⚠️ Caution: The Overfitting of Pretrained Word Embeddings P

⚠️ Caution: The Overfitting of Pretrained Word Embeddings

Pretrained word embeddings, like Word2Vec and GloVe, have revolutionized the field of Natural Language Processing (NLP) by providing powerful vector representations for words. These embeddings are trained on massive datasets and have learned to capture complex relationships between words. However, if not properly fine-tuned, they can lead to overfitting - a phenomenon where the model learns idiosyncrasies specific to the training data, rather than generalizable patterns.

Overfitting occurs when the model becomes too specialized in recognizing patterns that are unique to the training data, such as:

1. Domain-specific jargon: The model learns to recognize words and phrases that are only relevant to a specific domain or industry.
2. Training data biases: The model picks up on biases present in the training data, such as cultural or linguistic biases.
3. Noise in the data: The model learns to recognize patterns tha...

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