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feat(nlp-scraper): fix broken link and remove optional part with broken URL

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nprimo 2024-03-14 12:27:24 +00:00 committed by Niccolò Primo
parent 3f22fcf06b
commit 187ca1884b
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7
subjects/ai/nlp-scraper/README.md
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@ -56,7 +56,7 @@ SpaCy](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spa
The goal is to detect what the article is dealing with: Tech, Sport, Business,
Entertainment or Politics. To do so, a labelled dataset is provided: [training
data](bbc_news_train.csv) and [test data](bbc_news_test.csv). From this
data](bbc_news_train.csv) and [test data](bbc_news_tests.csv). From this
dataset, build a classifier that learns to detect the right topic in the
article. Save the training process to a python file because the audit requires
the auditor to test the model.
@ -68,11 +68,6 @@ that the model is trained correctly and not overfitted.
- Learning constraints: **Score on test: > 95%**
- **Optional**: If you want to train a news' topic classifier based on a more
challenging dataset, you can use the
[following](https://www.kaggle.com/rmisra/news-category-dataset) which is
based on 200k news headlines.
#### **3. Sentiment analysis:**
The goal is to detect the sentiment (positive, negative or neutral) of the news