📢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.
📢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.
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