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"@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Proyecto final Chatbots\n", + "Nombre: Paulina Garza Allende\n", + "\n", + "CU: 188456" + ], + "metadata": { + "id": "QhCouai2DygU" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Instalación de Bibliotecas\n", + "\n", + "Antes de comenzar a trabajar en un proyecto de aprendizaje automático, es esencial instalar las bibliotecas necesarias. En este caso, utilizamos datasets, accelerate, transformers y torch. Estas bibliotecas proporcionan herramientas para trabajar con conjuntos de datos, acelerar el entrenamiento de modelos, acceder a modelos preentrenados y manipular tensores, respectivamente." + ], + "metadata": { + "id": "VlbqGJzSD5aK" + } + }, + { + "cell_type": "markdown", + "source": [ + "Instalación de las bibliotecas necesarias para el proyecto" + ], + "metadata": { + "id": "GIGqMhI5PoLK" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "gtbY1T2-YajK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c059514f-8a17-4ebd-dd00-12a51ddf93fa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting datasets\n", + " Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: 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(from sympy->torch) (1.3.0)\n" + ] + } + ], + "source": [ + "!pip install datasets\n", + "!pip install accelerate\n", + "!pip install transformers\n", + "!pip install torch" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Carga del Conjunto de Datos\n", + "\n", + "Cargar un conjunto de datos es un paso crucial en el procesamiento de datos. En este ejemplo, utilizamos la biblioteca datasets para cargar un conjunto de datos de soluciones de código en Python desde una fuente externa. Luego, seleccionamos un subconjunto específico (como 'train') y lo convertimos en un DataFrame de Pandas para su manipulación y análisis." + ], + "metadata": { + "id": "-FdPu-ifEUBp" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "from datasets import load_dataset" + ], + "metadata": { + "id": "K1lCd4IxY7_M" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Cargar el conjunto de datos de soluciones de LeetCode en Python" + ], + "metadata": { + "id": "kC-q893NEk93" + } + }, + { + "cell_type": "code", + "source": [ + "dataset = load_dataset(\"mhhmm/leetcode-solutions-python\")" + ], + "metadata": { + "id": "HQhxrivgZBJV", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 205, + "referenced_widgets": [ + "b0ffdcdfdc1f4727b42faccdeb10455c", + "28169df6bcaa4f078f343d95556b0017", + "3a22170d364b476eb0cad03c71b134d1", + "d7fce66c7bcb461f9cd38506e5cb861f", + "8db8d906f1b54f9ba57abbbee00a18d1", + "fc989e84e0b2485384289fe254621d32", + "0590f3baba644135b7f12a5bf9c4c983", + "251f59b4860244ff81cfbc2bc3523094", + "df1958d6e8ba4510bd66f27212954022", + "968a7e56d037426d8b05ae42e0054b15", + "6f60da73f362439bbc50db9a0bc46afd", + "de4ce4641d39475f978ae57f4c65a606", + "6001fbd5b2f5478ba391c186d83b0f2c", + "43965b1bdf7745f0bfe9e0548ae9fda3", + "ef2d1001158d450ebafc751cbd88ade9", + "c3b3ea77355d413c83eae173e28574e1", + "87985a6e94f04fae91b728210202582c", + "9662a842d70d4942a9a6c65adeb21622", + "67abdce76c934fae95af8c96131feafa", + "b131082108f44f34937ce02c5692208b", + "42d163b1648044fba0ecdb5275dad1c5", + "8eca5856ecb54518b6ea14ef29c7fc4a", + "4bfc7fe1189a41b8beb574ebf81e8473", + "164547715062428eadc4bc071416e5e1", + "6816899ade6e4f3698d7e2637ad00ce2", + "e35eee70b1df4142b8dee9dde2f27a6e", + "ae0d2c0fbddd4ee095d2d1d9a2d5483b", + "471be68184284c3b8ef6d3e1b9955b39", + "82e17238e3674234a8cb9ce4b292c1f8", + "e143aec4499846de9887997dd1d8dbb7", + "4797129248904992bae3d3866a4d9231", + "7137d5b7b10b425ebf05c09ca2ff49a2", + "bc1bb8282ae346d5848d945bca0c617f", + "3aafee1176e74a1794553dc27576caae", + "6b5744d8ce1f4bcd926645011da9ffa4", + "8cf4b3185ca242ed967bea805a95d29a", + "d71676c9164b439aabec5d96a831b9b5", + "e2e11e959d4e4d60abaa08a1a55f2709", + "4a4a9da91684477b9a52fe085f50dfbe", + "e95e0d2adee6417b9e82400be6676171", + "98e139f5df394e2d85d018fb520e3e45", + "6a877f6c3f104330b2d70bfb2fc3b31e", + "ffde37d4a4924d1d83dc86c71d4436d7", + "ed79033fde8f45898ab2f2235a544b2b", + "1e945c2597ef4c7bba85bd16ce8bec6b", + "4bdefa0b3da7468cb3f46775bb20b285", + "2a7f11f7e7b347b1864503aebe95cd12", + "c9a894d63d5e4427920b339a267317fa", + "211b9d49fd3d4d0c88ea811579d652d4", + "b5f2af38caa84c7898c30ab4af24c483", + "2b240d57395d4b5ba014b878bc10dd48", + "65cb79bb126b4a01a0be1304a37550c3", + "20547dbd75ee413d8187c452e619ca01", + "3e4150b9d8c84dd2b510fe455cdaf47b", + "c76e8f64c4c1422ca444057218a5b4bd" + ] + }, + "outputId": "3c4da583-8b6d-4bd5-ed52-7c89c073e08e" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading readme: 0%| | 0.00/488 [00:00\n", + "
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idexplanation_onlycode_with_datacode_onlycode_with_problem
01The algorithm leverages a hash map (unordered_...# two-sum\\n# Two Sum\\n# Easy\\n# Given an array...```python\\ndef twoSum(nums, target):\\n map ...# Given an array of integers `nums` and an int...
121. Initialize a dummy ListNode with a value of...# add-two-numbers\\n# Add Two Numbers\\n# Medium...```python\\nclass ListNode:\\n def __init__(s...# You are given two **non-empty** linked lists...
23The algorithm uses a sliding window with two p...# longest-substring-without-repeating-characte...```python\\ndef length_of_longest_substring(s: ...# Given a string `s`, find the length of the *...
341. Choose the smaller array as nums1 so that t...# median-of-two-sorted-arrays\\n# Median of Two...```python\\ndef findMedianSortedArrays(nums1, n...# Given two sorted arrays `nums1` and `nums2` ...
451. Initialize `start` and `maxLength` for resu...# longest-palindromic-substring\\n# Longest Pal...```python\\ndef longest_palindromic_substring(s...# Given a string `s`, return _the longest_ _pa...
..................
23542608The algorithm starts by creating an adjacency ...# shortest-cycle-in-a-graph\\n# Shortest Cycle ...```python\\nfrom collections import deque\\n\\nde...# There is a **bi-directional** graph with `n`...
23552609The algorithm scans the string `s` twice. In t...# find-the-longest-balanced-substring-of-a-bin...```python\\ndef longestBalancedSubstring(s):\\n ...# You are given a binary string `s` consisting...
23562610The algorithm iterates through the input array...# convert-an-array-into-a-2d-array-with-condit...```python\\ndef distinct_rows_from(nums):\\n ...# You are given an integer array `nums`. You n...
235726111. For each type of cheese, calculate the diff...# mice-and-cheese\\n# Mice and Cheese\\n# Medium...```python\\ndef maxPoints(reward1, reward2, k):...# There are two mice and `n` different types o...
235826121. Create a set from the `banned` positions fo...# minimum-reverse-operations\\n# Minimum Revers...```python\\ndef reach_position(n, p, banned, k)...# You are given an integer `n` and an integer ...
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