11–20 of 32 posts
Disclaimer: No affiliation, only sharing for those who are curious
Did you try other solutions like ULMFiT [1]? Seems like the exact use case for that. Although it might be overkill for just 4 categories.
1. Experienced ML practitioners will be unimpressed with the ML task generally (simple problem, no comparison with common models, no use of common dataset, no lit review) and wish that there was more detail in model design.
2. Inexperienced ML practitioners will be happy with the birds-eye view of NLP tasks but wish there were more implementation details.
3. Potential clients (non-technical) will get lost in the details/lingo and wish there were case studies or a vision of what this service can accomplish for them/their business.
4. Potential clients (technical) and SWEs will wish they got a better look at the GUI, got an explanation of the stack, and wonder about APIs/integration with whatever it is they already do.
Perhaps this might explain why literally every other comment at the time I'm writing this is asking for additional details. Pick one or two!
Is Sculpt AI available for public use? I see sculptintel.com is down.
I agree with the other posters that the intro to NLP part is unnecessary. It reads like those recipe websites where they tell you their whole life story before the actual recipe. I get that it's good for SEO, but it's still annoying to read. Did you try other solutions like ULMFiT [1]? Seems like the exact use case for that. Although it might be overkill for just 4 categories. [1] https://arxiv.org/abs/1801.06146
I agree with the other posters that the intro to NLP part is unnecessary. It reads like those recipe websites where they tell you their whole life story before the actual recipe. I get that it's good for SEO, but it's still annoying to read. Did you try other solutions like ULMFiT [1]? Seems like the exact use case for that. Although it might be overkill for just 4 categories. [1] https://arxiv.org/abs/1801.06146
Yes! it's a great fit for this problem, we actually experimented with it: https://medium.com/sculpt/a-technique-for-building-nlp-class...
"This article has been written by Sculpt AI [...] in collaboration with Reza [Article’s author]", so we now use tools to write longer articles faster only to later feed them into tl;dr-bots/summarizers to get the gist without having to read all of it. ;)
If it was hard for humans to understand that, just imagine how difficult it would be for NLP to understand it. :)
- Snorkel (training data curation, weak supervision, heuristic labeling functions, uncertainty sampling, relation extraction): https://github.com/snorkel-team/snorkel
- AllenNLP (many pretrained NLP research models for tasks beyond text classification, model training and serving, visualization/interpretability utilities): https://github.com/allenai/allennlp
- Spacy (tokenization, NER/POS + tagging visualizer, pretrained word vectors, integration with DL models): https://github.com/allenai/allennlp
-huggingface Transformers (latest and greatest pretrained models, e.g. BERT): https://github.com/huggingface/transformers
- ...or a barebones “from scratch” solution in less than an hour with a Colab notebook and scikit-learn (preprocess text into tf-idf vectors, LSA/NMF to generate “document embeddings”, visualize embeddings with t-SNE/UMAP [facilitates weak supervision/active learning], classify with LogReg/RF/SVM/whatever). You could also tack on pretrained gensim/TF/PyTorch models quite easily as a next step. But this basic flow quickly gives you a handle on your corpus.
By the way, the docs for DeepDive (the predecessor of Snorkel) are some amazingly detailed background reading: http://deepdive.stanford.edu/example-spouse