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"""Helpdesk MultiLanguage |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1hukfVm3vIbdmE1jpGZUoZDhHeuTs-AlR |
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""" |
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!pip install datasets |
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import pandas as pd |
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import plotly.express as px |
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import os |
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import plotly.graph_objects as go |
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from plotly.subplots import make_subplots |
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from sklearn.model_selection import train_test_split |
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from sklearn.metrics import classification_report, confusion_matrix |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer |
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from datasets import Dataset |
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import torch |
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import numpy as np |
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!pip install wandb |
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import wandb |
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wandb.login(key='eb4c4a1fa7eec1ffbabc36420ba1166f797d4ac5') |
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data_path = "/content/ticket_helpdesk_labeled_multi_languages_english_spain_french_german.csv" |
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df = pd.read_csv(data_path) |
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print("First few rows of the dataset:") |
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print(df.head()) |
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print("\nEDA and Visualization") |
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print("\nSummary statistics:") |
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print(df.describe(include='all')) |
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fig_queue = px.histogram(df, x='queue', title='Distribution of Queue Categories', color='queue') |
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fig_queue.show() |
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fig_priority = px.histogram(df, x='priority', title='Distribution of Priority Levels', color='priority') |
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fig_priority.show() |
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fig_language = px.histogram(df, x='language', title='Distribution of Languages', color='language') |
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fig_language.show() |
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fig_software = px.histogram(df, x='software_used', title='Distribution of Software Used', color='software_used') |
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fig_software.show() |
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fig_hardware = px.histogram(df, x='hardware_used', title='Distribution of Hardware Used', color='hardware_used') |
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fig_hardware.show() |
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fig_accounting = px.histogram(df, x='accounting_category', title='Distribution of Accounting Categories', color='accounting_category') |
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fig_accounting.show() |
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fig = make_subplots(rows=3, cols=1, subplot_titles=('Priority Distribution', 'Language Distribution', 'Queue Distribution')) |
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fig.add_trace(go.Histogram(x=df['priority'], name='Priority'), row=1, col=1) |
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fig.add_trace(go.Histogram(x=df['language'], name='Language'), row=2, col=1) |
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fig.add_trace(go.Histogram(x=df['queue'], name='Queue'), row=3, col=1) |
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fig.update_layout(title_text='Distributions of Priority, Language, and Queue', showlegend=False) |
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fig.show() |
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fig_scatter = px.scatter(df, x='priority', y='queue', color='priority', title='Scatter Plot of Priority vs. Queue') |
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fig_scatter.show() |
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df = df.dropna(subset=['text']) |
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df['text'] = df['text'].astype(str) |
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df['queue_encoded'] = df['queue'].astype('category').cat.codes |
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queue_mapping = dict(enumerate(df['queue'].astype('category').cat.categories)) |
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X_train, X_test, y_train, y_test = train_test_split(df['text'], df['queue_encoded'], test_size=0.2, random_state=42) |
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train_data = Dataset.from_dict({'text': X_train.tolist(), 'label': y_train.tolist()}) |
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test_data = Dataset.from_dict({'text': X_test.tolist(), 'label': y_test.tolist()}) |
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model_name = "xlm-roberta-base" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=df['queue_encoded'].nunique()) |
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def preprocess_function(examples): |
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return tokenizer(examples['text'], truncation=True, padding=True) |
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train_data = train_data.map(preprocess_function, batched=True) |
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test_data = test_data.map(preprocess_function, batched=True) |
