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Bidirectional Encoder Representations from Transformers Score

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Beginner Guide to Building a Hate Speech Classifier with BERT
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Beginner Guide to Building a Hate Speech Classifier with BERT

Daniela Joaquin2025-07-08T13:54:15+00:00

You've seen the warnings: "This comment may violate our community guidelines," or you've watched online posts or videos get flagged and removed due to their hate speech content. This means that the platform is already using models to detect harmful content. However, have you ever questioned how these models work? How are these models able to understand human sarcasm, cultural slang, and even multilingual language in one sentence? In this guide, we'll walk through the process of building your OWN speech classifier using a well-known transformer model, BERT. This article guides you to a beginner-friendly beginning in modeling and machine learning. [...]

Beginner Guide to Building a Hate Speech Classifier with BERTDaniela Joaquin2025-07-08T13:54:15+00:00

What is BERTScore – Bidirectional Encoder Representations from Transformers Score?
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What is BERTScore – Bidirectional Encoder Representations from Transformers Score?

Lois Angelo Dar Juan2025-07-03T06:03:55+00:00

BERTScore (Bidirectional Encoder Representations from Transformers Score) Cheat Sheet BERTScore is an effective evaluation metric that looks beyond surface-level word matching to assess the meaning behind the generated text. Instead of counting overlapping words like traditional metrics such as BLEU or ROUGE, BERTScore taps into the power of pre-trained transformer models (like BERT) to compare the semantic similarity between tokens in the generated output and a reference sentence. It does this by calculating the cosine similarity between their contextual embeddings. Initially proposed by Zhang et al. (2020), BERTScore has quickly become a popular choice in natural language processing tasks where [...]

What is BERTScore – Bidirectional Encoder Representations from Transformers Score?Lois Angelo Dar Juan2025-07-03T06:03:55+00:00

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