in the
(Evaluating Machine Learning Models) webpage.
We can also use model-specific approaches to interpretability we we did in our embeddings lesson to identify the most influential n-grams in our text. ===>>
We can also use model-specific approaches to interpretability we did in our embeddings lesson to identify the most influential n-grams in our text.
there is an extra (we) in this text
Originally created by @mohammad-gh009 on GitHub (Jul 13, 2024).
Original GitHub issue: https://github.com/GokuMohandas/Made-With-ML/issues/261
in the
(Evaluating Machine Learning Models) webpage.
We can also use model-specific approaches to interpretability we we did in our [embeddings lesson](https://madewithml.com/courses/foundations/embeddings/#interpretability) to identify the most influential n-grams in our text. ===>>
We can also use model-specific approaches to interpretability we did in our [embeddings lesson](https://madewithml.com/courses/foundations/embeddings/#interpretability) to identify the most influential n-grams in our text.
there is an extra (we) in this text
@SanikaMunankar commented on GitHub (Apr 28, 2025):
I have reviewed the Page and the typo issue is Fixed. We can close this Issue.
<!-- gh-comment-id:2836698343 -->
@SanikaMunankar commented on GitHub (Apr 28, 2025):
I have reviewed the Page and the typo issue is Fixed. We can close this Issue.
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Originally created by @mohammad-gh009 on GitHub (Jul 13, 2024).
Original GitHub issue: https://github.com/GokuMohandas/Made-With-ML/issues/261
in the
(Evaluating Machine Learning Models) webpage.
We can also use model-specific approaches to interpretability we we did in our embeddings lesson to identify the most influential n-grams in our text. ===>>
We can also use model-specific approaches to interpretability we did in our embeddings lesson to identify the most influential n-grams in our text.
there is an extra (we) in this text
@SanikaMunankar commented on GitHub (Apr 28, 2025):
I have reviewed the Page and the typo issue is Fixed. We can close this Issue.