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"grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2c3d9cd976b34782ac0c4c154ef4fc83": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "v-zrB6upZjXy", "outputId": "e256eca1-e87d-4133-da72-04ac250fee64" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "the to be be to otothato ono be t be istot isthathationo t nor be to no be que o besthe tis or ior be ques otistio be be tior que que be nono no be bes ono io que o t que be no tothes tor que is tothe\n" ] } ], "source": [ "import random\n", "from collections import defaultdict\n", "\n", "class MarkovChain:\n", " def __init__(self):\n", " self.transitions = defaultdict(list)\n", "\n", " def train(self, text: str):\n", " for i in range(len(text) - 1):\n", " curr_char = text[i]\n", " next_char = text[i+1]\n", " self.transitions[curr_char].append(next_char)\n", "\n", " def generate(self, start_char: str, length: int = 100):\n", " result = [start_char]\n", " for _ in range(length - 1):\n", " curr_char = result[-1]\n", " if curr_char not in self.transitions:\n", " break\n", " next_char = random.choice(self.transitions[curr_char])\n", " result.append(next_char)\n", " return \"\".join(result)\n", "\n", "\n", "# Example corpus\n", "corpus = \"to be or not to be that is the question\"\n", "mc = MarkovChain()\n", "mc.train(corpus)\n", "\n", "# Generate text\n", "print(mc.generate(\"t\", 200))" ] }, { "cell_type": "markdown", "source": [ "- It “looks like English” in terms of letters, no illegal characters, the bigrams (to, be, th) show up a lot.\n", "\n", "- But it’s nonsense at the word level: it keeps looping (to to, ononot, nononor) because it only remembers the last letter, not words or phrases.\n", "\n", "- This is the pure Markov signature: local statistical correctness, global nonsense." ], "metadata": { "id": "_4dE8ZdwaGvz" } }, { "cell_type": "code", "source": [ "import random\n", "from collections import defaultdict\n", "\n", "class NGramMarkov:\n", " def __init__(self, n=5):\n", " self.n = n\n", " self.transitions = defaultdict(list)\n", "\n", " def train(self, text: str):\n", " for i in range(len(text) - self.n):\n", " gram = text[i:i+self.n] # current n-gram\n", " next_char = text[i+self.n]\n", " self.transitions[gram].append(next_char)\n", "\n", " def generate(self, start: str, length: int = 200):\n", " result = list(start)\n", " for _ in range(length - self.n):\n", " gram = \"\".join(result[-self.n:])\n", " if gram not in self.transitions:\n", " break\n", " next_char = random.choice(self.transitions[gram])\n", " result.append(next_char)\n", " return \"\".join(result)\n", "\n", "\n", "# Example\n", "corpus = \"\"\"I loved you: and perhaps this flame\n", "Has not entirely extinguished in my soul;\n", "But let it no longer trouble you;\n", "I do not want to cause you sorrow.\n", "I loved you silently, without hope,\n", "Tormented now by shyness, now by jealousy.\n", "I loved you so sincerely, so tenderly,\n", "As God grant you may be loved by another.\"\"\"\n", "mc2 = NGramMarkov(n=5)\n", "mc2.train(corpus)\n", "print(mc2.generate(\"to be\", 200))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Hojr2_g5aFWy", "outputId": "b36ee667-7ba2-43fd-8651-4ab4b0511175" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "to be\n" ] } ] }, { "cell_type": "code", "source": [ "# Example\n", "corpus = \"to be or not to be that is the question\"\n", "mc2 = NGramMarkov(n=5)\n", "mc2.train(corpus)\n", "print(mc2.generate(\"to be\", 200))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jBT0ZeXvck5U", "outputId": "2e7d6ab7-29cb-44c4-d0ce-b79ccc56601d" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "to be that is the question\n" ] } ] }, { "cell_type": "markdown", "source": [ "- This is n=3–5 magic: it doesn’t invent long-term structure, but it copies and reorders familiar chunks.\n", "\n", "- We’re literally watching the transition between gibberish -> coherence, depending on corpus size and n.\n", "\n", "- Perfect material for your Hugging Face card: shows why context length is the breakthrough in LLMs." ], "metadata": { "id": "GjHyMU4scywL" } }, { "cell_type": "code", "source": [ "pushkin_corpus = \"\"\"\n", "I loved you: and perhaps this flame\n", "Has not entirely extinguished in my soul;\n", "But let it no longer trouble you;\n", "I do not want to cause you sorrow.\n", "I loved you silently, without hope,\n", "Tormented now by shyness, now by jealousy.\n", "I loved you so sincerely, so tenderly,\n", "As God grant you may be loved by another.\n", "\"\"\"\n", "\n", "# Train & generate\n", "mc_pushkin = NGramMarkov(n=5)\n", "mc_pushkin.train(pushkin_corpus)\n", "print(mc_pushkin.generate(\"I loved you\", 300))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vEQ5sqhzbY__", "outputId": "de30aef5-0042-452a-e8ea-b268982f439e" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "I loved you silently, without hope,\n", "Tormented now by jealousy.\n", "I loved you: and perhaps this flame\n", "Has not entirely extinguished in my soul;\n", "But let it no longer trouble you;\n", "I do not entirely extinguished in my soul;\n", "But let it no longer trouble you;\n", "I do not entirely extinguished in my soul;\n", "But let it \n" ] } ] }, { "cell_type": "markdown", "source": [ "- n=5 really shows off: we’re getting full phrases like “I loved you so tenderly” stitched together.\n", "\n", "- But notice the loops: “so sincerely, so sincerely” — classic Markov artifact. It can’t “remember” it already said that.\n", "\n", "- It’s poetic-looking but shallow: once the local 5-character context is exhausted, it stumbles into repetition or abrupt stops." ], "metadata": { "id": "hmB_NdbLbicJ" } }, { "cell_type": "code", "source": [ "from transformers import pipeline\n", "\n", "generator = pipeline(\"text-generation\", model=\"gpt2-medium\")\n", "\n", "prompt = \"\"\"\n", "I loved you: and perhaps this flame\n", "Has not entirely extinguished in my soul;\n", "But let it no longer trouble you;\n", "I do not want to cause you sorrow.\n", "I loved you silently, without hope,\n", "Tormented now by shyness, now by jealousy.\n", "I loved you so sincerely, so tenderly,\n", "As God grant you may be loved by another.\n", "\"\"\"\n", "outputs = generator(prompt, max_length=80, num_return_sequences=1,\n", " do_sample=True, top_p=0.9, temperature=0.8)\n", "\n", "print(\"Prompt:\", prompt)\n", "print(\"GPT2-medium Output:\", outputs[0]['generated_text'])\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "c0e5cbada9714c0fa748cb78a7464353", "3f07a35f382146cdbca54061c6cc137a", "ffaf62053b2a4499a4acd7bbe43f794e", "fed4b15739ad4091b5fdc6ce0e0f355d", "a924a8a4f9ab4f6b994748c673c19c53", "8bc5ca9949504561a1a60e1ec1ddb5bf", "8d3b99c56bef449bb0e1be7e70cb8923", "f20b01e69f2e4145be73ff80ff8f4406", "52fd65292ba54b6aaeecab06eb5de8c8", "3940d2e66d824c13a92b95e6e973901d", "e9f7465a37294c4c86332d5defc985cf", "0c2ace7d3a85467aa3c4cfdb7dd58ab0", "770b83e186f841fca778c920481d7131", "787ea34b03284c649c7d5e32aff00301", "2a24701ce2734fffbc904c9dc4643445", "d310ee84830947fa97a0c8c3798cf988", "cc5a4ad527184aebb327907c4aa099b4", "d82944b0c23c44c5825eeb8e87a051cf", "37f553c51b674edc8c217f1cbcebae31", "c722a9833614475abfa6f4fc94509304", "3f2b5e80bdd64551a6feabecdf405465", "982cf5b6a7e042b09b8428e24cf95ead", "d7b5889c338046c5a6f4f30959c27c63", "5fedd5f2bb624cc69f791b019026771b", "5decd291134d4052b61aa185a650f0c8", "435669d77ed24fc9a9a3ce55c194dc9a", "78a23768c64a4f5ca0c84974f7685431", "10e51b94bac545af8619657b3c47b3de", "ed665adc50ac4029bef3360de9b6d087", "ddd5dc20c644473ba5924a0d5cec1dc5", "2b74a546438b4d008c177f2f8b98fc6d", "2312e0ec8a0c425888fc6c3f251047a5", "d4b64ae1ce78411ab0d13a0074f4aef5", "8605c093d4254083978d2956cd4b095e", "b45a787552d341e4a924204d05b0beb5", "f0937fc548be48c3900fa883ebd1e062", "1a7afdece4c24868a060f9a313599e89", "f1bc397d4eaf4f72bfaa32c885bbfb25", "27c2ac5270674914bdd2a324d2451a9d", "41d67e0817864249b37ce0f5f12a4589", "0db410eabf604dd58b7bd28f864b834a", "0feb86a5ac584511a3266bac5aaede5d", "f11d51342d524ce9896d6ba6c708802a", "357b875d20b94f3eabc1b564da64b647", "3e55045366084ea49a0097591aca3898", "19b547a024674276896b6b6a2e5c414d", "d418953a9420441185f3aa393f4a918c", "c59d6ac9bbce4bff9ff09a5881318709", "528a2d7799d8439799a59f9c29d4569e", "c2772e63e72c4eebbb4aafda30a21524", "e556fbb2e7f04fdb99c6cb120dc65fad", "820233d0bb834dd4975e57bb81936a8d", "bffc916248bf462b818696d6cdedb0a6", "5a444ac722fe4216b9b22b5e58be698b", "6e4368d5bd8a4481aad104ad8974eec7", "07f6ab22dc554ea3920f2eca0b198101", "cdedd397f5dd41bb8147c624fb838cb3", "a2291b0703c0427bbd594b3207457918", "231ce37fcd014253b356939466dec439", "ef049249fa1b4cb3a70d768f6ccb2e99", "03ddb6005b504b6bad40d0bf3959ba55", "937b2cf76aad40dca1733486921fa67d", "5ff1b797ac694c0ca5c2d977de8b8b36", "f82edc6f2f274275b51a415b42671a67", "7d234fdc91734fc7a3077bf2a746acde", "ddab4a9f3ab14125b4e020a56aadcc55", "c695251a31c844b9be04436c4cb62e43", "3983758d870a4073808986dd988d34ec", "8a02874f28f049689b487f4fd6089a72", "6104f06ab8e543588dfba55f18d5e66b", "315863f20660485387ea4bf28f6e7f48", "7f3343921675419ca00c0151398ee060", "4ee26ce9f101494abca1427eb5f3bedf", "ff2ece19dcdc4bb9a353fcfc23d59a99", "ccc8cd5ee0ed407e84c0ea5d67f4a6ad", "d7251585f46e4ac784cbf421c820eef5", "2c3d9cd976b34782ac0c4c154ef4fc83" ] }, "id": "npeoNKU1bq1h", "outputId": "766fe039-1355-4525-8138-99c1245d7f54" }, "execution_count": 5, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/718 [00:00 capture local structure, produce fragments and loops quickly.\n", "\n", "- Higher n = more coherence, but still shallow and repetitive.\n", "\n", "- GPT-2 medium -> extends poetically with creative phrasing, but eventually degenerates into repetition.\n", "\n", "- Key takeaway: context length + self-attention = why LLMs leapfrog n-grams, but sampling strategies still matter." ], "metadata": { "id": "l9WMP-3ykAJy" } } ] }