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https://github.com/ziadelsayed0/service-quality-ai-based-system
Ai Application For Enhancing Customer Services
https://github.com/ziadelsayed0/service-quality-ai-based-system
arbert bert django-rest-framework fine-tuning marbert sentiment-analysis text-classification topic-classification topic-modeling
Last synced: about 2 months ago
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Ai Application For Enhancing Customer Services
- Host: GitHub
- URL: https://github.com/ziadelsayed0/service-quality-ai-based-system
- Owner: ziadelsayed0
- Created: 2023-07-23T19:11:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-11T04:38:37.000Z (4 months ago)
- Last Synced: 2024-09-11T11:15:57.397Z (4 months ago)
- Topics: arbert, bert, django-rest-framework, fine-tuning, marbert, sentiment-analysis, text-classification, topic-classification, topic-modeling
- Homepage:
- Size: 606 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Service-Quality-Ai-based-System
### Service Quality Ai-based System is a cutting-edge platform designed to enhance customer service and support in various industries specifically in Arabic language.
#### The main three tasks of the Plateform:
- Topic classification
- Sentiment Analysis
- Solution Generation
The deployment process is applied using frontend and
backend development with Django framework.
---
## Used Models:
### ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic
### What is the repository is about?
This is the repository accompanying our project [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic].
In the paper, we:
* introduce ```ARBERT``` and ```MARBERT```, two powerful Transformer-based language models for Arabic;
* introduce ```ArBench```, a multi-domain, multi-variety benchmark for Arabic naturaal language understanding based on 41 datasets across 5 different tasks and task clusters;
* evaluate ARBERT and MARBERT on ArBench and compare against available language models.
Our model establish new state-of-the-art (SOTA) on all 5 tasks/task clusters on 37 out of the 41 datasets.
Our language models are publicaly available for research (see below).
The rest of this repository provides more information about our new language models, benchmark, and experiments.
### How to use ARBERT and MARBERT
#### Loading directly from Huggingface
You can use ARBERT and MARBERT with [Hugging Face's Transformers](https://github.com/huggingface/transformers) library as follow.
```python
from transformers import AutoTokenizer, AutoModel
#load AEBERT model from huggingface
ARBERT_tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/ARBERT")
ARBERT_model = AutoModel.from_pretrained("UBC-NLP/ARBERT")
#load MAEBERT model from huggingface
MARBERT_tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/MARBERT")
MARBERT_model = AutoModel.from_pretrained("UBC-NLP/MARBERT")
```
### 4.2 Example: Fine-tuning MARBERT for Sentiment Analysis
MARBERT Fine-Tuning demo with PyTorch checkpoint for Sentiment Analysis on the AJGT dataset [](https://colab.research.google.com/drive/1M0ls7EPUi1dwqIDh6HNfJ5y826XvcgGX?usp=sharing)