n",
       "      <td>0.938018</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9800</td>\n",
       "      <td>0.163700</td>\n",
       "      <td>0.182639</td>\n",
       "      <td>0.935799</td>\n",
       "      <td>0.935716</td>\n",
       "      <td>0.935752</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9900</td>\n",
       "      <td>0.162900</td>\n",
       "      <td>0.188293</td>\n",
       "      <td>0.938327</td>\n",
       "      <td>0.937884</td>\n",
       "      <td>0.938026</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>0.165500</td>\n",
       "      <td>0.190091</td>\n",
       "      <td>0.942920</td>\n",
       "      <td>0.942473</td>\n",
       "      <td>0.942617</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10100</td>\n",
       "      <td>0.167600</td>\n",
       "      <td>0.195751</td>\n",
       "      <td>0.943691</td>\n",
       "      <td>0.942262</td>\n",
       "      <td>0.942573</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10200</td>\n",
       "      <td>0.172700</td>\n",
       "      <td>0.182412</td>\n",
       "      <td>0.942743</td>\n",
       "      <td>0.942557</td>\n",
       "      <td>0.942630</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10300</td>\n",
       "      <td>0.159200</td>\n",
       "      <td>0.187636</td>\n",
       "      <td>0.938442</td>\n",
       "      <td>0.937842</td>\n",
       "      <td>0.938018</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10400</td>\n",
       "      <td>0.139800</td>\n",
       "      <td>0.196556</td>\n",
       "      <td>0.941027</td>\n",
       "      <td>0.940052</td>\n",
       "      <td>0.940296</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10500</td>\n",
       "      <td>0.159700</td>\n",
       "      <td>0.192160</td>\n",
       "      <td>0.940448</td>\n",
       "      <td>0.940263</td>\n",
       "      <td>0.940336</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10600</td>\n",
       "      <td>0.164500</td>\n",
       "      <td>0.193083</td>\n",
       "      <td>0.942097</td>\n",
       "      <td>0.941220</td>\n",
       "      <td>0.941449</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10700</td>\n",
       "      <td>0.187900</td>\n",
       "      <td>0.186416</td>\n",
       "      <td>0.934614</td>\n",
       "      <td>0.934758</td>\n",
       "      <td>0.934626</td>\n",
       "      <td>0.934633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10800</td>\n",
       "      <td>0.164100</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.935747</td>\n",
       "      <td>0.935884</td>\n",
       "      <td>0.935771</td>\n",
       "      <td>0.935780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10900</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.188822</td>\n",
       "      <td>0.938030</td>\n",
       "      <td>0.938095</td>\n",
       "      <td>0.938057</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11000</td>\n",
       "      <td>0.160400</td>\n",
       "      <td>0.189768</td>\n",
       "      <td>0.940388</td>\n",
       "      <td>0.940305</td>\n",
       "      <td>0.940342</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11100</td>\n",
       "      <td>0.182300</td>\n",
       "      <td>0.186722</td>\n",
       "      <td>0.939186</td>\n",
       "      <td>0.939221</td>\n",
       "      <td>0.939202</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11200</td>\n",
       "      <td>0.184800</td>\n",
       "      <td>0.184695</td>\n",
       "      <td>0.939186</td>\n",
       "      <td>0.939221</td>\n",
       "      <td>0.939202</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11300</td>\n",
       "      <td>0.162800</td>\n",
       "      <td>0.185151</td>\n",
       "      <td>0.941513</td>\n",
       "      <td>0.941473</td>\n",
       "      <td>0.941492</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11400</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.186144</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11500</td>\n",
       "      <td>0.154500</td>\n",
       "      <td>0.187034</td>\n",
       "      <td>0.942683</td>\n",
       "      <td>0.942599</td>\n",
       "      <td>0.942636</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11600</td>\n",
       "      <td>0.176100</td>\n",
       "      <td>0.186319</td>\n",
       "      <td>0.938026</td>\n",
       "      <td>0.938137</td>\n",
       "      <td>0.938062</td>\n",
       "      <td>0.938073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11700</td>\n",
       "      <td>0.160900</td>\n",
       "      <td>0.187314</td>\n",
       "      <td>0.939173</td>\n",
       "      <td>0.939263</td>\n",
       "      <td>0.939207</td>\n",
       "      <td>0.939220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11800</td>\n",
       "      <td>0.152700</td>\n",
       "      <td>0.187853</td>\n",
       "      <td>0.941480</td>\n",
       "      <td>0.941515</td>\n",
       "      <td>0.941496</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11900</td>\n",
       "      <td>0.152900</td>\n",
       "      <td>0.188446</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12000</td>\n",
       "      <td>0.153100</td>\n",
       "      <td>0.188191</td>\n",
       "      <td>0.941513</td>\n",
       "      <td>0.941473</td>\n",
       "      <td>0.941492</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12100</td>\n",
       "      <td>0.140400</td>\n",
       "      <td>0.188432</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940347</td>\n",
       "      <td>0.940367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12200</td>\n",
       "      <td>0.176100</td>\n",
       "      <td>0.188582</td>\n",
       "      <td>0.941513</td>\n",
       "      <td>0.941473</td>\n",
       "      <td>0.941492</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12300</td>\n",
       "      <td>0.150500</td>\n",
       "      <td>0.188766</td>\n",
       "      <td>0.941513</td>\n",
       "      <td>0.941473</td>\n",
       "      <td>0.941492</td>\n",
       "      <td>0.941514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12400</td>\n",
       "      <td>0.178500</td>\n",
       "      <td>0.188774</td>\n",
       "      <td>0.942683</td>\n",
       "      <td>0.942599</td>\n",
       "      <td>0.942636</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12500</td>\n",
       "      <td>0.154200</td>\n",
       "      <td>0.188761</td>\n",
       "      <td>0.942683</td>\n",
       "      <td>0.942599</td>\n",
       "      <td>0.942636</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12600</td>\n",
       "      <td>0.166700</td>\n",
       "      <td>0.188759</td>\n",
       "      <td>0.942683</td>\n",
       "      <td>0.942599</td>\n",
       "      <td>0.942636</td>\n",
       "      <td>0.942661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=12630, training_loss=0.21138816642459196, metrics={'train_runtime': 630.9595, 'train_samples_per_second': 320.222, 'train_steps_per_second': 20.017, 'total_flos': 16257300198672.0, 'train_loss': 0.21138816642459196, 'epoch': 3.0})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f212cb8-1e78-4101-bf03-bc2a62d976c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}