Datasets
Models
Downloads: 1
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{
"cells": [
{
"cell_type": "code",
"source": [
"# uncomment if working in colab\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "GYmWcjNhROnd",
"outputId": "da0b1a1c-f14d-4283-9ef4-7c76b14da9b6"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# uncomment if using colab\n",
"!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
"!pip install -q -U datasets\n",
"!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
"!pip install -q -U wandb\n",
"!pip install seqeval\n",
"!pip install -q -U evaluate"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RT7ju9chQ-TN",
"outputId": "8b28eb31-2f6d-4965-939c-70bbcee55193"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m62.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25h Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
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{
"cell_type": "code",
"source": [],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sfBVZ6-3RnEJ",
"outputId": "2c5d357d-e4e9-4c36-a0a3-75b9e2c3c2f4"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m195.4/195.4 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting seqeval\n",
" Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.25.2)\n",
"Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.10/dist-packages (from seqeval) (1.2.2)\n",
"Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.11.4)\n",
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.3.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.3.0)\n",
"Building wheels for collected packages: seqeval\n",
" Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16161 sha256=17c9afa7be0a513eb0bf5f9216667cf46b1911b007f15696165abf7ea6bb4a1a\n",
" Stored in directory: /root/.cache/pip/wheels/1a/67/4a/ad4082dd7dfc30f2abfe4d80a2ed5926a506eb8a972b4767fa\n",
"Successfully built seqeval\n",
"Installing collected packages: seqeval\n",
"Successfully installed seqeval-1.2.2\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pVzcHL0VQ2dm"
},
"source": [
"**Training of Roberta model for token classification on CHIA dataset**"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"id": "RIg5MsnUQ2do"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from transformers import AutoTokenizer, AutoModelForTokenClassification, DataCollatorForTokenClassification, Trainer, TrainingArguments\n",
"from datasets import load_dataset, load_metric\n",
"import evaluate\n",
"import wandb\n",
"import torch"
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
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"height": 145,
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"metadata": {
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"# dict for the entities (entity to int value)\n",
"simple_ent = {\"Condition\", \"Value\", \"Drug\", \"Procedure\", \"Measurement\", \"Temporal\", \"Observation\", \"Person\", \"Device\"}\n",
"sel_ent = {\n",
" \"O\": 0,\n",
" \"B-Condition\": 1,\n",
" \"I-Condition\": 2,\n",
" \"B-Value\": 3,\n",
" \"I-Value\": 4,\n",
" \"B-Drug\": 5,\n",
" \"I-Drug\": 6,\n",
" \"B-Procedure\": 7,\n",
" \"I-Procedure\": 8,\n",
" \"B-Measurement\": 9,\n",
" \"I-Measurement\": 10,\n",
" \"B-Temporal\": 11,\n",
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" \"B-Observation\": 13,\n",
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"\n",
"entities_list = list(sel_ent.keys())\n",
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]
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{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"id": "YRfsfOE_Q2dq"
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"outputs": [],
"source": [
"root = '..'\n",
"root = './drive/MyDrive/TER-LISN-2024'\n",
"data_path = f'{root}/data'\n",
"models_path = f'{root}/models'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "oqsXsCScQ2dq"
},
"outputs": [],
"source": [
"model_name = \"roberta-base\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "rmaT09W3Q2dr"
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_name, add_prefix_space=True)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "Y0e7r-R6Q2dr"
},
"outputs": [],
"source": [
"# tokenize and align the labels in the dataset\n",
"def tokenize_and_align_labels(sentence, tokenizer, flag = 'I'):\n",
" \"\"\"\n",
" Tokenize the sentence and align the labels\n",
" inputs:\n",
" sentence: dict, the sentence from the dataset\n",
" flag: str, the flag to indicate how to deal with the labels for subwords\n",
" - 'I': use the label of the first subword for all subwords but as intermediate (I-ENT)\n",
" - 'B': use the label of the first subword for all subwords as beginning (B-ENT)\n",
" - None: use -100 for subwords\n",
" outputs:\n",
" tokenized_sentence: dict, the tokenized sentence now with a field for the labels\n",
" \"\"\"\n",
" tokenized_sentence = tokenizer(sentence['tokens'], is_split_into_words=True, truncation=True)\n",
"\n",
" labels = []\n",
" for i, labels_s in enumerate(sentence['ner_tags']):\n",
" word_ids = tokenized_sentence.word_ids(batch_index=i)\n",
" previous_word_idx = None\n",
" label_ids = []\n",
" for word_idx in word_ids:\n",
" # if the word_idx is None, assign -100\n",
" if word_idx is None:\n",
" label_ids.append(-100)\n",
" # if it is a new word, assign the corresponding label\n",
" elif word_idx != previous_word_idx:\n",
" label_ids.append(labels_s[word_idx])\n",
" # if it is the same word, check the flag to assign\n",
" else:\n",
" if flag == 'I':\n",
" if entities_list[labels_s[word_idx]].startswith('I'):\n",
" label_ids.append(labels_s[word_idx])\n",
" else:\n",
" label_ids.append(labels_s[word_idx] + 1)\n",
" elif flag == 'B':\n",
" label_ids.append(labels_s[word_idx])\n",
" elif flag == None:\n",
" label_ids.append(-100)\n",
" previous_word_idx = word_idx\n",
" labels.append(label_ids)\n",
" tokenized_sentence['labels'] = labels\n",
" return tokenized_sentence"
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "Z-3Gzl41SbVZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "8_S9_gKQQ2dr"
},
"outputs": [],
"source": [
"dataset = load_dataset('JavierLopetegui/chia_v1')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "G9dj9zvtQ2dr"
},
"outputs": [],
"source": [
"# train_dataset = dataset['train']\n",
"# val_dataset = dataset['val']\n",
"# test_dataset = dataset['test']"
]
},
{
"cell_type": "code",
"source": [
"dataset"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dsE8qaxNUAW1",
"outputId": "9b154b7d-3cd2-403e-fdc5-947230a73c13"
},
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tokens', 'ner_tags', 'file', 'index'],\n",
" num_rows: 8881\n",
" })\n",
" test: Dataset({\n",
" features: ['tokens', 'ner_tags', 'file', 'index'],\n",
" num_rows: 1307\n",
" })\n",
" val: Dataset({\n",
" features: ['tokens', 'ner_tags', 'file', 'index'],\n",
" num_rows: 2221\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"id": "sz4iDo1FQ2dr"
},
"outputs": [],
"source": [
"# tokenize and align the labels in the dataset\n",
"dataset = dataset.map(lambda x: tokenize_and_align_labels(x, tokenizer, 'I'), batched = True)"
]
},
{
"cell_type": "code",
"source": [
"dataset"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-QI0OZpyVhu-",
"outputId": "71113efc-715d-42a0-e8d2-41cff34d6f58"
},
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels'],\n",
" num_rows: 8881\n",
" })\n",
" test: Dataset({\n",
" features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels'],\n",
" num_rows: 1307\n",
" })\n",
" val: Dataset({\n",
" features: ['tokens', 'ner_tags', 'file', 'index', 'input_ids', 'attention_mask', 'labels'],\n",
" num_rows: 2221\n",
" })\n",
"})"
]
},
"metadata": {},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 105,
"referenced_widgets": [
"d7a789dee9474a5186413894b45a34cd",
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"eea715c874904f3f9ed8b5d039282a95",
"9498b06aeb284dd1bec8be7840b87485",
"1ef908c3fe134104827d7af6d42c8e84",
"13bb9d5c92b44a3dafea7f5942d0b2e4",
"06711efe4ee640e4b4a0c10deddce098",
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"1c562c2545374dda93304d14c7db1cef",
"559937dcd4614af8a50e42fe2c7b7524"
]
},
"id": "0ljqQUM0Q2ds",
"outputId": "13a4b1a8-85e9-4fa1-9eff-e48f43657744"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/499M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d7a789dee9474a5186413894b45a34cd"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base 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"
]
}
],
"source": [
"# load the model\n",
"model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=len(entities_list))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"id": "ny-IijvWQ2ds"
},
"outputs": [],
"source": [
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "trZCdceUQ2ds",
"outputId": "c25b7fc8-3bbc-4277-e942-e8262e743628"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RobertaForTokenClassification(\n",
" (roberta): RobertaModel(\n",
" (embeddings): RobertaEmbeddings(\n",
" (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
" (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
" (token_type_embeddings): Embedding(1, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): RobertaEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x RobertaLayer(\n",
" (attention): RobertaAttention(\n",
" (self): RobertaSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): RobertaSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): RobertaIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): RobertaOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (classifier): Linear(in_features=768, out_features=19, bias=True)\n",
")"
]
},
"metadata": {},
"execution_count": 37
}
],
"source": [
"model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"id": "3iIccndnQ2ds"
},
"outputs": [],
"source": [
"# define the training arguments\n",
"args = TrainingArguments(\n",
" report_to = 'wandb',\n",
" run_name = 'chia_ner_with_roberta',\n",
" evaluation_strategy = \"steps\",\n",
" learning_rate=2e-5,\n",
" per_device_train_batch_size=16,\n",
" per_device_eval_batch_size=8,\n",
" num_train_epochs=3,\n",
" weight_decay=0.01,\n",
" logging_steps=50,\n",
" overwrite_output_dir = True,\n",
" eval_steps=50,\n",
" save_steps=1000,\n",
" output_dir = 'chia_ner_with_roberta',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"id": "shMTVQXMQ2ds"
},
"outputs": [],
"source": [
"data_collator = DataCollatorForTokenClassification(tokenizer)"
]
},
{
"cell_type": "code",
"source": [
"#load seqeval metric for evaluation\n",
"metric = load_metric(\"seqeval\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 176,
"referenced_widgets": [
"42288eee6bb94e06b4e5899c5e02347b",
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"679cd8726db64d65b6da6bbfee3f20ef",
"28cd5aa5e007475da90b1f4d9c0cc51d",
"f9d30994915d4433898c44c6a26f949d",
"49e1177f7ab144f7b12aea34d9eac563",
"8c8704ba5fb94ee898fb93882022502d",
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"633324d7f8ae49e795f7b688ebffb808",
"2266131a16f543aeab41b9d80224a0cc"
]
},
"id": "g-9XWW7tWP5i",
"outputId": "455a2f73-866f-42a7-ae8d-ed38d8bca65b"
},
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-45-653dc96d1cff>:2: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
" metric = load_metric(\"seqeval\")\n",
"/usr/local/lib/python3.10/dist-packages/datasets/load.py:756: FutureWarning: The repository for seqeval contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.18.0/metrics/seqeval/seqeval.py\n",
"You can avoid this message in future by passing the argument `trust_remote_code=True`.\n",
"Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading builder script: 0%| | 0.00/2.47k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
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}
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"id": "bCHH65EkQ2ds"
},
"outputs": [],
"source": [
"def compute_metrics_tr(p):\n",
" \"\"\"\n",
" Compute the metrics for the model\n",
" inputs:\n",
" p: tuple, the predictions and the labels\n",
" outputs:\n",
" dict: the metrics\n",
" \"\"\"\n",
" predictions, labels = p\n",
" predictions = np.argmax(predictions, axis=2)\n",
"\n",
" # Remove ignored index (special tokens)\n",
" true_predictions = [\n",
" [entities_list[p] for (p, l) in zip(prediction, label) if l != -100]\n",
" for prediction, label in zip(predictions, labels)\n",
" ]\n",
" true_labels = [\n",
" [entities_list[l] for (p, l) in zip(prediction, label) if l != -100]\n",
" for prediction, label in zip(predictions, labels)\n",
" ]\n",
"\n",
" results = metric.compute(predictions=true_predictions, references=true_labels)\n",
" return {\n",
" \"precision\": results[\"overall_precision\"],\n",
" \"recall\": results[\"overall_recall\"],\n",
" \"f1\": results[\"overall_f1\"],\n",
" \"accuracy\": results[\"overall_accuracy\"],\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ssgRZM7VQ2ds",
"outputId": "4e04857d-cb1b-4914-a4e9-b90abf5e2a84"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:432: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
"dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
" warnings.warn(\n"
]
}
],
"source": [
"# define the trainer\n",
"trainer = Trainer(\n",
" model,\n",
" args,\n",
" train_dataset=dataset['train'],\n",
" eval_dataset=dataset['val'],\n",
" data_collator=data_collator,\n",
" tokenizer=tokenizer,\n",
" compute_metrics=compute_metrics_tr\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 417,
"referenced_widgets": [
"bf1ed5b509d04ecb97e093aac9913644",
"81075d4fb934432b81b6f42f4114bea8",
"69db63ce5bc7457e8beeb860dce38196",
"a7be091111534c4ca37457f4e142f3fb",
"8a4356f3517349d38533c6a9f16e5f6c",
"bce85c205fcf4193af4d293ae5091231",
"65f17e980ba942abbe8cb5389e2b0117",
"25f72531ff19401c99e6ca6791efcccd"
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},
"id": "F1jkSOZoQ2ds",
"outputId": "055724dc-10ab-4579-e220-6389081f4605"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"Finishing last run (ID:6zm3ky1l) before initializing another..."
]
},
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},
{
"output_type": "display_data",
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"VBox(children=(Label(value='0.001 MB of 0.001 MB uploaded\\r'), FloatProgress(value=1.0, max=1.0)))"
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" 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/grad_norm</td><td>▁</td></tr><tr><td>train/learning_rate</td><td>▁</td></tr><tr><td>train/loss</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>0.09</td></tr><tr><td>train/global_step</td><td>50</td></tr><tr><td>train/grad_norm</td><td>8.97393</td></tr><tr><td>train/learning_rate</td><td>2e-05</td></tr><tr><td>train/loss</td><td>1.809</td></tr></table><br/></div></div>"
]
},
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{
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"text/html": [
" View run <strong style=\"color:#cdcd00\">snowy-sound-1</strong> at: <a href='https://wandb.ai/nlp-tasks/Chia_NER/runs/6zm3ky1l' target=\"_blank\">https://wandb.ai/nlp-tasks/Chia_NER/runs/6zm3ky1l</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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},
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},
{
"output_type": "display_data",
"data": {
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],
"text/html": [
"Find logs at: <code>./wandb/run-20240318_205422-6zm3ky1l/logs</code>"
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},
{
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"text/html": [
"Successfully finished last run (ID:6zm3ky1l). Initializing new run:<br/>"
]
},
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},
{
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"text/html": [
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]
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{
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"text/html": [
"Run data is saved locally in <code>/content/wandb/run-20240318_205551-tl4vnqb2</code>"
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"metadata": {}
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"output_type": "display_data",
"data": {
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"text/html": [
"Syncing run <strong><a href='https://wandb.ai/nlp-tasks/Chia_NER/runs/tl4vnqb2' target=\"_blank\">glamorous-galaxy-2</a></strong> to <a href='https://wandb.ai/nlp-tasks/Chia_NER' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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" View project at <a href='https://wandb.ai/nlp-tasks/Chia_NER' target=\"_blank\">https://wandb.ai/nlp-tasks/Chia_NER</a>"
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{
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"text/plain": [
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"text/html": [
" View run at <a href='https://wandb.ai/nlp-tasks/Chia_NER/runs/tl4vnqb2' target=\"_blank\">https://wandb.ai/nlp-tasks/Chia_NER/runs/tl4vnqb2</a>"
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},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/nlp-tasks/Chia_NER/runs/tl4vnqb2?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
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},
"metadata": {},
"execution_count": 48
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"wandb.init(project = \"Chia_NER\")"
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},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 1000
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"id": "BM2ROywqQ2dt",
"outputId": "556629f0-a5f9-42ac-b7af-c353a63103d3"
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{
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"\n",
" <div>\n",
" \n",
" <progress value='1668' max='1668' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [1668/1668 10:39, Epoch 3/3]\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",
" <th>Precision</th>\n",
" <th>Recall</th>\n",
" <th>F1</th>\n",
" <th>Accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.017100</td>\n",
" <td>0.928376</td>\n",
" <td>0.434856</td>\n",
" <td>0.418062</td>\n",
" <td>0.426293</td>\n",
" <td>0.731882</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>0.822500</td>\n",
" <td>0.813491</td>\n",
" <td>0.506574</td>\n",
" <td>0.503524</td>\n",
" <td>0.505045</td>\n",
" <td>0.765127</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>0.770200</td>\n",
" <td>0.762671</td>\n",
" <td>0.529176</td>\n",
" <td>0.523348</td>\n",
" <td>0.526246</td>\n",
" <td>0.779324</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>0.712500</td>\n",
" <td>0.710681</td>\n",
" <td>0.560302</td>\n",
" <td>0.566226</td>\n",
" <td>0.563249</td>\n",
" <td>0.786820</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>0.687600</td>\n",
" <td>0.706893</td>\n",
" <td>0.531826</td>\n",
" <td>0.580323</td>\n",
" <td>0.555017</td>\n",
" <td>0.788437</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>0.702200</td>\n",
" <td>0.666358</td>\n",
" <td>0.564002</td>\n",
" <td>0.613363</td>\n",
" <td>0.587648</td>\n",
" <td>0.792853</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>0.646700</td>\n",
" <td>0.653986</td>\n",
" <td>0.573650</td>\n",
" <td>0.628488</td>\n",
" <td>0.599818</td>\n",
" <td>0.800298</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>0.623300</td>\n",
" <td>0.634917</td>\n",
" <td>0.593040</td>\n",
" <td>0.605580</td>\n",
" <td>0.599244</td>\n",
" <td>0.804405</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>0.647000</td>\n",
" <td>0.635065</td>\n",
" <td>0.578706</td>\n",
" <td>0.628928</td>\n",
" <td>0.602773</td>\n",
" <td>0.801864</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>0.616800</td>\n",
" <td>0.613599</td>\n",
" <td>0.607627</td>\n",
" <td>0.631718</td>\n",
" <td>0.619438</td>\n",
" <td>0.807486</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>0.644300</td>\n",
" <td>0.601089</td>\n",
" <td>0.596063</td>\n",
" <td>0.649192</td>\n",
" <td>0.621494</td>\n",
" <td>0.808230</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>0.537800</td>\n",
" <td>0.601142</td>\n",
" <td>0.600027</td>\n",
" <td>0.663289</td>\n",
" <td>0.630074</td>\n",
" <td>0.811183</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>0.532700</td>\n",
" <td>0.598372</td>\n",
" <td>0.593594</td>\n",
" <td>0.664023</td>\n",
" <td>0.626837</td>\n",
" <td>0.813288</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>0.478100</td>\n",
" <td>0.584677</td>\n",
" <td>0.610991</td>\n",
" <td>0.664464</td>\n",
" <td>0.636607</td>\n",
" <td>0.817087</td>\n",
" </tr>\n",
" <tr>\n",
" <td>750</td>\n",
" <td>0.516000</td>\n",
" <td>0.605378</td>\n",
" <td>0.602448</td>\n",
" <td>0.664905</td>\n",
" <td>0.632137</td>\n",
" <td>0.810875</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>0.496500</td>\n",
" <td>0.588360</td>\n",
" <td>0.624573</td>\n",
" <td>0.644200</td>\n",
" <td>0.634234</td>\n",
" <td>0.816805</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>0.531000</td>\n",
" <td>0.574759</td>\n",
" <td>0.621539</td>\n",
" <td>0.659325</td>\n",
" <td>0.639875</td>\n",
" <td>0.818551</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>0.546900</td>\n",
" <td>0.555691</td>\n",
" <td>0.614453</td>\n",
" <td>0.670485</td>\n",
" <td>0.641247</td>\n",
" <td>0.822504</td>\n",
" </tr>\n",
" <tr>\n",
" <td>950</td>\n",
" <td>0.512700</td>\n",
" <td>0.570623</td>\n",
" <td>0.615395</td>\n",
" <td>0.678561</td>\n",
" <td>0.645436</td>\n",
" <td>0.820271</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>0.497300</td>\n",
" <td>0.574067</td>\n",
" <td>0.618721</td>\n",
" <td>0.676505</td>\n",
" <td>0.646324</td>\n",
" <td>0.821041</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1050</td>\n",
" <td>0.454000</td>\n",
" <td>0.578346</td>\n",
" <td>0.618678</td>\n",
" <td>0.679001</td>\n",
" <td>0.647438</td>\n",
" <td>0.821400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1100</td>\n",
" <td>0.498100</td>\n",
" <td>0.574136</td>\n",
" <td>0.618291</td>\n",
" <td>0.680029</td>\n",
" <td>0.647692</td>\n",
" <td>0.821041</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1150</td>\n",
" <td>0.457600</td>\n",
" <td>0.574290</td>\n",
" <td>0.630118</td>\n",
" <td>0.680176</td>\n",
" <td>0.654191</td>\n",
" <td>0.822273</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>0.437100</td>\n",
" <td>0.576165</td>\n",
" <td>0.611840</td>\n",
" <td>0.684435</td>\n",
" <td>0.646105</td>\n",
" <td>0.819398</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1250</td>\n",
" <td>0.443800</td>\n",
" <td>0.578306</td>\n",
" <td>0.626478</td>\n",
" <td>0.684435</td>\n",
" <td>0.654175</td>\n",
" <td>0.821683</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1300</td>\n",
" <td>0.421300</td>\n",
" <td>0.575019</td>\n",
" <td>0.634916</td>\n",
" <td>0.679295</td>\n",
" <td>0.656356</td>\n",
" <td>0.822581</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1350</td>\n",
" <td>0.399500</td>\n",
" <td>0.587560</td>\n",
" <td>0.630257</td>\n",
" <td>0.677827</td>\n",
" <td>0.653177</td>\n",
" <td>0.823428</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1400</td>\n",
" <td>0.409700</td>\n",
" <td>0.571972</td>\n",
" <td>0.633428</td>\n",
" <td>0.685609</td>\n",
" <td>0.658487</td>\n",
" <td>0.826381</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1450</td>\n",
" <td>0.435900</td>\n",
" <td>0.577606</td>\n",
" <td>0.630956</td>\n",
" <td>0.680617</td>\n",
" <td>0.654846</td>\n",
" <td>0.822478</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>0.443100</td>\n",
" <td>0.574967</td>\n",
" <td>0.628468</td>\n",
" <td>0.688546</td>\n",
" <td>0.657137</td>\n",
" <td>0.822478</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1550</td>\n",
" <td>0.432600</td>\n",
" <td>0.567493</td>\n",
" <td>0.632167</td>\n",
" <td>0.689721</td>\n",
" <td>0.659691</td>\n",
" <td>0.825148</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1600</td>\n",
" <td>0.428000</td>\n",
" <td>0.565049</td>\n",
" <td>0.631494</td>\n",
" <td>0.690749</td>\n",
" <td>0.659794</td>\n",
" <td>0.825841</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1650</td>\n",
" <td>0.405800</td>\n",
" <td>0.566147</td>\n",
" <td>0.628732</td>\n",
" <td>0.689574</td>\n",
" <td>0.657749</td>\n",
" <td>0.826021</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
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},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1668, training_loss=0.5500220998013906, metrics={'train_runtime': 639.2521, 'train_samples_per_second': 41.678, 'train_steps_per_second': 2.609, 'total_flos': 897186990438180.0, 'train_loss': 0.5500220998013906, 'epoch': 3.0})"
]
},
"metadata": {},
"execution_count": 49
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yKMnr0GCQ2dt",
"outputId": "fe3fbf27-2e77-4cb2-e287-b71274f857cf"
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{
"output_type": "execute_result",
"data": {
"text/plain": [
"RobertaForTokenClassification(\n",
" (roberta): RobertaModel(\n",
" (embeddings): RobertaEmbeddings(\n",
" (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
" (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
" (token_type_embeddings): Embedding(1, 768)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (encoder): RobertaEncoder(\n",
" (layer): ModuleList(\n",
" (0-11): 12 x RobertaLayer(\n",
" (attention): RobertaAttention(\n",
" (self): RobertaSelfAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" (output): RobertaSelfOutput(\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): RobertaIntermediate(\n",
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
" (intermediate_act_fn): GELUActivation()\n",
" )\n",
" (output): RobertaOutput(\n",
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (dropout): Dropout(p=0.1, inplace=False)\n",
" (classifier): Linear(in_features=768, out_features=19, bias=True)\n",
")"
]
},
"metadata": {},
"execution_count": 50
}
],
"source": [
"model.to('cpu')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"id": "lYJYGnb2Q2dt"
},
"outputs": [],
"source": [
"torch.save(model, f\"{models_path}/roberta-ner-chia.pt\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
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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",
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" </style>\n",
"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>▁▃▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇██████▇█████████</td></tr><tr><td>eval/f1</td><td>▁▃▄▅▅▆▆▆▆▇▇▇▇▇▇▇▇▇███████████████</td></tr><tr><td>eval/loss</td><td>█▆▅▄▄▃▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▂▁▁▁▁▁▁</td></tr><tr><td>eval/precision</td><td>▁▄▄▅▄▆▆▇▆▇▇▇▇▇▇██▇▇▇▇▇█▇█████████</td></tr><tr><td>eval/recall</td><td>▁▃▄▅▅▆▆▆▆▆▇▇▇▇▇▇▇▇███████████████</td></tr><tr><td>eval/runtime</td><td>▁▆▃█▃▄▃▆▃▇▂▂▃▁▃▂▇▂▄▂▃▂▃▂▂▄▆▂▇▂▇▂▇</td></tr><tr><td>eval/samples_per_second</td><td>█▂▆▁▆▄▆▃▆▂▆▇▆▇▅▇▂▇▄▇▆▇▆▇▆▄▃▇▂▆▂▇▂</td></tr><tr><td>eval/steps_per_second</td><td>█▂▆▁▆▄▆▃▆▂▆▇▆▇▅▇▂▇▄▇▆▇▆▇▆▄▃▇▂▆▂▇▂</td></tr><tr><td>train/epoch</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/global_step</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/grad_norm</td><td>▃█▂▅█▂▄▃▅▃▃█▂▄▃▂▁▅▃▃▃▅▁▂▂▃▂▄▅▆▃▂▂</td></tr><tr><td>train/learning_rate</td><td>███▇▇▇▇▆▆▆▆▆▅▅▅▅▅▄▄▄▄▃▃▃▃▃▂▂▂▂▁▁▁</td></tr><tr><td>train/loss</td><td>█▆▅▅▄▄▄▄▄▃▄▃▃▂▂▂▂▃▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>eval/accuracy</td><td>0.82602</td></tr><tr><td>eval/f1</td><td>0.65775</td></tr><tr><td>eval/loss</td><td>0.56615</td></tr><tr><td>eval/precision</td><td>0.62873</td></tr><tr><td>eval/recall</td><td>0.68957</td></tr><tr><td>eval/runtime</td><td>9.0171</td></tr><tr><td>eval/samples_per_second</td><td>246.31</td></tr><tr><td>eval/steps_per_second</td><td>30.83</td></tr><tr><td>total_flos</td><td>897186990438180.0</td></tr><tr><td>train/epoch</td><td>3.0</td></tr><tr><td>train/global_step</td><td>1668</td></tr><tr><td>train/grad_norm</td><td>7.42121</td></tr><tr><td>train/learning_rate</td><td>0.0</td></tr><tr><td>train/loss</td><td>0.4058</td></tr><tr><td>train_loss</td><td>0.55002</td></tr><tr><td>train_runtime</td><td>639.2521</td></tr><tr><td>train_samples_per_second</td><td>41.678</td></tr><tr><td>train_steps_per_second</td><td>2.609</td></tr></table><br/></div></div>"
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