---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
- proj-persona/PersonaHub
language:
- as
- an
metrics:
- bertscore
pipeline_tag: text-generation
tags:
- code
- not-for-all-audiences
library_name: flair
---
# Project Name

[![license](https://img.shields.io/badge/license-apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)


---

## 🌟 Overview

**Project Name** is a state-of-the-art natural language processing (NLP) model developed for [specific task]. The model is trained using the [base_model] architecture and is designed to work across multiple languages, including English, Spanish, French, German, and Chinese.

![Thumbnail](https://i.ibb.co/hDV7tRw/L0-BKJvb-Rt-KZz67i-Q1-Uf-g.webp) <!-- Replace with the actual thumbnail URL -->

---

## 🌐 Languages Supported

This model supports the following languages:

- **en**: English
- **es**: Spanish
- **fr**: French
- **de**: German
- **zh**: Chinese

---

## 📚 Datasets

The model has been trained and evaluated on the following datasets:

- **IMDB**: A dataset containing movie reviews for sentiment analysis.
- **SQuAD**: The Stanford Question Answering Dataset for reading comprehension.
- **Wikipedia**: A large-scale dataset of general knowledge.
- **COCO**: Common Objects in Context (COCO) dataset for various tasks.
- **CoNLL-2003**: A dataset for Named Entity Recognition (NER).

---

## 📊 Metrics

The performance of the model has been evaluated using the following metrics:

- **Accuracy**: The percentage of correct predictions.
- **F1-Score**: The harmonic mean of precision and recall.
- **Precision**: The percentage of relevant instances among the retrieved instances.
- **Recall**: The percentage of relevant instances that were retrieved.
- **BLEU**: A metric for evaluating the quality of machine-translated text.

---

## 🧠 Base Model

The model is based on the following architecture:

- **Base Model**: [bert-base-uncased](https://huggingface.co/bert-base-uncased) <!-- Replace with actual base model link -->

---

## ⚙️ Installation

To use this model, you need to install the required dependencies:

```bash
pip install flair transformers