Natural Language Processing (NLP) has seen significant progress in processing English and other widely spoken languages. However, there is a growing need to develop NLP techniques for African languages, particularly those with limited resources such as Amharic, which is the official language of Ethiopia. We followed the methodology outlined in the original study, using similar data sets but with an additional dataset of Amharic texts collected from various sources within Ethiopia. The NLP tasks include part-of-speech tagging and named entity recognition (NER). In our replication study, we observed a precision rate of 85% for part-of-speech tagging across all datasets, with slight variations in performance due to differences in text complexity and domain specificity. The findings suggest that the NLP techniques developed for English can be effectively applied to Amharic without substantial modifications. However, further research is needed to validate these results on larger datasets and in different contexts. Future studies should aim to identify and address potential biases or limitations specific to the Amharic language and Ethiopian context. Additionally, there is a need for more diverse and representative data sets to improve model generalization. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.