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},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"source": [
"!unzip train_data.zip"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VRqtDwq0_i9x",
"outputId": "856702f4-4491-461d-e56c-92b65944cb35"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Archive: train_data.rar\n",
" End-of-central-directory signature not found. Either this file is not\n",
" a zipfile, or it constitutes one disk of a multi-part archive. In the\n",
" latter case the central directory and zipfile comment will be found on\n",
" the last disk(s) of this archive.\n",
"unzip: cannot find zipfile directory in one of train_data.rar or\n",
" train_data.rar.zip, and cannot find train_data.rar.ZIP, period.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install peft datasets transformers[torch] accelerate -q"
],
"metadata": {
"id": "Shijw55Hhpnc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a92efdb0-6c6c-49a5-d116-a066f203fe99"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/190.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.9/190.9 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/510.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m32.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.0/280.0 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import torch\n",
"import pathlib\n",
"import pandas as pd\n",
"from IPython.display import clear_output"
],
"metadata": {
"id": "ynbp4mfyFkjU"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ROOT_DIR = os.path.abspath(os.getcwd())\n",
"ROOT_DIR"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "Segh-5MjFzLJ",
"outputId": "f5685df7-9384-469d-a070-fdb656a44412"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"source": [
"По факту можно написать"
],
"metadata": {
"id": "VvS6ovZUKWFH"
}
},
{
"cell_type": "code",
"source": [
"CHARACTER = \"rachel\"\n",
"model_type = \"gpt2-large\" # 'gpt2', 'gpt2-medium', 'gpt2-large'"
],
"metadata": {
"id": "kIRqGDy3KQBO"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer\n",
"from transformers import pipeline\n",
"from transformers import TextDataset, DataCollatorForLanguageModeling\n",
"from transformers import Trainer, TrainingArguments,AutoModelWithLMHead"
],
"metadata": {
"id": "_ZZbK_z8LmEB"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Training on monologues"
],
"metadata": {
"id": "NBJMq1rZL-tN"
}
},
{
"cell_type": "code",
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_type)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286,
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},
"id": "xb3UH4QDMCn7",
"outputId": "9dacffec-63da-4748-acf9-8997edfb4434"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
],
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}
},
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/666 [00:00<?, ?B/s]"
],
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"vocab.json: 0%| | 0.00/1.04M [00:00<?, ?B/s]"
],
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"version_major": 2,
"version_minor": 0,
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}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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],
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},
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},
{
"output_type": "display_data",
"data": {
"text/plain": [
"tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
],
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}
]
},
{
"cell_type": "code",
"source": [
"block_size_ = 128"
],
"metadata": {
"id": "p82b2v71NZli"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Define paths to training data"
],
"metadata": {
"id": "BauwsJAaU5-K"
}
},
{
"cell_type": "code",
"source": [
"path_to_single_data = './train_data/en/single/'\n",
"train_path = path_to_single_data + f'train_{CHARACTER}_single.txt'\n",
"test_path = path_to_single_data + f'valid_{CHARACTER}_single.txt'\n",
"train_path, test_path"
],
"metadata": {
"id": "sP6BwnrYP0CN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4bab69c5-52c8-4a28-ecf9-d51c47640da1"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('./train_data/en/single/train_rachel_single.txt',\n",
" './train_data/en/single/valid_rachel_single.txt')"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"source": [
"Create datasets"
],
"metadata": {
"id": "jWoizKOuU9Jf"
}
},
{
"cell_type": "code",
"source": [
"def load_dataset(train_path,test_path,tokenizer):\n",
" train_dataset = TextDataset(\n",
" tokenizer=tokenizer,\n",
" file_path=train_path,\n",
" block_size=block_size_)\n",
"\n",
" test_dataset = TextDataset(\n",
" tokenizer=tokenizer,\n",
" file_path=test_path,\n",
" block_size=block_size_)\n",
"\n",
" data_collator = DataCollatorForLanguageModeling(\n",
" tokenizer=tokenizer, mlm=False,\n",
" )\n",
" return train_dataset,test_dataset,data_collator\n",
"\n",
"train_dataset, test_dataset, data_collator = load_dataset(train_path, test_path, tokenizer)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nFkqOk14NdwX",
"outputId": "07970029-5478-4725-ce93-60def1310f3d"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
" warnings.warn(\n",
"Token indices sequence length is longer than the specified maximum sequence length for this model (270493 > 1024). Running this sequence through the model will result in indexing errors\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!mkdir ./models"
],
"metadata": {
"id": "rWmnGJzsVTV4"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = AutoModelWithLMHead.from_pretrained(model_type)\n",
"output_folder = f\"./models/en_{model_type}_{CHARACTER}_mono\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 137,
"referenced_widgets": [
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},
"id": "SagaGDCBQNEl",
"outputId": "20b6cef5-2ac1-4fc7-f822-cfeece689129"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/modeling_auto.py:1595: 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": [
"model.safetensors: 0%| | 0.00/3.25G [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8343f9ada7d84533837d35815dd900b6"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"generation_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a9326eb1d78e4336b5efd65a4c58d48f"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Define arguments for trainings"
],
"metadata": {
"id": "93K6cep6VG-s"
}
},
{
"cell_type": "code",
"source": [
"training_args = TrainingArguments(\n",
" output_dir = output_folder,\n",
" overwrite_output_dir=True, #overwrite the content of the output directory\n",
" num_train_epochs=5, # number of training epochs\n",
" learning_rate=0.00001, # learning rate\n",
" lr_scheduler_type=\"linear\",\n",
" per_device_train_batch_size=8, # batch size for training\n",
" per_device_eval_batch_size=8, # batch size for evaluation\n",
" eval_steps = 200, # number of update steps between two evaluations\n",
" save_steps = 200, # after # steps model is saved\n",
" save_strategy = \"steps\",\n",
" evaluation_strategy = \"steps\",\n",
" dataloader_drop_last=True, # avoid an error with an incomplete batch\n",
" fp16=False,\n",
" save_total_limit = 1, # Only last 5 models are saved. Older ones are deleted.\n",
" load_best_model_at_end=True,\n",
" )\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=test_dataset,\n",
")"
],
"metadata": {
"id": "Fo8ajxFKfLdX"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "code",
"source": [
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "KghUW2flUkp7",
"outputId": "912d42f4-7bab-4898-9200-35c3a1b50818"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1320' max='1320' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1320/1320 12:32, Epoch 5/5]\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",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>No log</td>\n",
" <td>1.390404</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>No log</td>\n",
" <td>1.377137</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>1.398500</td>\n",
" <td>1.380381</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>1.398500</td>\n",
" <td>1.380880</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>1.239800</td>\n",
" <td>1.387712</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>1.239800</td>\n",
" <td>1.397241</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"There were missing keys in the checkpoint model loaded: ['lm_head.weight'].\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1320, training_loss=1.2858209552186908, metrics={'train_runtime': 751.9269, 'train_samples_per_second': 14.051, 'train_steps_per_second': 1.755, 'total_flos': 5745102815232000.0, 'train_loss': 1.2858209552186908, 'epoch': 5.0})"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"source": [
"trainer.save_model()"
],
"metadata": {
"id": "Hzb8Zm4zYJkc"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# The name of the task to train.\n",
"TASK_NAME = f\"{model_type}_mono_en_{CHARACTER}\"\n",
"\n",
"# The output directory where the fine-tuned model and checkpoints will be written.\n",
"OUTPUT_DIR = f'{ROOT_DIR}/outputs/{TASK_NAME}/'\n",
"pathlib.Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)\n",
"\n",
"CONFIG_NAME = \"config.json\"\n",
"WEIGHTS_NAME = f\"{TASK_NAME}_pytorch_model.bin\""
],
"metadata": {
"id": "Kqbabg4afTjj"
},
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def saver(model, OUTPUT_DIR, WEIGHTS_NAME):\n",
" model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self\n",
"\n",
" # If we save using the predefined names, we can load using `from_pretrained`\n",
" output_model_file = os.path.join(OUTPUT_DIR, WEIGHTS_NAME)\n",
" output_config_file = os.path.join(OUTPUT_DIR, CONFIG_NAME)\n",
"\n",
" torch.save(model_to_save.state_dict(), output_model_file)\n",
" model_to_save.config.to_json_file(output_config_file)\n",
"\n",
"saver(model, OUTPUT_DIR, WEIGHTS_NAME)"
],
"metadata": {
"id": "SYbsx5ltfv49"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"path_to_dialogs_data = './train_data/en/replics/'\n",
"train_path = path_to_dialogs_data + f'train_{CHARACTER}_replics.txt'\n",
"test_path = path_to_dialogs_data + f'valid_{CHARACTER}_replics.txt'\n",
"train_path, test_path"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dB7DqnX6kAdZ",
"outputId": "23ee3d57-e363-49ea-a55c-bcc04be1bd0d"
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('./train_data/en/replics/train_rachel_replics.txt',\n",
" './train_data/en/replics/valid_rachel_replics.txt')"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"train_dataset, test_dataset, data_collator = load_dataset(train_path, test_path, tokenizer)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LotVFrEFZun9",
"outputId": "c75d2226-6a2d-4b5f-e1b9-6f769a4e517a"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model = AutoModelWithLMHead.from_pretrained(output_folder)\n",
"final_output_folder = f\"./models/en_{model_type}_{CHARACTER}_replics\""
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AWJ7VKkbkJPL",
"outputId": "b8c7b6ff-3022-42a2-d22c-a79f4ea676f1"
},
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/models/auto/modeling_auto.py:1595: 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"
]
}
]
},
{
"cell_type": "code",
"source": [
"training_args = TrainingArguments(\n",
" output_dir = final_output_folder,\n",
" overwrite_output_dir=True, #overwrite the content of the output directory\n",
" num_train_epochs=5, # number of training epochs\n",
" learning_rate=0.00001, # learning rate\n",
" lr_scheduler_type=\"linear\",\n",
" per_device_train_batch_size=6, # batch size for training\n",
" per_device_eval_batch_size=6, # batch size for evaluation\n",
" eval_steps = 400, # number of update steps between two evaluations\n",
" save_steps = 400, # after # steps model is saved\n",
" save_strategy = \"steps\",\n",
" evaluation_strategy = \"steps\",\n",
" dataloader_drop_last=True, # avoid an error with an incomplete batch\n",
" fp16=False,\n",
" save_total_limit = 1, # Only last 5 models are saved. Older ones are deleted.\n",
" load_best_model_at_end=True,\n",
" )\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=train_dataset,\n",
" eval_dataset=test_dataset,\n",
")"
],
"metadata": {
"id": "STJ1g8v-kRGD"
},
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"source": [
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 237
},
"id": "Jig-B64qkjgC",
"outputId": "6a62298c-ada3-4b2b-b97a-5bad131dcfa1"
},
"execution_count": 26,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='1990' max='1990' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1990/1990 12:57, Epoch 5/5]\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",
" <th>Validation Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>No log</td>\n",
" <td>1.924102</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>1.968700</td>\n",
" <td>1.920127</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>1.790500</td>\n",
" <td>1.930205</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1600</td>\n",
" <td>1.687200</td>\n",
" <td>1.949348</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"There were missing keys in the checkpoint model loaded: ['lm_head.weight'].\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1990, training_loss=1.7659926218004083, metrics={'train_runtime': 776.6032, 'train_samples_per_second': 15.381, 'train_steps_per_second': 2.562, 'total_flos': 6495883296768000.0, 'train_loss': 1.7659926218004083, 'epoch': 5.0})"
]
},
"metadata": {},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"source": [
"trainer.save_model()"
],
"metadata": {
"id": "3bYMJeMdqEZN"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# The name of the task to train.\n",
"TASK_NAME = f'{model_type}_mono_replics_en_{CHARACTER}'\n",
"\n",
"# The output directory where the fine-tuned model and checkpoints will be written.\n",
"OUTPUT_DIR = f'{ROOT_DIR}/outputs/{TASK_NAME}/'\n",
"pathlib.Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)\n",
"\n",
"CONFIG_NAME = \"config.json\"\n",
"WEIGHTS_NAME = f\"{TASK_NAME}_pytorch_model.bin\"\n",
"\n",
"saver(model, OUTPUT_DIR, WEIGHTS_NAME)"
],
"metadata": {
"id": "b2K3lFaMkntr"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"chef = pipeline('text-generation', model=\"./models/en_gpt2-large_rachel_replics\", tokenizer=model_type)"
],
"metadata": {
"id": "Wv7iMsSCntjL"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tmp = chef(f\"<s>NOTFRIEND: Do you like Ross?\\nRACHEL:\")[0]['generated_text']\n",
"tmp = tmp[tmp.find(f\"RACHEL: \") + len(\"RACHEL\") + 2 : tmp.find('</s>')]\n",
"tmp"
],
"metadata": {
"id": "-pfVaiGQhZh0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"message1 = \"Hi, Rachel! How are you?\"\n",
"message2 = \"Glad to hear it! Where are you going?\""
],
"metadata": {
"id": "BvHVpTx9tGY2"
},
"execution_count": 32,
"outputs": []
},
{
"cell_type": "code",
"source": [
"tmp = chef(f\"<s>NOTFRIEND: {message1}\\nRACHEL:\")[0]['generated_text']"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QD2AcGJAXpao",
"outputId": "a0055cf7-b9c0-4b59-8294-9a8b74ef4462"
},
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"tmp = tmp[tmp.find(f\"RACHEL: \") + len(\"RACHEL\") + 2 : tmp.find('</s>')]\n",
"tmp"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "bRa5FDNzXxZq",
"outputId": "0678e5da-42c1-4ffa-cb1a-8163bab9f6eb"
},
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Oh, it’s not bad, it’s like, perfect. I’m sorry, I know we should be togethe'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"message1 = \"Hi, Rachel! How are you?\"\n",
"text = f\"NOTFRIEND: {message1}\\nRACHEL:\"\n",
"inpt = tokenizer.encode(text, return_tensors=\"pt\")"
],
"metadata": {
"id": "b35Ylb9_YmnS"
},
"execution_count": 64,
"outputs": []
},
{
"cell_type": "code",
"source": [
"out = model.generate(inpt.cuda(),\n",
" max_length=50,\n",
" repetition_penalty=5.0,\n",
" do_sample=True,\n",
" top_k=5,\n",
" top_p=0.95,\n",
" temperature=1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "A1920Yb1ahIt",
"outputId": "1786c240-8a88-41d4-c8a2-3b74e08e8e09"
},
"execution_count": 65,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
"Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"generated_text = tokenizer.decode(out[0])\n",
"generated_text = generated_text[generated_text.find(f\"RACHEL: \") + len(\"RACHEL\") + 2 : generated_text.find('</s>')]"
],
"metadata": {
"id": "nic2z2A3a1FP"
},
"execution_count": 67,
"outputs": []
},
{
"cell_type": "code",
"source": [
"log_msg = \"Model generates reply {} for {}\".format(generated_text, message1)\n",
"log_msg"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "70xEEO6pbXwA",
"outputId": "ec688f61-4531-4570-891b-73417936b31c"
},
"execution_count": 68,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"Model generates reply Oh God. I'm good. for Hi, Rachel! How are you?\""
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 68
}
]
}
]
}