From 1ce8c1901885620994872d08e026dcb0f794e19a Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Thu, 5 Aug 2021 19:46:45 +0200
Subject: [PATCH 1/6] [Feature] ADD bert vectorization for classification
---
experiments/bert_experiments.py | 349 ++++++++++++++++++++++++++++++++
experiments/parameters.conf | 10 +
experiments/requierements.txt | 7 +
3 files changed, 366 insertions(+)
create mode 100644 experiments/bert_experiments.py
create mode 100644 experiments/parameters.conf
create mode 100644 experiments/requierements.txt
diff --git a/experiments/bert_experiments.py b/experiments/bert_experiments.py
new file mode 100644
index 0000000..b4e4bcd
--- /dev/null
+++ b/experiments/bert_experiments.py
@@ -0,0 +1,349 @@
+import pandas as pd
+import numpy as np
+import torch
+import transformers as ppb
+from sklearn.model_selection import train_test_split
+from sklearn import preprocessing
+import statistics
+import os
+import sys
+import argparse
+import configparser
+from transformers import CamembertModel, CamembertTokenizer
+from transformers import FlaubertModel, FlaubertTokenizer
+
+
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.linear_model import SGDClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import GridSearchCV
+
+
+import matplotlib.pyplot as plt
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+import seaborn as sns
+
+
+
+
+
+
+def evaluate_model(clf, X_test, y_test, y_pred, valid_y, classes, classesName, pathSave):
+
+ #classifier, label_list, test_x, valid_y, title = "Confusion matrix"):
+ precision = []
+ recall = []
+ f1 = []
+ support = []
+ weighted_avg = None
+ accuracy = None
+
+ df = pd.DataFrame(columns= ['className', 'precision', 'recall', 'f1-score', 'support', 'FP', 'FN', 'TP', 'TN'])
+ report = classification_report( y_pred, valid_y, output_dict = True)
+ for c in classes:
+ precision.append(report[c]['precision'])
+ recall.append(report[c]['recall'])
+ f1.append(report[c]['f1-score'])
+ support.append(report[c]['support'])
+
+ accuracy = report['accuracy']
+ weighted_avg = report['weighted avg']
+ cnf_matrix = confusion_matrix(valid_y, y_pred)
+ FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)
+ FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)
+ TP = np.diag(cnf_matrix)
+ TN = cnf_matrix.sum() - (FP + FN + TP)
+
+ df['className'] = classesName
+ df['precision'] = precision
+ df['recall'] = recall
+ df['f1-score'] = f1
+ df['support'] = support
+ df['FP'] = FP
+ df['FN'] = FN
+ df['TP'] = TP
+ df['TN'] = TN
+ #disp = plot_confusion_matrix(classifier, test_x, valid_y,
+ # display_labels= label_list,
+ # cmap=plt.cm.Blues,
+ # normalize=None)
+ #disp.ax_.set_title(title)
+
+ #print(title)
+ #print(disp.confusion_matrix)
+
+ #plt.show()
+ plt.rcParams["font.size"] = 3
+ plot_confusion_matrix(clf, X_test, y_test)
+ plt.savefig(pathSave)
+ return df, accuracy, weighted_avg
+
+
+
+def create_dict(df, classColumnName):
+ return dict(df[classColumnName].value_counts())
+
+def remove_weak_classes(df, classColumnName, threshold):
+
+ dictOfClassInstances = create_dict(df,classColumnName)
+
+
+ dictionary = {k: v for k, v in dictOfClassInstances.items() if v >= threshold }
+ keys = [*dictionary]
+ df_tmp = df[~ df[classColumnName].isin(keys)]
+ #df = df[df[columnTarget] not in keys]
+ #df = df.merge(df_tmp, how = 'outer' ,indicator=True)
+ df = pd.concat([df,df_tmp]).drop_duplicates(keep=False)
+ return df
+
+
+def split_class(df, columnProcessed):
+ i = 0
+ new_df = pd.DataFrame(columns= df.columns)
+ for index, row in df.iterrows():
+ #cls = re.split(';', row[columnProcessed])
+ cls = filter(None, row[columnProcessed].split(';'))
+ cls = list(cls)
+ #cls = re.findall(r"[\w']+", row [columnProcessed])
+ r = row
+ for categ in cls:
+ r[columnProcessed] = categ
+ #new_df.append(r, ignore_index = True)
+ new_df.loc[i] = r
+ i = i + 1
+
+ return new_df
+
+
+def resample_classes(df, classColumnName, numberOfInstances):
+ # numberOfInstances first elements
+ #return df.groupby(classColumnName).apply(lambda x: x[:numberOfInstances][df.columns])
+ #random numberOfInstances elements
+ replace = False # with replacement
+
+ fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]
+ return df.groupby(classColumnName, as_index=False).apply(fn)
+
+
+def select_classifier(argument):
+
+ classifiers = {
+
+ 'lr' :LogisticRegression(),
+ 'sgd' :SGDClassifier(),
+ 'svm' :SVC() ,
+ 'decisionTree' :DecisionTreeClassifier(),
+ 'rfc' :RandomForestClassifier(),
+ 'knn' : KNeighborsClassifier()
+ }
+
+ param_grid_svm = {'C':[1,10,100,1000],'gamma':[1,0.1,0.001,0.0001], 'kernel':['linear','rbf']}
+ param_grid_decisionTree = { 'criterion' : ['gini', 'entropy'], 'max_depth':range(5,10), 'min_samples_split': range(5,10), 'min_samples_leaf': range(1,5) }
+ param_grid_rfc = { 'n_estimators': [200, 500], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth' : [4,5,6,7,8], 'criterion' :['gini', 'entropy'] }
+ param_grid_lr = { "penalty":['none',"l2"]}
+ param_grid_sgd = { "loss" : ["hinge", "log", "squared_hinge", "modified_huber"], "alpha" : [0.0001, 0.001, 0.01, 0.1], "penalty" : ["l2", "l1", "none"], "max_iter" : [500]}
+ param_grid_knn = {'n_neighbors' : list(range(3,20)), 'weights' : ['uniform', 'distance'], 'metric' : ['euclidean', 'manhattan'] }
+
+ grid_params = {
+
+ 'lr': param_grid_lr,
+ 'sgd': param_grid_sgd ,
+ 'svm': param_grid_svm,
+ 'decisionTree': param_grid_decisionTree,
+ 'rfc': param_grid_rfc ,
+ 'knn': param_grid_knn,
+
+ }
+
+ return classifiers.get(argument), grid_params.get(argument)
+
+
+if __name__ == "__main__":
+
+
+
+
+
+ print('ok')
+ parser = argparse.ArgumentParser()
+ parser.add_argument("modelName", help="bert or distilBert or camembert or flaubert")
+ parser.add_argument("classifier", help="lr or knn or rfc or decisionTree or sgd or svm")
+
+
+ args = parser.parse_args()
+ arg = args.modelName
+ classifier = args.classifier
+
+ config = configparser.ConfigParser()
+ config.read('parameters.conf')
+
+ minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
+ maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
+
+ dataPath = config.get('data','dataPath')
+ columnText = config.get('data','columnText')
+ columnClass = config.get('data','columnClass')
+
+
+
+ if not os.path.exists('reports'):
+ os.makedirs('reports')
+
+ if not os.path.exists(os.path.join('reports', columnClass)):
+ os.makedirs(os.path.join('reports', columnClass))
+
+
+ dir_name_report = str(minOfInstancePerClass) + '_' + str(maxOfInstancePerClass)
+ if not os.path.exists(os.path.join('reports', columnClass, dir_name_report)):
+ os.makedirs(os.path.join('reports', columnClass, dir_name_report))
+
+
+
+ # read data
+ print(dataPath)
+ df = pd.read_csv(dataPath)
+ df = remove_weak_classes(df, columnClass, minOfInstancePerClass)
+ df = resample_classes(df, columnClass, maxOfInstancePerClass)
+
+ print(df.head())
+ print(df.shape)
+ #encode labels
+ df = df[df[columnClass] != 'unclassified']
+ y = df[columnClass]
+ encoder = preprocessing.LabelEncoder()
+ y = encoder.fit_transform(y)
+
+
+ sentences = df['firstParagraph']
+ labels = y.tolist()
+
+
+
+ # Features Extraction
+ #Bert
+ model_class_bert, tokenizer_class_bert, pretrained_weights_bert = (ppb.BertModel, ppb.BertTokenizer, 'bert-base-uncased')
+ tokenizer_bert = tokenizer_class_bert.from_pretrained(pretrained_weights_bert)
+ model_bert = model_class_bert.from_pretrained(pretrained_weights_bert)
+ #DistilBert
+ model_class_distilBert, tokenizer_class_distilBert, pretrained_weights_distilBert = (ppb.DistilBertModel, ppb.DistilBertTokenizer, 'distilbert-base-uncased')
+ tokenizer_distilBert = tokenizer_class_distilBert.from_pretrained(pretrained_weights_distilBert)
+ model_distilBert = model_class_distilBert.from_pretrained(pretrained_weights_distilBert)
+ #Camembert
+ camembert_tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base")
+ camembert = CamembertModel.from_pretrained("camembert/camembert-base")
+ #Flaubert
+
+ flaubert, log = FlaubertModel.from_pretrained('flaubert/flaubert_base_cased', output_loading_info=True)
+ flaubert_tokenizer = FlaubertTokenizer.from_pretrained('flaubert/flaubert_base_cased', do_lowercase=False)
+
+
+
+ models = {
+ 'bert': model_bert,
+ 'distilbert': model_distilBert ,
+ 'camembert': camembert,
+ 'flaubert': flaubert
+ }
+
+ tokenizers = {
+ 'bert': tokenizer_bert,
+ 'distilbert': tokenizer_distilBert ,
+ 'camembert': camembert_tokenizer,
+ 'flaubert': flaubert_tokenizer
+
+ }
+
+
+
+
+
+
+ if arg == 'flaubert':
+ model = flaubert
+ tokenizer = flaubert_tokenizer
+ elif arg == 'camembert':
+ model = camembert
+ tokenizer = camembert_tokenizer
+
+ elif arg == 'distilbert':
+ model = model_distilBert
+ tokenizer = tokenizer_distilBert
+
+ elif arg == 'bert':
+ model = model_bert
+ tokenizer = tokenizer_bert
+
+
+
+
+
+
+ tokenized = sentences.apply((lambda x: tokenizer.encode(x, add_special_tokens=True, max_length = 512, truncation = True)))
+
+ # padding the sequences
+ max_len = 0
+ for i in tokenized.values:
+ if len(i) > max_len:
+ max_len = len(i)
+
+ padded = np.array([i + [0]*(max_len-len(i)) for i in tokenized.values])
+
+
+
+ # attention mask
+
+ attention_mask = np.where(padded != 0, 1, 0)
+
+
+
+ # get features
+ input_ids = torch.tensor(padded)
+ attention_mask = torch.tensor(attention_mask)
+
+ with torch.no_grad():
+ last_hidden_states = model(input_ids, attention_mask=attention_mask)
+
+ features = last_hidden_states[0][:,0,:].numpy()
+ print(features.shape)
+
+ train_x, test_x, train_y, test_y = train_test_split(features, y, test_size=0.33, random_state=42, stratify = y )
+
+
+ # classification
+
+
+ clf, grid_param = select_classifier(classifier)
+
+ print(features)
+
+
+
+ clf = GridSearchCV(clf, grid_param, refit = True, verbose = 3)
+
+ clf.fit(train_x, train_y)
+
+ #evaluation
+
+
+ y_pred = clf.predict(test_x)
+
+
+ report, accuracy, weighted_avg = evaluate_model(clf, test_x, test_y, y_pred, test_y, [str(e) for e in encoder.transform(encoder.classes_)], encoder.classes_, os.path.join('reports', columnClass, dir_name_report, arg+ '_' + classifier+'.pdf'))
+
+ report.to_csv(os.path.join('reports', columnClass, dir_name_report, arg + '_' + classifier +'.csv'))
+ with open(os.path.join('reports', columnClass, dir_name_report, arg + '_' + classifier+'.txt'), 'w') as f:
+
+ sys.stdout = f # Change the standard output to the file we created.
+ print('accuracy : {}'.format(accuracy))
+ print('weighted_Precision : {}'.format(weighted_avg['precision']))
+ print('weighted_Recall : {}'.format(weighted_avg['recall']))
+ print('weighted_F-score : {}'.format(weighted_avg['f1-score']))
+ print('weighted_Support : {}'.format(weighted_avg['support']))
+ print(dict(zip(encoder.classes_, encoder.transform(encoder.classes_))))
+ #sys.stdout = sys.stdout # Reset the standard output to its original value
+ sys.stdout = sys.__stdout__
diff --git a/experiments/parameters.conf b/experiments/parameters.conf
new file mode 100644
index 0000000..df584e4
--- /dev/null
+++ b/experiments/parameters.conf
@@ -0,0 +1,10 @@
+[general]
+
+minOfInstancePerClass = 1200
+maxOfInstancePerClass = 7
+
+[data]
+
+dataPath = ../Data/dataframe_with_ensemble_domaine_enccre.csv
+columnText = contentWithoutClass
+columnClass = ensemble_domaine_enccre
diff --git a/experiments/requierements.txt b/experiments/requierements.txt
new file mode 100644
index 0000000..076fef8
--- /dev/null
+++ b/experiments/requierements.txt
@@ -0,0 +1,7 @@
+transformers==4.3.2
+sentencepiece
+sklearn
+pandas
+numpy
+torch==1.8.1
+
--
GitLab
From 7217823686c4ef8dcbeb7a05fc3156e2512445b0 Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Thu, 5 Aug 2021 20:04:02 +0200
Subject: [PATCH 2/6] [UPDATE] updating readme
---
README.md | 8 +++++++-
1 file changed, 7 insertions(+), 1 deletion(-)
diff --git a/README.md b/README.md
index b518c4c..6535fd5 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,13 @@ In order to run the classifiers, use the following command :
python experimentsClassicClassifiers.py <dataset_tsv_file> <content_column_name> <labels_column_name> <min_sample_per_class> <max_sample_per_class>
+In order to run Classification with pre-trained models, use the following command :
+
+
+ cd experiments/
+
+ python bert_experiments.py <model_Name> <classifier>
# Acknowledgment
-The authors are grateful to the ASLAN project (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR).
\ No newline at end of file
+The authors are grateful to the ASLAN project (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program "Investments for the Future" operated by the National Research Agency (ANR).
--
GitLab
From 21df7dd30824fa80778a6eb1144e64c858aeea6c Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Sat, 11 Sep 2021 14:26:24 +0200
Subject: [PATCH 3/6] [ADD] Notebook Bert Fine Tuning
---
BertFineTuning_.ipynb | 1098 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 1098 insertions(+)
create mode 100644 BertFineTuning_.ipynb
diff --git a/BertFineTuning_.ipynb b/BertFineTuning_.ipynb
new file mode 100644
index 0000000..6216aeb
--- /dev/null
+++ b/BertFineTuning_.ipynb
@@ -0,0 +1,1098 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "BertFineTuning_.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "8hzEGHl7gmzk"
+ },
+ "source": [
+ "## Setup GPU"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dPOU-Efhf4ui",
+ "outputId": "1e3142a8-6351-43f3-9147-68406520b7ee"
+ },
+ "source": [
+ "import torch\n",
+ "\n",
+ "# If there's a GPU available...\n",
+ "if torch.cuda.is_available(): \n",
+ "\n",
+ " # Tell PyTorch to use the GPU. \n",
+ " device = torch.device(\"cuda\")\n",
+ "\n",
+ " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
+ "\n",
+ " print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
+ "\n",
+ "# If not...\n",
+ "else:\n",
+ " print('No GPU available, using the CPU instead.')\n",
+ " device = torch.device(\"cpu\")"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "No GPU available, using the CPU instead.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jr-S9yYIgGkA"
+ },
+ "source": [
+ "## Install packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "pwmZ5bBvgGNh",
+ "outputId": "79c5fb08-a9f4-41bc-eb4d-ab448c5fb4a7"
+ },
+ "source": [
+ "pip install transformers"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.10.0)\n",
+ "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
+ "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (21.0)\n",
+ "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n",
+ "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.0)\n",
+ "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.6.4)\n",
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (5.4.1)\n",
+ "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.45)\n",
+ "Requirement already satisfied: huggingface-hub>=0.0.12 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.16)\n",
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
+ "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub>=0.0.12->transformers) (3.7.4.3)\n",
+ "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
+ "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.5.0)\n",
+ "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.5.30)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
+ "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
+ "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
+ "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "VFXEpG00gXkL",
+ "outputId": "2336f39a-78b7-4118-e754-508d876c51f9"
+ },
+ "source": [
+ "pip install sentencepiece"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.96)\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "12SA-qPFgsVo"
+ },
+ "source": [
+ "## Utils functions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WkIVcabUgxIl"
+ },
+ "source": [
+ "def create_dict(df, classColumnName):\n",
+ " return dict(df[classColumnName].value_counts())\n",
+ "\n",
+ "def remove_weak_classes(df, classColumnName, threshold):\n",
+ "\n",
+ " dictOfClassInstances = create_dict(df,classColumnName)\n",
+ "\n",
+ "\n",
+ " dictionary = {k: v for k, v in dictOfClassInstances.items() if v >= threshold }\n",
+ " keys = [*dictionary]\n",
+ " df_tmp = df[~ df[classColumnName].isin(keys)]\n",
+ " df = pd.concat([df,df_tmp]).drop_duplicates(keep=False)\n",
+ " return df\n",
+ "\n",
+ "\n",
+ "def resample_classes(df, classColumnName, numberOfInstances):\n",
+ " \n",
+ " #random numberOfInstances elements\n",
+ " replace = False # with replacement\n",
+ "\n",
+ " fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]\n",
+ " return df.groupby(classColumnName, as_index=False).apply(fn)\n",
+ " "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "c5QKcXulhNJ-"
+ },
+ "source": [
+ "## Load Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vonJ-d4Qg1g5"
+ },
+ "source": [
+ "import pandas as pd \n",
+ "import numpy as np\n",
+ "from sklearn import preprocessing\n",
+ "from sklearn.model_selection import train_test_split"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ouU5usvXg4PA"
+ },
+ "source": [
+ "dataPath = 'dataframe_with_ensemble_domaine_enccre.csv'\n",
+ "columnText = 'contentWithoutClass'\n",
+ "columnClass = 'ensemble_domaine_enccre'\n",
+ "minOfInstancePerClass = 200\n",
+ "maxOfInstancePerClass = 1500"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5u1acjunhoxe"
+ },
+ "source": [
+ "df = pd.read_csv(dataPath)\n",
+ "df = remove_weak_classes(df, columnClass, minOfInstancePerClass)\n",
+ "df = resample_classes(df, columnClass, maxOfInstancePerClass)\n",
+ "df = df[df[columnClass] != 'unclassified']"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zrjZvs2dhzAy"
+ },
+ "source": [
+ "y = df[columnClass]\n",
+ "numberOfClasses = y.nunique()\n",
+ "encoder = preprocessing.LabelEncoder()\n",
+ "y = encoder.fit_transform(y)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "u9AxxaA_h1CM"
+ },
+ "source": [
+ "train_x, test_x, train_y, test_y = train_test_split(df, y, test_size=0.33, random_state=42, stratify = y )\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Xt_PhH_6h1_3"
+ },
+ "source": [
+ "sentences = train_x[columnText].values\n",
+ "labels = train_y.tolist()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Gs4Agx_5h43M"
+ },
+ "source": [
+ "# Model\n",
+ "## Tokenisation & Input Formatting"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YZ5PhEYZiCEA"
+ },
+ "source": [
+ "tokeniser_bert = 'bert-base-multilingual-cased'\n",
+ "tokeniser_camembert = 'camembert-base'\n",
+ "\n",
+ "model_bert = \"bert-base-multilingual-cased\"\n",
+ "model_camembert = 'camembert-base'"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C4bigx_3ibuN",
+ "outputId": "9d54db26-9920-4a92-bb1e-4534f287140f"
+ },
+ "source": [
+ "from transformers import BertTokenizer, CamembertTokenizer\n",
+ "\n",
+ "# Load the BERT tokenizer.\n",
+ "print('Loading BERT tokenizer...')\n",
+ "tokenizer = BertTokenizer.from_pretrained(tokeniser_bert, do_lower_case=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Loading BERT tokenizer...\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5hNod5X9jDZN",
+ "outputId": "1166b782-d384-4388-de21-21091dc9f925"
+ },
+ "source": [
+ " # Tokenize all of the sentences and map the tokens to thier word IDs.\n",
+ "input_ids = []\n",
+ "\n",
+ "# For every sentence...\n",
+ "for sent in sentences:\n",
+ " # `encode` will:\n",
+ " # (1) Tokenize the sentence.\n",
+ " # (2) Prepend the `[CLS]` token to the start.\n",
+ " # (3) Append the `[SEP]` token to the end.\n",
+ " # (4) Map tokens to their IDs.\n",
+ " encoded_sent = tokenizer.encode(\n",
+ " sent, # Sentence to encode.\n",
+ " add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
+ "\n",
+ " # This function also supports truncation and conversion\n",
+ " # to pytorch tensors, but I need to do padding, so I\n",
+ " # can't use these features.\n",
+ " #max_length = 128, # Truncate all sentences.\n",
+ " #return_tensors = 'pt', # Return pytorch tensors.\n",
+ " )\n",
+ " \n",
+ " # Add the encoded sentence to the list.\n",
+ " input_ids.append(encoded_sent)\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Token indices sequence length is longer than the specified maximum sequence length for this model (866 > 512). Running this sequence through the model will result in indexing errors\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "W9EWv5JvjGH3",
+ "outputId": "9072122d-3586-40fe-9d75-5b6e9035d6d2"
+ },
+ "source": [
+ "print('Max sentence length: ', max([len(sen) for sen in input_ids])) "
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Max sentence length: 3462\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xh1TQJyvjOx5"
+ },
+ "source": [
+ "max_len = 180\n",
+ "padded = []\n",
+ "for i in input_ids:\n",
+ "\n",
+ " if len(i) > max_len:\n",
+ " padded.extend([i[:max_len]])\n",
+ " else:\n",
+ " padded.extend([i + [0] * (max_len - len(i))])\n",
+ "\n",
+ "\n",
+ "padded = input_ids = np.array(padded)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ZiwY6gn0jUkD"
+ },
+ "source": [
+ " # Create attention masks\n",
+ "attention_masks = []\n",
+ "\n",
+ "# For each sentence...\n",
+ "for sent in padded:\n",
+ " \n",
+ " # Create the attention mask.\n",
+ " # - If a token ID is 0, then it's padding, set the mask to 0.\n",
+ " # - If a token ID is > 0, then it's a real token, set the mask to 1.\n",
+ " att_mask = [int(token_id > 0) for token_id in sent]\n",
+ " \n",
+ " # Store the attention mask for this sentence.\n",
+ " attention_masks.append(att_mask)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oBTR5AfAjXJe"
+ },
+ "source": [
+ "# Use 90% for training and 10% for validation.\n",
+ "train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(padded, labels, \n",
+ " random_state=2018, test_size=0.1, stratify = labels )\n",
+ "# Do the same for the masks.\n",
+ "train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels,\n",
+ " random_state=2018, test_size=0.1, stratify = labels)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "b9Mw5kq3jhTb"
+ },
+ "source": [
+ "# Convert all inputs and labels into torch tensors, the required datatype \n",
+ "# for my model.\n",
+ "train_inputs = torch.tensor(train_inputs)\n",
+ "validation_inputs = torch.tensor(validation_inputs)\n",
+ "\n",
+ "train_labels = torch.tensor(train_labels)\n",
+ "validation_labels = torch.tensor(validation_labels)\n",
+ "\n",
+ "train_masks = torch.tensor(train_masks)\n",
+ "validation_masks = torch.tensor(validation_masks)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "UfFWzbENjnkw"
+ },
+ "source": [
+ "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
+ "\n",
+ "# The DataLoader needs to know the batch size for training, so I specify it here.\n",
+ "# For fine-tuning BERT on a specific task, the authors recommend a batch size of\n",
+ "# 16 or 32.\n",
+ "\n",
+ "batch_size = 32\n",
+ "\n",
+ "# Create the DataLoader for training set.\n",
+ "train_data = TensorDataset(train_inputs, train_masks, train_labels)\n",
+ "train_sampler = RandomSampler(train_data)\n",
+ "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
+ "\n",
+ "# Create the DataLoader for validation set.\n",
+ "validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)\n",
+ "validation_sampler = SequentialSampler(validation_data)\n",
+ "validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x45JNGqhkUn2"
+ },
+ "source": [
+ "## Training"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 463
+ },
+ "id": "C7M2Er1ajsTf",
+ "outputId": "fe4c13b7-5157-49b4-e878-6d7676d4d1a3"
+ },
+ "source": [
+ "from transformers import BertForSequenceClassification, AdamW, BertConfig, CamembertForSequenceClassification\n",
+ "\n",
+ "# Load BertForSequenceClassification, the pretrained BERT model with a single \n",
+ "# linear classification layer on top.\n",
+ "\n",
+ "model = BertForSequenceClassification.from_pretrained(\n",
+ " model_bert, # Use the 12-layer BERT model, with an uncased vocab.\n",
+ " num_labels = numberOfClasses, # The number of output labels--2 for binary classification.\n",
+ " # You can increase this for multi-class tasks. \n",
+ " output_attentions = False, # Whether the model returns attentions weights.\n",
+ " output_hidden_states = False, # Whether the model returns all hidden-states.\n",
+ ")\n",
+ "\n",
+ "# Tell pytorch to run this model on the GPU.\n",
+ "model.cuda()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias']\n",
+ "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+ "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+ ]
+ },
+ {
+ "output_type": "error",
+ "ename": "RuntimeError",
+ "evalue": "ignored",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-120-80c23ac5f353>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Tell pytorch to run this model on the GPU.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mcuda\u001b[0;34m(self, device)\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 636\u001b[0m \"\"\"\n\u001b[0;32m--> 637\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mxpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 529\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 532\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 529\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 532\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 529\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 532\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0;31m# `with torch.no_grad():`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m \u001b[0mparam_applied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 553\u001b[0m \u001b[0mshould_use_set_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 635\u001b[0m \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 636\u001b[0m \"\"\"\n\u001b[0;32m--> 637\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 639\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mxpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 352.00 MiB (GPU 0; 11.17 GiB total capacity; 10.43 GiB already allocated; 91.81 MiB free; 10.63 GiB reserved in total by PyTorch)"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xd_cG-8pj4Iw"
+ },
+ "source": [
+ "#Note: AdamW is a class from the huggingface library (as opposed to pytorch) \n",
+ "# I believe the 'W' stands for 'Weight Decay fix\"\n",
+ "optimizer = AdamW(model.parameters(),\n",
+ " lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
+ " eps = 1e-8 # args.adam_epsilon - default is 1e-8.\n",
+ " )"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "65G-uHuLj4_6"
+ },
+ "source": [
+ "from transformers import get_linear_schedule_with_warmup\n",
+ "\n",
+ "# Number of training epochs (authors recommend between 2 and 4)\n",
+ "epochs = 4\n",
+ "\n",
+ "# Total number of training steps is number of batches * number of epochs.\n",
+ "total_steps = len(train_dataloader) * epochs\n",
+ "\n",
+ "# Create the learning rate scheduler.\n",
+ "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
+ " num_warmup_steps = 0, # Default value in run_glue.py\n",
+ " num_training_steps = total_steps)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lHSOuwcMj9jf"
+ },
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "# Function to calculate the accuracy of our predictions vs labels\n",
+ "def flat_accuracy(preds, labels):\n",
+ " pred_flat = np.argmax(preds, axis=1).flatten()\n",
+ " labels_flat = labels.flatten()\n",
+ " return np.sum(pred_flat == labels_flat) / len(labels_flat) "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Z0S3br-7kASm"
+ },
+ "source": [
+ "import time\n",
+ "import datetime\n",
+ "\n",
+ "def format_time(elapsed):\n",
+ " '''\n",
+ " Takes a time in seconds and returns a string hh:mm:ss\n",
+ " '''\n",
+ " # Round to the nearest second.\n",
+ " elapsed_rounded = int(round((elapsed)))\n",
+ " \n",
+ " # Format as hh:mm:ss\n",
+ " return str(datetime.timedelta(seconds=elapsed_rounded))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SbHBbYpwkKaA"
+ },
+ "source": [
+ "import random\n",
+ "\n",
+ "# This training code is based on the `run_glue.py` script here:\n",
+ "# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n",
+ "\n",
+ "\n",
+ "# Set the seed value all over the place to make this reproducible.\n",
+ "seed_val = 42\n",
+ "\n",
+ "random.seed(seed_val)\n",
+ "np.random.seed(seed_val)\n",
+ "torch.manual_seed(seed_val)\n",
+ "torch.cuda.manual_seed_all(seed_val)\n",
+ "\n",
+ "# Store the average loss after each epoch so I can plot them.\n",
+ "loss_values = []\n",
+ "\n",
+ "# For each epoch...\n",
+ "for epoch_i in range(0, epochs):\n",
+ " \n",
+ " # ========================================\n",
+ " # Training\n",
+ " # ========================================\n",
+ " \n",
+ " # Perform one full pass over the training set.\n",
+ "\n",
+ " print(\"\")\n",
+ " print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n",
+ " print('Training...')\n",
+ "\n",
+ " # Measure how long the training epoch takes.\n",
+ " t0 = time.time()\n",
+ "\n",
+ " # Reset the total loss for this epoch.\n",
+ " total_loss = 0\n",
+ "\n",
+ " # Put the model into training mode.\n",
+ " model.train()\n",
+ "\n",
+ " # For each batch of training data...\n",
+ " for step, batch in enumerate(train_dataloader):\n",
+ "\n",
+ " # Progress update every 40 batches.\n",
+ " if step % 40 == 0 and not step == 0:\n",
+ " # Calculate elapsed time in minutes.\n",
+ " elapsed = format_time(time.time() - t0)\n",
+ " \n",
+ " # Report progress.\n",
+ " print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n",
+ "\n",
+ " # Unpack this training batch from the dataloader. \n",
+ " #\n",
+ " # As I unpack the batch, I'll also copy each tensor to the GPU using the \n",
+ " # `to` method.\n",
+ " #\n",
+ " # `batch` contains three pytorch tensors:\n",
+ " # [0]: input ids \n",
+ " # [1]: attention masks\n",
+ " # [2]: labels \n",
+ " b_input_ids = batch[0].to(device)\n",
+ " b_input_mask = batch[1].to(device)\n",
+ " b_labels = batch[2].to(device)\n",
+ "\n",
+ " # Always clear any previously calculated gradients before performing a\n",
+ " # backward pass. PyTorch doesn't do this automatically because \n",
+ " # accumulating the gradients is \"convenient while training RNNs\". \n",
+ " # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)\n",
+ " model.zero_grad() \n",
+ "\n",
+ " # Perform a forward pass (evaluate the model on this training batch).\n",
+ " # This will return the loss (rather than the model output) because I\n",
+ " # have provided the `labels`.\n",
+ " # The documentation for this `model` function is here: \n",
+ " # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
+ " outputs = model(b_input_ids, \n",
+ " token_type_ids=None, \n",
+ " attention_mask=b_input_mask, \n",
+ " labels=b_labels)\n",
+ " \n",
+ " # The call to `model` always returns a tuple, so I need to pull the \n",
+ " # loss value out of the tuple.\n",
+ " loss = outputs[0]\n",
+ "\n",
+ " # Accumulate the training loss over all of the batches so that I can\n",
+ " # calculate the average loss at the end. `loss` is a Tensor containing a\n",
+ " # single value; the `.item()` function just returns the Python value \n",
+ " # from the tensor.\n",
+ " total_loss += loss.item()\n",
+ "\n",
+ " # Perform a backward pass to calculate the gradients.\n",
+ " loss.backward()\n",
+ "\n",
+ " # Clip the norm of the gradients to 1.0.\n",
+ " # This is to help prevent the \"exploding gradients\" problem.\n",
+ " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
+ "\n",
+ " # Update parameters and take a step using the computed gradient.\n",
+ " # The optimizer dictates the \"update rule\"--how the parameters are\n",
+ " # modified based on their gradients, the learning rate, etc.\n",
+ " optimizer.step()\n",
+ "\n",
+ " # Update the learning rate.\n",
+ " scheduler.step()\n",
+ "\n",
+ " # Calculate the average loss over the training data.\n",
+ " avg_train_loss = total_loss / len(train_dataloader) \n",
+ " \n",
+ " # Store the loss value for plotting the learning curve.\n",
+ " loss_values.append(avg_train_loss)\n",
+ "\n",
+ " print(\"\")\n",
+ " print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n",
+ " print(\" Training epoch took: {:}\".format(format_time(time.time() - t0)))\n",
+ " \n",
+ " # ========================================\n",
+ " # Validation\n",
+ " # ========================================\n",
+ " # After the completion of each training epoch, measure the performance on\n",
+ " # the validation set.\n",
+ "\n",
+ " print(\"\")\n",
+ " print(\"Running Validation...\")\n",
+ "\n",
+ " t0 = time.time()\n",
+ "\n",
+ " # Put the model in evaluation mode--the dropout layers behave differently\n",
+ " # during evaluation.\n",
+ " model.eval()\n",
+ "\n",
+ " # Tracking variables \n",
+ " eval_loss, eval_accuracy = 0, 0\n",
+ " nb_eval_steps, nb_eval_examples = 0, 0\n",
+ "\n",
+ " # Evaluate data for one epoch\n",
+ " for batch in validation_dataloader:\n",
+ " \n",
+ " # Add batch to GPU\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ " \n",
+ " # Unpack the inputs from dataloader\n",
+ " b_input_ids, b_input_mask, b_labels = batch\n",
+ " \n",
+ " # Telling the model not to compute or store gradients, saving memory and\n",
+ " # speeding up validation\n",
+ " with torch.no_grad(): \n",
+ "\n",
+ " # Forward pass, calculate logit predictions.\n",
+ " # This will return the logits rather than the loss because we have\n",
+ " # not provided labels.\n",
+ " # token_type_ids is the same as the \"segment ids\", which \n",
+ " # differentiates sentence 1 and 2 in 2-sentence tasks.\n",
+ " # The documentation for this `model` function is here: \n",
+ " # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification\n",
+ " outputs = model(b_input_ids, \n",
+ " token_type_ids=None, \n",
+ " attention_mask=b_input_mask)\n",
+ " \n",
+ " # Get the \"logits\" output by the model. The \"logits\" are the output\n",
+ " # values prior to applying an activation function like the softmax.\n",
+ " logits = outputs[0]\n",
+ "\n",
+ " # Move logits and labels to CPU\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ " label_ids = b_labels.to('cpu').numpy()\n",
+ " \n",
+ " # Calculate the accuracy for this batch of test sentences.\n",
+ " tmp_eval_accuracy = flat_accuracy(logits, label_ids)\n",
+ " \n",
+ " # Accumulate the total accuracy.\n",
+ " eval_accuracy += tmp_eval_accuracy\n",
+ "\n",
+ " # Track the number of batches\n",
+ " nb_eval_steps += 1\n",
+ "\n",
+ " # Report the final accuracy for this validation run.\n",
+ " print(\" Accuracy: {0:.2f}\".format(eval_accuracy/nb_eval_steps))\n",
+ " print(\" Validation took: {:}\".format(format_time(time.time() - t0)))\n",
+ "\n",
+ "print(\"\")\n",
+ "print(\"Training complete!\")"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VJwyfmakkQyj"
+ },
+ "source": [
+ "## Test"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VAyzmfhZCGZo"
+ },
+ "source": [
+ "sentences_test = test_x[columnText].values\n",
+ "labels_test = test_y.tolist()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "lZFXr_sdCJcb"
+ },
+ "source": [
+ "# Tokenize all of the sentences and map the tokens to thier word IDs.\n",
+ "input_ids_test = []\n",
+ "# For every sentence...\n",
+ "for sent in sentences_test:\n",
+ " # `encode` will:\n",
+ " # (1) Tokenize the sentence.\n",
+ " # (2) Prepend the `[CLS]` token to the start.\n",
+ " # (3) Append the `[SEP]` token to the end.\n",
+ " # (4) Map tokens to their IDs.\n",
+ " encoded_sent = tokenizer.encode(\n",
+ " sent, # Sentence to encode.\n",
+ " add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
+ " )\n",
+ " \n",
+ " input_ids_test.append(encoded_sent)\n",
+ "\n",
+ "# Pad our input tokens\n",
+ "padded_test = []\n",
+ "for i in input_ids_test:\n",
+ "\n",
+ " if len(i) > max_len:\n",
+ " padded_test.extend([i[:max_len]])\n",
+ " else:\n",
+ " padded_test.extend([i + [0] * (max_len - len(i))])\n",
+ "input_ids_test = np.array(padded_test)\n",
+ "\n",
+ "# Create attention masks\n",
+ "attention_masks = []\n",
+ "\n",
+ "# Create a mask of 1s for each token followed by 0s for padding\n",
+ "for seq in input_ids_test:\n",
+ " seq_mask = [float(i>0) for i in seq]\n",
+ " attention_masks.append(seq_mask) \n",
+ "\n",
+ "# Convert to tensors.\n",
+ "prediction_inputs = torch.tensor(input_ids_test)\n",
+ "prediction_masks = torch.tensor(attention_masks)\n",
+ "prediction_labels = torch.tensor(labels_test)\n",
+ "\n",
+ "# Set the batch size. \n",
+ "batch_size = 32 \n",
+ "\n",
+ "# Create the DataLoader.\n",
+ "prediction_data = TensorDataset(prediction_inputs, prediction_masks, prediction_labels)\n",
+ "prediction_sampler = SequentialSampler(prediction_data)\n",
+ "prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SUDcxi03Cmf-"
+ },
+ "source": [
+ "print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs)))\n",
+ "\n",
+ "# Put model in evaluation mode\n",
+ "model.eval()\n",
+ "\n",
+ "# Tracking variables \n",
+ "predictions_test , true_labels = [], []\n",
+ "\n",
+ "# Predict \n",
+ "for batch in prediction_dataloader:\n",
+ "# Add batch to GPU\n",
+ " batch = tuple(t.to(device) for t in batch)\n",
+ " \n",
+ " # Unpack the inputs from the dataloader\n",
+ " b_input_ids, b_input_mask, b_labels = batch\n",
+ " \n",
+ " # Telling the model not to compute or store gradients, saving memory and \n",
+ " # speeding up prediction\n",
+ " with torch.no_grad():\n",
+ " # Forward pass, calculate logit predictions\n",
+ " outputs = model(b_input_ids, token_type_ids=None, \n",
+ " attention_mask=b_input_mask)\n",
+ "\n",
+ " logits = outputs[0]\n",
+ " #print(logits)\n",
+ "\n",
+ " # Move logits and labels to CPU\n",
+ " logits = logits.detach().cpu().numpy()\n",
+ " label_ids = b_labels.to('cpu').numpy()\n",
+ " #print(logits)\n",
+ " \n",
+ " # Store predictions and true labels\n",
+ " predictions_test.append(logits)\n",
+ " true_labels.append(label_ids)\n",
+ "\n",
+ "print(' DONE.')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "c3i7szp3Cn5u"
+ },
+ "source": [
+ "from sklearn.metrics import *\n",
+ "\n",
+ "pred_labels = []\n",
+ "\n",
+ "# Evaluate each test batch using many matrics\n",
+ "print('Calculating the matrics for each batch...')\n",
+ "\n",
+ "for i in range(len(true_labels)):\n",
+ " \n",
+ " # The predictions for this batch are a 2-column ndarray (one column for \"0\" \n",
+ " # and one column for \"1\"). Pick the label with the highest value and turn this\n",
+ " # in to a list of 0s and 1s.\n",
+ " pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()\n",
+ " pred_labels.append(pred_labels_i)\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0bU9-DsBCxSO"
+ },
+ "source": [
+ "pred_labels_ = [item for sublist in pred_labels for item in sublist]\n",
+ "true_labels_ = [item for sublist in true_labels for item in sublist]\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZUM_U2QlC4K5"
+ },
+ "source": [
+ "### Report & Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "d5n84N0xCfcU"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.metrics import plot_confusion_matrix\n",
+ "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.metrics import classification_report\n",
+ "import seaborn as sns"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "v4hXk-KjC-nq"
+ },
+ "source": [
+ "report = classification_report( pred_labels_, true_labels_, output_dict = True)\n",
+ " \n",
+ "accuracy = report['accuracy']\n",
+ "weighted_avg = report['weighted avg']"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xETMy1L6DAa5"
+ },
+ "source": [
+ "classes = [str(e) for e in encoder.transform(encoder.classes_)]\n",
+ "classesName = encoder.classes_"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dPjV_5g8DDQy"
+ },
+ "source": [
+ "precision = []\n",
+ "recall = []\n",
+ "f1 = []\n",
+ "support = []\n",
+ "dff = pd.DataFrame(columns= ['className', 'precision', 'recall', 'f1-score', 'support', 'FP', 'FN', 'TP', 'TN'])\n",
+ "for c in classes:\n",
+ " precision.append(report[c]['precision'])\n",
+ " recall.append(report[c]['recall'])\n",
+ " f1.append(report[c]['f1-score'])\n",
+ " support.append(report[c]['support'])\n",
+ "\n",
+ "accuracy = report['accuracy']\n",
+ "weighted_avg = report['weighted avg']\n",
+ "cnf_matrix = confusion_matrix(true_labels_, pred_labels_)\n",
+ "FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)\n",
+ "FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)\n",
+ "TP = np.diag(cnf_matrix)\n",
+ "TN = cnf_matrix.sum() - (FP + FN + TP)\n",
+ "\n",
+ "dff['className'] = classesName\n",
+ "dff['precision'] = precision\n",
+ "dff['recall'] = recall\n",
+ "dff['f1-score'] = f1\n",
+ "dff['support'] = support\n",
+ "dff['FP'] = FP\n",
+ "dff['FN'] = FN\n",
+ "dff['TP'] = TP\n",
+ "dff['TN'] = TN\n",
+ " \n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "vslzi9bHDKcv"
+ },
+ "source": [
+ "print(weighted_avg)\n",
+ "print(accuracy)\n",
+ "print(dff)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
--
GitLab
From e7f6f159fa4b99afbcec38a6c14a8745765f8775 Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Sun, 12 Sep 2021 00:45:06 +0200
Subject: [PATCH 4/6] [ADD] Training mode Bert Fine Tuning
---
training_bertFineTuning.py | 465 +++++++++++++++++++++++++++++++++++++
1 file changed, 465 insertions(+)
create mode 100644 training_bertFineTuning.py
diff --git a/training_bertFineTuning.py b/training_bertFineTuning.py
new file mode 100644
index 0000000..72a5929
--- /dev/null
+++ b/training_bertFineTuning.py
@@ -0,0 +1,465 @@
+import torch
+import pandas as pd
+import numpy as np
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from transformers import BertTokenizer, CamembertTokenizer
+from transformers import BertForSequenceClassification, AdamW, BertConfig, CamembertForSequenceClassification
+from transformers import get_linear_schedule_with_warmup
+import time
+import datetime
+import random
+
+
+
+
+
+###########################################################################
+########################## Utils Functions ################################
+###########################################################################
+
+def create_dict(df, classColumnName):
+ return dict(df[classColumnName].value_counts())
+
+def remove_weak_classes(df, classColumnName, threshold):
+
+ dictOfClassInstances = create_dict(df,classColumnName)
+
+
+ dictionary = {k: v for k, v in dictOfClassInstances.items() if v >= threshold }
+ keys = [*dictionary]
+ df_tmp = df[~ df[classColumnName].isin(keys)]
+ df = pd.concat([df,df_tmp]).drop_duplicates(keep=False)
+ return df
+
+
+def resample_classes(df, classColumnName, numberOfInstances):
+
+ #random numberOfInstances elements
+ replace = False # with replacement
+
+ fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]
+ return df.groupby(classColumnName, as_index=False).apply(fn)
+
+##############################################################################################################
+########################## Setup GPU #########################################################################
+##############################################################################################################
+
+# If there's a GPU available...
+if torch.cuda.is_available():
+
+ # Tell PyTorch to use the GPU.
+ device = torch.device("cuda")
+
+ print('There are %d GPU(s) available.' % torch.cuda.device_count())
+
+ print('We will use the GPU:', torch.cuda.get_device_name(0))
+
+# If not...
+else:
+ print('No GPU available, using the CPU instead.')
+ device = torch.device("cpu")
+
+
+
+
+#############################################################################################################
+########################## parameters ###################################################################
+###########################################################################################################
+
+config = configparser.ConfigParser()
+config.read('settings.conf')
+
+dataPath = config.get('general','dataPath')
+columnText = config.get('general','columnText')
+columnClass = config.get('general','columnClass')
+
+minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
+maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
+
+chosen_tokeniser = config.get('model','tokeniser')
+chosen_model = config.get('model','model')
+
+max_len = int(config.get('model','max_len_sequences'))
+
+
+#############################################################################################################
+########################## Load Data ###################################################################
+###########################################################################################################
+
+
+
+
+df = pd.read_csv(dataPath)
+df = remove_weak_classes(df, columnClass, minOfInstancePerClass)
+df = resample_classes(df, columnClass, maxOfInstancePerClass)
+df = df[df[columnClass] != 'unclassified']
+
+
+
+
+
+y = df[columnClass]
+numberOfClasses = y.nunique()
+encoder = preprocessing.LabelEncoder()
+y = encoder.fit_transform(y)
+
+
+
+sentences = train_x[columnText].values
+labels = train_y.tolist()
+
+
+
+############################################################################################################
+########################## Model: Tokenization & Input Formatting ###################################################################
+###########################################################################################################
+
+
+# Load the BERT tokenizer.
+print('Loading BERT tokenizer...')
+tokenizer = BertTokenizer.from_pretrained(tokeniser_bert, do_lower_case=True)
+
+
+ # Tokenize all of the sentences and map the tokens to thier word IDs.
+input_ids = []
+
+# For every sentence...
+for sent in sentences:
+ # `encode` will:
+ # (1) Tokenize the sentence.
+ # (2) Prepend the `[CLS]` token to the start.
+ # (3) Append the `[SEP]` token to the end.
+ # (4) Map tokens to their IDs.
+ encoded_sent = tokenizer.encode(
+ sent, # Sentence to encode.
+ add_special_tokens = True, # Add '[CLS]' and '[SEP]'
+
+ # This function also supports truncation and conversion
+ # to pytorch tensors, but I need to do padding, so I
+ # can't use these features.
+ #max_length = 128, # Truncate all sentences.
+ #return_tensors = 'pt', # Return pytorch tensors.
+ )
+
+ # Add the encoded sentence to the list.
+ input_ids.append(encoded_sent)
+
+
+
+
+padded = []
+for i in input_ids:
+
+ if len(i) > max_len:
+ padded.extend([i[:max_len]])
+ else:
+ padded.extend([i + [0] * (max_len - len(i))])
+
+
+padded = input_ids = np.array(padded)
+
+
+
+ # Create attention masks
+attention_masks = []
+
+# For each sentence...
+for sent in padded:
+
+ # Create the attention mask.
+ # - If a token ID is 0, then it's padding, set the mask to 0.
+ # - If a token ID is > 0, then it's a real token, set the mask to 1.
+ att_mask = [int(token_id > 0) for token_id in sent]
+
+ # Store the attention mask for this sentence.
+ attention_masks.append(att_mask)
+
+
+# Use 90% for training and 10% for validation.
+train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(padded, labels,
+ random_state=2018, test_size=0.1, stratify = labels )
+# Do the same for the masks.
+train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels,
+ random_state=2018, test_size=0.1, stratify = labels)
+
+
+# Convert all inputs and labels into torch tensors, the required datatype
+# for my model.
+train_inputs = torch.tensor(train_inputs)
+validation_inputs = torch.tensor(validation_inputs)
+
+train_labels = torch.tensor(train_labels)
+validation_labels = torch.tensor(validation_labels)
+
+train_masks = torch.tensor(train_masks)
+validation_masks = torch.tensor(validation_masks)
+
+
+
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+
+# The DataLoader needs to know the batch size for training, so I specify it here.
+# For fine-tuning BERT on a specific task, the authors recommend a batch size of
+# 16 or 32.
+
+batch_size = int(config.get('model','batch_size'))
+
+# Create the DataLoader for training set.
+train_data = TensorDataset(train_inputs, train_masks, train_labels)
+train_sampler = RandomSampler(train_data)
+train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
+
+# Create the DataLoader for validation set.
+validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)
+validation_sampler = SequentialSampler(validation_data)
+validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)
+
+
+
+
+############################################################################################################
+########################## Model: Training ###################################################################
+###########################################################################################################
+
+
+print(' Selecting a model .....')
+
+
+
+# Load BertForSequenceClassification, the pretrained BERT model with a single
+# linear classification layer on top.
+
+model = BertForSequenceClassification.from_pretrained(
+ chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+ num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
+ # You can increase this for multi-class tasks.
+ output_attentions = False, # Whether the model returns attentions weights.
+ output_hidden_states = False, # Whether the model returns all hidden-states.
+)
+
+# Tell pytorch to run this model on the GPU.
+model.cuda()
+
+
+#Note: AdamW is a class from the huggingface library (as opposed to pytorch)
+# I believe the 'W' stands for 'Weight Decay fix"
+optimizer = AdamW(model.parameters(),
+ lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
+ eps = 1e-8 # args.adam_epsilon - default is 1e-8.
+ )
+
+
+
+# Number of training epochs (authors recommend between 2 and 4)
+epochs = int(config.get('model','epochs'))
+
+# Total number of training steps is number of batches * number of epochs.
+total_steps = len(train_dataloader) * epochs
+
+# Create the learning rate scheduler.
+scheduler = get_linear_schedule_with_warmup(optimizer,
+ num_warmup_steps = 0, # Default value in run_glue.py
+ num_training_steps = total_steps)
+
+
+def flat_accuracy(preds, labels):
+ pred_flat = np.argmax(preds, axis=1).flatten()
+ labels_flat = labels.flatten()
+ return np.sum(pred_flat == labels_flat) / len(labels_flat)
+
+
+
+
+
+def format_time(elapsed):
+ '''
+ Takes a time in seconds and returns a string hh:mm:ss
+ '''
+ # Round to the nearest second.
+ elapsed_rounded = int(round((elapsed)))
+
+ # Format as hh:mm:ss
+ return str(datetime.timedelta(seconds=elapsed_rounded))
+
+
+
+
+# This training code is based on the `run_glue.py` script here:
+# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128
+
+
+# Set the seed value all over the place to make this reproducible.
+seed_val = 42
+
+random.seed(seed_val)
+np.random.seed(seed_val)
+torch.manual_seed(seed_val)
+torch.cuda.manual_seed_all(seed_val)
+
+# Store the average loss after each epoch so I can plot them.
+loss_values = []
+
+# For each epoch...
+for epoch_i in range(0, epochs):
+
+ # ========================================
+ # Training
+ # ========================================
+
+ # Perform one full pass over the training set.
+
+ print("")
+ print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
+ print('Training...')
+
+ # Measure how long the training epoch takes.
+ t0 = time.time()
+
+ # Reset the total loss for this epoch.
+ total_loss = 0
+
+ # Put the model into training mode.
+ model.train()
+
+ # For each batch of training data...
+ for step, batch in enumerate(train_dataloader):
+
+ # Progress update every 40 batches.
+ if step % 40 == 0 and not step == 0:
+ # Calculate elapsed time in minutes.
+ elapsed = format_time(time.time() - t0)
+
+ # Report progress.
+ print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
+
+ # Unpack this training batch from the dataloader.
+ #
+ # As I unpack the batch, I'll also copy each tensor to the GPU using the
+ # `to` method.
+ #
+ # `batch` contains three pytorch tensors:
+ # [0]: input ids
+ # [1]: attention masks
+ # [2]: labels
+ b_input_ids = batch[0].to(device)
+ b_input_mask = batch[1].to(device)
+ b_labels = batch[2].to(device)
+
+ # Always clear any previously calculated gradients before performing a
+ # backward pass. PyTorch doesn't do this automatically because
+ # accumulating the gradients is "convenient while training RNNs".
+ # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
+ model.zero_grad()
+
+ # Perform a forward pass (evaluate the model on this training batch).
+ # This will return the loss (rather than the model output) because I
+ # have provided the `labels`.
+ # The documentation for this `model` function is here:
+ # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
+ outputs = model(b_input_ids,
+ token_type_ids=None,
+ attention_mask=b_input_mask,
+ labels=b_labels)
+
+ # The call to `model` always returns a tuple, so I need to pull the
+ # loss value out of the tuple.
+ loss = outputs[0]
+
+ # Accumulate the training loss over all of the batches so that I can
+ # calculate the average loss at the end. `loss` is a Tensor containing a
+ # single value; the `.item()` function just returns the Python value
+ # from the tensor.
+ total_loss += loss.item()
+
+ # Perform a backward pass to calculate the gradients.
+ loss.backward()
+
+ # Clip the norm of the gradients to 1.0.
+ # This is to help prevent the "exploding gradients" problem.
+ torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+
+ # Update parameters and take a step using the computed gradient.
+ # The optimizer dictates the "update rule"--how the parameters are
+ # modified based on their gradients, the learning rate, etc.
+ optimizer.step()
+
+ # Update the learning rate.
+ scheduler.step()
+
+ # Calculate the average loss over the training data.
+ avg_train_loss = total_loss / len(train_dataloader)
+
+ # Store the loss value for plotting the learning curve.
+ loss_values.append(avg_train_loss)
+
+ print("")
+ print(" Average training loss: {0:.2f}".format(avg_train_loss))
+ print(" Training epoch took: {:}".format(format_time(time.time() - t0)))
+
+ # ========================================
+ # Validation
+ # ========================================
+ # After the completion of each training epoch, measure the performance on
+ # the validation set.
+
+ print("")
+ print("Running Validation...")
+
+ t0 = time.time()
+
+ # Put the model in evaluation mode--the dropout layers behave differently
+ # during evaluation.
+ model.eval()
+
+ # Tracking variables
+ eval_loss, eval_accuracy = 0, 0
+ nb_eval_steps, nb_eval_examples = 0, 0
+
+ # Evaluate data for one epoch
+ for batch in validation_dataloader:
+
+ # Add batch to GPU
+ batch = tuple(t.to(device) for t in batch)
+
+ # Unpack the inputs from dataloader
+ b_input_ids, b_input_mask, b_labels = batch
+
+ # Telling the model not to compute or store gradients, saving memory and
+ # speeding up validation
+ with torch.no_grad():
+
+ # Forward pass, calculate logit predictions.
+ # This will return the logits rather than the loss because we have
+ # not provided labels.
+ # token_type_ids is the same as the "segment ids", which
+ # differentiates sentence 1 and 2 in 2-sentence tasks.
+ # The documentation for this `model` function is here:
+ # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
+ outputs = model(b_input_ids,
+ token_type_ids=None,
+ attention_mask=b_input_mask)
+
+ # Get the "logits" output by the model. The "logits" are the output
+ # values prior to applying an activation function like the softmax.
+ logits = outputs[0]
+
+ # Move logits and labels to CPU
+ logits = logits.detach().cpu().numpy()
+ label_ids = b_labels.to('cpu').numpy()
+
+ # Calculate the accuracy for this batch of test sentences.
+ tmp_eval_accuracy = flat_accuracy(logits, label_ids)
+
+ # Accumulate the total accuracy.
+ eval_accuracy += tmp_eval_accuracy
+
+ # Track the number of batches
+ nb_eval_steps += 1
+
+ # Report the final accuracy for this validation run.
+ print(" Accuracy: {0:.2f}".format(eval_accuracy/nb_eval_steps))
+ print(" Validation took: {:}".format(format_time(time.time() - t0)))
+
+print("")
+print("Training complete!")
--
GitLab
From a5158391ec1bd55d595b03e34a358cba6253bb56 Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Fri, 17 Sep 2021 00:19:40 +0200
Subject: [PATCH 5/6] [ADD] train bert finetuning & predict & evaluate
---
evaluate_bertFineTuning.py | 54 ++++
main.py | 120 ++++++++
predict_bertFineTuning.py | 168 +++++++++++
training_bertFineTuning.py | 601 +++++++++++++++++--------------------
4 files changed, 610 insertions(+), 333 deletions(-)
create mode 100644 evaluate_bertFineTuning.py
create mode 100644 main.py
create mode 100644 predict_bertFineTuning.py
diff --git a/evaluate_bertFineTuning.py b/evaluate_bertFineTuning.py
new file mode 100644
index 0000000..3c9b52b
--- /dev/null
+++ b/evaluate_bertFineTuning.py
@@ -0,0 +1,54 @@
+import matplotlib.pyplot as plt
+from sklearn.metrics import plot_confusion_matrix
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+import seaborn as sns
+
+
+
+
+
+
+
+
+
+
+def evaluate_bertFineTuning(pred_labels_, true_labels_, encoder):
+ report = classification_report( pred_labels_, true_labels_, output_dict = True)
+
+ classes = [str(e) for e in encoder.transform(encoder.classes_)]
+ classesName = encoder.classes_
+
+ accuracy = report['accuracy']
+ weighted_avg = report['weighted avg']
+
+ precision = []
+ recall = []
+ f1 = []
+ support = []
+ dff = pd.DataFrame(columns= ['className', 'precision', 'recall', 'f1-score', 'support', 'FP', 'FN', 'TP', 'TN'])
+ for c in classes:
+ precision.append(report[c]['precision'])
+ recall.append(report[c]['recall'])
+ f1.append(report[c]['f1-score'])
+ support.append(report[c]['support'])
+
+ accuracy = report['accuracy']
+ weighted_avg = report['weighted avg']
+ cnf_matrix = confusion_matrix(true_labels_, pred_labels_)
+ FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix)
+ FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)
+ TP = np.diag(cnf_matrix)
+ TN = cnf_matrix.sum() - (FP + FN + TP)
+
+ dff['className'] = classesName
+ dff['precision'] = precision
+ dff['recall'] = recall
+ dff['f1-score'] = f1
+ dff['support'] = support
+ dff['FP'] = FP
+ dff['FN'] = FN
+ dff['TP'] = TP
+ dff['TN'] = TN
+
+ return dff, accuracy, weighted_avg
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..8301acc
--- /dev/null
+++ b/main.py
@@ -0,0 +1,120 @@
+import pandas as pd
+import numpy as np
+import configparser
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+
+from training_bertFineTuning import training_bertFineTuning
+from predict_bertFineTuning import predict_class_bertFineTuning, generate_prediction_dataloader
+from evaluate_bertFineTuning import evaluate_bertFineTuning
+
+
+
+
+
+
+def create_dict(df, classColumnName):
+ return dict(df[classColumnName].value_counts())
+
+def remove_weak_classes(df, classColumnName, threshold):
+
+ dictOfClassInstances = create_dict(df,classColumnName)
+
+
+ dictionary = {k: v for k, v in dictOfClassInstances.items() if v >= threshold }
+ keys = [*dictionary]
+ df_tmp = df[~ df[classColumnName].isin(keys)]
+ df = pd.concat([df,df_tmp]).drop_duplicates(keep=False)
+ return df
+
+
+def resample_classes(df, classColumnName, numberOfInstances):
+
+ #random numberOfInstances elements
+ replace = False # with replacement
+
+ fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]
+ return df.groupby(classColumnName, as_index=False).apply(fn)
+
+
+
+def main():
+
+ config = configparser.ConfigParser()
+ config.read('bert_settings.conf')
+
+ dataPath = config.get('general','dataPath')
+ columnText = config.get('general','columnText')
+ columnClass = config.get('general','columnClass')
+
+ minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
+ maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
+
+ chosen_tokeniser = config.get('model','tokeniser')
+ chosen_model = config.get('model','model')
+
+ max_len = int(config.get('model','max_len_sequences'))
+ batch_size = int(config.get('model','batch_size'))
+ epochs = int(config.get('model','epochs'))
+
+ df = pd.read_csv(dataPath)
+ df = remove_weak_classes(df, columnClass, minOfInstancePerClass)
+ df = resample_classes(df, columnClass, maxOfInstancePerClass)
+ df = df[df[columnClass] != 'unclassified']
+
+
+ y = df[columnClass]
+ numberOfClasses = y.nunique()
+ encoder = preprocessing.LabelEncoder()
+ y = encoder.fit_transform(y)
+
+
+ train_x, test_x, train_y, test_y = train_test_split(df, y, test_size=0.33, random_state=42, stratify = y )
+
+ sentences = train_x[columnText].values
+ labels = train_y.tolist()
+
+
+ #call train method
+
+ model = training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_size, epochs)
+ #save the model
+ model_save_name = config.get('model','modelName')
+ path = config.get('model','path')
+ torch.save(model, os.path.join(path,model_save_name))
+
+ #print the model parameters
+ params = list(model.named_parameters())
+
+ print('The BERT model has {:} different named parameters.\n'.format(len(params)))
+
+ print('==== Embedding Layer ====\n')
+
+ for p in params[0:5]:
+ print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
+
+ print('\n==== First Transformer ====\n')
+
+ for p in params[5:21]:
+ print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
+
+ print('\n==== Output Layer ====\n')
+
+ for p in params[-4:]:
+ print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
+
+ #call predict method
+ prediction_dataloader = generate_prediction_dataloader(chosen_model, sentences_to_predict, labels, max_len, batch_size = 32)
+ predicted_class, true_labels = predict_class_bertFineTuning(chosen_model, model, prediction_dataloader)
+
+ #call Evaluate
+ result_df, accuracy , weighted_avg = evaluate_bertFineTuning(predicted_class, true_labels, encoder)
+
+ print(result_df)
+ print(accuracy)
+ print(weighted_avg)
+
+
+
+if __name__ == "__main__":
+ main()
diff --git a/predict_bertFineTuning.py b/predict_bertFineTuning.py
new file mode 100644
index 0000000..4276122
--- /dev/null
+++ b/predict_bertFineTuning.py
@@ -0,0 +1,168 @@
+import torch
+
+import pandas as pd
+
+import numpy as np
+
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from transformers import BertTokenizer, CamembertTokenizer
+
+def generate_prediction_dataloader(chosen_model, sentences_to_predict, labels, batch_size = 32):
+
+ if chosen_model == 'bert-base-multilingual-cased' :
+ print('Loading Bert Tokenizer...')
+ tokenizer = BertTokenizer.from_pretrained(chosen_model, do_lower_case=True)
+ elif chosen_model == 'camembert-base':
+ print('Loading Camembert Tokenizer...')
+ tokenizer = CamembertTokenizer.from_pretrained(chosen_model , do_lower_case=True)
+
+ # Tokenize all of the sentences and map the tokens to thier word IDs.
+ input_ids_test = []
+ # For every sentence...
+ for sent in sentences_to_predict:
+ # `encode` will:
+ # (1) Tokenize the sentence.
+ # (2) Prepend the `[CLS]` token to the start.
+ # (3) Append the `[SEP]` token to the end.
+ # (4) Map tokens to their IDs.
+ encoded_sent = tokenizer.encode(
+ sent, # Sentence to encode.
+ add_special_tokens = True, # Add '[CLS]' and '[SEP]'
+ )
+
+ input_ids_test.append(encoded_sent)
+
+ # Pad our input tokens
+ padded_test = []
+ for i in input_ids_test:
+
+ if len(i) > max_len:
+ padded_test.extend([i[:max_len]])
+ else:
+ padded_test.extend([i + [0] * (max_len - len(i))])
+ input_ids_test = np.array(padded_test)
+
+ # Create attention masks
+ attention_masks = []
+
+ # Create a mask of 1s for each token followed by 0s for padding
+ for seq in input_ids_test:
+ seq_mask = [float(i>0) for i in seq]
+ attention_masks.append(seq_mask)
+
+ # Convert to tensors.
+ prediction_inputs = torch.tensor(input_ids_test)
+ prediction_masks = torch.tensor(attention_masks)
+ prediction_labels = torch.tensor(labels)
+
+ # Set the batch size.
+ batch_size = 32
+
+ # Create the DataLoader.
+ prediction_data = TensorDataset(prediction_inputs, prediction_masks, prediction_labels)
+ prediction_sampler = SequentialSampler(prediction_data)
+ prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)
+
+ return prediction_dataloader
+
+
+
+def predict_class_bertFineTuning(model, sentences_to_predict_dataloader):
+
+
+ # If there's a GPU available...
+ if torch.cuda.is_available():
+
+ # Tell PyTorch to use the GPU.
+ device = torch.device("cuda")
+
+ print('There are %d GPU(s) available.' % torch.cuda.device_count())
+
+ print('We will use the GPU:', torch.cuda.get_device_name(0))
+
+ # If not...
+ else:
+ print('No GPU available, using the CPU instead.')
+ device = torch.device("cpu")
+
+ # Put model in evaluation mode
+ model.eval()
+
+ # Tracking variables
+ predictions_test , true_labels = [], []
+
+ # Predict
+ for batch in prediction_dataloader:
+ # Add batch to GPU
+ batch = tuple(t.to(device) for t in batch)
+
+ # Unpack the inputs from the dataloader
+ b_input_ids, b_input_mask, b_labels = batch
+
+ # Telling the model not to compute or store gradients, saving memory and
+ # speeding up prediction
+ with torch.no_grad():
+ # Forward pass, calculate logit predictions
+ outputs = model(b_input_ids, token_type_ids=None,
+ attention_mask=b_input_mask)
+
+ logits = outputs[0]
+ #print(logits)
+
+ # Move logits and labels to CPU
+ logits = logits.detach().cpu().numpy()
+ label_ids = b_labels.to('cpu').numpy()
+ #print(logits)
+
+ # Store predictions and true labels
+ predictions_test.append(logits)
+ true_labels.append(label_ids)
+
+ print(' DONE.')
+
+ pred_labels = []
+
+
+ for i in range(len(true_labels)):
+
+ # The predictions for this batch are a 2-column ndarray (one column for "0"
+ # and one column for "1"). Pick the label with the highest value and turn this
+ # in to a list of 0s and 1s.
+ pred_labels_i = np.argmax(predictions_test[i], axis=1).flatten()
+ pred_labels.append(pred_labels_i)
+
+ pred_labels_ = [item for sublist in pred_labels for item in sublist]
+ true_labels_ = [item for sublist in true_labels for item in sublist]
+ return predictions_test_, true_labels_
+
+
+def predict_instance_bertFineTuning(chosen_model, model, sentences_to_predict):
+
+ if chosen_model == 'bert-base-multilingual-cased' :
+ print('Loading Bert Tokenizer...')
+ tokenizer = BertTokenizer.from_pretrained(chosen_model, do_lower_case=True)
+ elif chosen_model == 'camembert-base':
+ print('Loading Camembert Tokenizer...')
+ tokenizer = CamembertTokenizer.from_pretrained(chosen_model , do_lower_case=True)
+
+ # Tokenize all of the sentences and map the tokens to thier word IDs.
+ input_ids_test = []
+ # For every sentence...
+ for sent in sentences_to_predict:
+ # `encode` will:
+ # (1) Tokenize the sentence.
+ # (2) Prepend the `[CLS]` token to the start.
+ # (3) Append the `[SEP]` token to the end.
+ # (4) Map tokens to their IDs.
+ encoded_sent = tokenizer.encode(
+ sent, # Sentence to encode.
+ add_special_tokens = True, # Add '[CLS]' and '[SEP]'
+ )
+
+ input_ids_test.append(encoded_sent)
+ with torch.no_grad():
+ # Forward pass, calculate logit predictions
+ outputs = model(b_input_ids, token_type_ids=None,
+ attention_mask=b_input_mask)
+
+ logits = outputs[0]
diff --git a/training_bertFineTuning.py b/training_bertFineTuning.py
index 72a5929..285be2d 100644
--- a/training_bertFineTuning.py
+++ b/training_bertFineTuning.py
@@ -2,464 +2,399 @@ import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
-from sklearn.model_selection import train_test_split
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import BertTokenizer, CamembertTokenizer
from transformers import BertForSequenceClassification, AdamW, BertConfig, CamembertForSequenceClassification
from transformers import get_linear_schedule_with_warmup
import time
import datetime
import random
+import os
+def flat_accuracy(preds, labels):
+ pred_flat = np.argmax(preds, axis=1).flatten()
+ labels_flat = labels.flatten()
+ return np.sum(pred_flat == labels_flat) / len(labels_flat)
-###########################################################################
-########################## Utils Functions ################################
-###########################################################################
-
-def create_dict(df, classColumnName):
- return dict(df[classColumnName].value_counts())
-
-def remove_weak_classes(df, classColumnName, threshold):
-
- dictOfClassInstances = create_dict(df,classColumnName)
-
-
- dictionary = {k: v for k, v in dictOfClassInstances.items() if v >= threshold }
- keys = [*dictionary]
- df_tmp = df[~ df[classColumnName].isin(keys)]
- df = pd.concat([df,df_tmp]).drop_duplicates(keep=False)
- return df
-
-
-def resample_classes(df, classColumnName, numberOfInstances):
-
- #random numberOfInstances elements
- replace = False # with replacement
-
- fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]
- return df.groupby(classColumnName, as_index=False).apply(fn)
-
-##############################################################################################################
-########################## Setup GPU #########################################################################
-##############################################################################################################
-
-# If there's a GPU available...
-if torch.cuda.is_available():
- # Tell PyTorch to use the GPU.
- device = torch.device("cuda")
- print('There are %d GPU(s) available.' % torch.cuda.device_count())
- print('We will use the GPU:', torch.cuda.get_device_name(0))
+def format_time(elapsed):
+ '''
+ Takes a time in seconds and returns a string hh:mm:ss
+ '''
+ # Round to the nearest second.
+ elapsed_rounded = int(round((elapsed)))
-# If not...
-else:
- print('No GPU available, using the CPU instead.')
- device = torch.device("cpu")
+ # Format as hh:mm:ss
+ return str(datetime.timedelta(seconds=elapsed_rounded))
+def training_bertFineTuning(chosen_model, sentences, labels, max_len, batch_size, epochs = 4):
+ # If there's a GPU available...
+ if torch.cuda.is_available():
-#############################################################################################################
-########################## parameters ###################################################################
-###########################################################################################################
+ # Tell PyTorch to use the GPU.
+ device = torch.device("cuda")
-config = configparser.ConfigParser()
-config.read('settings.conf')
+ print('There are %d GPU(s) available.' % torch.cuda.device_count())
-dataPath = config.get('general','dataPath')
-columnText = config.get('general','columnText')
-columnClass = config.get('general','columnClass')
+ print('We will use the GPU:', torch.cuda.get_device_name(0))
-minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
-maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
+ # If not...
+ else:
+ print('No GPU available, using the CPU instead.')
+ device = torch.device("cpu")
-chosen_tokeniser = config.get('model','tokeniser')
-chosen_model = config.get('model','model')
-max_len = int(config.get('model','max_len_sequences'))
-#############################################################################################################
-########################## Load Data ###################################################################
+############################################################################################################
+########################## Model: Tokenization & Input Formatting ###################################################################
###########################################################################################################
+ if chosen_model == 'bert-base-multilingual-cased' :
+ print('Loading Bert Tokenizer...')
+ tokenizer = BertTokenizer.from_pretrained(chosen_model, do_lower_case=True)
+ elif chosen_model == 'camembert-base':
+ print('Loading Camembert Tokenizer...')
+ tokenizer = CamembertTokenizer.from_pretrained(chosen_model , do_lower_case=True)
-df = pd.read_csv(dataPath)
-df = remove_weak_classes(df, columnClass, minOfInstancePerClass)
-df = resample_classes(df, columnClass, maxOfInstancePerClass)
-df = df[df[columnClass] != 'unclassified']
-
+ # Tokenize all of the sentences and map the tokens to thier word IDs.
+ input_ids = []
+ # For every sentence...
+ for sent in sentences:
+ # `encode` will:
+ # (1) Tokenize the sentence.
+ # (2) Prepend the `[CLS]` token to the start.
+ # (3) Append the `[SEP]` token to the end.
+ # (4) Map tokens to their IDs.
+ encoded_sent = tokenizer.encode(
+ sent, # Sentence to encode.
+ add_special_tokens = True, # Add '[CLS]' and '[SEP]'
+ # This function also supports truncation and conversion
+ # to pytorch tensors, but I need to do padding, so I
+ # can't use these features.
+ #max_length = 128, # Truncate all sentences.
+ #return_tensors = 'pt', # Return pytorch tensors.
+ )
-y = df[columnClass]
-numberOfClasses = y.nunique()
-encoder = preprocessing.LabelEncoder()
-y = encoder.fit_transform(y)
+ # Add the encoded sentence to the list.
+ input_ids.append(encoded_sent)
-sentences = train_x[columnText].values
-labels = train_y.tolist()
+ padded = []
+ for i in input_ids:
+ if len(i) > max_len:
+ padded.extend([i[:max_len]])
+ else:
+ padded.extend([i + [0] * (max_len - len(i))])
-############################################################################################################
-########################## Model: Tokenization & Input Formatting ###################################################################
-###########################################################################################################
-
-
-# Load the BERT tokenizer.
-print('Loading BERT tokenizer...')
-tokenizer = BertTokenizer.from_pretrained(tokeniser_bert, do_lower_case=True)
+ padded = np.array(padded)
- # Tokenize all of the sentences and map the tokens to thier word IDs.
-input_ids = []
-# For every sentence...
-for sent in sentences:
- # `encode` will:
- # (1) Tokenize the sentence.
- # (2) Prepend the `[CLS]` token to the start.
- # (3) Append the `[SEP]` token to the end.
- # (4) Map tokens to their IDs.
- encoded_sent = tokenizer.encode(
- sent, # Sentence to encode.
- add_special_tokens = True, # Add '[CLS]' and '[SEP]'
- # This function also supports truncation and conversion
- # to pytorch tensors, but I need to do padding, so I
- # can't use these features.
- #max_length = 128, # Truncate all sentences.
- #return_tensors = 'pt', # Return pytorch tensors.
- )
+ # Create attention masks
+ attention_masks = []
- # Add the encoded sentence to the list.
- input_ids.append(encoded_sent)
+ # For each sentence...
+ for sent in padded:
+ # Create the attention mask.
+ # - If a token ID is 0, then it's padding, set the mask to 0.
+ # - If a token ID is > 0, then it's a real token, set the mask to 1.
+ att_mask = [int(token_id > 0) for token_id in sent]
+ # Store the attention mask for this sentence.
+ attention_masks.append(att_mask)
-padded = []
-for i in input_ids:
+ # Use 90% for training and 10% for validation.
+ train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(padded, labels, random_state=2018, test_size=0.1, stratify = labels )
+ # Do the same for the masks.
+ train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels, random_state=2018, test_size=0.1, stratify = labels)
- if len(i) > max_len:
- padded.extend([i[:max_len]])
- else:
- padded.extend([i + [0] * (max_len - len(i))])
+ # Convert all inputs and labels into torch tensors, the required datatype
+ # for my model.
+ train_inputs = torch.tensor(train_inputs)
+ validation_inputs = torch.tensor(validation_inputs)
-padded = input_ids = np.array(padded)
+ train_labels = torch.tensor(train_labels)
+ validation_labels = torch.tensor(validation_labels)
+ train_masks = torch.tensor(train_masks)
+ validation_masks = torch.tensor(validation_masks)
- # Create attention masks
-attention_masks = []
-# For each sentence...
-for sent in padded:
- # Create the attention mask.
- # - If a token ID is 0, then it's padding, set the mask to 0.
- # - If a token ID is > 0, then it's a real token, set the mask to 1.
- att_mask = [int(token_id > 0) for token_id in sent]
+ # The DataLoader needs to know the batch size for training, so I specify it here.
+ # For fine-tuning BERT on a specific task, the authors recommend a batch size of
+ # 16 or 32.
- # Store the attention mask for this sentence.
- attention_masks.append(att_mask)
+ # Create the DataLoader for training set.
+ train_data = TensorDataset(train_inputs, train_masks, train_labels)
+ train_sampler = RandomSampler(train_data)
+ train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
-# Use 90% for training and 10% for validation.
-train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(padded, labels,
- random_state=2018, test_size=0.1, stratify = labels )
-# Do the same for the masks.
-train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels,
- random_state=2018, test_size=0.1, stratify = labels)
+ # Create the DataLoader for validation set.
+ validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)
+ validation_sampler = SequentialSampler(validation_data)
+ validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)
-# Convert all inputs and labels into torch tensors, the required datatype
-# for my model.
-train_inputs = torch.tensor(train_inputs)
-validation_inputs = torch.tensor(validation_inputs)
-train_labels = torch.tensor(train_labels)
-validation_labels = torch.tensor(validation_labels)
-train_masks = torch.tensor(train_masks)
-validation_masks = torch.tensor(validation_masks)
+ print(' Selecting a model .....')
+ numberOfClasses = len(set(labels))
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
-# The DataLoader needs to know the batch size for training, so I specify it here.
-# For fine-tuning BERT on a specific task, the authors recommend a batch size of
-# 16 or 32.
+ # Load BertForSequenceClassification, the pretrained BERT model with a single
+ # linear classification layer on top.
+ if chosen_model == 'bert-base-multilingual-cased':
+ model = BertForSequenceClassification.from_pretrained(
+ chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+ num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
+ # You can increase this for multi-class tasks.
+ output_attentions = False, # Whether the model returns attentions weights.
+ output_hidden_states = False, # Whether the model returns all hidden-states.
+ )
+ elif chosen_model == 'camembert-base':
-batch_size = int(config.get('model','batch_size'))
+ model = CamembertForSequenceClassification.from_pretrained(
+ chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
+ num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
+ # You can increase this for multi-class tasks.
+ output_attentions = False, # Whether the model returns attentions weights.
+ output_hidden_states = False, # Whether the model returns all hidden-states.
+ )
-# Create the DataLoader for training set.
-train_data = TensorDataset(train_inputs, train_masks, train_labels)
-train_sampler = RandomSampler(train_data)
-train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
-# Create the DataLoader for validation set.
-validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)
-validation_sampler = SequentialSampler(validation_data)
-validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)
+ # Tell pytorch to run this model on the GPU.
+ model.cuda()
+ #Note: AdamW is a class from the huggingface library (as opposed to pytorch)
+ # I believe the 'W' stands for 'Weight Decay fix"
+ optimizer = AdamW(model.parameters(),
+ lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
+ eps = 1e-8 # args.adam_epsilon - default is 1e-8.
+ )
-############################################################################################################
-########################## Model: Training ###################################################################
-###########################################################################################################
-print(' Selecting a model .....')
+ # Total number of training steps is number of batches * number of epochs.
+ total_steps = len(train_dataloader) * epochs
+ # Create the learning rate scheduler.
+ scheduler = get_linear_schedule_with_warmup(optimizer,
+ num_warmup_steps = 0, # Default value in run_glue.py
+ num_training_steps = total_steps)
-# Load BertForSequenceClassification, the pretrained BERT model with a single
-# linear classification layer on top.
-model = BertForSequenceClassification.from_pretrained(
- chosen_model, # Use the 12-layer BERT model, with an uncased vocab.
- num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
- # You can increase this for multi-class tasks.
- output_attentions = False, # Whether the model returns attentions weights.
- output_hidden_states = False, # Whether the model returns all hidden-states.
-)
-# Tell pytorch to run this model on the GPU.
-model.cuda()
+ # This training code is based on the `run_glue.py` script here:
+ # https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128
-#Note: AdamW is a class from the huggingface library (as opposed to pytorch)
-# I believe the 'W' stands for 'Weight Decay fix"
-optimizer = AdamW(model.parameters(),
- lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
- eps = 1e-8 # args.adam_epsilon - default is 1e-8.
- )
+ # Set the seed value all over the place to make this reproducible.
+ seed_val = 42
+ random.seed(seed_val)
+ np.random.seed(seed_val)
+ torch.manual_seed(seed_val)
+ torch.cuda.manual_seed_all(seed_val)
+ # Store the average loss after each epoch so I can plot them.
+ loss_values = []
-# Number of training epochs (authors recommend between 2 and 4)
-epochs = int(config.get('model','epochs'))
+ # For each epoch...
+ for epoch_i in range(0, epochs):
-# Total number of training steps is number of batches * number of epochs.
-total_steps = len(train_dataloader) * epochs
+ # ========================================
+ # Training
+ # ========================================
-# Create the learning rate scheduler.
-scheduler = get_linear_schedule_with_warmup(optimizer,
- num_warmup_steps = 0, # Default value in run_glue.py
- num_training_steps = total_steps)
+ # Perform one full pass over the training set.
+ print("")
+ print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
+ print('Training...')
-def flat_accuracy(preds, labels):
- pred_flat = np.argmax(preds, axis=1).flatten()
- labels_flat = labels.flatten()
- return np.sum(pred_flat == labels_flat) / len(labels_flat)
+ # Measure how long the training epoch takes.
+ t0 = time.time()
+ # Reset the total loss for this epoch.
+ total_loss = 0
+ # Put the model into training mode.
+ model.train()
+ # For each batch of training data...
+ for step, batch in enumerate(train_dataloader):
+ # Progress update every 40 batches.
+ if step % 40 == 0 and not step == 0:
+ # Calculate elapsed time in minutes.
+ elapsed = format_time(time.time() - t0)
-def format_time(elapsed):
- '''
- Takes a time in seconds and returns a string hh:mm:ss
- '''
- # Round to the nearest second.
- elapsed_rounded = int(round((elapsed)))
+ # Report progress.
+ print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
- # Format as hh:mm:ss
- return str(datetime.timedelta(seconds=elapsed_rounded))
+ # Unpack this training batch from the dataloader.
+ #
+ # As I unpack the batch, I'll also copy each tensor to the GPU using the
+ # `to` method.
+ #
+ # `batch` contains three pytorch tensors:
+ # [0]: input ids
+ # [1]: attention masks
+ # [2]: labels
+ b_input_ids = batch[0].to(device)
+ b_input_mask = batch[1].to(device)
+ b_labels = batch[2].to(device)
+ # Always clear any previously calculated gradients before performing a
+ # backward pass. PyTorch doesn't do this automatically because
+ # accumulating the gradients is "convenient while training RNNs".
+ # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
+ model.zero_grad()
+ # Perform a forward pass (evaluate the model on this training batch).
+ # This will return the loss (rather than the model output) because I
+ # have provided the `labels`.
+ # The documentation for this `model` function is here:
+ # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
+ outputs = model(b_input_ids,
+ token_type_ids=None,
+ attention_mask=b_input_mask,
+ labels=b_labels)
+ # The call to `model` always returns a tuple, so I need to pull the
+ # loss value out of the tuple.
+ loss = outputs[0]
-# This training code is based on the `run_glue.py` script here:
-# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128
+ # Accumulate the training loss over all of the batches so that I can
+ # calculate the average loss at the end. `loss` is a Tensor containing a
+ # single value; the `.item()` function just returns the Python value
+ # from the tensor.
+ total_loss += loss.item()
+ # Perform a backward pass to calculate the gradients.
+ loss.backward()
-# Set the seed value all over the place to make this reproducible.
-seed_val = 42
+ # Clip the norm of the gradients to 1.0.
+ # This is to help prevent the "exploding gradients" problem.
+ torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-random.seed(seed_val)
-np.random.seed(seed_val)
-torch.manual_seed(seed_val)
-torch.cuda.manual_seed_all(seed_val)
+ # Update parameters and take a step using the computed gradient.
+ # The optimizer dictates the "update rule"--how the parameters are
+ # modified based on their gradients, the learning rate, etc.
+ optimizer.step()
-# Store the average loss after each epoch so I can plot them.
-loss_values = []
+ # Update the learning rate.
+ scheduler.step()
-# For each epoch...
-for epoch_i in range(0, epochs):
+ # Calculate the average loss over the training data.
+ avg_train_loss = total_loss / len(train_dataloader)
- # ========================================
- # Training
- # ========================================
+ # Store the loss value for plotting the learning curve.
+ loss_values.append(avg_train_loss)
- # Perform one full pass over the training set.
+ print("")
+ print(" Average training loss: {0:.2f}".format(avg_train_loss))
+ print(" Training epoch took: {:}".format(format_time(time.time() - t0)))
- print("")
- print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
- print('Training...')
-
- # Measure how long the training epoch takes.
- t0 = time.time()
-
- # Reset the total loss for this epoch.
- total_loss = 0
-
- # Put the model into training mode.
- model.train()
-
- # For each batch of training data...
- for step, batch in enumerate(train_dataloader):
-
- # Progress update every 40 batches.
- if step % 40 == 0 and not step == 0:
- # Calculate elapsed time in minutes.
- elapsed = format_time(time.time() - t0)
-
- # Report progress.
- print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
-
- # Unpack this training batch from the dataloader.
- #
- # As I unpack the batch, I'll also copy each tensor to the GPU using the
- # `to` method.
- #
- # `batch` contains three pytorch tensors:
- # [0]: input ids
- # [1]: attention masks
- # [2]: labels
- b_input_ids = batch[0].to(device)
- b_input_mask = batch[1].to(device)
- b_labels = batch[2].to(device)
-
- # Always clear any previously calculated gradients before performing a
- # backward pass. PyTorch doesn't do this automatically because
- # accumulating the gradients is "convenient while training RNNs".
- # (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
- model.zero_grad()
-
- # Perform a forward pass (evaluate the model on this training batch).
- # This will return the loss (rather than the model output) because I
- # have provided the `labels`.
- # The documentation for this `model` function is here:
- # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
- outputs = model(b_input_ids,
- token_type_ids=None,
- attention_mask=b_input_mask,
- labels=b_labels)
-
- # The call to `model` always returns a tuple, so I need to pull the
- # loss value out of the tuple.
- loss = outputs[0]
-
- # Accumulate the training loss over all of the batches so that I can
- # calculate the average loss at the end. `loss` is a Tensor containing a
- # single value; the `.item()` function just returns the Python value
- # from the tensor.
- total_loss += loss.item()
-
- # Perform a backward pass to calculate the gradients.
- loss.backward()
-
- # Clip the norm of the gradients to 1.0.
- # This is to help prevent the "exploding gradients" problem.
- torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-
- # Update parameters and take a step using the computed gradient.
- # The optimizer dictates the "update rule"--how the parameters are
- # modified based on their gradients, the learning rate, etc.
- optimizer.step()
-
- # Update the learning rate.
- scheduler.step()
-
- # Calculate the average loss over the training data.
- avg_train_loss = total_loss / len(train_dataloader)
-
- # Store the loss value for plotting the learning curve.
- loss_values.append(avg_train_loss)
+ # ========================================
+ # Validation
+ # ========================================
+ # After the completion of each training epoch, measure the performance on
+ # the validation set.
- print("")
- print(" Average training loss: {0:.2f}".format(avg_train_loss))
- print(" Training epoch took: {:}".format(format_time(time.time() - t0)))
+ print("")
+ print("Running Validation...")
- # ========================================
- # Validation
- # ========================================
- # After the completion of each training epoch, measure the performance on
- # the validation set.
+ t0 = time.time()
- print("")
- print("Running Validation...")
+ # Put the model in evaluation mode--the dropout layers behave differently
+ # during evaluation.
+ model.eval()
- t0 = time.time()
+ # Tracking variables
+ eval_loss, eval_accuracy = 0, 0
+ nb_eval_steps, nb_eval_examples = 0, 0
- # Put the model in evaluation mode--the dropout layers behave differently
- # during evaluation.
- model.eval()
+ # Evaluate data for one epoch
+ for batch in validation_dataloader:
- # Tracking variables
- eval_loss, eval_accuracy = 0, 0
- nb_eval_steps, nb_eval_examples = 0, 0
+ # Add batch to GPU
+ batch = tuple(t.to(device) for t in batch)
- # Evaluate data for one epoch
- for batch in validation_dataloader:
+ # Unpack the inputs from dataloader
+ b_input_ids, b_input_mask, b_labels = batch
- # Add batch to GPU
- batch = tuple(t.to(device) for t in batch)
+ # Telling the model not to compute or store gradients, saving memory and
+ # speeding up validation
+ with torch.no_grad():
- # Unpack the inputs from dataloader
- b_input_ids, b_input_mask, b_labels = batch
+ # Forward pass, calculate logit predictions.
+ # This will return the logits rather than the loss because we have
+ # not provided labels.
+ # token_type_ids is the same as the "segment ids", which
+ # differentiates sentence 1 and 2 in 2-sentence tasks.
+ # The documentation for this `model` function is here:
+ # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
+ outputs = model(b_input_ids,
+ token_type_ids=None,
+ attention_mask=b_input_mask)
- # Telling the model not to compute or store gradients, saving memory and
- # speeding up validation
- with torch.no_grad():
+ # Get the "logits" output by the model. The "logits" are the output
+ # values prior to applying an activation function like the softmax.
+ logits = outputs[0]
- # Forward pass, calculate logit predictions.
- # This will return the logits rather than the loss because we have
- # not provided labels.
- # token_type_ids is the same as the "segment ids", which
- # differentiates sentence 1 and 2 in 2-sentence tasks.
- # The documentation for this `model` function is here:
- # https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
- outputs = model(b_input_ids,
- token_type_ids=None,
- attention_mask=b_input_mask)
+ # Move logits and labels to CPU
+ logits = logits.detach().cpu().numpy()
+ label_ids = b_labels.to('cpu').numpy()
- # Get the "logits" output by the model. The "logits" are the output
- # values prior to applying an activation function like the softmax.
- logits = outputs[0]
+ # Calculate the accuracy for this batch of test sentences.
+ tmp_eval_accuracy = flat_accuracy(logits, label_ids)
- # Move logits and labels to CPU
- logits = logits.detach().cpu().numpy()
- label_ids = b_labels.to('cpu').numpy()
+ # Accumulate the total accuracy.
+ eval_accuracy += tmp_eval_accuracy
- # Calculate the accuracy for this batch of test sentences.
- tmp_eval_accuracy = flat_accuracy(logits, label_ids)
+ # Track the number of batches
+ nb_eval_steps += 1
- # Accumulate the total accuracy.
- eval_accuracy += tmp_eval_accuracy
+ # Report the final accuracy for this validation run.
+ print(" Accuracy: {0:.2f}".format(eval_accuracy/nb_eval_steps))
+ print(" Validation took: {:}".format(format_time(time.time() - t0)))
- # Track the number of batches
- nb_eval_steps += 1
+ print("")
+ print("Training complete!")
+ return model
- # Report the final accuracy for this validation run.
- print(" Accuracy: {0:.2f}".format(eval_accuracy/nb_eval_steps))
- print(" Validation took: {:}".format(format_time(time.time() - t0)))
-print("")
-print("Training complete!")
+'''print('Saving Model....')
+model_save_name = config.get('model','modelName')
+path = config.get('model','path')
+#torch.save(model.state_dict(), os.path.join(path,model_save_name))
+torch.save(model, os.path.join(path,model_save_name))'''
--
GitLab
From 66234a841ff8cc07647669b2c710ef40800f461f Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Fri, 17 Sep 2021 08:34:24 +0200
Subject: [PATCH 6/6] [ADD] bert config file
---
bert_settings.conf | 19 +++++++++++++++++++
1 file changed, 19 insertions(+)
create mode 100644 bert_settings.conf
diff --git a/bert_settings.conf b/bert_settings.conf
new file mode 100644
index 0000000..ccba612
--- /dev/null
+++ b/bert_settings.conf
@@ -0,0 +1,19 @@
+[general]
+dataPath = Data/dataframe_with_ensemble_domaine_enccre.csv
+columnText = contentWithoutClass
+columnClass = ensemble_domaine_enccre
+minOfInstancePerClass = 200
+maxOfInstancePerClass = 1500
+
+
+[model]
+
+tokeniser = bert-base-multilingual-cased
+#tokeniser = camembert-base
+model = bert-base-multilingual-cased
+#model = camembert-base
+max_len_sequences = 256
+batch_size = 32
+epochs = 4
+pathModel = ' '
+modelName = ' '
--
GitLab