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| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/aleksejalex/5de1c7b38c20e4515169193d7904766d/erbengpt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import os" | |
| ], | |
| "metadata": { | |
| "id": "WZInNJlECmHV" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "%%capture\n", | |
| "# Set the locale to a UTF-8 encoding\n", | |
| "os.environ['LC_ALL'] = 'en_US.UTF-8'\n", | |
| "os.environ['LANG'] = 'en_US.UTF-8'\n", | |
| "\n", | |
| "# Now you can run your pip install command\n", | |
| "!pip install numpy\n", | |
| "!pip install transformers[torch]\n", | |
| "!pip install accelerate -U\n", | |
| "!pip install datasets\n", | |
| "3!pip install wandb" | |
| ], | |
| "metadata": { | |
| "id": "Ugpaz2bxBXOg" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import torch" | |
| ], | |
| "metadata": { | |
| "id": "w1QoBLGuKZ_g" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "3import wandb" | |
| ], | |
| "metadata": { | |
| "id": "9dtVNU0F6hK9" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n", | |
| "\n", | |
| "model_name = \"gpt2\" # You can also try \"gpt2-medium\", \"gpt2-large\", or \"gpt2-xl\"\n", | |
| "model = GPT2LMHeadModel.from_pretrained(model_name)\n", | |
| "tokenizer = GPT2Tokenizer.from_pretrained(model_name)" | |
| ], | |
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| "colab": { | |
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| { | |
| "output_type": "display_data", | |
| "data": { | |
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| ], | |
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| "version_major": 2, | |
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| "metadata": {} | |
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| "output_type": "display_data", | |
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| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import requests\n", | |
| "\n", | |
| "url_kytice = \"https://gist.githubusercontent.com/aleksejalex/3d6efa777dbb9759bb02b0d2933438cc/raw/fbe305f8f3729352ebbaf802fcd49f38b4292a83/kytice.txt\"\n", | |
| "output_file = 'kytice.txt'\n", | |
| "\n", | |
| "response = requests.get(url_kytice)\n", | |
| "\n", | |
| "if response.status_code == 200:\n", | |
| " with open(output_file, 'wb') as f:\n", | |
| " f.write(response.content)\n", | |
| " print(f\"File downloaded successfully and saved as {output_file}\")\n", | |
| "else:\n", | |
| " print(f\"Failed to download file. Status code: {response.status_code}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GFoEqYFczb_L", | |
| "outputId": "291761a2-9046-429c-9042-d79e60e61664" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "File downloaded successfully and saved as kytice.txt\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Read your data from the .txt file\n", | |
| "with open(\"kytice.txt\", \"r\", encoding=\"utf-8\") as file:\n", | |
| " poems = file.read()\n", | |
| "\n", | |
| "# Tokenize the data\n", | |
| "tokenized_poems = tokenizer(poems, return_tensors=\"pt\", max_length=512, truncation=True)" | |
| ], | |
| "metadata": { | |
| "id": "R7UQUuFDzW2B" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "JdMjLHVLLs8L" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#run = wandb.init( project=\"ErbenGPT\")#, name=\"run_name\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 194 | |
| }, | |
| "id": "Ht8DeBpoB8NZ", | |
| "outputId": "e5d92c4f-53f7-4473-b298-b3f40e0d6b50" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.Javascript object>" | |
| ], | |
| "application/javascript": [ | |
| "\n", | |
| " window._wandbApiKey = new Promise((resolve, reject) => {\n", | |
| " function loadScript(url) {\n", | |
| " return new Promise(function(resolve, reject) {\n", | |
| " let newScript = document.createElement(\"script\");\n", | |
| " newScript.onerror = reject;\n", | |
| " newScript.onload = resolve;\n", | |
| " document.body.appendChild(newScript);\n", | |
| " newScript.src = url;\n", | |
| " });\n", | |
| " }\n", | |
| " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", | |
| " const iframe = document.createElement('iframe')\n", | |
| " iframe.style.cssText = \"width:0;height:0;border:none\"\n", | |
| " document.body.appendChild(iframe)\n", | |
| " const handshake = new Postmate({\n", | |
| " container: iframe,\n", | |
| " url: 'https://wandb.ai/authorize'\n", | |
| " });\n", | |
| " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", | |
| " handshake.then(function(child) {\n", | |
| " child.on('authorize', data => {\n", | |
| " clearTimeout(timeout)\n", | |
| " resolve(data)\n", | |
| " });\n", | |
| " });\n", | |
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| " });\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", | |
| "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", | |
| "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " ··········\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "Tracking run with wandb version 0.16.0" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "Run data is saved locally in <code>/content/wandb/run-20231205_143218-q2atmyu2</code>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "Syncing run <strong><a href='https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2' target=\"_blank\">fancy-pine-8</a></strong> to <a href='https://wandb.ai/sampleteam/erbenGPT' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| " View project at <a href='https://wandb.ai/sampleteam/erbenGPT' target=\"_blank\">https://wandb.ai/sampleteam/erbenGPT</a>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| " View run at <a href='https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2' target=\"_blank\">https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2</a>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "J1unVsMgZZLl", | |
| "outputId": "b6875870-649c-406f-f1f1-c31073d00b5d" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n", | |
| "Token is valid (permission: write).\n", | |
| "Your token has been saved to /root/.cache/huggingface/token\n", | |
| "Login successful\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from transformers import TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments\n", | |
| "\n", | |
| "dataset = TextDataset(\n", | |
| " tokenizer=tokenizer,\n", | |
| " file_path=\"kytice.txt\",\n", | |
| " block_size=256 # Adjust this based on the average length of your poems\n", | |
| ")\n", | |
| "\n", | |
| "data_collator = DataCollatorForLanguageModeling(\n", | |
| " tokenizer=tokenizer,\n", | |
| " mlm=False\n", | |
| ")\n", | |
| "\n", | |
| "training_args = TrainingArguments(\n", | |
| " output_dir=\"./ErbenGPT\",\n", | |
| " overwrite_output_dir=False, # bylo tu True...\n", | |
| " num_train_epochs=28, # 20 or 28 - nice\n", | |
| " per_device_train_batch_size=15,\n", | |
| " save_steps=10_000,\n", | |
| " save_total_limit=2,\n", | |
| " #output_dir=\"my-awesome-model\",\n", | |
| " #push_to_hub=True\n", | |
| ")\n", | |
| "\n", | |
| "trainer = Trainer(\n", | |
| " model=model,\n", | |
| " args=training_args,\n", | |
| " data_collator=data_collator,\n", | |
| " train_dataset=dataset\n", | |
| ")\n", | |
| "\n", | |
| "# Log hyperparameters to wandb\n", | |
| "wandb.config.update(training_args.__dict__)\n", | |
| "wandb.config.update({\"model_name\": \"erben_gpt2\"}) # Add any additional hyperparameters\n", | |
| "wandb.config.update({\"num_train_epochs\": training_args.num_train_epochs})\n", | |
| "wandb.config.update({\"per_device_train_batch_size\": training_args.per_device_train_batch_size})\n", | |
| "wandb.config.update({\"tba\": None})\n", | |
| "\n", | |
| "trainer.train()\n", | |
| "\n", | |
| "torch.save(model.state_dict(), 'ErbenGPT_model.pth')\n", | |
| "#artifact = wandb.Artifact('model', type='model')\n", | |
| "#artifact.add_file('ErbenGPT_model.pth')\n", | |
| "#run.log_artifact(artifact)\n", | |
| "\n", | |
| "#train_losses = trainer.state.log_history[\"loss\"]\n", | |
| "#plt.plot(train_losses, label='Training Loss')\n", | |
| "#plt.xlabel('Epoch or Step')\n", | |
| "#plt.ylabel('Loss')\n", | |
| "#plt.title('Training Loss Over Time')\n", | |
| "#plt.legend()\n", | |
| "#plt.show()\n", | |
| "\n", | |
| "wandb.finish()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 371, | |
| "referenced_widgets": [ | |
| "c16237b9c10141278216da422e35f793", | |
| "d3a367c43fd1413096393971e5743b2f", | |
| "af652e6aa770404095d95ec354d0435c", | |
| "87a7a405244945f0a60858bbc25ca8e5", | |
| "690c06288d5a4212a27f96725a184f27", | |
| "0d23ce6685bb457ba0880aaecc9ba8ae", | |
| "5965542dbb74412eac75c4b9c8125e25", | |
| "5fb048ed4784431f811e454796c0e607" | |
| ] | |
| }, | |
| "id": "yrSuxp5kzWwM", | |
| "outputId": "360aa8c3-a9d3-4aef-9eb8-4123a06202b7" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='364' max='364' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [364/364 05:44, Epoch 28/28]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Step</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "c16237b9c10141278216da422e35f793" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "<style>\n", | |
| " table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n", | |
| " .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n", | |
| " .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n", | |
| " </style>\n", | |
| "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁</td></tr><tr><td>train/global_step</td><td>▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>28.0</td></tr><tr><td>train/global_step</td><td>364</td></tr><tr><td>train/total_flos</td><td>662114009088000.0</td></tr><tr><td>train/train_loss</td><td>3.0831</td></tr><tr><td>train/train_runtime</td><td>348.8076</td></tr><tr><td>train/train_samples_per_second</td><td>14.53</td></tr><tr><td>train/train_steps_per_second</td><td>1.044</td></tr></table><br/></div></div>" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| " View run <strong style=\"color:#cdcd00\">fancy-pine-8</strong> at: <a href='https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2' target=\"_blank\">https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)" | |
| ] | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "Find logs at: <code>./wandb/run-20231205_143218-q2atmyu2/logs</code>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "trainer.state.log_history" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "DbzphQ3JOYv_", | |
| "outputId": "83dec880-1c7b-408e-a3b0-a7a5e0eee7b1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[{'train_runtime': 348.8076,\n", | |
| " 'train_samples_per_second': 14.53,\n", | |
| " 'train_steps_per_second': 1.044,\n", | |
| " 'total_flos': 662114009088000.0,\n", | |
| " 'train_loss': 3.083096682370364,\n", | |
| " 'epoch': 28.0,\n", | |
| " 'step': 364}]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 13 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Save the fine-tuned model\n", | |
| "model.save_pretrained(\"./ErbenGPT\")" | |
| ], | |
| "metadata": { | |
| "id": "7znRWG-0z-gz" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# PUSHING TOKENIZER TO HUGGINGFACE" | |
| ], | |
| "metadata": { | |
| "id": "24LXRrbCpF2l" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# PUSHING MODEL TO HUGGINGFACE" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 164, | |
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| "9574ee3edea74ccf8e2f3d3b219e15fc", | |
| "effbff934f544adeb27047f435be532d" | |
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| }, | |
| "id": "MpJkM9BwVLaN", | |
| "outputId": "0cb7acc3-1700-474a-f0ec-f4e4ed9cbbcc" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Upload 3 LFS files: 0%| | 0/3 [00:00<?, ?it/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b44ea30fe29b446e8ca040f060caabe7" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "eaed125d89e24d92a4a0c7af07677ff3" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "183c1424257445dc941c3d3ff4d56bac" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "'https://huggingface.co/aleksejalex/ErbenGPT/tree/main/'" | |
| ], | |
| "application/vnd.google.colaboratory.intrinsic+json": { | |
| "type": "string" | |
| } | |
| }, | |
| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# Step 6: Generate Poetry\n", | |
| "prompt = \"Dej sem dítě\"\n", | |
| "\n", | |
| "# Tokenize prompt and move to the same device as the model\n", | |
| "input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(model.device)\n", | |
| "\n", | |
| "# Generate poetry\n", | |
| "generated_poem = model.generate(\n", | |
| " input_ids,\n", | |
| " max_length=150, # Adjust as needed\n", | |
| " temperature=0.75, # Experiment with different temperature values\n", | |
| " num_return_sequences=3,\n", | |
| " no_repeat_ngram_size=0,\n", | |
| " top_k=25,\n", | |
| " top_p=0.95,\n", | |
| " pad_token_id=tokenizer.eos_token_id,\n", | |
| " do_sample=True\n", | |
| ")\n", | |
| "\n", | |
| "generated_text = tokenizer.decode(generated_poem[0], skip_special_tokens=True)\n", | |
| "print(generated_text)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "PvX_egZf1YKP", | |
| "outputId": "c3c0413c-f117-43e2-8887-2b522877e453" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dej sem dítě —\n", | |
| "sám tak to příže měsí:\n", | |
| "a tak to měsí, kde mi —\n", | |
| "však jemu ještě příže,\n", | |
| "však jednou děšné věží,\n", | |
| "a však jedna, však jedna!“\n", | |
| "\n", | |
| "Tak, to má, to má má;\n", | |
| "tu jí příže větě, to kde kde,\n", | |
| "a však jedna\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "generated_text = tokenizer.decode(generated_poem[1], skip_special_tokens=True)\n", | |
| "print(generated_text)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "EBlVC1ETYsXB", | |
| "outputId": "dc4b5667-5dcf-42cb-884e-4b4fc66ab4ff" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dej sem dítě — však sám,\n", | |
| "však již pachole vodní.\n", | |
| "\n", | |
| "„Odej se čas na viděláv:\n", | |
| "všej, jsem dítě — však sám,\n", | |
| "však již pachole vodní.“ —\n", | |
| "\n", | |
| "„Záhořím již na svatém pahouti?\n", | |
| "\n", | |
| "Nešejte již kostela v kostela?“\n", | |
| "\n", | |
| "„Ach, kde jsem,\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "generated_text = tokenizer.decode(generated_poem[2], skip_special_tokens=True)\n", | |
| "print(generated_text)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "hF3_ueYVUVwR", | |
| "outputId": "c268179c-dd7c-44f1-8c84-c7216c002e2f" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dej sem dítě nejte vrč,\n", | |
| "vzdlovavrá již a sotva jeho kost:\n", | |
| "„Nedá v růží, žírá na neslý,\n", | |
| "v ždycka vůru křeča v zemi klín,\n", | |
| "v dřítko vůru jsem klín,\n", | |
| "a zemi lého znamení\n", | |
| "a dne vůru jsem znamení!“\n", | |
| "\n", | |
| "A když a zemi se klín,\n", | |
| "zd\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "generated_text = tokenizer.decode(generated_poem[0], skip_special_tokens=True)\n", | |
| "print(generated_text)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "W1Uz3hacUVbY", | |
| "outputId": "d366dedd-c339-4fdf-cd72-197fcdca1192" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Dej sem dítě —\n", | |
| "sám tak to příže měsí:\n", | |
| "a tak to měsí, kde mi —\n", | |
| "však jemu ještě příže,\n", | |
| "však jednou děšné věží,\n", | |
| "a však jedna, však jedna!“\n", | |
| "\n", | |
| "Tak, to má, to má má;\n", | |
| "tu jí příže větě, to kde kde,\n", | |
| "a však jedna\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [], | |
| "metadata": { | |
| "id": "UZqi4c4qUVDv" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "first, raw result:\n", | |
| "\n", | |
| "prompt = `Your prompt goes here.`\n", | |
| "\n", | |
| "answer =\n", | |
| "```\n", | |
| "Ach, mé, ach!\n", | |
| "Má, kde, milá!“\n", | |
| "„Aj, toho, již, ještě,\n", | |
| "jaké má mď! —\n", | |
| "Jak, čám, řím! ší, zaře!\n", | |
| "Kdo, dČtí! kdo! dát! Čte! jak! její — ※\n", | |
| "a kdyť, nevĈ,? „Kdychůj! zlato, pojľ!?\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "Ungfy1oT1ha0" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "second result (batch increased, 28 epochs):\n", | |
| "\n", | |
| "prompt = `Stála`\n", | |
| "\n", | |
| "answer =\n", | |
| "```\n", | |
| "Stála,\n", | |
| "že mi se vrátil a na žel nízkaly\n", | |
| "a zas do hodiny mějí.\n", | |
| "\n", | |
| "A hledal a vĚč, ale klečil, —\n", | |
| "„Ach mé dítĽch oči!“\n", | |
| "Klekáním dáli tváři?\n", | |
| "Vġt�íť mi šál jiů všeň:\n", | |
| "tvám to bohabátí, buch bęeš,\n", | |
| "buch! buhabatuješ!\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "CYSoUaeV3rfQ" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "third result (batch increased, 28 epochs):\n", | |
| "\n", | |
| "prompt = `Dej sem dítě`\n", | |
| "\n", | |
| "answer =\n", | |
| "```\n", | |
| "Dej sem dítě,\n", | |
| "až i vyskům jiť poutník:\n", | |
| "„Haha, mama, co vidí!“\n", | |
| "vyskaře, jako vĚčný, —\n", | |
| "hle, hle! Hleď!\n", | |
| "\n", | |
| "Vydání, vydá, drahé, aň, královském,\n", | |
| "pœíchámĝí mġlo, pōímálo! \n", | |
| "\n", | |
| "\n", | |
| "IV\n", | |
| "I\n", | |
| "A kdy\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "6nGqXSb734TV" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "third result (batch increased, 28 epochs):\n", | |
| "\n", | |
| "prompt = `Dej sem dítě`\n", | |
| "\n", | |
| "params =\n", | |
| "```\n", | |
| "generated_poem = model.generate(\n", | |
| " input_ids,\n", | |
| " max_length=150, # Adjust as needed\n", | |
| " temperature=0.75, # Experiment with different temperature values\n", | |
| " num_return_sequences=1,\n", | |
| " no_repeat_ngram_size=0,\n", | |
| " top_k=50,\n", | |
| " top_p=0.95,\n", | |
| " pad_token_id=tokenizer.eos_token_id,\n", | |
| " do_sample=True\n", | |
| ")\n", | |
| "```\n", | |
| "\n", | |
| "answer =\n", | |
| "```\n", | |
| "Dej sem dítě říkou!\n", | |
| "Vchodám, vchodá, v křítíká,\n", | |
| "hraděčí sotva zvonu zvonu,\n", | |
| "hrobuje černého poutník.\n", | |
| "\n", | |
| "A dítě slyš, a sotva již:\n", | |
| "„Neba mám, nezpátíká,\n", | |
| "jako v něstném sotva již!“\n", | |
| "\n", | |
| "Tu někřikých rok se tíkou\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "Modn7jhQ9dkf" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "```\n", | |
| "Dej sem dítě!“ —\n", | |
| "\n", | |
| "„Sviťte a po ten čas, že svěš a krati,\n", | |
| "a na tak ruka za svěš a krati\n", | |
| "a přišli zas na svávaje:\n", | |
| "„Jedno po ten čas, že svatba,\n", | |
| "a na krati zas na zemi báti\n", | |
| "a ní muka zase zpět!“\n", | |
| "\n", | |
| "„Zaseť se, jaký bože, všeho se krati\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "pJeFF62AAVDO" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "```\n", | |
| "Dej sem dítě —\n", | |
| "sám tak to příže měsí:\n", | |
| "a tak to měsí, kde mi —\n", | |
| "však jemu ještě příže,\n", | |
| "však jednou děšné věží,\n", | |
| "a však jedna, však jedna!“\n", | |
| "\n", | |
| "Tak, to má, to má má;\n", | |
| "tu jí příže větě, to kde kde,\n", | |
| "a však jedna\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "KShawiO6miVx" | |
| } | |
| } | |
| ] | |
| } |
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