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"stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m45.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m880.6/880.6 kB\u001b[0m \u001b[31m45.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m82.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "markdown", "source": [ "Based off of [Philipp Schmid's](https://www.philschmid.de/philipp-schmid) [notebook](https://colab.research.google.com/github/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb#scrollTo=laDp891gO25V) with data from the [Trump Twitter Archive](https://www.thetrumparchive.com/?results=1).\n", "\n", "- GPT2 [Model Card](https://huggingface.co/gpt2)\n", "-[HuggingFace's Finetuning Docs](https://huggingface.co/learn/nlp-course/chapter3/3?fw=pt)" ], "metadata": { "id": "lw58eJhpyCww" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "iwZxNbIIbzbR" }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import pandas as pd\n", "import json\n", "from transformers import (\n", " TextDataset,\n", " DataCollatorForLanguageModeling,\n", " AutoTokenizer,\n", " AutoModelWithLMHead,\n", " get_linear_schedule_with_warmup,\n", " Trainer,\n", " TrainingArguments,\n", " pipeline\n", ")\n", "from sklearn.model_selection import train_test_split\n", "from tqdm.auto import tqdm\n", "import torch\n", "from pathlib import Path" ] }, { "cell_type": "code", "source": [ "model_name = \"gpt2\"\n", "\n", "if model_name == \"gpt2\":\n", " model_size = \"124M\"\n", "elif model_name == \"gpt2-medium\":\n", " model_size = \"355M\"\n", "elif model_name == \"gpt2-large\":\n", " model_size = \"774M\"\n", "elif model_name == \"gpt2-xl\":\n", " model_size = \"1.5B\"" ], "metadata": { "id": "GxBa9kFFsHaM" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "# define some params for model\n", "max_length = 100\n", "batch_size = 32\n", "epochs = 5\n", "learning_rate = 5e-4\n", "warmup_steps = 1e2\n", "epsilon = 1e-8\n", "\n", "# produce sample output every 100 steps\n", "sample_every = 100" ], "metadata": { "id": "1hFiQUbNcANl" }, "execution_count": 4, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Fetch / Load Data & Preprocess" ], "metadata": { "id": "9PY6SSKlcJNq" } }, { "cell_type": "code", "source": [ "tweets_path = Path(\"./data/tweets.json\")\n", "train_path = Path(\"./data/train_tweets.csv\")\n", "dev_path = Path(\"./data/dev_tweets.csv\")\n", "\n", "# fetch data if !exists already\n", "if not tweets_path.exists():\n", " !mkdir data\n", " !wget -O ./data/tweets.json \"https://drive.google.com/uc?export=download&id=16wm-2NTKohhcA26w-kaWfhLIGwl_oX95\"\n", "\n", "if not (train_path.exists() and dev_path.exists()):\n", " with open(tweets_path, 'rb') as f:\n", " # read json file into dict and then parse into df\n", " as_dict = json.loads(f.read())\n", " df = pd.DataFrame(as_dict)\n", " \n", " # filter df by !retweet\n", " df = df[df['isRetweet'] == \"f\"]\n", "\n", " # filter df to only text\n", " def is_multimedia(tweet: str):\n", " if tweet.startswith('https://t.co/'):\n", " return \"t\"\n", " else:\n", " return \"f\"\n", "\n", " df['isMultimedia'] = df['text'].apply(lambda x : is_multimedia(x))\n", " df = df[df['isMultimedia'] == \"f\"]\n", " df = df.reset_index(drop=True)\n", "\n", " # filter tweets to remove 'amp;'\n", " def remove_amp(tweet):\n", " tweet = tweet.replace('amp;', '')\n", " tweet = tweet.replace('amp', '')\n", " return tweet\n", " df['text'] = df['text'].apply(lambda x: remove_amp(x))\n", "\n", " # rename 'text' column to 'labels'\n", " # df = df.rename(columns={'text': 'labels'})\n", " \n", " # create train, validation splits\n", " train_data, dev_data = train_test_split(df[['text']], test_size=0.15) \n", " \n", " train_data.to_csv(train_path, index=False, header=None)\n", " dev_data.to_csv(dev_path, index=False, header=None)" ], "metadata": { "id": "aLQVWQ_dcB2h", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1f98aeec-6120-4772-d074-9cc7d65fe131" }, "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2023-04-23 20:45:15-- https://drive.google.com/uc?export=download&id=16wm-2NTKohhcA26w-kaWfhLIGwl_oX95\n", "Resolving drive.google.com (drive.google.com)... 108.177.126.113, 108.177.126.138, 108.177.126.102, ...\n", "Connecting to drive.google.com (drive.google.com)|108.177.126.113|:443... connected.\n", "HTTP request sent, awaiting response... 303 See Other\n", "Location: https://doc-0c-04-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/5h7rho54blq770420uluq9enivf39nuc/1682282700000/12919236576798385522/*/16wm-2NTKohhcA26w-kaWfhLIGwl_oX95?e=download&uuid=82f91d2d-5c4e-47b4-9acb-d781f8f5f78c [following]\n", "Warning: wildcards not supported in HTTP.\n", "--2023-04-23 20:45:21-- https://doc-0c-04-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/5h7rho54blq770420uluq9enivf39nuc/1682282700000/12919236576798385522/*/16wm-2NTKohhcA26w-kaWfhLIGwl_oX95?e=download&uuid=82f91d2d-5c4e-47b4-9acb-d781f8f5f78c\n", "Resolving doc-0c-04-docs.googleusercontent.com (doc-0c-04-docs.googleusercontent.com)... 108.177.126.132, 2a00:1450:4013:c01::84\n", "Connecting to doc-0c-04-docs.googleusercontent.com (doc-0c-04-docs.googleusercontent.com)|108.177.126.132|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 17227608 (16M) [application/json]\n", "Saving to: ‘./data/tweets.json’\n", "\n", "./data/tweets.json 100%[===================>] 16.43M 53.4MB/s in 0.3s \n", "\n", "2023-04-23 20:45:22 (53.4 MB/s) - ‘./data/tweets.json’ saved [17227608/17227608]\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "# create tokenized datasets\n", "tokenizer = AutoTokenizer.from_pretrained(\n", " model_name, \n", " pad_token='<|endoftext|>'\n", ")\n", "\n", "# custom load_dataset function because there are no labels\n", "def load_dataset(train_path, dev_path, tokenizer):\n", " block_size = 128\n", " # block_size = tokenizer.model_max_length\n", " \n", " train_dataset = TextDataset(\n", " tokenizer=tokenizer,\n", " file_path=train_path,\n", " block_size=block_size)\n", " \n", " dev_dataset = TextDataset(\n", " tokenizer=tokenizer,\n", " file_path=dev_path,\n", " block_size=block_size) \n", " \n", " data_collator = DataCollatorForLanguageModeling(\n", " tokenizer=tokenizer, mlm=False,\n", " )\n", " return train_dataset, dev_dataset, data_collator\n", "\n", "train_dataset, dev_dataset, data_collator = load_dataset(train_path, dev_path, tokenizer)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 220, "referenced_widgets": [ "911053acfcee4a0b99fdaa13014ecf1f", "0d29b6be52864c56884bfda196e55874", "a8021c1f961c4789800730dd347421f9", "642bf3de65f24422b51e1454a463beb1", "ae9c94dea4a14389853aa68ecd8d481c", "feadf0422faf47b395f45b790be964bf", "ecad22a54df24327b03c52cc0e7b4113", "40f54ee5a8cd43efa7a9371d075630a6", "9a993c8a4c0440068c08937bff1866d5", "83869b2be90d4a5999a8652d6c8a6d14", "238e6a9345094573ad2ecd6d54f8d5d0", "c0d126c128dd48c998dcd374fc182649", "1b4ad3e69eef46ffa190eaf8ca733d60", "b70e35bdac11466c82b2cbc27a182972", "00aa2d85364540d88648aba991074228", "6c6cce521ee745d98af6fec370f2d337", "5a3c3a4ed0d14debb85f9a55b69433e6", "e314714751ed4c74b176ca423fa1c520", "e6d4c4156de646cda2764401f99170b7", "f49e30bcf1a043d3b7c9c2bda894c169", "f4e9603647fd425dae2786f0405d2df0", "7767c82db2404a9097553cf7f540b07e", "3a82180b67cd4b988ac6389b06682a07", "b8a821410a0d465cb4db403a47130ff1", "65f936e2e5f14dad80ddf9fb476f95bd", "756c1c5e3a364ebcb8360ec41bdf6dd9", "1dbb067f28c64c46926ca201ada453e2", "f8f51c37325741c9a814de7a36842a1c", "b8500cb37b074231a1ded3f176fac43e", "93b3862ce5b0442dbeb1d726f3b5e1f7", "1ab239aa0f874d058678ff045749f009", "1b896a02d860416a89e07e7cd5c03414", "4ebd49d003db47c6944562290288f965", "df3fcbff8dbf46b8ab84cdfad41832d2", "a8a316be0eef437890ae0a9b0bfde076", "6fbb76c4b7e14416ab201ee35bab7434", "64bb8f7afd40423491b018388c298fe3", "d3ae66e2ac104b929072434187ce29f1", "ddb1075518c34fb2931b7ef24cccfc26", "bbcd7a8dd261459ca39c0da46c09016c", "2ff1219ba37a445d8353c3de82eb28da", "e8f44559c28441218ea6f8d4f683b624", "10479a9321ad4094939f191bd7e01b3e", "e3a6155dd3d547a7869e47b4bc331add" ] }, "id": "DAd1-0nLfcej", "outputId": "a95627fe-16aa-4f3d-d63f-f80b10ba9529" }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/665 [00:00 1024). Running this sequence through the model will result in indexing errors\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Finetune Model" ], "metadata": { "id": "6szJYteUf9L3" } }, { "cell_type": "code", "source": [ "# model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n", "model = AutoModelWithLMHead.from_pretrained(model_name, cache_dir=Path('cache').resolve())\n", "\n", "# necessary because of additional bos, eos, pad tokens to embeddings\n", "model.resize_token_embeddings(len(tokenizer))\n", "\n", "# create optimizer and learning rate schedule \n", "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, eps=epsilon)\n", "\n", "training_steps = len(train_dataset) * epochs\n", "\n", "# adjust learning rate during training\n", "scheduler = get_linear_schedule_with_warmup(optimizer, \n", " num_warmup_steps = warmup_steps, \n", " num_training_steps = training_steps)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 138, "referenced_widgets": [ "1df22013cc6a48c6b5fe61742c65aa48", "b72126e72996421ca81863423f2cb3c5", "657159a5b54e4924b1b6b5ef605b1700", "64ff22a46a9a4908a2d1472c5fa7e99f", "35d93ff67e0349fcb5390994d5d26bcd", "daacd6921e604328b09ee58bbaded02b", "306b15aac2734cf6b883a057e9f1fc56", "6e26600be4d04f3e943e0ab44c4845e6", "f50b3c09b56f4f70b06dc84ef2510624", "17460a9051ae45989b82f0039cf71947", "83caf28cd98c4c49ae34f89e1447c752", "28c5a75d84994cb49ab6e93d3e0c6c07", "d01fc3bfc2ac459f8a19f7d7b6fc875c", "9ef59238c50449c6a2430277c4aef15a", "6439123840de43568b89ff5208848af9", "ba1f8ae7d6d746d1b4c7e67fec7a6b1c", "82997a4ce4fb4cf2bfea595b1a518759", "71fbc75efbca443da2c64ac83899e9a6", "bcc3d514e5ca48e980b5d9543699bc47", "fb9bc6fb13784b5794b17890fdb8099a", "3542e2b6625b446c925f272358906eb2", "f220af18d641462c8de743f8ebb0d2dc" ] }, "id": "Nv-bFNB1f68X", "outputId": "04a81f6a-cac1-4fc0-a79a-2828efd66467" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.9/dist-packages/transformers/models/auto/modeling_auto.py:921: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/665 [00:00\n", " \n", " \n", " \n", " [ 875/1705 16:01 < 15:13, 0.91 it/s, Epoch 2.56/5]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Generate tweets" ], "metadata": { "id": "vJLUI-tSgtaX" } }, { "cell_type": "code", "source": [ "trump = pipeline(\"text-generation\", model=f\"./{model_name}-{model_size}-trump\", tokenizer=tokenizer, config={\"max_length\":140})" ], "metadata": { "id": "mnpf7Y9zgiUt" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "# give Trump a prompt\n", "trump('The democrats have')" ], "metadata": { "id": "gnVtF1K_h473", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c951da78-e606-4156-e27c-23fb78de0f2d" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[{'generated_text': 'The democrats have no respect for the people of this Country.” @CynthiaLM\\nThe GOP should move quickly to fix ObamaCare and defund ObamaCare fast!\\n\"\"\"\"\"@nathalie_k: @realDonaldTrump you\\'ll'}]" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "trump('Why does the lying news media')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H02NvY6lEPTJ", "outputId": "bb2f6c81-9505-46de-dc14-d6cf12d72067" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[{'generated_text': 'Why does the lying news media destroy credibility in the House?\"\\n@TrentBaxter8 Thanks--a great book.\\n\"With all due respect to @BarackObama, the great United States Military is not yet fully operational and is'}]" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "trump(\"Today I'll be\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "n8BoiGLGEScg", "outputId": "2772352f-5433-486d-f741-7c9d7b7a2652" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "[{'generated_text': 'Today I\\'ll be on @foxandfriends today live from Palm Beach... #CelebrityApprentice\\n\"....I am a winner, but also a winner with a great future. In this case, a great future for Americans at large!'}]" ] }, "metadata": {}, "execution_count": 13 } ] } ] }