diff --git "a/TRAINER (2).ipynb" "b/TRAINER (2).ipynb" deleted file mode 100644--- "a/TRAINER (2).ipynb" +++ /dev/null @@ -1,6676 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pdymVP95XA4_", - "outputId": "9dd22bfa-2e4f-4c5f-f544-c73431671713" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", - "Token is valid (permission: write).\n", - "Your token has been saved to /root/.cache/huggingface/token\n", - "Login successful\n" - ] - } - ], - "source": [ - "# Store your huggingface token as a environment variable.\n", - "# It is used to download or upload models to your account.\n", - "from google.colab import userdata\n", - "from huggingface_hub import login\n", - "login(userdata.get('TOKEN'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cBEG5Pq1lG-G" - }, - "outputs": [], - "source": [ - "%%capture\n", - "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", - "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", - "!pip install --upgrade transformers\n", - "\n", - "# We have to check which Torch version for Xformers (2.3 -> 0.0.27)\n", - "from torch import __version__; from packaging.version import Version as V\n", - "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", - "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "aRz4XCANNy8b", - "outputId": "0ccf0b95-5daf-452f-8bf7-37e224f5ce81" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" - ] - } - ], - "source": [ - "from unsloth import FastLanguageModel\n", - "import torch\n", - "\n", - "max_seq_length = 2048\n", - "\n", - "def load_model(model_name, max_seq_length=max_seq_length, dtype=None, load_in_4bit=True):\n", - " model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name=model_name,\n", - " max_seq_length=max_seq_length,\n", - " dtype=dtype,\n", - " load_in_4bit=load_in_4bit,\n", - " )\n", - " return model, tokenizer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 362, - "referenced_widgets": [ - "3689cf3776564eb0aeee4330dfa3e3d3", - "e041f54e04f54dcdb034b01061828560", - "f678ea6d3cae4b8584ad2554455170c7", - "0fdd0b53f0d8421582dc703055324f5a", - "9376591750f84fca8cb28e9c3547d7c2", - "a7dbef2655fd4910b4c77be13aeec794", - "2dd41449ffba43aaab27524ed293ac5c", - "ad69f28769ae4b058943d05f4f0106c9", - "5d1a2752ee08428f9a20744b1b0c1a13", - "89b90dada4a84f2784f8de3b56257237", - "3abd9a7962c44661b9711bc72f0ae1c1", - "b6ae505a739248ad9f4ae19fd2e27dfa", - "482f20a8b0e34585826572068f372caf", - "a44d2bb7eff54dba8b08cf688bb78e47", - "1456f51cf75c46419ae41c935e91ca6c", - "ee6ad031528e4a96821df3e4aa302aab", - "1600c0a5ca3b4957ba043162cc87a9d4", - "e2839ead63f34026a48b659b457cd287", - "a35995218e48452d8e1d06519178cbff", - "f682e8e835f54e118b31735241991610", - "4029a9c9b7d94462b1c7769d09371837", - "3423063d3cee4779890e0699f9942d87", - "14a2f3c4a8e545d4b51df59ef1c9fb57", - "2b9a63e62ddd4f7d9c662d894b8aba98", - "f8e88ed0e5fc456d9ca0af2921b84567", - "b44f4fff2e2c4d8b9fcf646c020ea120", - "0eec7d5de3ab4c079223d8ff6025b926", - "3d7bc27517644abebd7fc689c2572604", - "9b25f95b98ec444ea895b49ec9389fe0", - "7b26e464957d415692883cbb35eec48d", - "0f29692848b047e798847da2f622d989", - "5f32ee9393e8461c8d19ef9193db3883", - "9f430a851527456a809dee52f4f6fa1e", - "f253dd622f3e40639adac4e92121b850", - "27fe49df0ce346f8a11876ab748b8e62", - "47013ab3439741d09d1f480345cab67a", - "91319f286c9244699008214edbeaa37c", - "fea3d63aed214c31b85c7a61c223be70", - "a31433fafff14d6db5295da689d6a2dc", - "f755042c9d284beb9e62b46cabbce95a", - "1f92034da40c472aa6d4ae1ad72304fa", - "4fc8bb4fdfc34f4999ed4cb2f16cb9ec", - "58f8bb95982846409adef80ee31181c3", - "23f6e928e27749b28f146896adfb93c1", - "435a19574aa240d1820e042ea58db8ab", - "392f5e3c99054f9980820d566c82f9f3", - "12fc8d312dcf404280074026890b08c1", - "235195f2c3904776aabc1b3873ba73e6", - "7c453e5e85a348a08b45e82338fd1827", - "8883c661c0404f5c9eee1a50501b2049", - "31a55dd3528a48ceb6c88f9165271dc1", - "c8b909a793394f159e4539d42ee63efe", - "66dda996d9ee48579583bb0d15056bef", - "827108f23a1d45f493abe37e2026efce", - "bb25cc648bd04e6da1a7bc431a9e3a1d", - "c4326ba898bd4741b5dd1c26c1bdbe5b", - "09e2dee7f7684deea98df4f04ad67a45", - "93dc969e22dc4430962979f74965458e", - "41cd9cc5172045aaa3eed8ca0d3715ca", - "5ecda50fe7b64895b3869d238697f523", - "90106cc2003f4968aa4c32a66ef8a5cf", - "6147cf84651d455ebc91606ef761752c", - "58aee35908e845f08bae7b2082fa139f", - "107bf752d7194e45bb2e45031ec964f1", - "6be7918e435a43819bdd95e04f502012", - "9e6fd5d822f845628e4cae4bb1cc9260", - "10249ba56f2c4718b938c741b0787dcc", - "1ad218f809744ea48ba2f5f430c4d342", - "cc1308b0d90a452981e72e1f3b4ba19a", - "0dfd7601605b4ea189a844e329aa388d", - "4a071fc3531c4d968483d81e9a91d5b4", - "17b807ada6034094bfc17e8905d26293", - "08f570ac3f6645b791dec4fc8f034313", - "9d9f79126c4b4c629a6d863cd09f6c6c", - "e41e17aa9d594c9b9df9c4cda16ff259", - "6f8a14f3850b414ba6607b3560d869b0", - "9a31dacd9bff4ac5a42f6549cb686819" - ] - }, - "id": "urDIg3QRlCMx", - "outputId": "b45d8a03-31ab-4d4a-ea31-bb9f7e6314fc" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "==((====))== Unsloth 2024.9.post4: Fast Llama patching. Transformers = 4.45.1.\n", - " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", - "O^O/ \\_/ \\ Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", - "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post1. FA2 = False]\n", - " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", - "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "model.safetensors: 0%| | 0.00/2.26G [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "3689cf3776564eb0aeee4330dfa3e3d3" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "generation_config.json: 0%| | 0.00/140 [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "b6ae505a739248ad9f4ae19fd2e27dfa" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/3.37k [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "14a2f3c4a8e545d4b51df59ef1c9fb57" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer.model: 0%| | 0.00/500k [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "f253dd622f3e40639adac4e92121b850" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "added_tokens.json: 0%| | 0.00/293 [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "435a19574aa240d1820e042ea58db8ab" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "special_tokens_map.json: 0%| | 0.00/571 [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "c4326ba898bd4741b5dd1c26c1bdbe5b" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer.json: 0%| | 0.00/1.84M [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "10249ba56f2c4718b938c741b0787dcc" - } - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Unsloth 2024.9.post4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" - ] - } - ], - "source": [ - "models = [\n", - " 'Phi-3.5-mini-instruct-bnb-4bit', # |Min Gpu: T4, Max Model size: 14.748GB|\n", - " 'gemma-2-27b-it-bnb-4bit', # |Min Gpu: A100, Max Model size: 39.564GB|\n", - " 'Meta-Llama-3.1-8B-Instruct-bnb-4bit' # |Min Gpu: L4, Max Model size: 22.168GB|\n", - " ]\n", - "\n", - "# Model Selection.\n", - "model_name = models[0]\n", - "\n", - "# Required for uploading models to or downloading models from huggingface repositories.\n", - "HfUsername = \"CooperW\"\n", - "\n", - "base_model = f\"unsloth/{model_name}\"\n", - "LoRa_Adapters = f\"{HfUsername}/{model_name.replace('-', '_').replace('.', '_')}_128prompt\"\n", - "\n", - "# Load the base model.\n", - "model, tokenizer = load_model(base_model)\n", - "\n", - "try:\n", - " # For continued training load LoRa adapters to model.\n", - " from peft import PeftModel\n", - " model = PeftModel.from_pretrained(model, LoRa_Adapters)\n", - "\n", - " from huggingface_hub import snapshot_download\n", - " import os\n", - "\n", - " download_path = snapshot_download(repo_id=LoRa_Adapters, ignore_patterns=[\"*.md\", \"*.safetensors\"])\n", - " last_checkpoint_path = os.path.join(download_path, \"last-checkpoint\")\n", - "except:\n", - " # Initialise LoRa adapters.\n", - " model = FastLanguageModel.get_peft_model(\n", - " model,\n", - " r = 128,\n", - " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", - " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", - " lora_alpha = 8,\n", - " lora_dropout = 0,\n", - " bias = \"none\",\n", - " use_gradient_checkpointing = \"unsloth\",\n", - " random_state = 3407,\n", - " use_rslora = True,\n", - " loftq_config = None,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QVEYv-pnlh9s" - }, - "outputs": [], - "source": [ - "from unsloth.chat_templates import get_chat_template\n", - "\n", - "tokenizer = get_chat_template(\n", - " tokenizer,\n", - " chat_template = \"phi-3\", #Use Template of Current model.\n", - " mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"},\n", - ")\n", - "\n", - "def formatting_prompts_func(examples):\n", - " convos = examples[\"conversations\"]\n", - " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", - " return { \"text\" : texts, }\n", - "pass" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iVqRqJrqlpvD", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 113, - "referenced_widgets": [ - "a11401c0389a431f91f121ac2c7a3b71", - "4ee22d17a3cb47fda1719e794dfd19e9", - "a1baf0fbc8034cb6a15930b9e698a0f4", - "c6afc697668b4315a856fe991e1d7b8c", - "f4dae16dc8a24d83b0b916a26a0255a1", - "01e9840b9fa340b8a6a3be102bf13645", - "2320db9057174d90883c07b4cd172ae7", - "f78e4df64f9a473f8380c2f1f9b40414", - "39dfd87409ce40b39576b07cff3674d0", - "98c19e00fc8b4705872b3d397dd794e3", - "b171a32eb8164515a651530b2ccd1573", - "2d1c4c9228144ea79896dd01bad82e14", - "8a948f04b0034c1f816bde4b29e89fba", - "3300c67eed17403ebe38878931203d6b", - "4e797a5bc60a4e1981497f536022f590", - "f1c585c40fec428188d9c51d60504021", - "2c955921188f440099398d30bfd2a4a2", - "c2e9be71122945ed9e64b17039ee12e6", - "81a1d102fed24ff79ee883ee7f3972de", - "83cc0345dbb34a8ea025d13593d32f35", - "9274b9e3a4874b09b55c1c59a0ac9674", - "f7886b3fdf1c4988b7f201d728f44513", - "8b99cb2061e04a6c82e2cb9d8bf4db4d", - "b8f676098c9145448030032f80feeb6c", - "f259acb3cd1948738dba98d6b2997e30", - "d15e5841c3e441389f295966596f02e1", - "76d38d8ee7d64ec2859f311eade510fa", - "d3193af4ec29498b9d40f1cda371cd64", - "aee5798e5e664aa0a728c949ff555d2a", - "15ef70d1863f4757856566638830be3c", - "ff154273b8e04867abbfe59848bc630d", - "6095912b466b46f5834cfff9d55a9159", - "685c9503fa1646258af5d20b54af346f" - ] - }, - "outputId": "d4ab65e0-7c53-476c-e351-2c73f891a556" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "train_network.jsonl: 0%| | 0.00/136M [00:00, ?B/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "a11401c0389a431f91f121ac2c7a3b71" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Generating train split: 0 examples [00:00, ? examples/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "2d1c4c9228144ea79896dd01bad82e14" - } - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map: 0%| | 0/168834 [00:00, ? examples/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "8b99cb2061e04a6c82e2cb9d8bf4db4d" - } - }, - "metadata": {} - } - ], - "source": [ - "from datasets import load_dataset\n", - "\n", - "dataset_repo = f\"{HfUsername}/jadidi\"\n", - "dataset_name = \"train_network.jsonl\"\n", - "\n", - "train_dataset = load_dataset(dataset_repo, data_files=dataset_name, split='train')\n", - "train_dataset = train_dataset.map(formatting_prompts_func, batched = True,)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "U9TEfn5l290J" - }, - "outputs": [], - "source": [ - "from transformers import Trainer, TrainingArguments\n", - "from trl import SFTTrainer\n", - "import os\n", - "\n", - "training_args = TrainingArguments(\n", - " output_dir=\"outputs\",\n", - " per_device_train_batch_size=8,\n", - " gradient_accumulation_steps=16,\n", - " lr_scheduler_type = \"cosine_with_restarts\",\n", - " lr_scheduler_kwargs = { \"num_cycles\": len(trainer.get_train_dataloader()) / 100 },\n", - " optim=\"adamw_8bit\",\n", - " weight_decay=0.01,\n", - " seed=3407,\n", - " warmup_steps=10,\n", - " # max_steps=100,\n", - " num_train_epochs=3,\n", - " learning_rate=2e-4,\n", - " fp16=not torch.cuda.is_bf16_supported(),\n", - " bf16=torch.cuda.is_bf16_supported(),\n", - " logging_steps=1,\n", - ")\n", - "\n", - "# Define the name of the repo your model is uploaded. By default : (YourHuggingFaceAccount/baseModelName)\n", - "training_args = training_args.set_push_to_hub(\n", - " model_id=LoRa_Adapters,\n", - " strategy=\"checkpoint\",\n", - " private_repo=True,\n", - " always_push=False\n", - ")\n", - "\n", - "# Define how often your model is uploaded during training.\n", - "# Only LoRa adapters are saved.\n", - "training_args = training_args.set_save(\n", - " strategy='steps',\n", - " steps=50,\n", - " total_limit=2,\n", - ")\n", - "\n", - "trainer = SFTTrainer(\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " train_dataset=train_dataset,\n", - " dataset_text_field=\"text\",\n", - " max_seq_length=max_seq_length,\n", - " dataset_num_proc=os.cpu_count(),\n", - " packing=False,\n", - " args=training_args,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "0ajwRxIR4GXc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "183abc90-a621-4882-be16-52b11e44d4c1" - }, - "outputs": [ - { - "metadata": { - "tags": null - }, - "name": "stderr", - "output_type": "stream", - "text": [ - "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", - " \\\\ /| Num examples = 168,834 | Num Epochs = 3\n", - "O^O/ \\_/ \\ Batch size per device = 8 | Gradient Accumulation steps = 16\n", - "\\ / Total batch size = 128 | Total steps = 3,957\n", - " \"-____-\" Number of trainable parameters = 239,075,328\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
Step | \n", - "Training Loss | \n", - "
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1 | \n", - "1.190300 | \n", - "
2 | \n", - "1.170200 | \n", - "
3 | \n", - "1.127500 | \n", - "
4 | \n", - "0.966100 | \n", - "
5 | \n", - "0.900900 | \n", - "
6 | \n", - "0.679400 | \n", - "
7 | \n", - "0.508900 | \n", - "
8 | \n", - "0.372600 | \n", - "
9 | \n", - "0.314400 | \n", - "
10 | \n", - "0.239900 | \n", - "
11 | \n", - "0.224400 | \n", - "
12 | \n", - "0.220800 | \n", - "
13 | \n", - "0.210900 | \n", - "
14 | \n", - "0.198400 | \n", - "
15 | \n", - "0.168700 | \n", - "
16 | \n", - "0.177600 | \n", - "
17 | \n", - "0.165700 | \n", - "
18 | \n", - "0.165200 | \n", - "
19 | \n", - "0.158600 | \n", - "
20 | \n", - "0.160900 | \n", - "
21 | \n", - "0.150700 | \n", - "
22 | \n", - "0.164300 | \n", - "
23 | \n", - "0.153900 | \n", - "
24 | \n", - "0.152200 | \n", - "
25 | \n", - "0.164800 | \n", - "
26 | \n", - "0.157500 | \n", - "
27 | \n", - "0.147100 | \n", - "
28 | \n", - "0.153500 | \n", - "
29 | \n", - "0.154900 | \n", - "
30 | \n", - "0.140600 | \n", - "
31 | \n", - "0.148100 | \n", - "
32 | \n", - "0.150900 | \n", - "
33 | \n", - "0.133300 | \n", - "
34 | \n", - "0.137600 | \n", - "
35 | \n", - "0.134300 | \n", - "
36 | \n", - "0.134500 | \n", - "
37 | \n", - "0.143400 | \n", - "
38 | \n", - "0.131400 | \n", - "
39 | \n", - "0.129300 | \n", - "
40 | \n", - "0.134000 | \n", - "
41 | \n", - "0.122100 | \n", - "
42 | \n", - "0.122700 | \n", - "
43 | \n", - "0.130300 | \n", - "
44 | \n", - "0.126000 | \n", - "
45 | \n", - "0.122300 | \n", - "
46 | \n", - "0.133500 | \n", - "
47 | \n", - "0.116900 | \n", - "
48 | \n", - "0.125700 | \n", - "
49 | \n", - "0.138400 | \n", - "
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- " \n",
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\n",
- " \n",
- " \n",
- " \n",
- " Step \n",
- " Training Loss \n",
- " \n",
- " \n",
- " 1 \n",
- " 1.190300 \n",
- " \n",
- " \n",
- " 2 \n",
- " 1.170200 \n",
- " \n",
- " \n",
- " 3 \n",
- " 1.127500 \n",
- " \n",
- " \n",
- " 4 \n",
- " 0.966100 \n",
- " \n",
- " \n",
- " 5 \n",
- " 0.900900 \n",
- " \n",
- " \n",
- " 6 \n",
- " 0.679400 \n",
- " \n",
- " \n",
- " 7 \n",
- " 0.508900 \n",
- " \n",
- " \n",
- " 8 \n",
- " 0.372600 \n",
- " \n",
- " \n",
- " 9 \n",
- " 0.314400 \n",
- " \n",
- " \n",
- " 10 \n",
- " 0.239900 \n",
- " \n",
- " \n",
- " 11 \n",
- " 0.224400 \n",
- " \n",
- " \n",
- " 12 \n",
- " 0.220800 \n",
- " \n",
- " \n",
- " 13 \n",
- " 0.210900 \n",
- " \n",
- " \n",
- " 14 \n",
- " 0.198400 \n",
- " \n",
- " \n",
- " 15 \n",
- " 0.168700 \n",
- " \n",
- " \n",
- " 16 \n",
- " 0.177600 \n",
- " \n",
- " \n",
- " 17 \n",
- " 0.165700 \n",
- " \n",
- " \n",
- " 18 \n",
- " 0.165200 \n",
- " \n",
- " \n",
- " 19 \n",
- " 0.158600 \n",
- " \n",
- " \n",
- " 20 \n",
- " 0.160900 \n",
- " \n",
- " \n",
- " 21 \n",
- " 0.150700 \n",
- " \n",
- " \n",
- " 22 \n",
- " 0.164300 \n",
- " \n",
- " \n",
- " 23 \n",
- " 0.153900 \n",
- " \n",
- " \n",
- " 24 \n",
- " 0.152200 \n",
- " \n",
- " \n",
- " 25 \n",
- " 0.164800 \n",
- " \n",
- " \n",
- " 26 \n",
- " 0.157500 \n",
- " \n",
- " \n",
- " 27 \n",
- " 0.147100 \n",
- " \n",
- " \n",
- " 28 \n",
- " 0.153500 \n",
- " \n",
- " \n",
- " 29 \n",
- " 0.154900 \n",
- " \n",
- " \n",
- " 30 \n",
- " 0.140600 \n",
- " \n",
- " \n",
- " 31 \n",
- " 0.148100 \n",
- " \n",
- " \n",
- " 32 \n",
- " 0.150900 \n",
- " \n",
- " \n",
- " 33 \n",
- " 0.133300 \n",
- " \n",
- " \n",
- " 34 \n",
- " 0.137600 \n",
- " \n",
- " \n",
- " 35 \n",
- " 0.134300 \n",
- " \n",
- " \n",
- " 36 \n",
- " 0.134500 \n",
- " \n",
- " \n",
- " 37 \n",
- " 0.143400 \n",
- " \n",
- " \n",
- " 38 \n",
- " 0.131400 \n",
- " \n",
- " \n",
- " 39 \n",
- " 0.129300 \n",
- " \n",
- " \n",
- " 40 \n",
- " 0.134000 \n",
- " \n",
- " \n",
- " 41 \n",
- " 0.122100 \n",
- " \n",
- " \n",
- " 42 \n",
- " 0.122700 \n",
- " \n",
- " \n",
- " 43 \n",
- " 0.130300 \n",
- " \n",
- " \n",
- " 44 \n",
- " 0.126000 \n",
- " \n",
- " \n",
- " 45 \n",
- " 0.122300 \n",
- " \n",
- " \n",
- " 46 \n",
- " 0.133500 \n",
- " \n",
- " \n",
- " 47 \n",
- " 0.116900 \n",
- " \n",
- " \n",
- " 48 \n",
- " 0.125700 \n",
- " \n",
- " \n",
- " 49 \n",
- " 0.138400 \n",
- " \n",
- " \n",
- " 50 \n",
- " 0.137300 \n",
- " \n",
- " \n",
- " 51 \n",
- " 0.126000 \n",
- " \n",
- " \n",
- " 52 \n",
- " 0.125800 \n",
- " \n",
- " \n",
- " 53 \n",
- " 0.119500 \n",
- " \n",
- " \n",
- " 54 \n",
- " 0.115100 \n",
- " \n",
- " \n",
- " 55 \n",
- " 0.119800 \n",
- " \n",
- " \n",
- " 56 \n",
- " 0.113900 \n",
- " \n",
- " \n",
- " 57 \n",
- " 0.106700 \n",
- " \n",
- " \n",
- " 58 \n",
- " 0.102900 \n",
- " \n",
- " \n",
- " 59 \n",
- " 0.114900 \n",
- " \n",
- " \n",
- " 60 \n",
- " 0.108800 \n",
- " \n",
- " \n",
- " 61 \n",
- " 0.107000 \n",
- " \n",
- " \n",
- " 62 \n",
- " 0.107100 \n",
- " \n",
- " \n",
- " 63 \n",
- " 0.123400 \n",
- " \n",
- " \n",
- " 64 \n",
- " 0.111700 \n",
- " \n",
- " \n",
- " 65 \n",
- " 0.119400 \n",
- " \n",
- " \n",
- " 66 \n",
- " 0.105800 \n",
- " \n",
- " \n",
- " 67 \n",
- " 0.118200 \n",
- " \n",
- " \n",
- " 68 \n",
- " 0.103700 \n",
- " \n",
- " \n",
- " 69 \n",
- " 0.103800 \n",
- " \n",
- " \n",
- " 70 \n",
- " 0.114400 \n",
- " \n",
- " \n",
- " 71 \n",
- " 0.116800 \n",
- " \n",
- " \n",
- " 72 \n",
- " 0.110800 \n",
- " \n",
- " \n",
- " 73 \n",
- " 0.112000 \n",
- " \n",
- " \n",
- " 74 \n",
- " 0.097700 \n",
- " \n",
- " \n",
- " 75 \n",
- " 0.097700 \n",
- " \n",
- " \n",
- " 76 \n",
- " 0.104100 \n",
- " \n",
- " \n",
- " 77 \n",
- " 0.100000 \n",
- " \n",
- " \n",
- " 78 \n",
- " 0.097500 \n",
- " \n",
- " \n",
- " 79 \n",
- " 0.094400 \n",
- " \n",
- " \n",
- " 80 \n",
- " 0.099400 \n",
- " \n",
- " \n",
- " 81 \n",
- " 0.099900 \n",
- " \n",
- " \n",
- " 82 \n",
- " 0.105500 \n",
- " \n",
- " \n",
- " 83 \n",
- " 0.111300 \n",
- " \n",
- " \n",
- " 84 \n",
- " 0.091300 \n",
- " \n",
- " \n",
- " 85 \n",
- " 0.094300 \n",
- " \n",
- " \n",
- " 86 \n",
- " 0.111600 \n",
- " \n",
- " \n",
- " 87 \n",
- " 0.095900 \n",
- " \n",
- " \n",
- " 88 \n",
- " 0.099900 \n",
- " \n",
- " \n",
- " 89 \n",
- " 0.100800 \n",
- " \n",
- " \n",
- " 90 \n",
- " 0.093300 \n",
- " \n",
- " \n",
- " 91 \n",
- " 0.094600 \n",
- " \n",
- " \n",
- " 92 \n",
- " 0.107300 \n",
- " \n",
- " \n",
- " 93 \n",
- " 0.090600 \n",
- " \n",
- " \n",
- " 94 \n",
- " 0.089100 \n",
- " \n",
- " \n",
- " 95 \n",
- " 0.099000 \n",
- " \n",
- " \n",
- " 96 \n",
- " 0.090400 \n",
- " \n",
- " \n",
- " 97 \n",
- " 0.098100 \n",
- " \n",
- " \n",
- " 98 \n",
- " 0.087900 \n",
- " \n",
- " \n",
- " 99 \n",
- " 0.097600 \n",
- " \n",
- " \n",
- " 100 \n",
- " 0.092100 \n",
- " \n",
- " \n",
- " 101 \n",
- " 0.094300 \n",
- " \n",
- " \n",
- " 102 \n",
- " 0.089800 \n",
- " \n",
- " \n",
- " 103 \n",
- " 0.092700 \n",
- " \n",
- " \n",
- " 104 \n",
- " 0.092900 \n",
- " \n",
- " \n",
- " 105 \n",
- " 0.087100 \n",
- " \n",
- " \n",
- " 106 \n",
- " 0.099300 \n",
- " \n",
- " \n",
- " 107 \n",
- " 0.098400 \n",
- " \n",
- " \n",
- " 108 \n",
- " 0.095700 \n",
- " \n",
- " \n",
- " 109 \n",
- " 0.086000 \n",
- " \n",
- " \n",
- " 110 \n",
- " 0.092600 \n",
- " \n",
- " \n",
- " 111 \n",
- " 0.095200 \n",
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- " \n",
- " 112 \n",
- " 0.092300 \n",
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- " \n",
- " 113 \n",
- " 0.088600 \n",
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- " \n",
- " 114 \n",
- " 0.086800 \n",
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- " \n",
- " 115 \n",
- " 0.093900 \n",
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- " 116 \n",
- " 0.089400 \n",
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- " 117 \n",
- " 0.085200 \n",
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- " 118 \n",
- " 0.088000 \n",
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- " 119 \n",
- " 0.084600 \n",
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- " \n",
- " 120 \n",
- " 0.085000 \n",
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- " \n",
- " 121 \n",
- " 0.077800 \n",
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- " \n",
- " 122 \n",
- " 0.081400 \n",
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- " \n",
- " 123 \n",
- " 0.079400 \n",
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- " \n",
- " 124 \n",
- " 0.085200 \n",
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- " \n",
- " 125 \n",
- " 0.086700 \n",
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- " \n",
- " 126 \n",
- " 0.090500 \n",
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- " \n",
- " 127 \n",
- " 0.094300 \n",
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- " \n",
- " 128 \n",
- " 0.088800 \n",
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- " \n",
- " 129 \n",
- " 0.086400 \n",
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- " \n",
- " 130 \n",
- " 0.085100 \n",
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- " \n",
- " 131 \n",
- " 0.089200 \n",
- " \n",
- " \n",
- " 132 \n",
- " 0.086400 \n",
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- " \n",
- " 133 \n",
- " 0.085700 \n",
- " \n",
- " \n",
- " 134 \n",
- " 0.086600 \n",
- " \n",
- " \n",
- " 135 \n",
- " 0.085100 \n",
- " \n",
- " \n",
- " 136 \n",
- " 0.086100 \n",
- " \n",
- " \n",
- " 137 \n",
- " 0.089200 \n",
- " \n",
- " \n",
- " 138 \n",
- " 0.090800 \n",
- " \n",
- " \n",
- " 139 \n",
- " 0.089900 \n",
- " \n",
- " \n",
- " 140 \n",
- " 0.081000 \n",
- " \n",
- " \n",
- " 141 \n",
- " 0.081300 \n",
- " \n",
- " \n",
- " 142 \n",
- " 0.081300 \n",
- " \n",
- " \n",
- " 143 \n",
- " 0.084400 \n",
- " \n",
- " \n",
- " 144 \n",
- " 0.096700 \n",
- " \n",
- " \n",
- " 145 \n",
- " 0.084600 \n",
- " \n",
- " \n",
- " 146 \n",
- " 0.091900 \n",
- " \n",
- " \n",
- " 147 \n",
- " 0.083300 \n",
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- " \n",
- " 148 \n",
- " 0.086900 \n",
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- " \n",
- " 149 \n",
- " 0.086500 \n",
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- " \n",
- " 150 \n",
- " 0.079600 \n",
- " \n",
- " \n",
- " 151 \n",
- " 0.078700 \n",
- " \n",
- " \n",
- " 152 \n",
- " 0.082500 \n",
- " \n",
- " \n",
- " 153 \n",
- " 0.077400 \n",
- " \n",
- " \n",
- " 154 \n",
- " 0.080600 \n",
- " \n",
- " \n",
- " 155 \n",
- " 0.079600 \n",
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- " \n",
- " 156 \n",
- " 0.091500 \n",
- " \n",
- " \n",
- " 157 \n",
- " 0.081000 \n",
- " \n",
- " \n",
- " 158 \n",
- " 0.082900 \n",
- " \n",
- " \n",
- " 159 \n",
- " 0.089100 \n",
- " \n",
- " \n",
- " 160 \n",
- " 0.076200 \n",
- " \n",
- " \n",
- " 161 \n",
- " 0.084900 \n",
- " \n",
- " \n",
- " 162 \n",
- " 0.091200 \n",
- " \n",
- " \n",
- " 163 \n",
- " 0.091000 \n",
- " \n",
- " \n",
- " 164 \n",
- " 0.081500 \n",
- " \n",
- " \n",
- " 165 \n",
- " 0.075500 \n",
- " \n",
- " \n",
- " 166 \n",
- " 0.081600 \n",
- " \n",
- " \n",
- " 167 \n",
- " 0.077400 \n",
- " \n",
- " \n",
- " 168 \n",
- " 0.081500 \n",
- " \n",
- " \n",
- " 169 \n",
- " 0.091400 \n",
- " \n",
- " \n",
- " 170 \n",
- " 0.086800 \n",
- " \n",
- " \n",
- " 171 \n",
- " 0.086300 \n",
- " \n",
- " \n",
- " 172 \n",
- " 0.078600 \n",
- " \n",
- " \n",
- " 173 \n",
- " 0.088400 \n",
- " \n",
- " \n",
- " 174 \n",
- " 0.086700 \n",
- " \n",
- " \n",
- " 175 \n",
- " 0.077100 \n",
- " \n",
- " \n",
- " 176 \n",
- " 0.077000 \n",
- " \n",
- " \n",
- " 177 \n",
- " 0.075900 \n",
- " \n",
- " \n",
- " 178 \n",
- " 0.091300 \n",
- " \n",
- " \n",
- " 179 \n",
- " 0.082400 \n",
- " \n",
- " \n",
- " 180 \n",
- " 0.078000 \n",
- " \n",
- " \n",
- " 181 \n",
- " 0.079000 \n",
- " \n",
- " \n",
- " 182 \n",
- " 0.098200 \n",
- " \n",
- " \n",
- " 183 \n",
- " 0.081100 \n",
- " \n",
- " \n",
- " 184 \n",
- " 0.081700 \n",
- " \n",
- " \n",
- " 185 \n",
- " 0.080200 \n",
- " \n",
- " \n",
- " 186 \n",
- " 0.087200 \n",
- " \n",
- " \n",
- " 187 \n",
- " 0.086300 \n",
- " \n",
- " \n",
- " 188 \n",
- " 0.082700 \n",
- " \n",
- " \n",
- " 189 \n",
- " 0.075700 \n",
- " \n",
- " \n",
- " 190 \n",
- " 0.082800 \n",
- " \n",
- " \n",
- " 191 \n",
- " 0.082600 \n",
- " \n",
- " \n",
- " 192 \n",
- " 0.080300 \n",
- " \n",
- " \n",
- " 193 \n",
- " 0.081000 \n",
- " \n",
- " \n",
- " 194 \n",
- " 0.077900 \n",
- " \n",
- " \n",
- " 195 \n",
- " 0.080100 \n",
- " \n",
- " \n",
- " 196 \n",
- " 0.079100 \n",
- " \n",
- " \n",
- " 197 \n",
- " 0.077100 \n",
- " \n",
- " \n",
- " 198 \n",
- " 0.075200 \n",
- " \n",
- " \n",
- " 199 \n",
- " 0.076800 \n",
- " \n",
- " \n",
- " 200 \n",
- " 0.076000 \n",
- " \n",
- " \n",
- " 201 \n",
- " 0.082700 \n",
- " \n",
- " \n",
- " 202 \n",
- " 0.080900 \n",
- " \n",
- " \n",
- " 203 \n",
- " 0.081000 \n",
- " \n",
- " \n",
- " 204 \n",
- " 0.077900 \n",
- " \n",
- " \n",
- " 205 \n",
- " 0.080600 \n",
- " \n",
- " \n",
- " 206 \n",
- " 0.092600 \n",
- " \n",
- " \n",
- " 207 \n",
- " 0.081000 \n",
- " \n",
- " \n",
- " 208 \n",
- " 0.073900 \n",
- " \n",
- " \n",
- " 209 \n",
- " 0.077800 \n",
- " \n",
- " \n",
- " 210 \n",
- " 0.079000 \n",
- " \n",
- " \n",
- " 211 \n",
- " 0.073600 \n",
- " \n",
- " \n",
- " 212 \n",
- " 0.079200 \n",
- " \n",
- " \n",
- " 213 \n",
- " 0.070000 \n",
- " \n",
- " \n",
- " 214 \n",
- " 0.072000 \n",
- " \n",
- " \n",
- " 215 \n",
- " 0.081300 \n",
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- " \n",
- " 216 \n",
- " 0.076300 \n",
- " \n",
- " \n",
- " 217 \n",
- " 0.075100 \n",
- " \n",
- " \n",
- " 218 \n",
- " 0.077500 \n",
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- " \n",
- " 219 \n",
- " 0.072600 \n",
- " \n",
- " \n",
- " 220 \n",
- " 0.079000 \n",
- " \n",
- " \n",
- " 221 \n",
- " 0.083400 \n",
- " \n",
- " \n",
- " 222 \n",
- " 0.078400 \n",
- " \n",
- " \n",
- " 223 \n",
- " 0.082300 \n",
- " \n",
- " \n",
- " 224 \n",
- " 0.081200 \n",
- " \n",
- " \n",
- " 225 \n",
- " 0.076200 \n",
- " \n",
- " \n",
- " 226 \n",
- " 0.078500 \n",
- " \n",
- " \n",
- " 227 \n",
- " 0.069100 \n",
- " \n",
- " \n",
- " 228 \n",
- " 0.079800 \n",
- " \n",
- " \n",
- " 229 \n",
- " 0.084200 \n",
- " \n",
- " \n",
- " 230 \n",
- " 0.075800 \n",
- " \n",
- " \n",
- " 231 \n",
- " 0.073700 \n",
- " \n",
- " \n",
- " 232 \n",
- " 0.081600 \n",
- " \n",
- " \n",
- " 233 \n",
- " 0.080200 \n",
- " \n",
- " \n",
- " 234 \n",
- " 0.072400 \n",
- " \n",
- " \n",
- " 235 \n",
- " 0.079200 \n",
- " \n",
- " \n",
- " 236 \n",
- " 0.074500 \n",
- " \n",
- " \n",
- " 237 \n",
- " 0.074300 \n",
- " \n",
- " \n",
- " 238 \n",
- " 0.076100 \n",
- " \n",
- " \n",
- " 239 \n",
- " 0.074900 \n",
- " \n",
- " \n",
- " 240 \n",
- " 0.070300 \n",
- " \n",
- " \n",
- " 241 \n",
- " 0.074700 \n",
- " \n",
- " \n",
- " 242 \n",
- " 0.077300 \n",
- " \n",
- " \n",
- " 243 \n",
- " 0.078800 \n",
- " \n",
- " \n",
- " 244 \n",
- " 0.070700 \n",
- " \n",
- " \n",
- " 245 \n",
- " 0.072600 \n",
- " \n",
- " \n",
- " 246 \n",
- " 0.080200 \n",
- " \n",
- " \n",
- " 247 \n",
- " 0.073800 \n",
- " \n",
- " \n",
- " 248 \n",
- " 0.069400 \n",
- " \n",
- " \n",
- " 249 \n",
- " 0.080000 \n",
- " \n",
- " \n",
- " 250 \n",
- " 0.076700 \n",
- " \n",
- " \n",
- " 251 \n",
- " 0.072000 \n",
- " \n",
- " \n",
- " 252 \n",
- " 0.072700 \n",
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- " \n",
- " 253 \n",
- " 0.076100 \n",
- " \n",
- " \n",
- " 254 \n",
- " 0.074600 \n",
- " \n",
- " \n",
- " 255 \n",
- " 0.072800 \n",
- " \n",
- " \n",
- " 256 \n",
- " 0.080100 \n",
- " \n",
- " \n",
- " 257 \n",
- " 0.086300 \n",
- " \n",
- " \n",
- " 258 \n",
- " 0.084900 \n",
- " \n",
- " \n",
- " 259 \n",
- " 0.077100 \n",
- " \n",
- " \n",
- " 260 \n",
- " 0.071000 \n",
- " \n",
- " \n",
- " 261 \n",
- " 0.068500 \n",
- " \n",
- " \n",
- " 262 \n",
- " 0.076300 \n",
- " \n",
- " \n",
- " 263 \n",
- " 0.064500 \n",
- " \n",
- " \n",
- " 264 \n",
- " 0.072700 \n",
- " \n",
- " \n",
- " 265 \n",
- " 0.073000 \n",
- " \n",
- " \n",
- " 266 \n",
- " 0.074900 \n",
- " \n",
- " \n",
- " 267 \n",
- " 0.082100 \n",
- " \n",
- " \n",
- " 268 \n",
- " 0.074900 \n",
- " \n",
- " \n",
- " 269 \n",
- " 0.073800 \n",
- " \n",
- " \n",
- " 270 \n",
- " 0.077600 \n",
- " \n",
- " \n",
- " 271 \n",
- " 0.076300 \n",
- " \n",
- " \n",
- " 272 \n",
- " 0.075700 \n",
- " \n",
- " \n",
- " 273 \n",
- " 0.067600 \n",
- " \n",
- " \n",
- " 274 \n",
- " 0.071000 \n",
- " \n",
- " \n",
- " 275 \n",
- " 0.071800 \n",
- " \n",
- " \n",
- " 276 \n",
- " 0.075600 \n",
- " \n",
- " \n",
- " 277 \n",
- " 0.076600 \n",
- " \n",
- " \n",
- " 278 \n",
- " 0.080500 \n",
- " \n",
- " \n",
- " 279 \n",
- " 0.071600 \n",
- " \n",
- " \n",
- " 280 \n",
- " 0.069700 \n",
- " \n",
- " \n",
- " 281 \n",
- " 0.076500 \n",
- " \n",
- " \n",
- " 282 \n",
- " 0.072700 \n",
- " \n",
- " \n",
- " 283 \n",
- " 0.073800 \n",
- " \n",
- " \n",
- " 284 \n",
- " 0.073000 \n",
- " \n",
- " \n",
- " 285 \n",
- " 0.074000 \n",
- " \n",
- " \n",
- " 286 \n",
- " 0.070800 \n",
- " \n",
- " \n",
- " 287 \n",
- " 0.069300 \n",
- " \n",
- " \n",
- " 288 \n",
- " 0.073700 \n",
- " \n",
- " \n",
- " 289 \n",
- " 0.072500 \n",
- " \n",
- " \n",
- " 290 \n",
- " 0.071000 \n",
- " \n",
- " \n",
- " 291 \n",
- " 0.068400 \n",
- " \n",
- " \n",
- " 292 \n",
- " 0.074700 \n",
- " \n",
- " \n",
- " 293 \n",
- " 0.070700 \n",
- " \n",
- " \n",
- " 294 \n",
- " 0.076200 \n",
- " \n",
- " \n",
- " 295 \n",
- " 0.073800 \n",
- " \n",
- " \n",
- " 296 \n",
- " 0.078400 \n",
- " \n",
- " \n",
- " 297 \n",
- " 0.071900 \n",
- " \n",
- " \n",
- " 298 \n",
- " 0.071300 \n",
- " \n",
- " \n",
- " 299 \n",
- " 0.074200 \n",
- " \n",
- " \n",
- " 300 \n",
- " 0.077000 \n",
- " \n",
- " \n",
- " 301 \n",
- " 0.075700 \n",
- " \n",
- " \n",
- " 302 \n",
- " 0.077400 \n",
- " \n",
- " \n",
- " 303 \n",
- " 0.069400 \n",
- " \n",
- " \n",
- " 304 \n",
- " 0.073200 \n",
- " \n",
- " \n",
- " 305 \n",
- " 0.069400 \n",
- " \n",
- " \n",
- " 306 \n",
- " 0.070000 \n",
- " \n",
- " \n",
- " 307 \n",
- " 0.067700 \n",
- " \n",
- " \n",
- " 308 \n",
- " 0.071400 \n",
- " \n",
- " \n",
- " 309 \n",
- " 0.073300 \n",
- " \n",
- " \n",
- " 310 \n",
- " 0.076000 \n",
- " \n",
- " \n",
- " 311 \n",
- " 0.071200 \n",
- " \n",
- " \n",
- " 312 \n",
- " 0.074500 \n",
- " \n",
- " \n",
- " 313 \n",
- " 0.072700 \n",
- " \n",
- " \n",
- " 314 \n",
- " 0.081700 \n",
- " \n",
- " \n",
- " 315 \n",
- " 0.069900 \n",
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- " \n",
- " 316 \n",
- " 0.075000 \n",
- " \n",
- " \n",
- " 317 \n",
- " 0.074700 \n",
- " \n",
- " \n",
- " 318 \n",
- " 0.078000 \n",
- " \n",
- " \n",
- " 319 \n",
- " 0.073400 \n",
- " \n",
- " \n",
- " 320 \n",
- " 0.083300 \n",
- " \n",
- " \n",
- " 321 \n",
- " 0.070800 \n",
- " \n",
- " \n",
- " 322 \n",
- " 0.067300 \n",
- " \n",
- " \n",
- " 323 \n",
- " 0.078400 \n",
- " \n",
- " \n",
- " 324 \n",
- " 0.075700 \n",
- " \n",
- " \n",
- " 325 \n",
- " 0.073500 \n",
- " \n",
- " \n",
- " 326 \n",
- " 0.071200 \n",
- " \n",
- " \n",
- " 327 \n",
- " 0.073300 \n",
- " \n",
- " \n",
- " 328 \n",
- " 0.075900 \n",
- " \n",
- " \n",
- " 329 \n",
- " 0.069500 \n",
- " \n",
- " \n",
- " 330 \n",
- " 0.072200 \n",
- " \n",
- " \n",
- " 331 \n",
- " 0.070200 \n",
- " \n",
- " \n",
- " 332 \n",
- " 0.070300 \n",
- " \n",
- " \n",
- " 333 \n",
- " 0.076900 \n",
- " \n",
- " \n",
- " 334 \n",
- " 0.071200 \n",
- " \n",
- " \n",
- " 335 \n",
- " 0.072200 \n",
- " \n",
- " \n",
- " 336 \n",
- " 0.070600 \n",
- " \n",
- " \n",
- " 337 \n",
- " 0.070700 \n",
- " \n",
- " \n",
- " 338 \n",
- " 0.077800 \n",
- " \n",
- " \n",
- " 339 \n",
- " 0.076100 \n",
- " \n",
- " \n",
- " 340 \n",
- " 0.073700 \n",
- " \n",
- " \n",
- " 341 \n",
- " 0.077700 \n",
- " \n",
- " \n",
- " 342 \n",
- " 0.072300 \n",
- " \n",
- " \n",
- " 343 \n",
- " 0.068200 \n",
- " \n",
- " \n",
- " 344 \n",
- " 0.068600 \n",
- " \n",
- " \n",
- " 345 \n",
- " 0.068300 \n",
- " \n",
- " \n",
- " 346 \n",
- " 0.074800 \n",
- " \n",
- " \n",
- " 347 \n",
- " 0.065200 \n",
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- " \n",
- " 348 \n",
- " 0.069900 \n",
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- " 349 \n",
- " 0.071500 \n",
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- " \n",
- " 350 \n",
- " 0.066800 \n",
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- " \n",
- " 351 \n",
- " 0.070000 \n",
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- " \n",
- " 352 \n",
- " 0.078400 \n",
- " \n",
- " \n",
- " 353 \n",
- " 0.077900 \n",
- " \n",
- " \n",
- " 354 \n",
- " 0.076800 \n",
- " \n",
- " \n",
- " 355 \n",
- " 0.076200 \n",
- " \n",
- " \n",
- " 356 \n",
- " 0.081700 \n",
- " \n",
- " \n",
- " 357 \n",
- " 0.071600 \n",
- " \n",
- " \n",
- " 358 \n",
- " 0.076300 \n",
- " \n",
- " \n",
- " 359 \n",
- " 0.075600 \n",
- " \n",
- " \n",
- " 360 \n",
- " 0.066200 \n",
- " \n",
- " \n",
- " 361 \n",
- " 0.065700 \n",
- " \n",
- " \n",
- " 362 \n",
- " 0.067700 \n",
- " \n",
- " \n",
- " 363 \n",
- " 0.084000 \n",
- " \n",
- " \n",
- " 364 \n",
- " 0.066600 \n",
- " \n",
- " \n",
- " 365 \n",
- " 0.069300 \n",
- " \n",
- " \n",
- " 366 \n",
- " 0.071600 \n",
- " \n",
- " \n",
- " 367 \n",
- " 0.068500 \n",
- " \n",
- " \n",
- " 368 \n",
- " 0.073200 \n",
- " \n",
- " \n",
- " 369 \n",
- " 0.074700 \n",
- " \n",
- " \n",
- " 370 \n",
- " 0.075100 \n",
- " \n",
- " \n",
- " 371 \n",
- " 0.078100 \n",
- " \n",
- " \n",
- " 372 \n",
- " 0.071100 \n",
- " \n",
- " \n",
- " 373 \n",
- " 0.067800 \n",
- " \n",
- " \n",
- " 374 \n",
- " 0.074400 \n",
- " \n",
- " \n",
- " 375 \n",
- " 0.074700 \n",
- " \n",
- " \n",
- " 376 \n",
- " 0.069600 \n",
- " \n",
- " \n",
- " 377 \n",
- " 0.075600 \n",
- " \n",
- " \n",
- " 378 \n",
- " 0.071000 \n",
- " \n",
- " \n",
- " 379 \n",
- " 0.065700 \n",
- " \n",
- " \n",
- " 380 \n",
- " 0.074100 \n",
- " \n",
- " \n",
- " 381 \n",
- " 0.069700 \n",
- " \n",
- " \n",
- " 382 \n",
- " 0.073000 \n",
- " \n",
- " \n",
- " 383 \n",
- " 0.074000 \n",
- " \n",
- " \n",
- " 384 \n",
- " 0.068400 \n",
- " \n",
- " \n",
- " 385 \n",
- " 0.070700 \n",
- " \n",
- " \n",
- " 386 \n",
- " 0.073500 \n",
- " \n",
- " \n",
- " 387 \n",
- " 0.071800 \n",
- " \n",
- " \n",
- " 388 \n",
- " 0.074700 \n",
- " \n",
- " \n",
- " 389 \n",
- " 0.079000 \n",
- " \n",
- " \n",
- " 390 \n",
- " 0.068800 \n",
- " \n",
- " \n",
- " 391 \n",
- " 0.072400 \n",
- " \n",
- " \n",
- " 392 \n",
- " 0.086500 \n",
- " \n",
- " \n",
- " 393 \n",
- " 0.071700 \n",
- " \n",
- " \n",
- " 394 \n",
- " 0.072700 \n",
- " \n",
- " \n",
- " 395 \n",
- " 0.073100 \n",
- " \n",
- " \n",
- " 396 \n",
- " 0.069200 \n",
- " \n",
- " \n",
- " 397 \n",
- " 0.071600 \n",
- " \n",
- " \n",
- " 398 \n",
- " 0.066700 \n",
- " \n",
- " \n",
- " 399 \n",
- " 0.067500 \n",
- " \n",
- " \n",
- " 400 \n",
- " 0.069300 \n",
- " \n",
- " \n",
- " 401 \n",
- " 0.072200 \n",
- " \n",
- " \n",
- " 402 \n",
- " 0.072300 \n",
- " \n",
- " \n",
- " 403 \n",
- " 0.073000 \n",
- " \n",
- " \n",
- " 404 \n",
- " 0.068700 \n",
- " \n",
- " \n",
- " 405 \n",
- " 0.064400 \n",
- " \n",
- " \n",
- " 406 \n",
- " 0.068100 \n",
- " \n",
- " \n",
- " 407 \n",
- " 0.068200 \n",
- " \n",
- " \n",
- " 408 \n",
- " 0.088000 \n",
- " \n",
- " \n",
- " 409 \n",
- " 0.073100 \n",
- " \n",
- " \n",
- " 410 \n",
- " 0.067500 \n",
- " \n",
- " \n",
- " 411 \n",
- " 0.077400 \n",
- " \n",
- " \n",
- " 412 \n",
- " 0.073900 \n",
- " \n",
- " \n",
- " 413 \n",
- " 0.067900 \n",
- " \n",
- " \n",
- " 414 \n",
- " 0.066400 \n",
- " \n",
- " \n",
- " 415 \n",
- " 0.072700 \n",
- " \n",
- " \n",
- " 416 \n",
- " 0.071400 \n",
- " \n",
- " \n",
- " 417 \n",
- " 0.065600 \n",
- " \n",
- " \n",
- " 418 \n",
- " 0.074600 \n",
- " \n",
- " \n",
- " 419 \n",
- " 0.069800 \n",
- " \n",
- " \n",
- " 420 \n",
- " 0.064000 \n",
- " \n",
- " \n",
- " 421 \n",
- " 0.071300 \n",
- " \n",
- " \n",
- " 422 \n",
- " 0.066300 \n",
- " \n",
- " \n",
- " 423 \n",
- " 0.065200 \n",
- " \n",
- " \n",
- " 424 \n",
- " 0.075600 \n",
- " \n",
- " \n",
- " 425 \n",
- " 0.072600 \n",
- " \n",
- " \n",
- " 426 \n",
- " 0.068900 \n",
- " \n",
- " \n",
- " 427 \n",
- " 0.071700 \n",
- " \n",
- " \n",
- " 428 \n",
- " 0.067400 \n",
- " \n",
- " \n",
- " 429 \n",
- " 0.066900 \n",
- " \n",
- " \n",
- " 430 \n",
- " 0.073400 \n",
- " \n",
- " \n",
- " 431 \n",
- " 0.069400 \n",
- " \n",
- " \n",
- " 432 \n",
- " 0.063300 \n",
- " \n",
- " \n",
- " 433 \n",
- " 0.069600 \n",
- " \n",
- " \n",
- " 434 \n",
- " 0.075400 \n",
- " \n",
- " \n",
- " 435 \n",
- " 0.066600 \n",
- " \n",
- " \n",
- " 436 \n",
- " 0.071200 \n",
- " \n",
- " \n",
- " \n",
- "437 \n",
- " 0.073500 \n",
- "