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"cells": [
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"cell_type": "markdown",
"source": [
"Задача - определить диалект португальского языка по фразе.\n",
"\n",
"Данные взяты из субтитров кофнеренции TED"
],
"metadata": {
"id": "qF-Rki_SBT7-"
}
},
{
"cell_type": "markdown",
"source": [
"### Подготовка данных"
],
"metadata": {
"id": "gjsF2U3a9_HR"
}
},
{
"cell_type": "code",
"source": [
"!wget https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt.txt.gz\n",
"!wget https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt_br.txt.gz\n",
"!gunzip pt.txt.gz\n",
"!gunzip pt_br.txt.gz\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XwRRwPFp9-JV",
"outputId": "acfbaa31-8efd-45ed-cc42-8b124fac681e"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2023-04-15 10:19:27-- https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt.txt.gz\n",
"Resolving object.pouta.csc.fi (object.pouta.csc.fi)... 86.50.254.19\n",
"Connecting to object.pouta.csc.fi (object.pouta.csc.fi)|86.50.254.19|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 11610902 (11M) [application/gzip]\n",
"Saving to: ‘pt.txt.gz’\n",
"\n",
"pt.txt.gz 100%[===================>] 11.07M 4.32MB/s in 2.6s \n",
"\n",
"2023-04-15 10:19:31 (4.32 MB/s) - ‘pt.txt.gz’ saved [11610902/11610902]\n",
"\n",
"--2023-04-15 10:19:31-- https://object.pouta.csc.fi/OPUS-TED2020/v1/mono/pt_br.txt.gz\n",
"Resolving object.pouta.csc.fi (object.pouta.csc.fi)... 86.50.254.19\n",
"Connecting to object.pouta.csc.fi (object.pouta.csc.fi)|86.50.254.19|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 14802314 (14M) [application/gzip]\n",
"Saving to: ‘pt_br.txt.gz’\n",
"\n",
"pt_br.txt.gz 100%[===================>] 14.12M 4.94MB/s in 2.9s \n",
"\n",
"2023-04-15 10:19:35 (4.94 MB/s) - ‘pt_br.txt.gz’ saved [14802314/14802314]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"pt = pd.read_csv('pt.txt', sep='\\t', names=['text'])\n",
"pt['target'] = 'pt'\n",
"pt_br = pd.read_csv('pt_br.txt', sep='\\t', names=['text'])\n",
"pt_br['target'] = 'pt_br'\n",
"data = pd.concat([pt[:10000], pt_br[:10000]], ignore_index=True)"
],
"metadata": {
"id": "DIZD8gBB-6mo"
},
"execution_count": 35,
"outputs": []
},
{
"cell_type": "code",
"source": [
"data"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 423
},
"id": "FQdiHJihD46w",
"outputId": "5de3ab8d-032a-4ed9-dab5-8961ad3b19e3"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" text target\n",
"0 Em meados do século XVI, os italianos eram cat... pt\n",
"1 Porém, este dom tinha um alto preço. pt\n",
"2 Para impedir a alteração da voz, esses cantore... pt\n",
"3 Conhecidos por \"castrati\", estas vozes agudas ... pt\n",
"4 Embora o crescimento vocal retardado possa pro... pt\n",
"... ... ...\n",
"49995 Como você acha que seria o mundo se você fosse... pt_br\n",
"49996 Bem, a resposta é nada. pt_br\n",
"49997 A razão por que você não consegue enxergar no ... pt_br\n",
"49998 Para ver uma maçã, a luz tem que atingir a maç... pt_br\n",
"49999 Então, as retinas em seus olhos recebem o refl... pt_br\n",
"\n",
"[50000 rows x 2 columns]"
],
"text/html": [
"\n",
" <div id=\"df-f5ef419f-52cd-4661-92e3-493f6c907f3e\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <tbody>\n",
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" <td>pt</td>\n",
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" <tr>\n",
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" <td>Porém, este dom tinha um alto preço.</td>\n",
" <td>pt</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>Para impedir a alteração da voz, esses cantore...</td>\n",
" <td>pt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Conhecidos por \"castrati\", estas vozes agudas ...</td>\n",
" <td>pt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Embora o crescimento vocal retardado possa pro...</td>\n",
" <td>pt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49995</th>\n",
" <td>Como você acha que seria o mundo se você fosse...</td>\n",
" <td>pt_br</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49996</th>\n",
" <td>Bem, a resposta é nada.</td>\n",
" <td>pt_br</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49997</th>\n",
" <td>A razão por que você não consegue enxergar no ...</td>\n",
" <td>pt_br</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49998</th>\n",
" <td>Para ver uma maçã, a luz tem que atingir a maç...</td>\n",
" <td>pt_br</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49999</th>\n",
" <td>Então, as retinas em seus olhos recebem o refl...</td>\n",
" <td>pt_br</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>50000 rows × 2 columns</p>\n",
"</div>\n",
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" title=\"Convert this dataframe to an interactive table.\"\n",
" style=\"display:none;\">\n",
" \n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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" </svg>\n",
" </button>\n",
" \n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" flex-wrap:wrap;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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"\n",
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" const buttonEl =\n",
" document.querySelector('#df-f5ef419f-52cd-4661-92e3-493f6c907f3e button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-f5ef419f-52cd-4661-92e3-493f6c907f3e');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
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]
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "markdown",
"source": [
"### Файнтюн модели"
],
"metadata": {
"id": "u-03LX5EBtED"
}
},
{
"cell_type": "code",
"execution_count": 5,
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"outputId": "1933d7b0-0ea1-485c-a1b9-05066786f448"
},
"outputs": [
{
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"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting transformers\n",
" Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m98.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
" Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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"Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
]
}
],
"source": [
"!pip install transformers\n",
"import transformers"
]
},
{
"cell_type": "code",
"source": [
"!pip install datasets"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "D8OtthEoJ-jJ",
"outputId": "aea9ef62-08cd-40c5-8d26-4590fb87de8b"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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"Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets\n",
"Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer, TrainingArguments, Trainer, IntervalStrategy\n",
"from datasets import load_metric, Dataset, ClassLabel\n",
"\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" data,\n",
" data['target'],\n",
" test_size=0.1,\n",
" random_state=42\n",
")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False)\n",
"\n",
"class_label = ClassLabel(names=['pt','pt_br'])\n",
"\n",
"def tokenize(text):\n",
" tokens = tokenizer(text['text'], padding=True, truncation=True, max_length=256, add_special_tokens = True)\n",
" tokens['label'] = class_label.str2int(text['target'])\n",
" return tokens\n",
"\n",
"train_ds = Dataset.from_pandas(X_train).map(tokenize, batched=True)\n",
"test_ds = Dataset.from_pandas(X_test).map(tokenize, batched=True)\n",
"\n",
"train_ds.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])\n",
"test_ds.set_format('torch', columns=['input_ids', 'attention_mask', 'label'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
"79e7afed6f204ced8012f9853e8ce153",
"92e3b0a9ee344022b0bf16a6617239b3",
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"3b905f68919049608a3eb3c48da12032",
"eee35ecb15c84ee994f9ffa479feeaaf",
"c17b6f3b3436490e923835add11f8d74",
"4d5a02eb385d470f8b3c68a8db60561c",
"7946964d5e8741ba86b2662397ccb891",
"5624c10fc93943dda1e0c93c1fc2fbba",
"101f117dfb0a441e807757524b857350",
"77819fe20f514982b8547abff9361b2d",
"8f76ba596bdc427aa868685b151f51d8",
"a14ec2755a2a477aa654287af8a463a3",
"cb82b162aae342888f353ea2fe507015",
"ebb3365ce29e476ab28201e9b825e302",
"31e7fabdde2f4129ab38d49b5c5b1d50",
"b2fb5695afd04756975281c5a04fbcaf",
"6e358ea9a32a486db0c81a5f54a112e9",
"647a42b85a8044229d71fd1e7318878b",
"f439095945fb413e916059f01f67bc79",
"fece699d83854b5e876164da8a3b6f3a",
"814444ea270d4739aa8588937801800b"
]
},
"id": "Nf7Cew2HLKi6",
"outputId": "f107d518-4016-4d4a-d3b8-161f4724bce4"
},
"execution_count": 46,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/18000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "79e7afed6f204ced8012f9853e8ce153"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/2000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8f76ba596bdc427aa868685b151f51d8"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
"\n",
"model = BertForSequenceClassification.from_pretrained(\n",
" 'adalbertojunior/distilbert-portuguese-cased',\n",
" num_labels = 2,\n",
" output_attentions = False, \n",
" output_hidden_states = False,\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 105,
"referenced_widgets": [
"00047affc56f494c82268e7a8bc2c53c",
"dd7a5a0c04fa4b66a0b5078b0a3e59e0",
"eeaf71899a6743a081f79552dfc1672b",
"23f2d28b36c84ea78422ef020d0d5e4b",
"02a6351920ba4a0689b19bb9531dc8df",
"2859d235432e4d12a78f55412ba8c569",
"7a8e9cb5072b47fa9a5cee174d29bc47",
"b6f515df241e44f7ba04114423fc1f7e",
"7fad565b40de40c2b3d8d72d8c50111e",
"0c271f3f91314bd094d8db4834c90e41",
"243a2bc40dbd48d2910616fa0e1a01a6"
]
},
"id": "xe7ooV6LXHrm",
"outputId": "f4e0e785-ce91-4be3-cb19-675f94dc3f46"
},
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/266M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "00047affc56f494c82268e7a8bc2c53c"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at adalbertojunior/distilbert-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"model.to(device)\n",
"device"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "0PZkmqv9br7j",
"outputId": "8710153b-357e-4116-93d7-ced770ef137b"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'cuda'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"params = list(model.named_parameters())\n",
"\n",
"print('The BERT model has {:} different named parameters.\\n'.format(len(params)))\n",
"\n",
"print('==== Embedding Layer ====\\n')\n",
"\n",
"for p in params[0:5]:\n",
" print(\"{:<55} {:>12} {}\".format(p[0], str(tuple(p[1].size())), p[1].requires_grad))\n",
"\n",
"print('\\n==== First Transformer ====\\n')\n",
"\n",
"for p in params[5:21]:\n",
" print(\"{:<55} {:>12} {}\".format(p[0], str(tuple(p[1].size())), p[1].requires_grad))\n",
"\n",
"print('\\n==== Output Layer ====\\n')\n",
"\n",
"for p in params[-4:]:\n",
" print(\"{:<55} {:>12} {}\".format(p[0], str(tuple(p[1].size())), p[1].requires_grad))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6KDOKbGCXHuQ",
"outputId": "5d4cd088-3a0e-4be8-e7a4-427f3ba087c7"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The BERT model has 105 different named parameters.\n",
"\n",
"==== Embedding Layer ====\n",
"\n",
"bert.embeddings.word_embeddings.weight (29794, 768) False\n",
"bert.embeddings.position_embeddings.weight (512, 768) False\n",
"bert.embeddings.token_type_embeddings.weight (2, 768) False\n",
"bert.embeddings.LayerNorm.weight (768,) False\n",
"bert.embeddings.LayerNorm.bias (768,) False\n",
"\n",
"==== First Transformer ====\n",
"\n",
"bert.encoder.layer.0.attention.self.query.weight (768, 768) False\n",
"bert.encoder.layer.0.attention.self.query.bias (768,) False\n",
"bert.encoder.layer.0.attention.self.key.weight (768, 768) False\n",
"bert.encoder.layer.0.attention.self.key.bias (768,) False\n",
"bert.encoder.layer.0.attention.self.value.weight (768, 768) False\n",
"bert.encoder.layer.0.attention.self.value.bias (768,) False\n",
"bert.encoder.layer.0.attention.output.dense.weight (768, 768) False\n",
"bert.encoder.layer.0.attention.output.dense.bias (768,) False\n",
"bert.encoder.layer.0.attention.output.LayerNorm.weight (768,) False\n",
"bert.encoder.layer.0.attention.output.LayerNorm.bias (768,) False\n",
"bert.encoder.layer.0.intermediate.dense.weight (3072, 768) False\n",
"bert.encoder.layer.0.intermediate.dense.bias (3072,) False\n",
"bert.encoder.layer.0.output.dense.weight (768, 3072) False\n",
"bert.encoder.layer.0.output.dense.bias (768,) False\n",
"bert.encoder.layer.0.output.LayerNorm.weight (768,) False\n",
"bert.encoder.layer.0.output.LayerNorm.bias (768,) False\n",
"\n",
"==== Output Layer ====\n",
"\n",
"bert.pooler.dense.weight (768, 768) False\n",
"bert.pooler.dense.bias (768,) False\n",
"classifier.weight (2, 768) True\n",
"classifier.bias (2,) True\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"for parameter in model.parameters():\n",
" parameter.requires_grad = False\n",
"for parameter in model.classifier.parameters():\n",
" parameter.requires_grad = True"
],
"metadata": {
"id": "9DOy7eY5Y_px"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./trainer_out\",\n",
" learning_rate=2e-4,\n",
" per_device_train_batch_size=256,\n",
" per_device_eval_batch_size=256,\n",
" num_train_epochs=5,\n",
" weight_decay=0.01,\n",
" evaluation_strategy=IntervalStrategy.EPOCH,\n",
")\n",
"\n",
"metric = load_metric('accuracy')\n",
"\n",
"def compute_metrics(eval_pred):\n",
" logits, labels = eval_pred\n",
" predictions = np.argmax(logits, axis=-1)\n",
" return metric.compute(predictions=predictions, references=labels)\n",
"\n",
"# # transfer learning\n",
"# for module in list(model.modules())[:-3]:\n",
"# for param in module.parameters():\n",
"# param.requires_grad = False\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=train_ds,\n",
" eval_dataset=test_ds,\n",
" tokenizer=tokenizer,\n",
" compute_metrics=compute_metrics,\n",
")\n",
"\n",
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 362
},
"id": "WiAQCmLZXHzy",
"outputId": "a9f508ae-3eb8-4b7d-d333-193f2d236796"
},
"execution_count": 47,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
"You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='355' max='355' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [355/355 12:02, Epoch 5/5]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>0.634915</td>\n",
" <td>0.663500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>No log</td>\n",
" <td>0.628911</td>\n",
" <td>0.670500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>No log</td>\n",
" <td>0.625601</td>\n",
" <td>0.669000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>No log</td>\n",
" <td>0.623909</td>\n",
" <td>0.664000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>No log</td>\n",
" <td>0.623280</td>\n",
" <td>0.664500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=355, training_loss=0.6338723196110255, metrics={'train_runtime': 724.7707, 'train_samples_per_second': 124.177, 'train_steps_per_second': 0.49, 'total_flos': 5961032939520000.0, 'train_loss': 0.6338723196110255, 'epoch': 5.0})"
]
},
"metadata": {},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"source": [
"trainer.train()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 287
},
"id": "ounT1oDOniUI",
"outputId": "50d5bd72-eab2-4be8-9f1d-04120b97cce9"
},
"execution_count": 53,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='355' max='355' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [355/355 12:01, Epoch 5/5]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Epoch</th>\n",
" <th>Training Loss</th>\n",
" <th>Validation Loss</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>No log</td>\n",
" <td>0.623280</td>\n",
" <td>0.664500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>No log</td>\n",
" <td>0.623280</td>\n",
" <td>0.664500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>No log</td>\n",
" <td>0.623280</td>\n",
" <td>0.664500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>No log</td>\n",
" <td>0.623280</td>\n",
" <td>0.664500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>No log</td>\n",
" <td>0.623280</td>\n",
" <td>0.664500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=355, training_loss=0.6281896456866197, metrics={'train_runtime': 723.41, 'train_samples_per_second': 124.411, 'train_steps_per_second': 0.491, 'total_flos': 5961032939520000.0, 'train_loss': 0.6281896456866197, 'epoch': 5.0})"
]
},
"metadata": {},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"source": [
"trainer.save_model('./pt_br_model')"
],
"metadata": {
"id": "Bp7qkdjkXH2j"
},
"execution_count": 54,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!tar jcpvf model.tar.bz2 pt_br_model"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Wmu5JQSue0Hb",
"outputId": "54dae7e4-0c29-46ad-e304-04ab5703f1aa"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"pt_br_model/\n",
"pt_br_model/tokenizer_config.json\n",
"pt_br_model/training_args.bin\n",
"pt_br_model/vocab.txt\n",
"pt_br_model/pytorch_model.bin\n",
"pt_br_model/tokenizer.json\n",
"pt_br_model/config.json\n",
"pt_br_model/special_tokens_map.json\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!ls -al ./pt_br_model/*"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "evbwLlLIi3QK",
"outputId": "c1736d23-8d93-4597-ed76-3428d8d75e82"
},
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-rw-r--r-- 1 root root 752 Apr 15 10:51 ./pt_br_model/config.json\n",
"-rw-r--r-- 1 root root 265629621 Apr 15 10:51 ./pt_br_model/pytorch_model.bin\n",
"-rw-r--r-- 1 root root 125 Apr 15 10:51 ./pt_br_model/special_tokens_map.json\n",
"-rw-r--r-- 1 root root 395 Apr 15 10:51 ./pt_br_model/tokenizer_config.json\n",
"-rw-r--r-- 1 root root 678043 Apr 15 10:51 ./pt_br_model/tokenizer.json\n",
"-rw-r--r-- 1 root root 3579 Apr 15 10:51 ./pt_br_model/training_args.bin\n",
"-rw-r--r-- 1 root root 209528 Apr 15 10:51 ./pt_br_model/vocab.txt\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!ls -al"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "y68jWdM5jJJD",
"outputId": "f9910474-5a0b-44da-b88d-ecaed8fae13a"
},
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 317924\n",
"drwxr-xr-x 1 root root 4096 Apr 15 10:51 .\n",
"drwxr-xr-x 1 root root 4096 Apr 15 10:18 ..\n",
"drwxr-xr-x 4 root root 4096 Apr 13 13:29 .config\n",
"-rw-r--r-- 1 root root 252222673 Apr 15 10:52 model.tar.bz2\n",
"drwxr-xr-x 2 root root 4096 Apr 15 10:51 pt_br_model\n",
"-rw-r--r-- 1 root root 41144160 Dec 3 2020 pt_br.txt\n",
"-rw-r--r-- 1 root root 32155453 Dec 3 2020 pt.txt\n",
"drwxr-xr-x 1 root root 4096 Apr 13 13:30 sample_data\n",
"drwxr-xr-x 3 root root 4096 Apr 15 10:25 trainer_out\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!cp model.tar.bz2 /content/drive/MyDrive"
],
"metadata": {
"id": "_4BB3YnvRHZw"
},
"execution_count": 56,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(transformers.__version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K-B-mwGryWOw",
"outputId": "1742e546-04d3-47d7-e1bf-34da07ef35b8"
},
"execution_count": 58,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"4.28.1\n"
]
}
]
}
]
}