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https://github.com/wassname/detect_bs_text.git
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wassname
3489 lines
222 KiB
Plaintext
3489 lines
222 KiB
Plaintext
{
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"cells": [
|
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{
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"cell_type": "markdown",
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||
"metadata": {},
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"source": [
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"https://github.com/huggingface/peft/blob/main/examples/fp4_finetuning/finetune_fp4_opt_bnb_peft.py"
|
||
]
|
||
},
|
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{
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||
"cell_type": "code",
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||
"execution_count": 3,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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"%reload_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from torch import optim\n",
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"import lightning as pl\n",
|
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"from matplotlib import pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1"
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||
]
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},
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"execution_count": 5,
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||
"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from loguru import logger\n",
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"import sys\n",
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"\n",
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"# only if you want it shorter\n",
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"logger.remove()\n",
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"logger.add(sys.stderr, format=\"<level>{message}</level>\", level=\"WARNING\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES']=\"1\"\n",
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"# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import transformers\n",
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"from datasets import load_dataset\n",
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"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoConfig\n",
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"import numpy as np\n",
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"from tqdm.auto import tqdm\n",
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"import pandas as pd\n",
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"import warnings\n",
|
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"from peft import LoraConfig, get_peft_model, IA3Config"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use('seaborn-v0_8')\n",
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"torch.set_float32_matmul_precision('medium')\n",
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"warnings.filterwarnings(\"ignore\", \".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", \".*Was asked to gather along dimension 0.*\")\n",
|
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"warnings.filterwarnings(\"ignore\", \".*There is an imbalance between your GPUs.*\")"
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]
|
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"max_chars = 2000\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22\n",
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"model_name = \"unsloth/Llama-3.2-1B\"\n",
|
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"model_name = \"unsloth/Llama-3.2-1B-bnb-4bit\"\n",
|
||
"# Model Release Date: Sept 25, 2024\n",
|
||
"# launch date 9/25/2024 https://github.com/meta-llama/llama-models/blob/main/README.md\n",
|
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"# https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing\n",
|
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"# unsloth/Llama-3.2-3B\n",
|
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"# Data Freshness: The pretraining data has a cutoff of December 2023.\n",
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"\n",
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"def load_model():\n",
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"\n",
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" model = AutoModelForCausalLM.from_pretrained(\n",
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" model_name,\n",
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" # quantization_config=BitsAndBytesConfig(\n",
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" # load_in_4bit=True,\n",
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" # llm_int8_threshold=6.0,\n",
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" # llm_int8_has_fp16_weight=False,\n",
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" # bnb_4bit_compute_dtype=torch.float16,\n",
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" # bnb_4bit_use_double_quant=True,\n",
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" # bnb_4bit_quant_type=\"nf4\",\n",
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" # ),\n",
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" torch_dtype=torch.float16,\n",
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" trust_remote_code=True,\n",
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" )\n",
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"\n",
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"\n",
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" # config = AutoConfig.from_pretrained(model_name, trust_remote_code=True,)\n",
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" # config.quantization_config['use_exllama'] = False\n",
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" # config.quantization_config['disable_exllama'] = True\n",
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" # model = AutoModelForCausalLM.from_pretrained(\n",
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" # model_name,\n",
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" # torch_dtype=torch.bfloat16,\n",
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" # trust_remote_code=True,\n",
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" # config=config,\n",
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||
" # )\n",
|
||
" return model\n"
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||
]
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},
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{
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"cell_type": "code",
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||
"execution_count": 10,
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||
"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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||
"output_type": "stream",
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"text": [
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"Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n",
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"`low_cpu_mem_usage` was None, now default to True since model is quantized.\n"
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]
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}
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],
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"source": [
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"base_model = load_model()\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True,)\n",
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||
"tokenizer.pad_token = tokenizer.eos_token"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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||
"metadata": {},
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||
"outputs": [],
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"source": [
|
||
"def reset_model(base_model):\n",
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||
" # peft_config = LoraConfig(\n",
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" # # task_type=TaskType.TOKEN_CLS, \n",
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" # target_modules=[ \"fc2\", \"Wqkv\",],\n",
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" # inference_mode=False, r=4, lora_alpha=4, \n",
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" # # lora_dropout=0.1, \n",
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" # # bias=\"all\"\n",
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" # )\n",
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" # peft_config = IA3Config(\n",
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" # target_modules=[ \"fc2\", \"Wqkv\",], \n",
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" # feedforward_modules=[\"fc2\"],\n",
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" # inference_mode=False,\n",
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" # )\n",
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" peft_config = IA3Config(\n",
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" # target_modules=[ \"fc2\", \"Wqkv\", 'out_proj', 'fc1'], \n",
|
||
" # feedforward_modules=[\"fc2\", 'fc1', 'out_proj'],\n",
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" # inference_mode=False,\n",
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||
" )\n",
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" random_name = \"peft_\" + str(np.random.randint(0, 100000))\n",
|
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" model = get_peft_model(base_model, peft_config, adapter_name=random_name)\n",
|
||
" model.config.use_cache = False\n",
|
||
" return model\n",
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||
"\n",
|
||
"model = reset_model(base_model)"
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||
]
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},
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{
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||
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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||
"from bs_writing_detector.data.load_md import load_md_df\n",
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"from bs_writing_detector.metrics.ppx import perplexity_compute_ds\n",
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"from pathlib import Path\n",
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||
"df = load_md_df(Path(\"../samples/\"))"
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]
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},
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{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
|
||
"## Training"
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||
]
|
||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 13,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"from torch.nn import functional as F\n",
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"from torch.utils.data import DataLoader, TensorDataset\n",
|
||
"from datasets import Dataset"
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]
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},
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{
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||
"cell_type": "markdown",
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||
"metadata": {},
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"source": [
|
||
"## Lightning helpers"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": []
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 14,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"def eval(model, tokenizer, ds_val: Dataset):\n",
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||
" model.eval();\n",
|
||
" with torch.no_grad():\n",
|
||
" with model.disable_adapter():\n",
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" results = perplexity_compute_ds(ds=ds_val, model=model, tokenizer=tokenizer, device='cuda')['nlls'][0]\n",
|
||
" results2 = perplexity_compute_ds(ds=ds_val, model=model, tokenizer=tokenizer, device='cuda')['nlls'][0]\n",
|
||
" return dict(before=results, after=results2)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Train"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from datasets import Dataset\n",
|
||
"\n",
|
||
"\n",
|
||
"def compute_metrics(eval_prediction):\n",
|
||
" return {}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Trainer docs\n",
|
||
"\n",
|
||
"- https://huggingface.co/docs/transformers/v4.36.1/en/main_classes/trainer#transformers.Trainer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Dataset({\n",
|
||
" features: ['input_ids', 'attention_mask', 'overflow_to_sample_mapping'],\n",
|
||
" num_rows: 2\n",
|
||
"})"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"def tokenize_and_split(examples):\n",
|
||
" l = len(tokenizer(examples).input_ids[0])\n",
|
||
" max_len = min(l//3, max_chars) # break into at least 5\n",
|
||
" max_len = max(max_len, 10)\n",
|
||
"\n",
|
||
"\n",
|
||
" result = tokenizer(\n",
|
||
" examples,\n",
|
||
" add_special_tokens=False,\n",
|
||
" truncation=True,\n",
|
||
" stride=2, # – If set to a number along with max_length, the overflowing tokens returned will contain some tokens from the main sequence returned. The value of this argument defines the number of additional tokens.\n",
|
||
" max_length=max_len,\n",
|
||
" return_overflowing_tokens=True,\n",
|
||
" return_attention_mask=True,\n",
|
||
" )\n",
|
||
" return result\n",
|
||
"\n",
|
||
"sample = df.sample(1).iloc[0]\n",
|
||
"s = sample['content']\n",
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||
"d = Dataset.from_dict(tokenize_and_split([s]))\n",
|
||
"d2 = d.train_test_split(test_size=0.5, seed=42)\n",
|
||
"ds_train = d2['train']\n",
|
||
"ds_val = d2['test']\n",
|
||
"ds_val"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def learn_sample(sample):\n",
|
||
" # device = 'cuda'\n",
|
||
" # lr = 4e-3\n",
|
||
" # epochs = 3\n",
|
||
" # accum_steps = 1\n",
|
||
" batch_size = 1\n",
|
||
" verbose = False\n",
|
||
"\n",
|
||
" s = sample['content']\n",
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||
"\n",
|
||
" d = Dataset.from_dict(tokenize_and_split([s]))\n",
|
||
" d2 = d.train_test_split(test_size=0.5, seed=42)\n",
|
||
" ds_train = d2['train']\n",
|
||
" ds_val = d2['test']\n",
|
||
"\n",
|
||
" print(model.peft_config)\n",
|
||
" model = reset_model(base_model)\n",
|
||
"\n",
|
||
" # verify that we have reset it\n",
|
||
" print(model.peft_config)\n",
|
||
"\n",
|
||
"\n",
|
||
" # eval(model, tokenizer, ds_train)\n",
|
||
"\n",
|
||
" # https://huggingface.co/docs/transformers/v4.36.1/en/main_classes/trainer#transformers.Trainer\n",
|
||
" trainer = transformers.Trainer(\n",
|
||
" model=model,\n",
|
||
" train_dataset=ds_train,\n",
|
||
" eval_dataset=ds_val,\n",
|
||
" compute_metrics=compute_metrics, # without this it wont even give val loss\n",
|
||
" args=transformers.TrainingArguments(\n",
|
||
" # checkpoint='epoch',\n",
|
||
" save_strategy='epoch',\n",
|
||
" label_names=['labels',],\n",
|
||
" per_device_train_batch_size=batch_size,\n",
|
||
" # gradient_accumulation_steps=1,\n",
|
||
" # warmup_steps=6,\n",
|
||
" warmup_ratio=0.1,\n",
|
||
" # max_steps=50,\n",
|
||
" num_train_epochs=3,\n",
|
||
" learning_rate=1e-3,\n",
|
||
" fp16=True,\n",
|
||
" logging_steps=1,\n",
|
||
" output_dir=\"outputs\",\n",
|
||
" log_level='error',\n",
|
||
" # do_eval=True,\n",
|
||
" evaluation_strategy=\"epoch\",\n",
|
||
" eval_steps=1,\n",
|
||
" load_best_model_at_end=True,\n",
|
||
" \n",
|
||
" # disable_tqdm=not verbose,\n",
|
||
" ),\n",
|
||
" data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
|
||
" )\n",
|
||
" trainer._signature_columns = ['input_ids', 'attention_mask', 'labels',]\n",
|
||
" model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
|
||
" train_output = trainer.train()\n",
|
||
"\n",
|
||
" df_hist = pd.DataFrame(trainer.state.log_history)\n",
|
||
" df_hist_epoch = df_hist.groupby('epoch').last().drop(columns=['step'])\n",
|
||
" df_hist_step = df_hist.set_index('step').dropna(thresh=2, axis=1)\n",
|
||
" if verbose:\n",
|
||
" df_hist_epoch['loss'].plot()\n",
|
||
" plt.twinx()\n",
|
||
" df_hist_epoch['eval_loss'].plot(c='b', label='eval')\n",
|
||
" plt.legend()\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
"\n",
|
||
" result_train = {f'train/{k}':v for k,v in eval(model, tokenizer, ds_train).items()}\n",
|
||
" result = eval(model, tokenizer, ds_val)\n",
|
||
" result['hist'] = df_hist_epoch\n",
|
||
" result.update(result_train)\n",
|
||
" return result\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"ename": "NameError",
|
||
"evalue": "name 'data' is not defined",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mkeys()\n",
|
||
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"data[0].keys()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "46ca413f7112493bbeb2be6403b68e9f",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
" 0%| | 0/30 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/media/wassname/SGIronWolf/projects5/bs_writing_detector/.venv/lib/python3.11/site-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
|
||
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"{'eval_loss': 2.890993595123291, 'eval_runtime': 0.1859, 'eval_samples_per_second': 10.761, 'eval_steps_per_second': 5.381, 'epoch': 2.0}\n",
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"{'loss': 1.3094, 'grad_norm': 1.8891417980194092, 'learning_rate': 0.0002, 'epoch': 2.5}\n",
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"{'loss': 1.5913, 'grad_norm': 1.575945496559143, 'learning_rate': 0.0, 'epoch': 3.0}\n",
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"{'eval_loss': 2.9214653968811035, 'eval_runtime': 0.1873, 'eval_samples_per_second': 10.677, 'eval_steps_per_second': 5.338, 'epoch': 3.0}\n",
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||
"{'train_runtime': 3.8221, 'train_samples_per_second': 1.57, 'train_steps_per_second': 1.57, 'train_loss': 2.2379422187805176, 'epoch': 3.0}\n",
|
||
"The Laws of Large Numbers\n",
|
||
"{'before': [11.152969360351562, 12.381094932556152, 6.883550643920898, 5.000158309936523], 'after': [10.98746109008789, 12.829581260681152, 6.23975944519043, 4.902842998504639]}\n"
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]
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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||
"/media/wassname/SGIronWolf/projects5/bs_writing_detector/.venv/lib/python3.11/site-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
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||
"output_type": "stream",
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||
"text": [
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"{'loss': 3.4465, 'grad_norm': 3.0251810550689697, 'learning_rate': 0.001, 'epoch': 0.5}\n",
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"{'loss': 2.4625, 'grad_norm': 3.3443939685821533, 'learning_rate': 0.0008, 'epoch': 1.0}\n",
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||
"{'eval_loss': 3.2333736419677734, 'eval_runtime': 0.1783, 'eval_samples_per_second': 11.22, 'eval_steps_per_second': 5.61, 'epoch': 1.0}\n",
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||
"{'loss': 2.6116, 'grad_norm': 2.0492935180664062, 'learning_rate': 0.0006, 'epoch': 1.5}\n",
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||
"{'loss': 1.4007, 'grad_norm': 1.8274493217468262, 'learning_rate': 0.0004, 'epoch': 2.0}\n",
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||
"{'eval_loss': 3.223329782485962, 'eval_runtime': 0.1792, 'eval_samples_per_second': 11.162, 'eval_steps_per_second': 5.581, 'epoch': 2.0}\n",
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||
"{'loss': 1.7269, 'grad_norm': 1.6054573059082031, 'learning_rate': 0.0002, 'epoch': 2.5}\n",
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||
"{'loss': 0.9109, 'grad_norm': 1.4826263189315796, 'learning_rate': 0.0, 'epoch': 3.0}\n",
|
||
"{'eval_loss': 3.2280447483062744, 'eval_runtime': 0.1846, 'eval_samples_per_second': 10.834, 'eval_steps_per_second': 5.417, 'epoch': 3.0}\n",
|
||
"{'train_runtime': 3.8591, 'train_samples_per_second': 1.555, 'train_steps_per_second': 1.555, 'train_loss': 2.0931734144687653, 'epoch': 3.0}\n",
|
||
"The subset parity learning problem: much more than you wanted to know\n",
|
||
"{'before': [6.899591445922852, 0.8442123532295227, 0.49031174182891846, 9.700416564941406], 'after': [5.6730499267578125, 0.8620076775550842, 0.47212398052215576, 10.04554271697998]}\n"
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||
]
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||
},
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||
{
|
||
"name": "stderr",
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||
"output_type": "stream",
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||
"text": [
|
||
"/media/wassname/SGIronWolf/projects5/bs_writing_detector/.venv/lib/python3.11/site-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'loss': 4.1168, 'grad_norm': 3.2236294746398926, 'learning_rate': 0.001, 'epoch': 0.5}\n",
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||
"{'loss': 3.8383, 'grad_norm': 3.7380311489105225, 'learning_rate': 0.0008, 'epoch': 1.0}\n",
|
||
"{'eval_loss': 2.892657995223999, 'eval_runtime': 0.1945, 'eval_samples_per_second': 10.282, 'eval_steps_per_second': 5.141, 'epoch': 1.0}\n",
|
||
"{'loss': 3.2994, 'grad_norm': 2.064343214035034, 'learning_rate': 0.0006, 'epoch': 1.5}\n",
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||
"{'loss': 2.7109, 'grad_norm': 2.8733603954315186, 'learning_rate': 0.0004, 'epoch': 2.0}\n",
|
||
"{'eval_loss': 2.893580198287964, 'eval_runtime': 0.1934, 'eval_samples_per_second': 10.341, 'eval_steps_per_second': 5.17, 'epoch': 2.0}\n",
|
||
"{'loss': 2.3681, 'grad_norm': 2.0598092079162598, 'learning_rate': 0.0002, 'epoch': 2.5}\n",
|
||
"{'loss': 2.1504, 'grad_norm': 2.0436267852783203, 'learning_rate': 0.0, 'epoch': 3.0}\n",
|
||
"{'eval_loss': 2.8934695720672607, 'eval_runtime': 0.1953, 'eval_samples_per_second': 10.239, 'eval_steps_per_second': 5.12, 'epoch': 3.0}\n",
|
||
"{'train_runtime': 4.0349, 'train_samples_per_second': 1.487, 'train_steps_per_second': 1.487, 'train_loss': 3.080639600753784, 'epoch': 3.0}\n",
|
||
"What’s the short timeline plan?\n",
|
||
"{'before': [9.168852806091309, 3.9152371883392334, 6.799325942993164, 6.304879188537598], 'after': [4.647651195526123, 4.194304466247559, 7.104080677032471, 6.2407636642456055]}\n"
|
||
]
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||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/media/wassname/SGIronWolf/projects5/bs_writing_detector/.venv/lib/python3.11/site-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'loss': 2.9721, 'grad_norm': 6.88749885559082, 'learning_rate': 0.001, 'epoch': 0.5}\n",
|
||
"{'loss': 2.8245, 'grad_norm': 6.573474407196045, 'learning_rate': 0.0008, 'epoch': 1.0}\n",
|
||
"{'eval_loss': 2.963982105255127, 'eval_runtime': 0.2374, 'eval_samples_per_second': 8.426, 'eval_steps_per_second': 4.213, 'epoch': 1.0}\n",
|
||
"{'loss': 2.7296, 'grad_norm': 6.738276481628418, 'learning_rate': 0.0006, 'epoch': 1.5}\n",
|
||
"{'loss': 2.2587, 'grad_norm': 5.604180812835693, 'learning_rate': 0.0004, 'epoch': 2.0}\n",
|
||
"{'eval_loss': 2.7031450271606445, 'eval_runtime': 0.2389, 'eval_samples_per_second': 8.372, 'eval_steps_per_second': 4.186, 'epoch': 2.0}\n",
|
||
"{'loss': 2.0789, 'grad_norm': 5.735146522521973, 'learning_rate': 0.0002, 'epoch': 2.5}\n",
|
||
"{'loss': 1.7098, 'grad_norm': 3.3593287467956543, 'learning_rate': 0.0, 'epoch': 3.0}\n",
|
||
"{'eval_loss': 2.540477991104126, 'eval_runtime': 0.2464, 'eval_samples_per_second': 8.117, 'eval_steps_per_second': 4.059, 'epoch': 3.0}\n",
|
||
"{'train_runtime': 4.1935, 'train_samples_per_second': 1.431, 'train_steps_per_second': 1.431, 'train_loss': 2.4289310773213706, 'epoch': 3.0}\n",
|
||
"Lorem ipsum\n",
|
||
"{'before': [13.19483470916748, 4.822676658630371, 4.694229602813721, 0.15062260627746582, 0.009142794646322727], 'after': [12.946345329284668, 4.517403602600098, 5.635333061218262, 0.7315716743469238, 0.001257344032637775]}\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/media/wassname/SGIronWolf/projects5/bs_writing_detector/.venv/lib/python3.11/site-packages/transformers/training_args.py:1575: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"{'loss': 3.3232, 'grad_norm': 2.512242555618286, 'learning_rate': 0.001, 'epoch': 0.5}\n",
|
||
"{'loss': 3.0244, 'grad_norm': 2.4470341205596924, 'learning_rate': 0.0008, 'epoch': 1.0}\n",
|
||
"{'eval_loss': 3.506387233734131, 'eval_runtime': 0.1841, 'eval_samples_per_second': 10.862, 'eval_steps_per_second': 5.431, 'epoch': 1.0}\n",
|
||
"{'loss': 2.4132, 'grad_norm': 1.939867615699768, 'learning_rate': 0.0006, 'epoch': 1.5}\n",
|
||
"{'loss': 1.8838, 'grad_norm': 2.473356246948242, 'learning_rate': 0.0004, 'epoch': 2.0}\n",
|
||
"{'eval_loss': 3.5382893085479736, 'eval_runtime': 0.1772, 'eval_samples_per_second': 11.287, 'eval_steps_per_second': 5.644, 'epoch': 2.0}\n",
|
||
"{'loss': 1.4453, 'grad_norm': 1.9782445430755615, 'learning_rate': 0.0002, 'epoch': 2.5}\n",
|
||
"{'loss': 1.3757, 'grad_norm': 1.529227375984192, 'learning_rate': 0.0, 'epoch': 3.0}\n",
|
||
"{'eval_loss': 3.5465643405914307, 'eval_runtime': 0.1858, 'eval_samples_per_second': 10.765, 'eval_steps_per_second': 5.382, 'epoch': 3.0}\n",
|
||
"{'train_runtime': 3.7878, 'train_samples_per_second': 1.584, 'train_steps_per_second': 1.584, 'train_loss': 2.2442721724510193, 'epoch': 3.0}\n",
|
||
"politics is the mind-killer\n",
|
||
"{'before': [11.374530792236328, 3.393611192703247, 13.047080993652344, 3.0926449298858643, 6.4303483963012695, 8.23534870147705], 'after': [11.683854103088379, 3.5927183628082275, 13.514053344726562, 3.821317672729492, 7.558355808258057, 7.392338275909424]}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"data = []\n",
|
||
"for i in tqdm(range(len(df))):\n",
|
||
" sample = df.iloc[i]\n",
|
||
" r = learn_sample(sample)\n",
|
||
" print(sample['title'])\n",
|
||
" print(dict(before=r['before'], after=r['after']))\n",
|
||
" data.append(dict(**r, **sample))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x550 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# example training\n",
|
||
"df_hist = data[-1]['hist']#.groupby('epoch').last().dropna(axis=1).drop(columns=['step'])\n",
|
||
"df_hist['loss'].plot(label='train')\n",
|
||
"plt.twinx()\n",
|
||
"df_hist['eval_loss'].plot(c='b', label='eval')\n",
|
||
"plt.legend()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# df_hist['learning_rate'].plot(logy=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"\n",
|
||
"### Perplexity\n",
|
||
"\n",
|
||
"Perplexity measures how well a language model predicts a text sample. Lower is better\n",
|
||
"\n",
|
||
"It’s calculated as the average number of bits per word a model needs to represent the same\n",
|
||
"\n",
|
||
"https://huggingface.co/docs/transformers/perplexity\n",
|
||
"https://thegradient.pub/understanding-evaluation-metrics-for-language-models/\n",
|
||
"\n",
|
||
"The **improvement** column, is perplexity decrease"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 44,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# df_res = pd.DataFrame(data)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 84,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"sum\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"train/diff_sum -0.474407\n",
|
||
"train/diff_mean -0.383682\n",
|
||
"train/diff%_sum -0.253703\n",
|
||
"train/diff%_mean -0.253703\n",
|
||
"train/diff_std -0.226926\n",
|
||
"train/diff%_std -0.184180\n",
|
||
"train/diff_min -0.082520\n",
|
||
"train/after_std 0.016230\n",
|
||
"train/before_max 0.026255\n",
|
||
"train/diff%_max 0.056223\n",
|
||
"train/diff_max 0.059453\n",
|
||
"train/after_mean 0.073286\n",
|
||
"train/after_max 0.082784\n",
|
||
"train/before_min 0.126469\n",
|
||
"train/after_min 0.176288\n",
|
||
"train/after_sum 0.182927\n",
|
||
"train/before_std 0.281097\n",
|
||
"train/diff%_min 0.342018\n",
|
||
"train/before_mean 0.388784\n",
|
||
"train/before_sum 0.455190\n",
|
||
"novelty 1.000000\n",
|
||
"Name: novelty, dtype: float64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_res = pd.DataFrame(data)\n",
|
||
"for stat in ['mean', 'std', 'min', 'max', 'sum']:\n",
|
||
" agg = getattr(np, stat)\n",
|
||
" df_res[f'train/before_{stat}'] = df_res['train/before'].apply(lambda x: agg(x))\n",
|
||
" df_res[f'train/after_{stat}'] = df_res['train/after'].apply(lambda x: agg(x)) \n",
|
||
"\n",
|
||
" # df_res[f'before_{stat}'] = df_res['before'].apply(lambda x: agg(x))\n",
|
||
" # df_res[f'after_{stat}'] = df_res['after'].apply(lambda x: agg(x))\n",
|
||
"\n",
|
||
" # df_res[f\"diff_{stat}\"] = - df_res[f'before_{stat}'] + df_res[f'after_{stat}']\n",
|
||
" # df_res[f\"diff%_{stat}\"] = df_res[f\"diff_{stat}\"] / df_res[f'before_{stat}'] * 100\n",
|
||
"\n",
|
||
" df_res[f\"train/diff_{stat}\"] = -df_res[f'train/before_{stat}'] + df_res[f'train/after_{stat}']\n",
|
||
" df_res[f\"train/diff%_{stat}\"] = df_res[f\"train/diff_{stat}\"] / df_res[f'train/before_{stat}'] * 100\n",
|
||
"\n",
|
||
"r = df_res.select_dtypes(include=np.number).corr()['novelty'].sort_values()\n",
|
||
"print(stat)\n",
|
||
"display(r)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 89,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"only lesswrong\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_1883910/1966928096.py:2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
|
||
" m = df_res.url.str.contains('lesswrong').fillna(False)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"train/diff_sum -0.247629\n",
|
||
"train/diff_mean -0.159304\n",
|
||
"train/before_max -0.077736\n",
|
||
"train/diff_std -0.041644\n",
|
||
"train/diff%_sum -0.034815\n",
|
||
"train/diff%_mean -0.034815\n",
|
||
"train/before_min -0.007299\n",
|
||
"train/after_min -0.005681\n",
|
||
"train/diff%_std -0.001165\n",
|
||
"train/diff_min 0.007247\n",
|
||
"train/after_std 0.111126\n",
|
||
"train/after_mean 0.168236\n",
|
||
"train/after_max 0.178731\n",
|
||
"train/before_std 0.202097\n",
|
||
"train/after_sum 0.261667\n",
|
||
"train/diff_max 0.264107\n",
|
||
"train/diff%_max 0.274232\n",
|
||
"train/diff%_min 0.318505\n",
|
||
"train/before_mean 0.390433\n",
|
||
"train/before_sum 0.418176\n",
|
||
"novelty 1.000000\n",
|
||
"Name: novelty, dtype: float64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"without lesswrong\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"train/diff_sum -0.524070\n",
|
||
"train/diff_mean -0.289291\n",
|
||
"train/diff%_sum -0.212006\n",
|
||
"train/diff%_mean -0.212006\n",
|
||
"train/diff_std -0.205211\n",
|
||
"train/diff%_max -0.173305\n",
|
||
"train/diff_max -0.153685\n",
|
||
"train/diff%_std -0.135741\n",
|
||
"train/diff_min 0.065099\n",
|
||
"train/after_sum 0.075106\n",
|
||
"train/after_mean 0.077967\n",
|
||
"train/before_min 0.133288\n",
|
||
"train/after_std 0.148123\n",
|
||
"train/before_mean 0.209628\n",
|
||
"train/before_sum 0.217732\n",
|
||
"train/after_max 0.219580\n",
|
||
"train/before_max 0.319630\n",
|
||
"train/before_std 0.337727\n",
|
||
"train/after_min 0.384317\n",
|
||
"train/diff%_min 0.484895\n",
|
||
"novelty 1.000000\n",
|
||
"Name: novelty, dtype: float64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"only new\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"train/diff_sum -0.445037\n",
|
||
"train/diff_mean -0.296686\n",
|
||
"train/diff_std -0.175885\n",
|
||
"train/diff%_sum -0.169818\n",
|
||
"train/diff%_mean -0.169818\n",
|
||
"train/diff%_std -0.149809\n",
|
||
"train/diff_min -0.063278\n",
|
||
"train/after_std -0.029953\n",
|
||
"train/before_max -0.012773\n",
|
||
"train/after_mean 0.102790\n",
|
||
"train/after_max 0.116678\n",
|
||
"train/before_min 0.129897\n",
|
||
"train/diff%_max 0.131804\n",
|
||
"train/diff_max 0.133860\n",
|
||
"train/before_std 0.172009\n",
|
||
"train/after_min 0.238759\n",
|
||
"train/after_sum 0.306655\n",
|
||
"train/before_mean 0.357417\n",
|
||
"train/diff%_min 0.367850\n",
|
||
"train/before_sum 0.536616\n",
|
||
"novelty 1.000000\n",
|
||
"Name: novelty, dtype: float64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# also try with only new stuff, no less wrong, only less wrong...\n",
|
||
"m = df_res.url.str.contains('lesswrong').fillna(False)\n",
|
||
"r = df_res[m].select_dtypes(include=np.number).corr()['novelty'].sort_values()\n",
|
||
"print('only lesswrong')\n",
|
||
"display(r)\n",
|
||
"\n",
|
||
"r = df_res[~m].select_dtypes(include=np.number).corr()['novelty'].sort_values()\n",
|
||
"print('without lesswrong')\n",
|
||
"display(r)\n",
|
||
"\n",
|
||
"r = df_res[~df_res.in_training].select_dtypes(include=np.number).corr()['novelty'].sort_values()\n",
|
||
"print('only new')\n",
|
||
"display(r)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 93,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>f</th>\n",
|
||
" <th>train/diff%_sum</th>\n",
|
||
" <th>novelty</th>\n",
|
||
" <th>in_training</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>23</th>\n",
|
||
" <td>../samples/2025_lw_the-field-of-ai-alignment-a...</td>\n",
|
||
" <td>-57.706054</td>\n",
|
||
" <td>0.925483</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>24</th>\n",
|
||
" <td>../samples/2025_lw_the-intelligence-curse.md</td>\n",
|
||
" <td>-54.846207</td>\n",
|
||
" <td>0.688044</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>../samples/2025_lw_preference-inversion.md</td>\n",
|
||
" <td>-52.294728</td>\n",
|
||
" <td>0.633321</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>../samples/2025_lw_my-agi-safety-research-2024...</td>\n",
|
||
" <td>-49.874378</td>\n",
|
||
" <td>0.756925</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>../samples/2025_lw_review-planecrash.md</td>\n",
|
||
" <td>-49.581118</td>\n",
|
||
" <td>0.933950</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>26</th>\n",
|
||
" <td>../samples/2025_lw_the-subset-parity-learning-...</td>\n",
|
||
" <td>-47.673055</td>\n",
|
||
" <td>0.697946</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>../samples/2024_trump_appointment.md</td>\n",
|
||
" <td>-45.133784</td>\n",
|
||
" <td>0.300000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>../samples/2024_lesswrong_slop.md</td>\n",
|
||
" <td>-44.930269</td>\n",
|
||
" <td>0.100000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>../samples/2025_lw_debating-buying-nvda-in-201...</td>\n",
|
||
" <td>-44.381423</td>\n",
|
||
" <td>0.526975</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>../samples/2025_h5n1_report.md</td>\n",
|
||
" <td>-44.069677</td>\n",
|
||
" <td>0.750000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>../samples/2024_lw_by-default-capital-will-mat...</td>\n",
|
||
" <td>-43.184356</td>\n",
|
||
" <td>0.906388</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>../samples/2025_lw_human-study-on-ai-spear-phi...</td>\n",
|
||
" <td>-41.879381</td>\n",
|
||
" <td>0.677458</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>../samples/2024_news_anthropic.md</td>\n",
|
||
" <td>-41.733471</td>\n",
|
||
" <td>0.500000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>../samples/2024_how_to_focus.md</td>\n",
|
||
" <td>-38.998412</td>\n",
|
||
" <td>0.500000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>../samples/2025_lw_comment-on-death-and-the-go...</td>\n",
|
||
" <td>-38.898521</td>\n",
|
||
" <td>0.750253</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>../samples/2024_openai_emails.md</td>\n",
|
||
" <td>-37.535013</td>\n",
|
||
" <td>0.700000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>../samples/2025_lw_2024-in-ai-predictions.md</td>\n",
|
||
" <td>-37.078135</td>\n",
|
||
" <td>0.789190</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>../samples/2024_gpt4_fake_paper.md</td>\n",
|
||
" <td>-35.067661</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>../samples/2024_deliberative_alignment.md</td>\n",
|
||
" <td>-34.208421</td>\n",
|
||
" <td>0.600000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>../samples/2024_anthropic_palintir.md</td>\n",
|
||
" <td>-31.636323</td>\n",
|
||
" <td>0.200000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>29</th>\n",
|
||
" <td>../samples/politics_is_the_mind_killer.md</td>\n",
|
||
" <td>-26.009715</td>\n",
|
||
" <td>0.500000</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>28</th>\n",
|
||
" <td>../samples/lorem_ipsum.md</td>\n",
|
||
" <td>-25.975428</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>../samples/2025_lw_the-laws-of-large-numbers.md</td>\n",
|
||
" <td>-25.415931</td>\n",
|
||
" <td>0.540932</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>../samples/2024_gwern_reddit.md</td>\n",
|
||
" <td>-25.314383</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>../samples/2025_lw_parkinson-s-law-and-the-ide...</td>\n",
|
||
" <td>-21.706555</td>\n",
|
||
" <td>0.677458</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>../samples/2024_lw_the-plan-2024-update.md</td>\n",
|
||
" <td>-20.314768</td>\n",
|
||
" <td>0.785170</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>../samples/2024_bob_fanfic2.md</td>\n",
|
||
" <td>-20.237010</td>\n",
|
||
" <td>0.400000</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>../samples/2024_bob_fanfic.md</td>\n",
|
||
" <td>-20.153819</td>\n",
|
||
" <td>0.300000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>../samples/2024_arxiv_meh.md</td>\n",
|
||
" <td>-19.115730</td>\n",
|
||
" <td>0.150000</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>27</th>\n",
|
||
" <td>../samples/2025_lw_what-s-the-short-timeline-p...</td>\n",
|
||
" <td>-16.388113</td>\n",
|
||
" <td>0.898161</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" f train/diff%_sum \\\n",
|
||
"23 ../samples/2025_lw_the-field-of-ai-alignment-a... -57.706054 \n",
|
||
"24 ../samples/2025_lw_the-intelligence-curse.md -54.846207 \n",
|
||
"21 ../samples/2025_lw_preference-inversion.md -52.294728 \n",
|
||
"19 ../samples/2025_lw_my-agi-safety-research-2024... -49.874378 \n",
|
||
"22 ../samples/2025_lw_review-planecrash.md -49.581118 \n",
|
||
"26 ../samples/2025_lw_the-subset-parity-learning-... -47.673055 \n",
|
||
"13 ../samples/2024_trump_appointment.md -45.133784 \n",
|
||
"8 ../samples/2024_lesswrong_slop.md -44.930269 \n",
|
||
"17 ../samples/2025_lw_debating-buying-nvda-in-201... -44.381423 \n",
|
||
"14 ../samples/2025_h5n1_report.md -44.069677 \n",
|
||
"9 ../samples/2024_lw_by-default-capital-will-mat... -43.184356 \n",
|
||
"18 ../samples/2025_lw_human-study-on-ai-spear-phi... -41.879381 \n",
|
||
"11 ../samples/2024_news_anthropic.md -41.733471 \n",
|
||
"7 ../samples/2024_how_to_focus.md -38.998412 \n",
|
||
"16 ../samples/2025_lw_comment-on-death-and-the-go... -38.898521 \n",
|
||
"12 ../samples/2024_openai_emails.md -37.535013 \n",
|
||
"15 ../samples/2025_lw_2024-in-ai-predictions.md -37.078135 \n",
|
||
"5 ../samples/2024_gpt4_fake_paper.md -35.067661 \n",
|
||
"4 ../samples/2024_deliberative_alignment.md -34.208421 \n",
|
||
"0 ../samples/2024_anthropic_palintir.md -31.636323 \n",
|
||
"29 ../samples/politics_is_the_mind_killer.md -26.009715 \n",
|
||
"28 ../samples/lorem_ipsum.md -25.975428 \n",
|
||
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|
||
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|
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||
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|
||
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|
||
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|
||
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|
||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
" <th>../samples/2024_trump_appointment.md</th>\n",
|
||
" <td>President Trump Announces Morgan Ortagus as De...</td>\n",
|
||
" <td>0.300000</td>\n",
|
||
" <td>2025-01-04 00:00:00+00:00</td>\n",
|
||
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|
||
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||
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|
||
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||
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|
||
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||
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|
||
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|
||
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|
||
" <td>-82.696441</td>\n",
|
||
" <td>17.917255</td>\n",
|
||
" <td>11.689822</td>\n",
|
||
" <td>-6.227433</td>\n",
|
||
" <td>-34.756625</td>\n",
|
||
" <td>284.586479</td>\n",
|
||
" <td>156.141833</td>\n",
|
||
" <td>-128.444646</td>\n",
|
||
" <td>-45.133784</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>../samples/2024_how_to_focus.md</th>\n",
|
||
" <td>How to Focus</td>\n",
|
||
" <td>0.500000</td>\n",
|
||
" <td>2024-06-01 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2.030182</td>\n",
|
||
" <td>1.238443</td>\n",
|
||
" <td>-0.791739</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>-2.135250</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <th>../samples/2024_gpt4_fake_paper.md</th>\n",
|
||
" <td>fake ai hoax paper made up by gpt-4</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>2024-01-01 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2.890543</td>\n",
|
||
" <td>1.876897</td>\n",
|
||
" <td>-1.013646</td>\n",
|
||
" <td>-35.067661</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>-14.135986</td>\n",
|
||
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|
||
" <td>227.104543</td>\n",
|
||
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|
||
" <td>-35.067661</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>../samples/2025_h5n1_report.md</th>\n",
|
||
" <td>CDC Report on Missouri H5N1 Serology Testing</td>\n",
|
||
" <td>0.750000</td>\n",
|
||
" <td>2025-01-05 00:00:00+00:00</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>2.737036</td>\n",
|
||
" <td>2.004289</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.002226</td>\n",
|
||
" <td>-77.666175</td>\n",
|
||
" <td>11.928956</td>\n",
|
||
" <td>9.404486</td>\n",
|
||
" <td>-2.524470</td>\n",
|
||
" <td>-21.162542</td>\n",
|
||
" <td>355.013296</td>\n",
|
||
" <td>198.560084</td>\n",
|
||
" <td>-156.453212</td>\n",
|
||
" <td>-44.069677</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_arxiv_meh.md</th>\n",
|
||
" <td>TradingAgents: Multi-Agents LLM Financial Trad...</td>\n",
|
||
" <td>0.150000</td>\n",
|
||
" <td>2024-12-28 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.329921</td>\n",
|
||
" <td>2.693382</td>\n",
|
||
" <td>-0.636539</td>\n",
|
||
" <td>-19.115730</td>\n",
|
||
" <td>3.075030</td>\n",
|
||
" <td>2.940876</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000330</td>\n",
|
||
" <td>-61.776143</td>\n",
|
||
" <td>13.523319</td>\n",
|
||
" <td>13.253697</td>\n",
|
||
" <td>-0.269622</td>\n",
|
||
" <td>-1.993755</td>\n",
|
||
" <td>386.270819</td>\n",
|
||
" <td>312.432330</td>\n",
|
||
" <td>-73.838489</td>\n",
|
||
" <td>-19.115730</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_lw_by-default-capital-will-matter-more-than-ever-after-agi.md</th>\n",
|
||
" <td>By default, capital will matter more than ever...</td>\n",
|
||
" <td>0.906388</td>\n",
|
||
" <td>2024-12-30 19:47:19.838000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2.935460</td>\n",
|
||
" <td>1.667801</td>\n",
|
||
" <td>-1.267660</td>\n",
|
||
" <td>-43.184356</td>\n",
|
||
" <td>2.497172</td>\n",
|
||
" <td>2.004676</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.007733</td>\n",
|
||
" <td>-65.756404</td>\n",
|
||
" <td>12.706497</td>\n",
|
||
" <td>11.517975</td>\n",
|
||
" <td>-1.188522</td>\n",
|
||
" <td>-9.353658</td>\n",
|
||
" <td>393.351672</td>\n",
|
||
" <td>223.485284</td>\n",
|
||
" <td>-169.866388</td>\n",
|
||
" <td>-43.184356</td>\n",
|
||
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|
||
" <tr>\n",
|
||
" <th>../samples/2024_news_anthropic.md</th>\n",
|
||
" <td>Amazon-backed Anthropic debuts AI agents that ...</td>\n",
|
||
" <td>0.500000</td>\n",
|
||
" <td>2025-01-05 05:03:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2.437276</td>\n",
|
||
" <td>1.420116</td>\n",
|
||
" <td>-1.017160</td>\n",
|
||
" <td>-41.733471</td>\n",
|
||
" <td>2.880203</td>\n",
|
||
" <td>2.245643</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000029</td>\n",
|
||
" <td>-71.181132</td>\n",
|
||
" <td>12.790111</td>\n",
|
||
" <td>12.667040</td>\n",
|
||
" <td>-0.123071</td>\n",
|
||
" <td>-0.962233</td>\n",
|
||
" <td>424.085983</td>\n",
|
||
" <td>247.100184</td>\n",
|
||
" <td>-176.985800</td>\n",
|
||
" <td>-41.733471</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_the-laws-of-large-numbers.md</th>\n",
|
||
" <td>The Laws of Large Numbers</td>\n",
|
||
" <td>0.540932</td>\n",
|
||
" <td>2025-01-04 18:06:02.387000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>2.887975</td>\n",
|
||
" <td>2.153969</td>\n",
|
||
" <td>-0.734006</td>\n",
|
||
" <td>-25.415931</td>\n",
|
||
" <td>2.576897</td>\n",
|
||
" <td>2.052082</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.001162</td>\n",
|
||
" <td>-29.038092</td>\n",
|
||
" <td>11.118690</td>\n",
|
||
" <td>8.321499</td>\n",
|
||
" <td>-2.797191</td>\n",
|
||
" <td>-25.157557</td>\n",
|
||
" <td>424.532354</td>\n",
|
||
" <td>316.633504</td>\n",
|
||
" <td>-107.898850</td>\n",
|
||
" <td>-25.415931</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/politics_is_the_mind_killer.md</th>\n",
|
||
" <td>politics is the mind-killer</td>\n",
|
||
" <td>0.500000</td>\n",
|
||
" <td>2007-02-19 00:00:00+00:00</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>3.285058</td>\n",
|
||
" <td>2.430624</td>\n",
|
||
" <td>-0.854434</td>\n",
|
||
" <td>-26.009715</td>\n",
|
||
" <td>2.930715</td>\n",
|
||
" <td>2.513867</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.004398</td>\n",
|
||
" <td>-65.298689</td>\n",
|
||
" <td>11.889731</td>\n",
|
||
" <td>11.973468</td>\n",
|
||
" <td>0.083736</td>\n",
|
||
" <td>0.704275</td>\n",
|
||
" <td>446.767846</td>\n",
|
||
" <td>330.564804</td>\n",
|
||
" <td>-116.203043</td>\n",
|
||
" <td>-26.009715</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_lesswrong_slop.md</th>\n",
|
||
" <td>Deontic Explorations In \"Paying To Talk To Sla...</td>\n",
|
||
" <td>0.100000</td>\n",
|
||
" <td>2024-04-12 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.078750</td>\n",
|
||
" <td>1.695459</td>\n",
|
||
" <td>-1.383291</td>\n",
|
||
" <td>-44.930269</td>\n",
|
||
" <td>2.946400</td>\n",
|
||
" <td>2.349493</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000778</td>\n",
|
||
" <td>-75.973899</td>\n",
|
||
" <td>15.304575</td>\n",
|
||
" <td>12.805290</td>\n",
|
||
" <td>-2.499285</td>\n",
|
||
" <td>-16.330311</td>\n",
|
||
" <td>449.497469</td>\n",
|
||
" <td>247.537046</td>\n",
|
||
" <td>-201.960423</td>\n",
|
||
" <td>-44.930269</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_parkinson-s-law-and-the-ideology-of-statistics-1.md</th>\n",
|
||
" <td>Parkinson's Law and the Ideology of Statistics</td>\n",
|
||
" <td>0.677458</td>\n",
|
||
" <td>2025-01-04 22:59:57.376000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.550214</td>\n",
|
||
" <td>2.779585</td>\n",
|
||
" <td>-0.770629</td>\n",
|
||
" <td>-21.706555</td>\n",
|
||
" <td>3.111395</td>\n",
|
||
" <td>2.862761</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.004117</td>\n",
|
||
" <td>-90.026555</td>\n",
|
||
" <td>12.598433</td>\n",
|
||
" <td>12.334391</td>\n",
|
||
" <td>-0.264043</td>\n",
|
||
" <td>-2.095839</td>\n",
|
||
" <td>450.877187</td>\n",
|
||
" <td>353.007281</td>\n",
|
||
" <td>-97.869906</td>\n",
|
||
" <td>-21.706555</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_anthropic_palintir.md</th>\n",
|
||
" <td>Anthropic and Palantir Partner to Bring Claude...</td>\n",
|
||
" <td>0.200000</td>\n",
|
||
" <td>2024-07-11 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.575668</td>\n",
|
||
" <td>2.444458</td>\n",
|
||
" <td>-1.131210</td>\n",
|
||
" <td>-31.636323</td>\n",
|
||
" <td>3.246451</td>\n",
|
||
" <td>2.673633</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000074</td>\n",
|
||
" <td>-75.181568</td>\n",
|
||
" <td>13.271792</td>\n",
|
||
" <td>12.891874</td>\n",
|
||
" <td>-0.379918</td>\n",
|
||
" <td>-2.862598</td>\n",
|
||
" <td>454.109781</td>\n",
|
||
" <td>310.446145</td>\n",
|
||
" <td>-143.663636</td>\n",
|
||
" <td>-31.636323</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_preference-inversion.md</th>\n",
|
||
" <td>Preference Inversion</td>\n",
|
||
" <td>0.633321</td>\n",
|
||
" <td>2025-01-03 23:49:06.168000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.358938</td>\n",
|
||
" <td>1.602391</td>\n",
|
||
" <td>-1.756548</td>\n",
|
||
" <td>-52.294728</td>\n",
|
||
" <td>2.745416</td>\n",
|
||
" <td>1.724408</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.003750</td>\n",
|
||
" <td>-88.162939</td>\n",
|
||
" <td>11.404502</td>\n",
|
||
" <td>8.079728</td>\n",
|
||
" <td>-3.324774</td>\n",
|
||
" <td>-29.153170</td>\n",
|
||
" <td>456.815627</td>\n",
|
||
" <td>217.925137</td>\n",
|
||
" <td>-238.890490</td>\n",
|
||
" <td>-52.294728</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_deliberative_alignment.md</th>\n",
|
||
" <td>Deliberative Alignment: Reasoning Enables Safe...</td>\n",
|
||
" <td>0.600000</td>\n",
|
||
" <td>2024-12-20 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.623786</td>\n",
|
||
" <td>2.384146</td>\n",
|
||
" <td>-1.239640</td>\n",
|
||
" <td>-34.208421</td>\n",
|
||
" <td>3.441515</td>\n",
|
||
" <td>2.720267</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000561</td>\n",
|
||
" <td>-29.432378</td>\n",
|
||
" <td>14.758335</td>\n",
|
||
" <td>12.259495</td>\n",
|
||
" <td>-2.498840</td>\n",
|
||
" <td>-16.931722</td>\n",
|
||
" <td>467.468432</td>\n",
|
||
" <td>307.554861</td>\n",
|
||
" <td>-159.913571</td>\n",
|
||
" <td>-34.208421</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_the-subset-parity-learning-problem-much-more-than-you-wanted.md</th>\n",
|
||
" <td>The subset parity learning problem: much more ...</td>\n",
|
||
" <td>0.697946</td>\n",
|
||
" <td>2025-01-04 09:59:16.158000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.322798</td>\n",
|
||
" <td>1.738719</td>\n",
|
||
" <td>-1.584079</td>\n",
|
||
" <td>-47.673055</td>\n",
|
||
" <td>2.968027</td>\n",
|
||
" <td>2.028070</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.005546</td>\n",
|
||
" <td>-71.898714</td>\n",
|
||
" <td>12.878409</td>\n",
|
||
" <td>10.411036</td>\n",
|
||
" <td>-2.467374</td>\n",
|
||
" <td>-19.158995</td>\n",
|
||
" <td>471.837339</td>\n",
|
||
" <td>246.898066</td>\n",
|
||
" <td>-224.939272</td>\n",
|
||
" <td>-47.673055</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_human-study-on-ai-spear-phishing-campaigns.md</th>\n",
|
||
" <td>Human study on AI spear phishing campaigns</td>\n",
|
||
" <td>0.677458</td>\n",
|
||
" <td>2025-01-03 19:03:28.406000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.436050</td>\n",
|
||
" <td>1.997053</td>\n",
|
||
" <td>-1.438996</td>\n",
|
||
" <td>-41.879381</td>\n",
|
||
" <td>3.160585</td>\n",
|
||
" <td>2.289065</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.002553</td>\n",
|
||
" <td>-85.223138</td>\n",
|
||
" <td>14.798123</td>\n",
|
||
" <td>12.255587</td>\n",
|
||
" <td>-2.542537</td>\n",
|
||
" <td>-17.181481</td>\n",
|
||
" <td>477.610894</td>\n",
|
||
" <td>277.590406</td>\n",
|
||
" <td>-200.020488</td>\n",
|
||
" <td>-41.879381</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_the-intelligence-curse.md</th>\n",
|
||
" <td>The Intelligence Curse</td>\n",
|
||
" <td>0.688044</td>\n",
|
||
" <td>2025-01-04 18:16:58.921000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.539096</td>\n",
|
||
" <td>1.598036</td>\n",
|
||
" <td>-1.941060</td>\n",
|
||
" <td>-54.846207</td>\n",
|
||
" <td>3.480139</td>\n",
|
||
" <td>2.130229</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000008</td>\n",
|
||
" <td>2.610249</td>\n",
|
||
" <td>17.431973</td>\n",
|
||
" <td>11.515382</td>\n",
|
||
" <td>-5.916591</td>\n",
|
||
" <td>-33.941028</td>\n",
|
||
" <td>484.856104</td>\n",
|
||
" <td>218.930921</td>\n",
|
||
" <td>-265.925183</td>\n",
|
||
" <td>-54.846207</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/lorem_ipsum.md</th>\n",
|
||
" <td>Lorem ipsum</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1900-01-01 00:00:00+00:00</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>2.465992</td>\n",
|
||
" <td>1.825440</td>\n",
|
||
" <td>-0.640552</td>\n",
|
||
" <td>-25.975428</td>\n",
|
||
" <td>2.463530</td>\n",
|
||
" <td>2.008885</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000011</td>\n",
|
||
" <td>-19.999573</td>\n",
|
||
" <td>12.736030</td>\n",
|
||
" <td>12.230991</td>\n",
|
||
" <td>-0.505038</td>\n",
|
||
" <td>-3.965429</td>\n",
|
||
" <td>490.732376</td>\n",
|
||
" <td>363.262540</td>\n",
|
||
" <td>-127.469837</td>\n",
|
||
" <td>-25.975428</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_the-field-of-ai-alignment-a-postmortem-and-what-to-do-about.md</th>\n",
|
||
" <td>The Field of AI Alignment: A Postmortem, and W...</td>\n",
|
||
" <td>0.925483</td>\n",
|
||
" <td>2025-01-01 00:42:36.538000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.735233</td>\n",
|
||
" <td>1.579777</td>\n",
|
||
" <td>-2.155455</td>\n",
|
||
" <td>-57.706054</td>\n",
|
||
" <td>3.116857</td>\n",
|
||
" <td>2.056279</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000621</td>\n",
|
||
" <td>378.210283</td>\n",
|
||
" <td>12.190395</td>\n",
|
||
" <td>10.575907</td>\n",
|
||
" <td>-1.614489</td>\n",
|
||
" <td>-13.243940</td>\n",
|
||
" <td>500.521191</td>\n",
|
||
" <td>211.690162</td>\n",
|
||
" <td>-288.831029</td>\n",
|
||
" <td>-57.706054</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_openai_emails.md</th>\n",
|
||
" <td>OpenAI Email Archives from Musk v. Altman</td>\n",
|
||
" <td>0.700000</td>\n",
|
||
" <td>2024-11-01 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.426089</td>\n",
|
||
" <td>2.140106</td>\n",
|
||
" <td>-1.285983</td>\n",
|
||
" <td>-37.535013</td>\n",
|
||
" <td>2.755313</td>\n",
|
||
" <td>2.184135</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000420</td>\n",
|
||
" <td>-95.082707</td>\n",
|
||
" <td>10.482502</td>\n",
|
||
" <td>9.602067</td>\n",
|
||
" <td>-0.880435</td>\n",
|
||
" <td>-8.399092</td>\n",
|
||
" <td>517.339424</td>\n",
|
||
" <td>323.156004</td>\n",
|
||
" <td>-194.183420</td>\n",
|
||
" <td>-37.535013</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_debating-buying-nvda-in-2019.md</th>\n",
|
||
" <td>debating buying NVDA in 2019</td>\n",
|
||
" <td>0.526975</td>\n",
|
||
" <td>2025-01-04 18:07:35.832000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.498877</td>\n",
|
||
" <td>1.946026</td>\n",
|
||
" <td>-1.552851</td>\n",
|
||
" <td>-44.381423</td>\n",
|
||
" <td>2.970988</td>\n",
|
||
" <td>2.091860</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000716</td>\n",
|
||
" <td>144.210285</td>\n",
|
||
" <td>12.902117</td>\n",
|
||
" <td>9.158434</td>\n",
|
||
" <td>-3.743683</td>\n",
|
||
" <td>-29.016036</td>\n",
|
||
" <td>517.833801</td>\n",
|
||
" <td>288.011791</td>\n",
|
||
" <td>-229.822011</td>\n",
|
||
" <td>-44.381423</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_my-agi-safety-research-2024-review-25-plans.md</th>\n",
|
||
" <td>My AGI safety research—2024 review, ’25 plans</td>\n",
|
||
" <td>0.756925</td>\n",
|
||
" <td>2025-01-01 22:01:48.820000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.153156</td>\n",
|
||
" <td>1.580539</td>\n",
|
||
" <td>-1.572617</td>\n",
|
||
" <td>-49.874378</td>\n",
|
||
" <td>3.112932</td>\n",
|
||
" <td>2.097326</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.001612</td>\n",
|
||
" <td>-86.930632</td>\n",
|
||
" <td>13.688265</td>\n",
|
||
" <td>11.766024</td>\n",
|
||
" <td>-1.922241</td>\n",
|
||
" <td>-14.042987</td>\n",
|
||
" <td>523.423819</td>\n",
|
||
" <td>262.369446</td>\n",
|
||
" <td>-261.054373</td>\n",
|
||
" <td>-49.874378</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_bob_fanfic.md</th>\n",
|
||
" <td>Flower Crowns and Furry Mishaps by MyPalAI</td>\n",
|
||
" <td>0.300000</td>\n",
|
||
" <td>2024-12-24 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.314768</td>\n",
|
||
" <td>2.646716</td>\n",
|
||
" <td>-0.668052</td>\n",
|
||
" <td>-20.153819</td>\n",
|
||
" <td>3.029083</td>\n",
|
||
" <td>2.704339</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000615</td>\n",
|
||
" <td>-35.032627</td>\n",
|
||
" <td>13.226252</td>\n",
|
||
" <td>11.086496</td>\n",
|
||
" <td>-2.139755</td>\n",
|
||
" <td>-16.178093</td>\n",
|
||
" <td>527.048090</td>\n",
|
||
" <td>420.827773</td>\n",
|
||
" <td>-106.220317</td>\n",
|
||
" <td>-20.153819</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_gwern_reddit.md</th>\n",
|
||
" <td>Hardware Hedging Against Scaling Regime Shifts...</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>2024-08-21 00:00:00+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.907376</td>\n",
|
||
" <td>2.918248</td>\n",
|
||
" <td>-0.989128</td>\n",
|
||
" <td>-25.314383</td>\n",
|
||
" <td>3.043581</td>\n",
|
||
" <td>2.630350</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000549</td>\n",
|
||
" <td>46.703333</td>\n",
|
||
" <td>15.920402</td>\n",
|
||
" <td>14.091064</td>\n",
|
||
" <td>-1.829337</td>\n",
|
||
" <td>-11.490521</td>\n",
|
||
" <td>562.662201</td>\n",
|
||
" <td>420.227737</td>\n",
|
||
" <td>-142.434464</td>\n",
|
||
" <td>-25.314383</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_comment-on-death-and-the-gorgon.md</th>\n",
|
||
" <td>Comment on 'Death and the Gorgon'</td>\n",
|
||
" <td>0.750253</td>\n",
|
||
" <td>2025-01-01 06:00:24.498000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>4.084834</td>\n",
|
||
" <td>2.495894</td>\n",
|
||
" <td>-1.588940</td>\n",
|
||
" <td>-38.898521</td>\n",
|
||
" <td>3.372985</td>\n",
|
||
" <td>2.744499</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.001318</td>\n",
|
||
" <td>-65.735003</td>\n",
|
||
" <td>16.383120</td>\n",
|
||
" <td>15.471860</td>\n",
|
||
" <td>-0.911260</td>\n",
|
||
" <td>-5.562186</td>\n",
|
||
" <td>616.809882</td>\n",
|
||
" <td>376.879962</td>\n",
|
||
" <td>-239.929921</td>\n",
|
||
" <td>-38.898521</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_bob_fanfic2.md</th>\n",
|
||
" <td>Paradox's Box (Bobiverse) by Mark4man</td>\n",
|
||
" <td>0.400000</td>\n",
|
||
" <td>2022-02-17 00:00:00+00:00</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>4.136299</td>\n",
|
||
" <td>3.299236</td>\n",
|
||
" <td>-0.837063</td>\n",
|
||
" <td>-20.237010</td>\n",
|
||
" <td>3.103517</td>\n",
|
||
" <td>2.757547</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.001771</td>\n",
|
||
" <td>-35.546726</td>\n",
|
||
" <td>13.576470</td>\n",
|
||
" <td>12.110069</td>\n",
|
||
" <td>-1.466401</td>\n",
|
||
" <td>-10.801048</td>\n",
|
||
" <td>620.444836</td>\n",
|
||
" <td>494.885355</td>\n",
|
||
" <td>-125.559481</td>\n",
|
||
" <td>-20.237010</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_what-s-the-short-timeline-plan.md</th>\n",
|
||
" <td>What’s the short timeline plan?</td>\n",
|
||
" <td>0.898161</td>\n",
|
||
" <td>2025-01-05 00:10:28.708000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.970715</td>\n",
|
||
" <td>3.319989</td>\n",
|
||
" <td>-0.650725</td>\n",
|
||
" <td>-16.388113</td>\n",
|
||
" <td>3.171675</td>\n",
|
||
" <td>2.998089</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000231</td>\n",
|
||
" <td>159.805322</td>\n",
|
||
" <td>13.405236</td>\n",
|
||
" <td>17.062244</td>\n",
|
||
" <td>3.657008</td>\n",
|
||
" <td>27.280446</td>\n",
|
||
" <td>623.402177</td>\n",
|
||
" <td>521.238323</td>\n",
|
||
" <td>-102.163854</td>\n",
|
||
" <td>-16.388113</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2024_lw_the-plan-2024-update.md</th>\n",
|
||
" <td>The Plan - 2024 Update</td>\n",
|
||
" <td>0.785170</td>\n",
|
||
" <td>2024-12-31 19:29:23.013000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.741762</td>\n",
|
||
" <td>2.981632</td>\n",
|
||
" <td>-0.760130</td>\n",
|
||
" <td>-20.314768</td>\n",
|
||
" <td>2.872466</td>\n",
|
||
" <td>2.731928</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000043</td>\n",
|
||
" <td>70.526800</td>\n",
|
||
" <td>11.613052</td>\n",
|
||
" <td>11.740885</td>\n",
|
||
" <td>0.127832</td>\n",
|
||
" <td>1.100765</td>\n",
|
||
" <td>673.517207</td>\n",
|
||
" <td>536.693749</td>\n",
|
||
" <td>-136.823458</td>\n",
|
||
" <td>-20.314768</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_review-planecrash.md</th>\n",
|
||
" <td>Review: Planecrash</td>\n",
|
||
" <td>0.933950</td>\n",
|
||
" <td>2025-01-01 01:31:26.864000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>3.475632</td>\n",
|
||
" <td>1.752375</td>\n",
|
||
" <td>-1.723257</td>\n",
|
||
" <td>-49.581118</td>\n",
|
||
" <td>3.319073</td>\n",
|
||
" <td>2.316245</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>-0.000083</td>\n",
|
||
" <td>-79.884876</td>\n",
|
||
" <td>14.514912</td>\n",
|
||
" <td>10.882781</td>\n",
|
||
" <td>-3.632131</td>\n",
|
||
" <td>-25.023443</td>\n",
|
||
" <td>684.699408</td>\n",
|
||
" <td>345.217788</td>\n",
|
||
" <td>-339.481621</td>\n",
|
||
" <td>-49.581118</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>../samples/2025_lw_2024-in-ai-predictions.md</th>\n",
|
||
" <td>2024 in AI predictions</td>\n",
|
||
" <td>0.789190</td>\n",
|
||
" <td>2025-01-02 02:09:13.093000+00:00</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>4.635499</td>\n",
|
||
" <td>2.916743</td>\n",
|
||
" <td>-1.718757</td>\n",
|
||
" <td>-37.078135</td>\n",
|
||
" <td>3.080130</td>\n",
|
||
" <td>2.548945</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000238</td>\n",
|
||
" <td>50.615295</td>\n",
|
||
" <td>13.301698</td>\n",
|
||
" <td>12.898935</td>\n",
|
||
" <td>-0.402762</td>\n",
|
||
" <td>-3.027902</td>\n",
|
||
" <td>801.941370</td>\n",
|
||
" <td>504.596465</td>\n",
|
||
" <td>-297.344904</td>\n",
|
||
" <td>-37.078135</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>30 rows × 24 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" title \\\n",
|
||
"f \n",
|
||
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|
||
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|
||
"../samples/2024_gpt4_fake_paper.md fake ai hoax paper made up by gpt-4 \n",
|
||
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|
||
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|
||
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|
||
"../samples/2024_news_anthropic.md Amazon-backed Anthropic debuts AI agents that ... \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md The Laws of Large Numbers \n",
|
||
"../samples/politics_is_the_mind_killer.md politics is the mind-killer \n",
|
||
"../samples/2024_lesswrong_slop.md Deontic Explorations In \"Paying To Talk To Sla... \n",
|
||
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|
||
"../samples/2024_anthropic_palintir.md Anthropic and Palantir Partner to Bring Claude... \n",
|
||
"../samples/2025_lw_preference-inversion.md Preference Inversion \n",
|
||
"../samples/2024_deliberative_alignment.md Deliberative Alignment: Reasoning Enables Safe... \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... The subset parity learning problem: much more ... \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... Human study on AI spear phishing campaigns \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md The Intelligence Curse \n",
|
||
"../samples/lorem_ipsum.md Lorem ipsum \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... The Field of AI Alignment: A Postmortem, and W... \n",
|
||
"../samples/2024_openai_emails.md OpenAI Email Archives from Musk v. Altman \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md debating buying NVDA in 2019 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... My AGI safety research—2024 review, ’25 plans \n",
|
||
"../samples/2024_bob_fanfic.md Flower Crowns and Furry Mishaps by MyPalAI \n",
|
||
"../samples/2024_gwern_reddit.md Hardware Hedging Against Scaling Regime Shifts... \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... Comment on 'Death and the Gorgon' \n",
|
||
"../samples/2024_bob_fanfic2.md Paradox's Box (Bobiverse) by Mark4man \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... What’s the short timeline plan? \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md The Plan - 2024 Update \n",
|
||
"../samples/2025_lw_review-planecrash.md Review: Planecrash \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md 2024 in AI predictions \n",
|
||
"\n",
|
||
" novelty \\\n",
|
||
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|
||
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||
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||
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||
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||
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||
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||
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|
||
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||
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||
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||
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||
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||
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|
||
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|
||
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||
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|
||
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|
||
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||
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|
||
"\n",
|
||
" date \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md 2025-01-04 00:00:00+00:00 \n",
|
||
"../samples/2024_how_to_focus.md 2024-06-01 00:00:00+00:00 \n",
|
||
"../samples/2024_gpt4_fake_paper.md 2024-01-01 00:00:00+00:00 \n",
|
||
"../samples/2025_h5n1_report.md 2025-01-05 00:00:00+00:00 \n",
|
||
"../samples/2024_arxiv_meh.md 2024-12-28 00:00:00+00:00 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... 2024-12-30 19:47:19.838000+00:00 \n",
|
||
"../samples/2024_news_anthropic.md 2025-01-05 05:03:00+00:00 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md 2025-01-04 18:06:02.387000+00:00 \n",
|
||
"../samples/politics_is_the_mind_killer.md 2007-02-19 00:00:00+00:00 \n",
|
||
"../samples/2024_lesswrong_slop.md 2024-04-12 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... 2025-01-04 22:59:57.376000+00:00 \n",
|
||
"../samples/2024_anthropic_palintir.md 2024-07-11 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_preference-inversion.md 2025-01-03 23:49:06.168000+00:00 \n",
|
||
"../samples/2024_deliberative_alignment.md 2024-12-20 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... 2025-01-04 09:59:16.158000+00:00 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... 2025-01-03 19:03:28.406000+00:00 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md 2025-01-04 18:16:58.921000+00:00 \n",
|
||
"../samples/lorem_ipsum.md 1900-01-01 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... 2025-01-01 00:42:36.538000+00:00 \n",
|
||
"../samples/2024_openai_emails.md 2024-11-01 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md 2025-01-04 18:07:35.832000+00:00 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... 2025-01-01 22:01:48.820000+00:00 \n",
|
||
"../samples/2024_bob_fanfic.md 2024-12-24 00:00:00+00:00 \n",
|
||
"../samples/2024_gwern_reddit.md 2024-08-21 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... 2025-01-01 06:00:24.498000+00:00 \n",
|
||
"../samples/2024_bob_fanfic2.md 2022-02-17 00:00:00+00:00 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 2025-01-05 00:10:28.708000+00:00 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 2024-12-31 19:29:23.013000+00:00 \n",
|
||
"../samples/2025_lw_review-planecrash.md 2025-01-01 01:31:26.864000+00:00 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md 2025-01-02 02:09:13.093000+00:00 \n",
|
||
"\n",
|
||
" in_training \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md False \n",
|
||
"../samples/2024_how_to_focus.md False \n",
|
||
"../samples/2024_gpt4_fake_paper.md False \n",
|
||
"../samples/2025_h5n1_report.md False \n",
|
||
"../samples/2024_arxiv_meh.md False \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... False \n",
|
||
"../samples/2024_news_anthropic.md False \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md False \n",
|
||
"../samples/politics_is_the_mind_killer.md True \n",
|
||
"../samples/2024_lesswrong_slop.md False \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... False \n",
|
||
"../samples/2024_anthropic_palintir.md False \n",
|
||
"../samples/2025_lw_preference-inversion.md False \n",
|
||
"../samples/2024_deliberative_alignment.md False \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... False \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... False \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md False \n",
|
||
"../samples/lorem_ipsum.md True \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... False \n",
|
||
"../samples/2024_openai_emails.md False \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md False \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... False \n",
|
||
"../samples/2024_bob_fanfic.md False \n",
|
||
"../samples/2024_gwern_reddit.md False \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... False \n",
|
||
"../samples/2024_bob_fanfic2.md True \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... False \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md False \n",
|
||
"../samples/2025_lw_review-planecrash.md False \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md False \n",
|
||
"\n",
|
||
" train/before_mean \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md 3.602360 \n",
|
||
"../samples/2024_how_to_focus.md 2.030182 \n",
|
||
"../samples/2024_gpt4_fake_paper.md 2.890543 \n",
|
||
"../samples/2025_h5n1_report.md 2.840106 \n",
|
||
"../samples/2024_arxiv_meh.md 3.329921 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... 2.935460 \n",
|
||
"../samples/2024_news_anthropic.md 2.437276 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md 2.887975 \n",
|
||
"../samples/politics_is_the_mind_killer.md 3.285058 \n",
|
||
"../samples/2024_lesswrong_slop.md 3.078750 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... 3.550214 \n",
|
||
"../samples/2024_anthropic_palintir.md 3.575668 \n",
|
||
"../samples/2025_lw_preference-inversion.md 3.358938 \n",
|
||
"../samples/2024_deliberative_alignment.md 3.623786 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... 3.322798 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... 3.436050 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md 3.539096 \n",
|
||
"../samples/lorem_ipsum.md 2.465992 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... 3.735233 \n",
|
||
"../samples/2024_openai_emails.md 3.426089 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md 3.498877 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... 3.153156 \n",
|
||
"../samples/2024_bob_fanfic.md 3.314768 \n",
|
||
"../samples/2024_gwern_reddit.md 3.907376 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... 4.084834 \n",
|
||
"../samples/2024_bob_fanfic2.md 4.136299 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 3.970715 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 3.741762 \n",
|
||
"../samples/2025_lw_review-planecrash.md 3.475632 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md 4.635499 \n",
|
||
"\n",
|
||
" train/after_mean \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md 1.976479 \n",
|
||
"../samples/2024_how_to_focus.md 1.238443 \n",
|
||
"../samples/2024_gpt4_fake_paper.md 1.876897 \n",
|
||
"../samples/2025_h5n1_report.md 1.588481 \n",
|
||
"../samples/2024_arxiv_meh.md 2.693382 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... 1.667801 \n",
|
||
"../samples/2024_news_anthropic.md 1.420116 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md 2.153969 \n",
|
||
"../samples/politics_is_the_mind_killer.md 2.430624 \n",
|
||
"../samples/2024_lesswrong_slop.md 1.695459 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... 2.779585 \n",
|
||
"../samples/2024_anthropic_palintir.md 2.444458 \n",
|
||
"../samples/2025_lw_preference-inversion.md 1.602391 \n",
|
||
"../samples/2024_deliberative_alignment.md 2.384146 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... 1.738719 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... 1.997053 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md 1.598036 \n",
|
||
"../samples/lorem_ipsum.md 1.825440 \n",
|
||
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|
||
"../samples/2024_openai_emails.md 2.140106 \n",
|
||
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|
||
"../samples/2025_lw_my-agi-safety-research-2024-... 1.580539 \n",
|
||
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"../samples/2024_trump_appointment.md 2.116151 ... \n",
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"../samples/2025_lw_the-subset-parity-learning-p... 10.411036 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... 12.255587 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md 11.515382 \n",
|
||
"../samples/lorem_ipsum.md 12.230991 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... 10.575907 \n",
|
||
"../samples/2024_openai_emails.md 9.602067 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md 9.158434 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... 11.766024 \n",
|
||
"../samples/2024_bob_fanfic.md 11.086496 \n",
|
||
"../samples/2024_gwern_reddit.md 14.091064 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... 15.471860 \n",
|
||
"../samples/2024_bob_fanfic2.md 12.110069 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 17.062244 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 11.740885 \n",
|
||
"../samples/2025_lw_review-planecrash.md 10.882781 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md 12.898935 \n",
|
||
"\n",
|
||
" train/diff_max \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md -6.227433 \n",
|
||
"../samples/2024_how_to_focus.md -0.287336 \n",
|
||
"../samples/2024_gpt4_fake_paper.md -1.615372 \n",
|
||
"../samples/2025_h5n1_report.md -2.524470 \n",
|
||
"../samples/2024_arxiv_meh.md -0.269622 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... -1.188522 \n",
|
||
"../samples/2024_news_anthropic.md -0.123071 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md -2.797191 \n",
|
||
"../samples/politics_is_the_mind_killer.md 0.083736 \n",
|
||
"../samples/2024_lesswrong_slop.md -2.499285 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... -0.264043 \n",
|
||
"../samples/2024_anthropic_palintir.md -0.379918 \n",
|
||
"../samples/2025_lw_preference-inversion.md -3.324774 \n",
|
||
"../samples/2024_deliberative_alignment.md -2.498840 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... -2.467374 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... -2.542537 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md -5.916591 \n",
|
||
"../samples/lorem_ipsum.md -0.505038 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... -1.614489 \n",
|
||
"../samples/2024_openai_emails.md -0.880435 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md -3.743683 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... -1.922241 \n",
|
||
"../samples/2024_bob_fanfic.md -2.139755 \n",
|
||
"../samples/2024_gwern_reddit.md -1.829337 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... -0.911260 \n",
|
||
"../samples/2024_bob_fanfic2.md -1.466401 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 3.657008 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 0.127832 \n",
|
||
"../samples/2025_lw_review-planecrash.md -3.632131 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md -0.402762 \n",
|
||
"\n",
|
||
" train/diff%_max \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md -34.756625 \n",
|
||
"../samples/2024_how_to_focus.md -2.135250 \n",
|
||
"../samples/2024_gpt4_fake_paper.md -14.135986 \n",
|
||
"../samples/2025_h5n1_report.md -21.162542 \n",
|
||
"../samples/2024_arxiv_meh.md -1.993755 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... -9.353658 \n",
|
||
"../samples/2024_news_anthropic.md -0.962233 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md -25.157557 \n",
|
||
"../samples/politics_is_the_mind_killer.md 0.704275 \n",
|
||
"../samples/2024_lesswrong_slop.md -16.330311 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... -2.095839 \n",
|
||
"../samples/2024_anthropic_palintir.md -2.862598 \n",
|
||
"../samples/2025_lw_preference-inversion.md -29.153170 \n",
|
||
"../samples/2024_deliberative_alignment.md -16.931722 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... -19.158995 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... -17.181481 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md -33.941028 \n",
|
||
"../samples/lorem_ipsum.md -3.965429 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... -13.243940 \n",
|
||
"../samples/2024_openai_emails.md -8.399092 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md -29.016036 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... -14.042987 \n",
|
||
"../samples/2024_bob_fanfic.md -16.178093 \n",
|
||
"../samples/2024_gwern_reddit.md -11.490521 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... -5.562186 \n",
|
||
"../samples/2024_bob_fanfic2.md -10.801048 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 27.280446 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 1.100765 \n",
|
||
"../samples/2025_lw_review-planecrash.md -25.023443 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md -3.027902 \n",
|
||
"\n",
|
||
" train/before_sum \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md 284.586479 \n",
|
||
"../samples/2024_how_to_focus.md 347.161163 \n",
|
||
"../samples/2024_gpt4_fake_paper.md 349.755680 \n",
|
||
"../samples/2025_h5n1_report.md 355.013296 \n",
|
||
"../samples/2024_arxiv_meh.md 386.270819 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... 393.351672 \n",
|
||
"../samples/2024_news_anthropic.md 424.085983 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md 424.532354 \n",
|
||
"../samples/politics_is_the_mind_killer.md 446.767846 \n",
|
||
"../samples/2024_lesswrong_slop.md 449.497469 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... 450.877187 \n",
|
||
"../samples/2024_anthropic_palintir.md 454.109781 \n",
|
||
"../samples/2025_lw_preference-inversion.md 456.815627 \n",
|
||
"../samples/2024_deliberative_alignment.md 467.468432 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... 471.837339 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... 477.610894 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md 484.856104 \n",
|
||
"../samples/lorem_ipsum.md 490.732376 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... 500.521191 \n",
|
||
"../samples/2024_openai_emails.md 517.339424 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md 517.833801 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... 523.423819 \n",
|
||
"../samples/2024_bob_fanfic.md 527.048090 \n",
|
||
"../samples/2024_gwern_reddit.md 562.662201 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... 616.809882 \n",
|
||
"../samples/2024_bob_fanfic2.md 620.444836 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 623.402177 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 673.517207 \n",
|
||
"../samples/2025_lw_review-planecrash.md 684.699408 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md 801.941370 \n",
|
||
"\n",
|
||
" train/after_sum \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md 156.141833 \n",
|
||
"../samples/2024_how_to_focus.md 211.773822 \n",
|
||
"../samples/2024_gpt4_fake_paper.md 227.104543 \n",
|
||
"../samples/2025_h5n1_report.md 198.560084 \n",
|
||
"../samples/2024_arxiv_meh.md 312.432330 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... 223.485284 \n",
|
||
"../samples/2024_news_anthropic.md 247.100184 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md 316.633504 \n",
|
||
"../samples/politics_is_the_mind_killer.md 330.564804 \n",
|
||
"../samples/2024_lesswrong_slop.md 247.537046 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... 353.007281 \n",
|
||
"../samples/2024_anthropic_palintir.md 310.446145 \n",
|
||
"../samples/2025_lw_preference-inversion.md 217.925137 \n",
|
||
"../samples/2024_deliberative_alignment.md 307.554861 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... 246.898066 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... 277.590406 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md 218.930921 \n",
|
||
"../samples/lorem_ipsum.md 363.262540 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... 211.690162 \n",
|
||
"../samples/2024_openai_emails.md 323.156004 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md 288.011791 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... 262.369446 \n",
|
||
"../samples/2024_bob_fanfic.md 420.827773 \n",
|
||
"../samples/2024_gwern_reddit.md 420.227737 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... 376.879962 \n",
|
||
"../samples/2024_bob_fanfic2.md 494.885355 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... 521.238323 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md 536.693749 \n",
|
||
"../samples/2025_lw_review-planecrash.md 345.217788 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md 504.596465 \n",
|
||
"\n",
|
||
" train/diff_sum \\\n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md -128.444646 \n",
|
||
"../samples/2024_how_to_focus.md -135.387340 \n",
|
||
"../samples/2024_gpt4_fake_paper.md -122.651137 \n",
|
||
"../samples/2025_h5n1_report.md -156.453212 \n",
|
||
"../samples/2024_arxiv_meh.md -73.838489 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... -169.866388 \n",
|
||
"../samples/2024_news_anthropic.md -176.985800 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md -107.898850 \n",
|
||
"../samples/politics_is_the_mind_killer.md -116.203043 \n",
|
||
"../samples/2024_lesswrong_slop.md -201.960423 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... -97.869906 \n",
|
||
"../samples/2024_anthropic_palintir.md -143.663636 \n",
|
||
"../samples/2025_lw_preference-inversion.md -238.890490 \n",
|
||
"../samples/2024_deliberative_alignment.md -159.913571 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... -224.939272 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... -200.020488 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md -265.925183 \n",
|
||
"../samples/lorem_ipsum.md -127.469837 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... -288.831029 \n",
|
||
"../samples/2024_openai_emails.md -194.183420 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md -229.822011 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... -261.054373 \n",
|
||
"../samples/2024_bob_fanfic.md -106.220317 \n",
|
||
"../samples/2024_gwern_reddit.md -142.434464 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... -239.929921 \n",
|
||
"../samples/2024_bob_fanfic2.md -125.559481 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... -102.163854 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md -136.823458 \n",
|
||
"../samples/2025_lw_review-planecrash.md -339.481621 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md -297.344904 \n",
|
||
"\n",
|
||
" train/diff%_sum \n",
|
||
"f \n",
|
||
"../samples/2024_trump_appointment.md -45.133784 \n",
|
||
"../samples/2024_how_to_focus.md -38.998412 \n",
|
||
"../samples/2024_gpt4_fake_paper.md -35.067661 \n",
|
||
"../samples/2025_h5n1_report.md -44.069677 \n",
|
||
"../samples/2024_arxiv_meh.md -19.115730 \n",
|
||
"../samples/2024_lw_by-default-capital-will-matt... -43.184356 \n",
|
||
"../samples/2024_news_anthropic.md -41.733471 \n",
|
||
"../samples/2025_lw_the-laws-of-large-numbers.md -25.415931 \n",
|
||
"../samples/politics_is_the_mind_killer.md -26.009715 \n",
|
||
"../samples/2024_lesswrong_slop.md -44.930269 \n",
|
||
"../samples/2025_lw_parkinson-s-law-and-the-ideo... -21.706555 \n",
|
||
"../samples/2024_anthropic_palintir.md -31.636323 \n",
|
||
"../samples/2025_lw_preference-inversion.md -52.294728 \n",
|
||
"../samples/2024_deliberative_alignment.md -34.208421 \n",
|
||
"../samples/2025_lw_the-subset-parity-learning-p... -47.673055 \n",
|
||
"../samples/2025_lw_human-study-on-ai-spear-phis... -41.879381 \n",
|
||
"../samples/2025_lw_the-intelligence-curse.md -54.846207 \n",
|
||
"../samples/lorem_ipsum.md -25.975428 \n",
|
||
"../samples/2025_lw_the-field-of-ai-alignment-a-... -57.706054 \n",
|
||
"../samples/2024_openai_emails.md -37.535013 \n",
|
||
"../samples/2025_lw_debating-buying-nvda-in-2019.md -44.381423 \n",
|
||
"../samples/2025_lw_my-agi-safety-research-2024-... -49.874378 \n",
|
||
"../samples/2024_bob_fanfic.md -20.153819 \n",
|
||
"../samples/2024_gwern_reddit.md -25.314383 \n",
|
||
"../samples/2025_lw_comment-on-death-and-the-gor... -38.898521 \n",
|
||
"../samples/2024_bob_fanfic2.md -20.237010 \n",
|
||
"../samples/2025_lw_what-s-the-short-timeline-pl... -16.388113 \n",
|
||
"../samples/2024_lw_the-plan-2024-update.md -20.314768 \n",
|
||
"../samples/2025_lw_review-planecrash.md -49.581118 \n",
|
||
"../samples/2025_lw_2024-in-ai-predictions.md -37.078135 \n",
|
||
"\n",
|
||
"[30 rows x 24 columns]"
|
||
]
|
||
},
|
||
"execution_count": 94,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_res.sort_values('train/before_sum').set_index('f').drop(columns=['content', 'hist', 'before', 'after', 'train/before', 'train/after', 'url'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"ename": "NameError",
|
||
"evalue": "name 'df_res' is not defined",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdf_res\u001b[49m\u001b[38;5;241m.\u001b[39mbefore\n",
|
||
"\u001b[0;31mNameError\u001b[0m: name 'df_res' is not defined"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df_res.before"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 95,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# df_res.sort_values('improvement%', ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# print(df_res.to_markdown())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"# DEBUG"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from IPython.display import display, HTML, Markdown\n",
|
||
"import torch\n",
|
||
"\n",
|
||
"@torch.no_grad()\n",
|
||
"def gen(model, inputs, tokenizer, clean=True):\n",
|
||
" s = model.generate(\n",
|
||
" input_ids=inputs[\"input_ids\"][None, :].to(model.device),\n",
|
||
" attention_mask=inputs[\"attention_mask\"][None, :].to(model.device),\n",
|
||
" use_cache=False,\n",
|
||
" max_new_tokens=100,\n",
|
||
" min_new_tokens=100,\n",
|
||
" do_sample=False,\n",
|
||
" early_stopping=False,\n",
|
||
" )\n",
|
||
" input_l = inputs[\"input_ids\"].shape[0]\n",
|
||
" tokenizer_kwargs=dict(clean_up_tokenization_spaces=clean, skip_special_tokens=clean)\n",
|
||
" old = tokenizer.decode(\n",
|
||
" s[0, :input_l][-100:], **tokenizer_kwargs\n",
|
||
" )\n",
|
||
" new = tokenizer.decode(\n",
|
||
" s[0, input_l:], **tokenizer_kwargs\n",
|
||
" )\n",
|
||
" s_old = \"\"+old.replace('\\n', '<br>')\n",
|
||
" s_new = '<b>' + new.replace('\\n', '<br>')+ '<br><br><b/>'\n",
|
||
" # print(s_old, s_new)\n",
|
||
" display(HTML(f\"{s_old}{s_new}\"))\n",
|
||
" # print([old, new])\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"sample = samples[-1]\n",
|
||
"s = sample['content']\n",
|
||
"first_half = s[:len(s)//2]\n",
|
||
"second_half = s[len(s)//2:]\n",
|
||
"ds_train = Dataset.from_dict(tokenizer([first_half]))\n",
|
||
"ds_val = Dataset.from_dict(tokenizer([second_half]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"with model.disable_adapter():\n",
|
||
" gen(model, ds_train.with_format('pt')[0], tokenizer)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"gen(model, ds_train.with_format('pt')[0], tokenizer)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"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": "3.11.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|