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"!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)" | |
], | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/", | |
"height": 209, | |
"referenced_widgets": [ | |
"dd171a589b6249c78e89725a9aa40816", | |
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"309e50ee7ef14c8f937c83a180bdafdc", | |
"d9b21f4cffa54e2c8a1059c2644c3ce8", | |
"d524508c94fa42e4a8eaa0553472c87d", | |
"665a4ae974334f94bdfa2eb852896e2d", | |
"2b5d8e91e8c74897a8ab9ef4c361c4e6", | |
"ec1361f9b613432e964310f96834d59d", | |
"e425fb5965c84235bcf0d76b7625602a", | |
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"eff8075fa5e646cd8fe3e9073bdaa731", | |
"476a89dfee73423cbf0807447984bcef", | |
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"990637c2f5e24b9e8ce00af5f1d0425c", | |
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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", | |
" })\n", | |
" });\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": [ | |
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"87a7a405244945f0a60858bbc25ca8e5", | |
"690c06288d5a4212a27f96725a184f27", | |
"0d23ce6685bb457ba0880aaecc9ba8ae", | |
"5965542dbb74412eac75c4b9c8125e25", | |
"5fb048ed4784431f811e454796c0e607" | |
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}, | |
"id": "yrSuxp5kzWwM", | |
"outputId": "360aa8c3-a9d3-4aef-9eb8-4123a06202b7" | |
}, | |
"execution_count": null, | |
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{ | |
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"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)))" | |
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"application/vnd.jupyter.widget-view+json": { | |
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"<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, | |
"referenced_widgets": [ | |
"b44ea30fe29b446e8ca040f060caabe7", | |
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"b77cb375529c4d41a6cc31412683e96d", | |
"9e032b1d38a74e3cafaa1dd39fb262ab", | |
"a883fd798593400aae1f3eeb46ee107e", | |
"1a4dc35672b74feeb546d62a008cfb1a", | |
"c008c92cce0746c797bb5304952fe920", | |
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"24cc6ae2f0ba4d058bc1efb99ef6ce97", | |
"af8a17694d6545e183708378694a14b6", | |
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"3c3a58cbfd814acb8d0794898a0ff260", | |
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"adae0ec06a7f4060aa7e405e1fc2961f", | |
"ec65619ac5114bfc985f683deebc7711", | |
"94b3b6d37f114241a1e6d26b202b8fa7", | |
"cc0615a8bc9c42eab84722e3a0cd42ce", | |
"368086cf9cb145ada88f3bf6a0e88e6a", | |
"ff343ed68adf41c8a20ac742243c8b49", | |
"05d140a1a15f458ebd7110db8ae8a559", | |
"7904fbccd1c048f68eeb981fa91b3521", | |
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] | |
}, | |
"id": "MpJkM9BwVLaN", | |
"outputId": "0cb7acc3-1700-474a-f0ec-f4e4ed9cbbcc" | |
}, | |
"execution_count": null, | |
"outputs": [ | |
{ | |
"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": "b44ea30fe29b446e8ca040f060caabe7" | |
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}, | |
"metadata": {} | |
}, | |
{ | |
"output_type": "display_data", | |
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}, | |
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{ | |
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"model_id": "183c1424257445dc941c3d3ff4d56bac" | |
} | |
}, | |
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{ | |
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"model_id": "7904fbccd1c048f68eeb981fa91b3521" | |
} | |
}, | |
"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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