📢Day 15/100: The Rise of Telegram E-Commerce in Ethiopia

Telegram is transforming e-commerce in Ethiopia, but its fragmented nature poses challenges. Vendors operate in silos, and customers struggle to navigate multiple channels.



EthioMart's Vision:



We aim to create a centralized platform aggregating data from Telegram channels, simplifying product discovery for customers and enhancing visibility for vendors.



💡 Question of the day: How can centralized platforms improve Ethiopia’s digital shopping experience?





#Ethiopia #ECommerce #DigitalTransformation #Telegram #FintechInnovation

📢Day 16/100: Tackling Amharic NLP Challenges

Amharic presents unique challenges in natural language processing (NLP), from its complex script to a lack of annotated datasets.



My approach: Fine-tune Large Language Models (LLMs) for Amharic Named Entity Recognition (NER) to extract product names, prices, and locations from Telegram messages.



💡 Discussion: What strategies can we adopt to make NLP more accessible for low-resource languages like Amharic?

#NLP #AI #Amharic #FintechEthiopia

📢Day 17/100: From Data to Insights



My journey started with collecting and cleaning data from Telegram channels, a hub for Ethiopian e-commerce.



Key steps:

1️⃣ Scraping Telegram messages to capture product details.

2️⃣ Preprocessing Amharic text to handle non-text characters and normalize content.

3️⃣ Tokenizing text for labeling.



💡 Takeaway: High-quality data preparation is the backbone of effective machine learning models.


#DataScience #AmharicNLP #FintechEthiopia

📢Day 18/100: Labeling Amharic Text for NER

Labeling Amharic text for Named Entity Recognition is no small task.

Our algorithm identifies:

Prices using patterns like "ብር" (currency).

Locations from a predefined list.

Products through contextual analysis.

💡 Example: "ዋጋ 4800 ብር" -> "B-PRICE I-PRICE I-PRICE"

💡 Discussion: How can we simplify labeling entities in low-resource languages?

#NER #Amharic #DataLabeling #Ethiopia

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📢Day 19/100: Choosing the Right Language Model

For Amharic Named Entity Recognition, we fine-tuned three models:

1️⃣ XLM-Roberta: Best for multilingual NLP.

2️⃣ mBERT: Balanced performance.

3️⃣ DistilBERT: Lightweight but slightly less accurate.

💡 Insight: XLM-Roberta outperformed others in accuracy and entity recognition for Amharic e-commerce data.

💡 Question: What’s your experience with fine-tuning NLP models for underrepresented languages?

#AI #NLP #ModelSelection #FintechAfrica

📢Day 20/100: Overcoming Tokenization Challenges
Tokenization is critical for NLP tasks like Named Entity Recognition.

Key steps:
1️⃣ Aligning tokens with Amharic text.
2️⃣ Preserving the relationship between tokens and their labels.
3️⃣ Using model-specific tokenizers (XLM-Roberta, mBERT).

💡 Takeaway: Tokenization errors can significantly impact the accuracy of entity recognition models.

#AI #Tokenization #AmharicNLP #FintechInnovation

𝘼𝙄 𝙄𝙨 𝙍𝙚𝙫𝙤𝙡𝙪𝙩𝙞𝙤𝙣𝙖𝙧𝙮, 𝘽𝙪𝙩 𝘼𝙧𝙚 𝙒𝙚 𝙊𝙫𝙚𝙧𝙡𝙤𝙤𝙠𝙞𝙣𝙜 𝙌𝙪𝙖𝙣𝙩𝙪𝙢 𝘾𝙤𝙢𝙥𝙪𝙩𝙞𝙣𝙜?
In the tech world, discussions of Artificial Intelligence dominate the stage—and rightly so. AI has transformed industries, revolutionized how we work, and opened the door to possibilities once thought unattainable.
But here’s a question for the experts: Are we paying enough attention to quantum computing?
Quantum computing isn't just a buzzword; it has the potential to supercharge AI by solving problems that classical computers can’t handle in a practical timeframe. From optimizing complex systems to enabling breakthroughs in drug discovery and cryptography, the synergy between AI and quantum computing could redefine innovation.
Yet, in many discussions about AI, I rarely hear about how we’re preparing for this convergence.
How do we ensure our AI models are ready to harness quantum power?
What are the ethical considerations as we bridge these two transformative technologies?
To those immersed in AI, have you explored the potential of quantum computing in your field? If not, why? Let’s start a conversation about how these technologies can shape the future—together.

hashtag#AI hashtag#QuantumComputing hashtag#Innovation hashtag#FutureTech https://medium.com/@epythonlab/whats-next-after-ai-the-emerging-frontiers-of-technology-822c73b9c7c9

15 𝘽𝙚𝙨𝙩 𝙋𝙮𝙩𝙝𝙤𝙣 𝘼𝙄/ 𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙋𝙧𝙤𝙟𝙚𝙘𝙩𝙨 𝙩𝙤 𝘽𝙤𝙤𝙨𝙩 𝙔𝙤𝙪𝙧 𝙎𝙠𝙞𝙡𝙡𝙨 https://medium.com/p/96677345b57d

📢𝗗𝗮𝘆 𝟮𝟭/𝟭𝟬𝟬: 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗔𝗺𝗵𝗮𝗿𝗶𝗰 𝗡𝗘𝗥 𝗠𝗼𝗱𝗲𝗹𝘀

I fine-tuned models on 27,989 labeled examples, optimizing key parameters:

- Learning rate: Experimented to find the sweet spot.

- Batch size: Limited to 16 to manage memory constraints.

- Metrics: Focused on precision, recall, and F1-score.



💡 Finding: Smaller batches helped balance performance and computational efficiency.

💡 Question: How do you optimize parameters for low-resource NLP tasks?

#AI #ModelTraining #Ethiopia #NLP

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📢𝘿𝙖𝙮 22/100: 𝙏𝙝𝙚 𝙑𝙖𝙡𝙪𝙚 𝙤𝙛 𝘾𝙚𝙣𝙩𝙧𝙖𝙡𝙞𝙯𝙚𝙙 𝘿𝙖𝙩𝙖

Why is centralizing e-commerce data critical for Ethiopia?



- For vendors: Better visibility and reach.

- For customers: Streamlined product discovery.

- For analytics: Real-time insights into market trends.



💡 Question: What are the key challenges to centralizing data in emerging markets?

#ECommerce #DigitalTransformation #Ethiopia

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🌟 𝘿𝙖𝙮 23/100: 𝙏𝙧𝙪𝙩𝙝 𝙤𝙧 𝙇𝙞𝙚: 𝙉𝙖𝙫𝙞𝙜𝙖𝙩𝙞𝙣𝙜 𝙅𝙤𝙗 𝙄𝙣𝙩𝙚𝙧𝙫𝙞𝙚𝙬𝙨 🌟

This morning, I received an exciting email: "Interview Invitation: AI Python and .NET Developer."

While I’m proficient in AI Python and have tackled many projects, .NET isn’t in my skill set. I faced a dilemma:

Exaggerate my expertise?
Or be honest about my strengths and gaps?
I chose truth. I emphasized my Python expertise and willingness to learn .NET.

💡 Lesson: Honesty builds trust and keeps doors open for the right opportunities.

Have you faced a similar situation? Let’s discuss in the comments! 🙌

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🌟𝘿𝙖𝙮 24/100: 𝙉𝙚𝙭𝙩 𝙎𝙩𝙚𝙥𝙨 𝙛𝙤𝙧 𝘾𝙚𝙣𝙩𝙧𝙖𝙡𝙞𝙯𝙚𝙙 𝙀-𝙘𝙤𝙢𝙢𝙚𝙧𝙘𝙚🌟



I'm moving closer to deploying a centralized e-commerce platform for Ethiopia.



Next steps:

1️⃣ Integrating XLM-Roberta for real-time entity extraction.

2️⃣ Expanding the dataset for even better performance.

3️⃣ Collaborating with vendors to enrich product listings.



💡 Takeaway: NLP-driven platforms like central e-commerce can redefine how e-commerce works in Ethiopia.



💡 Discussion: How can we scale similar platforms for other underrepresented markets?

#AI #ECommerce #FintechAfrica #Amharic

🌟𝘿𝙖𝙮 25/100: 𝙐𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜 𝙀𝙩𝙝𝙞𝙤𝙥𝙞𝙖𝙣 𝙁𝙞𝙣𝙩𝙚𝙘𝙝 🌟



Ethiopia's fintech ecosystem is a mix of challenges and opportunities. 📈🌍

From low formal banking penetration to an increasingly digital population, it’s clear that innovation in financial services is critical.



Key insights from my research today:

- Low banking penetration but high mobile adoption: Over 75% of transactions are cash-based, yet mobile payment systems like Telebirr are gaining traction.

- Regulatory frameworks: Ethiopia’s regulatory approach emphasizes financial inclusion but poses innovation challenges, especially for Buy-Now-Pay-Later services.

- Unique consumer behaviors: The dominance of informal financial systems and cash reliance shapes how Ethiopians engage with digital financial services.



💡 Question of the day: How can fintech drive financial literacy in Ethiopia to accelerate digital adoption?



#FintechAfrica #Ethiopia #Buy-Now-Pay-Later #FinancialLiteracy #DigitalTransformation

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