Why is this company called HuggingFace?
I assume it is a reference to the movie Alien.
Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
31–40 of 45 posts
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#32I can't believe the level of productivity this Hugging face team has. They seemed to have found the ideal balance of software engineering capability and Neural network knowledge, in a team of highly effective and efficient employees. Idk what their monetization plan is as a startup, but it is 100% undervalued at 20 million, and that is just the quality of that team. Now, if only I can figure out how to put a few thou…
> Idk what their monetization plan is as a startup > put a few thousand $ in a series-A Not a good idea.
I see them as an acqui-hire target. Especially form Facebook since they are so geographically close to FAIR labs in NY or Google and get integrated into Google AI like Deep Mind did. (esp. since google uses a ton of Transformers any ways)
I can't think of many small teams that can be acquired and can build a company's ML infrastructure as fast as this team.
If they have the money for it, OCI and Azure may also be keeping a look out for them.
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#33Are there examples on how this can be used for topic modeling, document similarity etc? All the examples I’ve seen (gensim) use bag-of-words which seems to be outdated.
They don't use huggingface, but some of the modern approaches for topic modeling use variational auto-encoders, see:
Open-SESAME (2017): https://arxiv.org/abs/1706.09528 / https://github.com/swabhs/open-sesame
VAMPIRE (2019): https://arxiv.org/abs/1906.02242 / https://github.com/allenai/vampire
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#34Great! Just did a quick test and got a 6-7x speedup on tokenization.
Mind sharing what tests your ran & with which setup? Thanks!
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#35Earlier quoted context omitted.
The reason is that you do not need to finely understand the structure of individual sentences to group documents by similar topics. Word order does not matter much for this task. Hence the success of methods that use Bag of Words (eg TFIDF) as their input representation.
It might be that the corpus I was trying to cluster needs better preprocessing, or perhaps better n-grams. Using Bigrams only I saw a lot of common words that were meaningless, but adding them as stop words made the results worse. Hence my wondering if some other vectorization would produce better results. On a related note, as a newcomer just trying to get things done (i.e. applied NLP) I find the whole ecosystem gr…
If I understand you problem clearly, you can use TFIDF to reduce the weight of meaningless words.
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#36I love the work done and made freely available by both spaCy and HuggingFace. I had my own NLP libraries for about 20 years, simple ones were examples in my books, and more complex and not so understandable ones I sold as products and pulled in lots of consulting work with. I have completely given up my own work developing NLP tools, and generally I use the Python bindings (via the Hy language (hylang) which is a Lis…
Hybrid symbolic and NN will be my next area of hobby research, currently getting my masters degree in NLP. Do you have a few good resources to get startedor/read about?
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#37[deleted]
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#38I didn't realize that particular emoji had a name. I thought it was a play on this: https://en.wikipedia.org/wiki/Alien_(creature_in_Alien_franc...
All emoji have a name. I've found emojipedia to be a good source of info about emoji. https://emojipedia.org/hugging-face/
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#39Earlier quoted context omitted.
It might be that the corpus I was trying to cluster needs better preprocessing, or perhaps better n-grams. Using Bigrams only I saw a lot of common words that were meaningless, but adding them as stop words made the results worse. Hence my wondering if some other vectorization would produce better results. On a related note, as a newcomer just trying to get things done (i.e. applied NLP) I find the whole ecosystem gr…
If I understand you problem clearly, you can use TFIDF to reduce the weight of meaningless words.
It’s not meaningless words - it’s common English words that are overloaded and I think considering their position in sentences instead would give better results.
I haven’t yet tried TFIDF though so I’ll see what that will do.
Re: Show HN: HuggingFace – Fast tokenization library for deep-learning NLP pipelines
#40Are there examples on how this can be used for topic modeling, document similarity etc? All the examples I’ve seen (gensim) use bag-of-words which seems to be outdated.
They don't use huggingface, but some of the modern approaches for topic modeling use variational auto-encoders, see: Open-SESAME (2017): https://arxiv.org/abs/1706.09528 / https://github.com/swabhs/open-sesame VAMPIRE (2019): https://arxiv.org/abs/1906.02242 / https://github.com/allenai/vampire
Thanks! I hadn’t seen VAMPIRE! So stoked to see a new approach to topic modeling. SVD etc are very much a local max