XLM-RoBERTa (base-sized model) Fine-tuned for ticket classification 💕

This model has already been fine-tuned for the classification of IT tickets in English and Italian, you can use XLM-RoBERTa basic (no fine-tuned) at the following link: https://huggingface.co/FacebookAI/xlm-roberta-base

🚨 CODE TO AUTONOMOUSLY FINE-TUNE YOUR MODEL WITH YOUR DATA FOR BOTH BALANCED AND UNBALANCED CLASSES 🧸

You can find it in files and version under the name of: NOTEBOOK_FINE-TUNING_XLM-ROBERTA-BASE.ipynb

Fine-Tuned Model description

The fine tuned was done to recognize 18 classes related to the following macros:

Database 3 class
Hardware 6 class
Inquiry/help 2 class
Network 5 class
software 2 class

The data was generated using GPT 3.5-turbo and does not contain any sensitive data. A total of 100 examples were generated for each class, all different from each other and not translated, 50 in Italian and 50 in English.

The dataset contains a total of 1800 ticket texts, divided into the following numbers:

Training 1440 (80%)
Validation 180 (10%)
Test 180 (10%)

You can find dataset in: https://huggingface.co/Noise144/xlm-roberta-base-finetuned-IT-EN-classification-ticket/tree/main/Data

Fine-tuning is performed according to the following parameters:

Batch size = 16
Epochs = 6
Learning rate = 2e-5
Weight decay = 0.01

Model description

XLM-RoBERTa model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. It was introduced in the paper Unsupervised Cross-lingual Representation Learning at Scale by Conneau et al. and first released in this repository.

XLM-RoBERTa is a multilingual version of RoBERTa. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. RoBERTa is a transformers model pretrained on a large corpus in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.

More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.

This way, the model learns an inner representation of 100 languages that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the XLM-RoBERTa model as inputs.

Notes:

This model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering, not for text generation.
This model is fine-tuned for classification of ticket