From e02c7de847749e41ee4a060ea08daf86b5aa3230 Mon Sep 17 00:00:00 2001 From: Kyle Corbitt Date: Thu, 17 Oct 2024 17:09:49 -0700 Subject: [PATCH] stories trainer seems to work --- prepare-dataset.ipynb | 2 +- pyproject.toml | 2 + rate-random-comments.ipynb | 4 +- stories_train_model_v1.py | 136 ++++++ utils.py | 67 +++ uv.lock | 18 + we-doing-stories-now.ipynb | 938 +++++++++++++++++++++++++++++++++++++ 7 files changed, 1164 insertions(+), 3 deletions(-) create mode 100644 stories_train_model_v1.py create mode 100644 we-doing-stories-now.ipynb diff --git a/prepare-dataset.ipynb b/prepare-dataset.ipynb index 9ddca83..b4ab583 100644 --- a/prepare-dataset.ipynb +++ b/prepare-dataset.ipynb @@ -1158,7 +1158,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Building prompts: 2%|▏ | 6448/340702 [00:04<02:47, 1991.79it/s]" + "Building prompts: 100%|██████████| 340702/340702 [02:44<00:00, 2065.44it/s]\n" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 6fafe71..726bbf0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,8 @@ dependencies = [ "peft>=0.13.2", "polars>=1.9.0", "python-dotenv>=1.0.1", + "scikit-learn>=1.5.2", + "seaborn>=0.13.2", "sglang[all]>=0.3.3.post1", "torch==2.4.0", "tqdm>=4.66.5", diff --git a/rate-random-comments.ipynb b/rate-random-comments.ipynb index b94ba19..42d0caf 100644 --- a/rate-random-comments.ipynb +++ b/rate-random-comments.ipynb @@ -7,9 +7,9 @@ "outputs": [], "source": [ "import polars as pl\n", - "from utils import dataset, build_all_prompts, run_inference_sglang\n", + "from utils import augmented_comments, build_all_prompts, run_inference_sglang\n", "\n", - "df = dataset()" + "df = augmented_comments()" ] }, { diff --git a/stories_train_model_v1.py b/stories_train_model_v1.py new file mode 100644 index 0000000..0c594ee --- /dev/null +++ b/stories_train_model_v1.py @@ -0,0 +1,136 @@ +import torch +from datasets import load_dataset, Dataset +from transformers import ( + AutoModelForSequenceClassification, + AutoTokenizer, + Trainer, + TrainingArguments, +) +from peft.tuners.lora import LoraConfig +from peft.mapping import get_peft_model +import wandb +from dotenv import load_dotenv +import polars as pl +from utils import stories_dataset +from sklearn.metrics import mean_squared_error +from liger_kernel.transformers import _apply_liger_kernel_to_instance + +load_dotenv("/workspace/.env") + +# Configuration +base_model = "unsloth/Meta-Llama-3.1-8B" +run_name = "stories_model_v1" +output_dir = f"./models/{run_name}" +num_epochs = 1 +batch_size = 8 +learning_rate = 2e-4 +max_length = 4096 + +# Initialize wandb +wandb.init(project="hn_stories_model_training", name=run_name) + + +def create_dataset(split, num_rows, tokenizer): + stories = stories_dataset() + stories = stories.filter(pl.col("split") == split).head(num_rows) + + stories = stories.with_columns( + [ + pl.col("serialized").alias("text"), + pl.col("log_score").alias("label"), + ] + ) + + stories = stories.with_columns( + [ + pl.col("text") + .map_elements( + lambda x: tokenizer(x)["input_ids"], return_dtype=pl.List(pl.Int64) + ) + .alias("input_ids"), + ] + ).select(["input_ids", "label"]) + return Dataset.from_polars(stories) + + +print("Loading tokenizer and model...") +tokenizer = AutoTokenizer.from_pretrained( + base_model, + truncation=True, + padding=True, + max_length=max_length, +) + +model = AutoModelForSequenceClassification.from_pretrained( + base_model, + num_labels=1, # Regression task + device_map="auto", + attn_implementation="flash_attention_2", + torch_dtype=torch.bfloat16, +) +_apply_liger_kernel_to_instance(model=model) + +model.config.pad_token_id = tokenizer.pad_token_id +tokenizer.padding_side = "right" + +print("Configuring LoRA...") +model = get_peft_model( + model, + LoraConfig( + task_type="SEQ_CLS", + r=8, + lora_alpha=16, + lora_dropout=0, + ), +) + +print("Loading dataset...") +train_stories = create_dataset("train", 30000, tokenizer) +validation_stories = create_dataset("val", 500, tokenizer) + + +# Configure training arguments +training_args = TrainingArguments( + output_dir=output_dir, + num_train_epochs=num_epochs, + per_device_train_batch_size=batch_size, + per_device_eval_batch_size=batch_size, + learning_rate=learning_rate, + weight_decay=0, + evaluation_strategy="steps", + eval_steps=0.1, + logging_steps=100, + save_strategy="steps", + save_steps=1000, + report_to="wandb", + no_cuda=False, + bf16=True, + warmup_steps=100, + # use_liger_kernel=True, +) + + +def compute_metrics(eval_pred): + predictions, labels = eval_pred + rmse = mean_squared_error(labels, predictions, squared=False) + return {"rmse": rmse} + + +print("Initializing Trainer...") +trainer = Trainer( + model=model, + args=training_args, + train_dataset=train_stories, + eval_dataset=validation_stories, + tokenizer=tokenizer, + compute_metrics=compute_metrics, +) + +print("Starting model training...") +trainer.train() + +print("Saving final model...") +trainer.save_model(output_dir) +tokenizer.save_pretrained(output_dir) + +print("Stories model training complete") diff --git a/utils.py b/utils.py index c8ba430..f40e655 100644 --- a/utils.py +++ b/utils.py @@ -6,6 +6,7 @@ import os import html import re +import numpy as np def cache_dataframe(path): @@ -24,6 +25,14 @@ def wrapper(*args, **kwargs): cache[path] = df return df + def bust_cache(): + if path in cache: + del cache[path] + if os.path.exists(path): + os.remove(path) + print(f"Cache busted for {path}") + + wrapper.bust_cache = bust_cache return wrapper return decorator @@ -234,3 +243,61 @@ def with_story_info(comments_df: pl.DataFrame) -> pl.DataFrame: return comments_df.join( stories_df, left_on="top_level_parent", right_on="story_id", how="left" ) + + +@cache_dataframe("./data/stories_dataset.parquet") +def stories_dataset() -> pl.DataFrame: + stories = full_dataset().filter( + (pl.col("type") == "story") + & pl.col("time").is_not_null() + & pl.col("text").is_not_null() + & pl.col("url").is_null() + & pl.col("deleted").is_null() + & pl.col("dead").is_null() + ) + + # There's a weird discontinuity in late 2015, just ignore it + stories = stories.filter(pl.col("time") >= pl.datetime(2016, 1, 1)) + + # Add a log score, it's a very skewed distribution + stories = stories.with_columns(pl.col("score").log().alias("log_score")) + + progress_bar = tqdm.tqdm(total=len(stories), desc="Serializing stories") + + def serialize_story(story): + progress_bar.update(1) + return f""" +{story["title"]} +{story["by"]}, {story["time"].strftime("%Y-%m-%d")} + +{html.unescape(story["text"]).replace("

", "\n\n")} +""" + + stories = stories.with_columns( + pl.struct(["title", "by", "time", "text"]) + .map_elements(serialize_story, return_dtype=pl.Utf8) + .alias("serialized") + ) + + progress_bar.close() + + stories = stories.sample(fraction=1, shuffle=True, seed=42) + + split_assignments = np.random.choice( + ["train", "test", "val"], size=len(stories), p=[0.8, 0.1, 0.1] + ) + + stories = stories.with_columns(pl.Series("split", split_assignments)) + + return stories.select( + "id", + "title", + "by", + "text", + "score", + "descendants", + "time", + "log_score", + "serialized", + "split", + ) diff --git a/uv.lock b/uv.lock index 806968e..72afe7a 100644 --- a/uv.lock +++ b/uv.lock @@ -289,6 +289,8 @@ dependencies = [ { name = "peft" }, { name = "polars" }, { name = "python-dotenv" }, + { name = "scikit-learn" }, + { name = "seaborn" }, { name = "sglang", extra = ["all"] }, { name = "torch" }, { name = "tqdm" }, @@ -312,6 +314,8 @@ requires-dist = [ { name = "peft", specifier = ">=0.13.2" }, { name = "polars", specifier = ">=1.9.0" }, { name = "python-dotenv", specifier = ">=1.0.1" }, + { name = "scikit-learn", specifier = ">=1.5.2" }, + { name = "seaborn", specifier = ">=0.13.2" }, { name = "sglang", extras = ["all"], editable = "deps/sglang/python" }, { name = "torch", specifier = "==2.4.0" }, { name = "tqdm", specifier = ">=4.66.5" }, @@ -3117,6 +3121,20 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f5/1b/6ee032251bf4cdb0cc50059374e86a9f076308c1512b61c4e003e241efb7/scipy-1.14.1-cp313-cp313-win_amd64.whl", hash = "sha256:baff393942b550823bfce952bb62270ee17504d02a1801d7fd0719534dfb9c84", size = 44469524 }, ] +[[package]] +name = "seaborn" +version = "0.13.2" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "matplotlib" }, + { name = "numpy" }, + { name = "pandas" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/86/59/a451d7420a77ab0b98f7affa3a1d78a313d2f7281a57afb1a34bae8ab412/seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7", size = 1457696 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987", size = 294914 }, +] + [[package]] name = "send2trash" version = "1.8.3" diff --git a/we-doing-stories-now.ipynb b/we-doing-stories-now.ipynb new file mode 100644 index 0000000..65a44c1 --- /dev/null +++ b/we-doing-stories-now.ipynb @@ -0,0 +1,938 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "import utils\n", + "\n", + "df = utils.full_dataset()\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['id',\n", + " 'type',\n", + " 'by',\n", + " 'time',\n", + " 'title',\n", + " 'text',\n", + " 'url',\n", + " 'score',\n", + " 'parent',\n", + " 'top_level_parent',\n", + " 'descendants',\n", + " 'kids',\n", + " 'deleted',\n", + " 'dead']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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While ┆ 1 ┆ 1 ┆ 2024-10-11 │\n", + "│ ┆ does GTM for ┆ ┆ designing a ┆ ┆ ┆ 18:15:28 │\n", + "│ ┆ pre-r… ┆ ┆ pitch d… ┆ ┆ ┆ │\n", + "│ 41812150 ┆ Ask HN: Did ┆ paliderek ┆ I have this ┆ 4 ┆ 1 ┆ 2024-10-11 │\n", + "│ ┆ the rejection ┆ ┆ notion that ┆ ┆ ┆ 18:41:35 │\n", + "│ ┆ XHTM… ┆ ┆ the ma… ┆ ┆ ┆ │\n", + "│ 41812350 ┆ Ask HN: I have ┆ clickzyn ┆ I would like ┆ 1 ┆ 2 ┆ 2024-10-11 │\n", + "│ ┆ a quick ┆ ┆ to create a ┆ ┆ ┆ 18:58:46 │\n", + "│ ┆ questio… ┆ ┆ solut… ┆ ┆ ┆ │\n", + "│ 41812549 ┆ Ask HN: Are ┆ clpm4j ┆ It's been ┆ 2 ┆ 0 ┆ 2024-10-11 │\n", + "│ ┆ you running ┆ ┆ over a year, ┆ ┆ ┆ 19:13:04 │\n", + "│ ┆ RAG in… ┆ ┆ gi… ┆ ┆ ┆ │\n", + "│ 41812842 ┆ Next.js, ┆ ilrwbwrkhv ┆ I can't ┆ 2 ┆ 3 ┆ 2024-10-11 │\n", + "│ ┆ TypeScript are ┆ ┆ stand these ┆ ┆ ┆ 19:39:30 │\n", + "│ ┆ instan… ┆ ┆ codeb… ┆ ┆ ┆ │\n", + "└──────────┴────────────────┴───────────────┴────────────────┴───────┴─────────────┴───────────────┘" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stories = df.filter(\n", + " (\n", + " (pl.col(\"type\") == \"story\")\n", + " & pl.col(\"time\").is_not_null()\n", + " & pl.col(\"text\").is_not_null()\n", + " & pl.col(\"url\").is_null()\n", + " & pl.col(\"deleted\").is_null()\n", + " & pl.col(\"dead\").is_null()\n", + " )\n", + ").select([\"id\", \"title\", \"by\", \"text\", \"score\", \"descendants\", \"time\"])\n", + "\n", + "stories" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (208, 2)\n", + "┌─────────┬─────────────┐\n", + "│ month ┆ avg_upvotes │\n", + "│ --- ┆ --- │\n", + "│ str ┆ f64 │\n", + "╞═════════╪═════════════╡\n", + "│ 2007-07 ┆ 12.307692 │\n", + "│ 2007-08 ┆ 18.375 │\n", + "│ 2007-09 ┆ 21.518519 │\n", + "│ 2007-10 ┆ 20.673913 │\n", + "│ 2007-11 ┆ 28.774194 │\n", + "│ … ┆ … │\n", + "│ 2024-06 ┆ 11.67331 │\n", + "│ 2024-07 ┆ 12.408938 │\n", + "│ 2024-08 ┆ 11.071203 │\n", + "│ 2024-09 ┆ 15.071553 │\n", + "│ 2024-10 ┆ 10.819413 │\n", + "└─────────┴─────────────┘\n" + ] + } + ], + "source": [ + "# Convert time to day\n", + "stories_with_day = stories.with_columns(\n", + " pl.col(\"time\").dt.strftime(\"%Y-%m\").alias(\"month\")\n", + ")\n", + "\n", + "# Calculate average score (upvotes) per day\n", + "daily_avg_upvotes = (\n", + " stories_with_day.group_by(\"month\")\n", + " .agg(pl.col(\"score\").mean().alias(\"avg_upvotes\"))\n", + " .sort(\"month\")\n", + ")\n", + "\n", + "# Plot the results\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(30, 6)) # Increased width to accommodate more data points\n", + "plt.plot(daily_avg_upvotes[\"month\"], daily_avg_upvotes[\"avg_upvotes\"])\n", + "plt.title(\"Average Number of Upvotes per Story by Month\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Average Upvotes\")\n", + "plt.xticks(rotation=90) # Increased rotation for better readability\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Display the data\n", + "print(daily_avg_upvotes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape: (208, 2)\n", + "┌─────────┬─────────────┐\n", + "│ month ┆ num_stories │\n", + "│ --- ┆ --- │\n", + "│ str ┆ u32 │\n", + "╞═════════╪═════════════╡\n", + "│ 2007-07 ┆ 13 │\n", + "│ 2007-08 ┆ 24 │\n", + "│ 2007-09 ┆ 27 │\n", + "│ 2007-10 ┆ 46 │\n", + "│ 2007-11 ┆ 31 │\n", + "│ … ┆ … │\n", + "│ 2024-06 ┆ 1154 │\n", + "│ 2024-07 ┆ 1186 │\n", + "│ 2024-08 ┆ 1264 │\n", + "│ 2024-09 ┆ 1146 │\n", + "│ 2024-10 ┆ 443 │\n", + "└─────────┴─────────────┘\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the number of stories per month\n", + "stories_with_month = stories.with_columns(\n", + " pl.col(\"time\").dt.strftime(\"%Y-%m\").alias(\"month\")\n", + ")\n", + "\n", + "stories_per_month = (\n", + " stories_with_month.group_by(\"month\")\n", + " .agg(pl.col(\"id\").count().alias(\"num_stories\"))\n", + " .sort(\"month\")\n", + ")\n", + "\n", + "plt.figure(figsize=(30, 6))\n", + "plt.plot(stories_per_month[\"month\"], stories_per_month[\"num_stories\"])\n", + "plt.title(\"Number of Stories by Month\")\n", + "plt.xlabel(\"Month\")\n", + "plt.ylabel(\"Number of Stories\")\n", + "plt.xticks(rotation=90)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (255, 7)
idtitlebytextscoredescendantstime
i64strstrstri64i64datetime[μs]
9809333"Ask HN: Can you get to AWS?""cornellwright""We&#x27;re having lots of trou…60402015-07-01 00:50:16
9812245"Ask HN: Who is hiring? (July 2…"whoishiring""Please lead with the location …67310872015-07-01 15:02:02
9812246"Ask HN: Freelancer? Seeking fr…"whoishiring""Please lead with either SEEKIN…891082015-07-01 15:02:02
9812247"Ask HN: Who wants to be hired?…"whoishiring""Share your information if you …721402015-07-01 15:02:02
9812261"Ask HN: Why Do We Act Like Elo…"cconcepts""Granted, he is an insanely gif…26502015-07-01 15:03:05
…………………
9981996"Ask HN: What can be done to fi…"JulianMorrison""Hacker News, I appeal to your …18422015-07-31 14:34:34
9982884"Ask HN: Who's looking for a co…"dmnd""Last month this tread was resu…1572015-07-31 17:07:04
9982957"Show HN: WP Pluginator – A Wor…"outsourceappz""Hello All,<p>We&#x27;ve create…9222015-07-31 17:21:52
9984235"Ask HN: How do you draw the li…"719Ben""Hi HN,<p>I am currently workin…482015-07-31 20:26:27
9984485"Ask HN: Would you pay to use R…"workerdee""If reddit had fees, how much w…9242015-07-31 21:04:32
" + ], + "text/plain": [ + "shape: (255, 7)\n", + "┌─────────┬────────────────┬────────────────┬────────────────┬───────┬─────────────┬───────────────┐\n", + "│ id ┆ title ┆ by ┆ text ┆ score ┆ descendants ┆ time │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ str ┆ i64 ┆ i64 ┆ datetime[μs] │\n", + "╞═════════╪════════════════╪════════════════╪════════════════╪═══════╪═════════════╪═══════════════╡\n", + "│ 9809333 ┆ Ask HN: Can ┆ cornellwright ┆ We're ┆ 60 ┆ 40 ┆ 2015-07-01 │\n", + "│ ┆ you get to ┆ ┆ having lots of ┆ ┆ ┆ 00:50:16 │\n", + "│ ┆ AWS? ┆ ┆ trou… ┆ ┆ ┆ │\n", + "│ 9812245 ┆ Ask HN: Who is ┆ whoishiring ┆ Please lead ┆ 673 ┆ 1087 ┆ 2015-07-01 │\n", + "│ ┆ hiring? (July ┆ ┆ with the ┆ ┆ ┆ 15:02:02 │\n", + "│ ┆ 2… ┆ ┆ location … ┆ ┆ ┆ │\n", + "│ 9812246 ┆ Ask HN: ┆ whoishiring ┆ Please lead ┆ 89 ┆ 108 ┆ 2015-07-01 │\n", + "│ ┆ Freelancer? ┆ ┆ with either ┆ ┆ ┆ 15:02:02 │\n", + "│ ┆ Seeking fr… ┆ ┆ SEEKIN… ┆ ┆ ┆ │\n", + "│ 9812247 ┆ Ask HN: Who ┆ whoishiring ┆ Share your ┆ 72 ┆ 140 ┆ 2015-07-01 │\n", + "│ ┆ wants to be ┆ ┆ information if ┆ ┆ ┆ 15:02:02 │\n", + "│ ┆ hired?… ┆ ┆ you … ┆ ┆ ┆ │\n", + "│ 9812261 ┆ Ask HN: Why Do ┆ cconcepts ┆ Granted, he is ┆ 26 ┆ 50 ┆ 2015-07-01 │\n", + "│ ┆ We Act Like ┆ ┆ an insanely ┆ ┆ ┆ 15:03:05 │\n", + "│ ┆ Elo… ┆ ┆ gif… ┆ ┆ ┆ │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 9981996 ┆ Ask HN: What ┆ JulianMorrison ┆ Hacker News, I ┆ 18 ┆ 42 ┆ 2015-07-31 │\n", + "│ ┆ can be done to ┆ ┆ appeal to your ┆ ┆ ┆ 14:34:34 │\n", + "│ ┆ fi… ┆ ┆ … ┆ ┆ ┆ │\n", + "│ 9982884 ┆ Ask HN: Who's ┆ dmnd ┆ Last month ┆ 15 ┆ 7 ┆ 2015-07-31 │\n", + "│ ┆ looking for a ┆ ┆ this tread was ┆ ┆ ┆ 17:07:04 │\n", + "│ ┆ co… ┆ ┆ resu… ┆ ┆ ┆ │\n", + "│ 9982957 ┆ Show HN: WP ┆ outsourceappz ┆ Hello All,

W ┆ 9 ┆ 22 ┆ 2015-07-31 │\n", + "│ ┆ Pluginator – A ┆ ┆ e've ┆ ┆ ┆ 17:21:52 │\n", + "│ ┆ Wor… ┆ ┆ create… ┆ ┆ ┆ │\n", + "│ 9984235 ┆ Ask HN: How do ┆ 719Ben ┆ Hi HN,

I am ┆ 4 ┆ 8 ┆ 2015-07-31 │\n", + "│ ┆ you draw the ┆ ┆ currently ┆ ┆ ┆ 20:26:27 │\n", + "│ ┆ li… ┆ ┆ workin… ┆ ┆ ┆ │\n", + "│ 9984485 ┆ Ask HN: Would ┆ workerdee ┆ If reddit had ┆ 9 ┆ 24 ┆ 2015-07-31 │\n", + "│ ┆ you pay to use ┆ ┆ fees, how much ┆ ┆ ┆ 21:04:32 │\n", + "│ ┆ R… ┆ ┆ w… ┆ ┆ ┆ │\n", + "└─────────┴────────────────┴────────────────┴────────────────┴───────┴─────────────┴───────────────┘" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# find the stories that were in July 2015\n", + "stories_in_july_2015 = stories.filter(\n", + " (pl.col(\"time\").dt.year() == 2015) & (pl.col(\"time\").dt.month() == 7)\n", + ")\n", + "\n", + "stories_in_july_2015\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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10820883"Ask HN: Offering dev. assistan…"shivakaush""Hi All,<p>I feel my skills are…512016-01-01 02:33:52
10821162"What can we expect from 2016 w…"umeryounis""CRM has already worked wonders…302016-01-01 05:17:51
10821555"A robust shell manager for fis…"bucaran""Long time Oh My Fish! contribu…122016-01-01 10:26:53
10821565"Ask HN: Job Differences with B…"whiff""I&#x27;m currently studying Ba…332016-01-01 10:42:13
10821695"When to use modal?""asakapab0i""I&#x27;m really interested in …102016-01-01 12:11:17
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11007847"Ask HN: What did you achieve i…"spIrr""A month into 2016, I think it&…21322016-01-31 20:14:01
11007871"Common Lisp Tips""such_a_casual""http:&#x2F;&#x2F;lisptips.com<…1112016-01-31 20:20:24
11008115"Ask HN: Parse customers – Wher…"srik1234""Parse Customers: Have you made…202016-01-31 21:17:49
11008574"Ask HN: Australian freelancers…"panjaro""Interested to know the freelan…34442016-01-31 23:00:18
11008630"Ask HN: Evernote alternatives …"Spooky23""I do alot of research for both…62492016-01-31 23:15:05
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I ┆ 5 ┆ 1 ┆ 2016-01-01 │\n", + "│ ┆ Offering dev. ┆ ┆ feel my skills ┆ ┆ ┆ 02:33:52 │\n", + "│ ┆ assistan… ┆ ┆ are… ┆ ┆ ┆ │\n", + "│ 10821162 ┆ What can we ┆ umeryounis ┆ CRM has ┆ 3 ┆ 0 ┆ 2016-01-01 │\n", + "│ ┆ expect from ┆ ┆ already worked ┆ ┆ ┆ 05:17:51 │\n", + "│ ┆ 2016 w… ┆ ┆ wonders… ┆ ┆ ┆ │\n", + "│ 10821555 ┆ A robust shell ┆ bucaran ┆ Long time Oh ┆ 1 ┆ 2 ┆ 2016-01-01 │\n", + "│ ┆ manager for ┆ ┆ My Fish! ┆ ┆ ┆ 10:26:53 │\n", + "│ ┆ fis… ┆ ┆ contribu… ┆ ┆ ┆ │\n", + "│ 10821565 ┆ Ask HN: Job ┆ whiff ┆ I'm ┆ 3 ┆ 3 ┆ 2016-01-01 │\n", + "│ ┆ Differences ┆ ┆ currently ┆ ┆ ┆ 10:42:13 │\n", + "│ ┆ with B… ┆ ┆ studying Ba… ┆ ┆ ┆ │\n", + "│ 10821695 ┆ When to use ┆ asakapab0i ┆ I'm ┆ 1 ┆ 0 ┆ 2016-01-01 │\n", + "│ ┆ modal? ┆ ┆ really ┆ ┆ ┆ 12:11:17 │\n", + "│ ┆ ┆ ┆ interested in ┆ ┆ ┆ │\n", + "│ ┆ ┆ ┆ … ┆ ┆ ┆ │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 11007847 ┆ Ask HN: What ┆ spIrr ┆ A month into ┆ 21 ┆ 32 ┆ 2016-01-31 │\n", + "│ ┆ did you ┆ ┆ 2016, I think ┆ ┆ ┆ 20:14:01 │\n", + "│ ┆ achieve i… ┆ ┆ it&… ┆ ┆ ┆ │\n", + "│ 11007871 ┆ Common Lisp ┆ such_a_casual ┆ http:/&#x ┆ 11 ┆ 1 ┆ 2016-01-31 │\n", + "│ ┆ Tips ┆ ┆ 2F;lisptips.co ┆ ┆ ┆ 20:20:24 │\n", + "│ ┆ ┆ ┆ m<… ┆ ┆ ┆ │\n", + "│ 11008115 ┆ Ask HN: Parse ┆ srik1234 ┆ Parse ┆ 2 ┆ 0 ┆ 2016-01-31 │\n", + "│ ┆ customers – ┆ ┆ Customers: ┆ ┆ ┆ 21:17:49 │\n", + "│ ┆ Wher… ┆ ┆ Have you made… ┆ ┆ ┆ │\n", + "│ 11008574 ┆ Ask HN: ┆ panjaro ┆ Interested to ┆ 34 ┆ 44 ┆ 2016-01-31 │\n", + "│ ┆ Australian ┆ ┆ know the ┆ ┆ ┆ 23:00:18 │\n", + "│ ┆ freelancers… ┆ ┆ freelan… ┆ ┆ ┆ │\n", + "│ 11008630 ┆ Ask HN: ┆ Spooky23 ┆ I do alot of ┆ 62 ┆ 49 ┆ 2016-01-31 │\n", + "│ ┆ Evernote ┆ ┆ research for ┆ ┆ ┆ 23:15:05 │\n", + "│ ┆ alternatives … ┆ ┆ both… ┆ ┆ ┆ │\n", + "└──────────┴────────────────┴───────────────┴────────────────┴───────┴─────────────┴───────────────┘" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find the stories in January 2016\n", + "stories_in_january_2016 = stories.filter(\n", + " (pl.col(\"time\").dt.year() == 2016) & (pl.col(\"time\").dt.month() == 1)\n", + ")\n", + "\n", + "stories_in_january_2016\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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10820883"Ask HN: Offering dev. assistan…"shivakaush""Hi All,<p>I feel my skills are…512016-01-01 02:33:52
10821162"What can we expect from 2016 w…"umeryounis""CRM has already worked wonders…302016-01-01 05:17:51
10821555"A robust shell manager for fis…"bucaran""Long time Oh My Fish! contribu…122016-01-01 10:26:53
10821565"Ask HN: Job Differences with B…"whiff""I&#x27;m currently studying Ba…332016-01-01 10:42:13
10821695"When to use modal?""asakapab0i""I&#x27;m really interested in …102016-01-01 12:11:17
…………………
41811839"Ask HN: How does GTM for pre-r…"kunarmando""Hey! While designing a pitch d…112024-10-11 18:15:28
41812150"Ask HN: Did the rejection XHTM…"paliderek""I have this notion that the ma…412024-10-11 18:41:35
41812350"Ask HN: I have a quick questio…"clickzyn""I would like to create a solut…122024-10-11 18:58:46
41812549"Ask HN: Are you running RAG in…"clpm4j""It&#x27;s been over a year, gi…202024-10-11 19:13:04
41812842"Next.js, TypeScript are instan…"ilrwbwrkhv""I can&#x27;t stand these codeb…232024-10-11 19:39:30
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I ┆ 5 ┆ 1 ┆ 2016-01-01 │\n", + "│ ┆ Offering dev. ┆ ┆ feel my skills ┆ ┆ ┆ 02:33:52 │\n", + "│ ┆ assistan… ┆ ┆ are… ┆ ┆ ┆ │\n", + "│ 10821162 ┆ What can we ┆ umeryounis ┆ CRM has already ┆ 3 ┆ 0 ┆ 2016-01-01 │\n", + "│ ┆ expect from ┆ ┆ worked wonders… ┆ ┆ ┆ 05:17:51 │\n", + "│ ┆ 2016 w… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 10821555 ┆ A robust shell ┆ bucaran ┆ Long time Oh My ┆ 1 ┆ 2 ┆ 2016-01-01 │\n", + "│ ┆ manager for ┆ ┆ Fish! contribu… ┆ ┆ ┆ 10:26:53 │\n", + "│ ┆ fis… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 10821565 ┆ Ask HN: Job ┆ whiff ┆ I'm ┆ 3 ┆ 3 ┆ 2016-01-01 │\n", + "│ ┆ Differences ┆ ┆ currently ┆ ┆ ┆ 10:42:13 │\n", + "│ ┆ with B… ┆ ┆ studying Ba… ┆ ┆ ┆ │\n", + "│ 10821695 ┆ When to use ┆ asakapab0i ┆ I'm really ┆ 1 ┆ 0 ┆ 2016-01-01 │\n", + "│ ┆ modal? ┆ ┆ interested in … ┆ ┆ ┆ 12:11:17 │\n", + "│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │\n", + "│ 41811839 ┆ Ask HN: How ┆ kunarmando ┆ Hey! While ┆ 1 ┆ 1 ┆ 2024-10-11 │\n", + "│ ┆ does GTM for ┆ ┆ designing a ┆ ┆ ┆ 18:15:28 │\n", + "│ ┆ pre-r… ┆ ┆ pitch d… ┆ ┆ ┆ │\n", + "│ 41812150 ┆ Ask HN: Did the ┆ paliderek ┆ I have this ┆ 4 ┆ 1 ┆ 2024-10-11 │\n", + "│ ┆ rejection XHTM… ┆ ┆ notion that the ┆ ┆ ┆ 18:41:35 │\n", + "│ ┆ ┆ ┆ ma… ┆ ┆ ┆ │\n", + "│ 41812350 ┆ Ask HN: I have ┆ clickzyn ┆ I would like to ┆ 1 ┆ 2 ┆ 2024-10-11 │\n", + "│ ┆ a quick ┆ ┆ create a solut… ┆ ┆ ┆ 18:58:46 │\n", + "│ ┆ questio… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 41812549 ┆ Ask HN: Are you ┆ clpm4j ┆ It's been ┆ 2 ┆ 0 ┆ 2024-10-11 │\n", + "│ ┆ running RAG in… ┆ ┆ over a year, ┆ ┆ ┆ 19:13:04 │\n", + "│ ┆ ┆ ┆ gi… ┆ ┆ ┆ │\n", + "│ 41812842 ┆ Next.js, ┆ ilrwbwrkhv ┆ I can't ┆ 2 ┆ 3 ┆ 2024-10-11 │\n", + "│ ┆ TypeScript are ┆ ┆ stand these ┆ ┆ ┆ 19:39:30 │\n", + "│ ┆ instan… ┆ ┆ codeb… ┆ ┆ ┆ │\n", + "└──────────┴─────────────────┴────────────┴─────────────────┴───────┴─────────────┴────────────────┘" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "modern_stories = stories.filter((pl.col(\"time\").dt.year() >= 2016))\n", + "\n", + "modern_stories\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# add a new log_score column that is the log of the score\n", + "modern_stories = modern_stories.with_columns(pl.col(\"score\").log().alias(\"log_score\"))\n", + "\n", + "# show a histogram of the log_score column\n", + "plt.hist(modern_stories[\"log_score\"], bins=20)\n", + "plt.title(\"Histogram of Log Score\")\n", + "plt.xlabel(\"Log Score\")\n", + "plt.ylabel(\"Frequency\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (142_886, 8)
idtitlebytextscoredescendantstimelog_score
i64strstrstri64i64datetime[μs]f64
10820883"Ask HN: Offering dev. assistan…"shivakaush""Hi All,<p>I feel my skills are…512016-01-01 02:33:521.609438
10821162"What can we expect from 2016 w…"umeryounis""CRM has already worked wonders…302016-01-01 05:17:511.098612
10821555"A robust shell manager for fis…"bucaran""Long time Oh My Fish! contribu…122016-01-01 10:26:530.0
10821565"Ask HN: Job Differences with B…"whiff""I&#x27;m currently studying Ba…332016-01-01 10:42:131.098612
10821695"When to use modal?""asakapab0i""I&#x27;m really interested in …102016-01-01 12:11:170.0
……………………
41811839"Ask HN: How does GTM for pre-r…"kunarmando""Hey! While designing a pitch d…112024-10-11 18:15:280.0
41812150"Ask HN: Did the rejection XHTM…"paliderek""I have this notion that the ma…412024-10-11 18:41:351.386294
41812350"Ask HN: I have a quick questio…"clickzyn""I would like to create a solut…122024-10-11 18:58:460.0
41812549"Ask HN: Are you running RAG in…"clpm4j""It&#x27;s been over a year, gi…202024-10-11 19:13:040.693147
41812842"Next.js, TypeScript are instan…"ilrwbwrkhv""I can&#x27;t stand these codeb…232024-10-11 19:39:300.693147
" + ], + "text/plain": [ + "shape: (142_886, 8)\n", + "┌──────────┬─────────────┬────────────┬─────────────┬───────┬─────────────┬────────────┬───────────┐\n", + "│ id ┆ title ┆ by ┆ text ┆ score ┆ descendants ┆ time ┆ log_score │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ str ┆ str ┆ i64 ┆ i64 ┆ datetime[μ ┆ f64 │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ s] ┆ │\n", + "╞══════════╪═════════════╪════════════╪═════════════╪═══════╪═════════════╪════════════╪═══════════╡\n", + "│ 10820883 ┆ Ask HN: ┆ shivakaush ┆ Hi All,

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10821162"What can we expect from 2016 w…"umeryounis""CRM has already worked wonders…302016-01-01 05:17:511.0986120.950714"val"
10821555"A robust shell manager for fis…"bucaran""Long time Oh My Fish! contribu…122016-01-01 10:26:530.00.731994"train"
10821565"Ask HN: Job Differences with B…"whiff""I&#x27;m currently studying Ba…332016-01-01 10:42:131.0986120.598658"train"
10821695"When to use modal?""asakapab0i""I&#x27;m really interested in …102016-01-01 12:11:170.00.156019"train"
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41811839"Ask HN: How does GTM for pre-r…"kunarmando""Hey! While designing a pitch d…112024-10-11 18:15:280.00.382187"train"
41812150"Ask HN: Did the rejection XHTM…"paliderek""I have this notion that the ma…412024-10-11 18:41:351.3862940.926159"val"
41812350"Ask HN: I have a quick questio…"clickzyn""I would like to create a solut…122024-10-11 18:58:460.00.399347"train"
41812549"Ask HN: Are you running RAG in…"clpm4j""It&#x27;s been over a year, gi…202024-10-11 19:13:040.6931470.96424"val"
41812842"Next.js, TypeScript are instan…"ilrwbwrkhv""I can&#x27;t stand these codeb…232024-10-11 19:39:300.6931470.783462"train"
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\", \"\\n\\n\")}\n", + "\"\"\"\n", + "\n", + "\n", + "print(serialize_story(modern_stories.row(0, named=True)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cache busted for ./data/stories_dataset.parquet\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Serializing stories: 100%|██████████| 142886/142886 [00:07<00:00, 18604.53it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Caching dataframe to ./data/stories_dataset.parquet\n" + ] + }, + { + "data": { + "text/html": [ + "

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idtitlebytextscoredescendantstimelog_scoreserializedsplit
i64strstrstri64i64datetime[μs]f64strstr
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34006028"Ask HN: What do you look for i…"inasmuch""I&#x27;m a designer (product, …122022-12-15 21:29:030.0"\n", + "Ask HN: What do you look for …"train"
37741338"Ask HN: Should I Keep the “Co-…"kidsil""This month marks a time for a …372023-10-02 17:18:011.098612"\n", + "Ask HN: Should I Keep the “Co…"train"
40231402"Ask cloud-file-storage and sem…"math101""I am looking for a service whi…102024-05-02 00:34:030.0"\n", + "Ask cloud-file-storage and se…"train"
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14671460"Ask HN: What are some investig…"cpete""This is inspired by the &quot;…202017-06-30 15:46:220.693147"\n", + "Ask HN: What are some investi…"train"
32573243"Ask HN: Monochrome LCD display…"dv35z""Hey HN crew - \n", + "Trying to cobbl…122022-08-23 23:56:580.0"\n", + "Ask HN: Monochrome LCD displa…"train"
18537743"Kharagpur Winter of Code is a …"rava-dosa""Hi! We are organizing third ed…302018-11-26 23:18:481.098612"\n", + "Kharagpur Winter of Code is a…"train"
18782738"Jobs to be Done (JTBD) – do yo…"kakashi141""I&#x27;ve been reading about h…402018-12-29 08:58:211.386294"\n", + "Jobs to be Done (JTBD) – do y…"train"
14862516"Show HN: C/C++ API documentati…"vovkos""https:&#x2F;&#x2F;github.com&#…202017-07-27 03:37:120.693147"\n", + "Show HN: C/C++ API documentat…"train"
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We are ┆ … ┆ 2018-11-26 ┆ 1.098612 ┆ ┆ train │\n", + "│ ┆ Winter of ┆ ┆ organizing ┆ ┆ 23:18:48 ┆ ┆ Kharagpur ┆ │\n", + "│ ┆ Code is a ┆ ┆ third ed… ┆ ┆ ┆ ┆ Winter of ┆ │\n", + "│ ┆ … ┆ ┆ ┆ ┆ ┆ ┆ Code is ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ a… ┆ │\n", + "│ 18782738 ┆ Jobs to be ┆ kakashi141 ┆ I've ┆ … ┆ 2018-12-29 ┆ 1.386294 ┆ ┆ train │\n", + "│ ┆ Done ┆ ┆ been ┆ ┆ 08:58:21 ┆ ┆ Jobs to ┆ │\n", + "│ ┆ (JTBD) – ┆ ┆ reading ┆ ┆ ┆ ┆ be Done ┆ │\n", + "│ ┆ do yo… ┆ ┆ about h… ┆ ┆ ┆ ┆ (JTBD) – ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ do y… ┆ │\n", + "│ 14862516 ┆ Show HN: ┆ vovkos ┆ https: ┆ … ┆ 2017-07-27 ┆ 0.693147 ┆ ┆ train │\n", + "│ ┆ C/C++ API ┆ ┆ F;/gi ┆ ┆ 03:37:12 ┆ ┆ Show HN: ┆ │\n", + "│ ┆ documentat ┆ ┆ thub.com&# ┆ ┆ ┆ ┆ C/C++ API ┆ │\n", + "│ ┆ i… ┆ ┆ … ┆ ┆ ┆ ┆ documenta ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ t… ┆ │\n", + "└──────────┴────────────┴────────────┴────────────┴───┴────────────┴───────────┴───────────┴───────┘" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "import utils\n", + "\n", + "importlib.reload(utils)\n", + "utils.stories_dataset.bust_cache()\n", + "utils.stories_dataset()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'tokenizer' 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[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtokenizer\u001b[49m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124masdf\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'tokenizer' is not defined" + ] + } + ], + "source": [ + "tokenizer[\"asdf\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.\n" + ] + }, + { + "data": { + "text/plain": [ + "[3, 26, 7, 9, 4, 6, 3, 8, 1, 269]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from datasets import Dataset\n", + "import polars as pl\n", + "from transformers import AutoTokenizer\n", + "import utils\n", + "\n", + "\n", + "def create_dataset(split, num_rows, tokenizer):\n", + " stories = utils.stories_dataset()\n", + " stories = stories.filter(pl.col(\"split\") == split).head(num_rows)\n", + "\n", + " stories = stories.with_columns(\n", + " [\n", + " pl.col(\"serialized\").alias(\"text\"),\n", + " pl.col(\"score\").alias(\"label\").cast(pl.Float32),\n", + " ]\n", + " )\n", + "\n", + " stories = stories.with_columns(\n", + " [\n", + " pl.col(\"text\")\n", + " .map_elements(lambda x: tokenizer(x)[\"input_ids\"])\n", + " .alias(\"input_ids\"),\n", + " pl.col(\"label\"),\n", + " ]\n", + " ).select([\"input_ids\", \"label\"])\n", + " return Dataset.from_polars(stories)\n", + "\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"unsloth/Meta-Llama-3.1-8B\")\n", + "\n", + "create_dataset(\"val\", 10, tokenizer)[\"label\"]" + ] + } + ], + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}