📢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
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 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
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