NLP Cloud is an API for natural language processing.
Embeddings are vector representations of pieces of texts. If 2 pieces of text have a similar vector representation, it most likely means that they have a similar meaning.
Imagine you have the 3 following sentences:
NLP Cloud is an API for natural language processing.
NLP Cloud proposes an API dedicated to NLP at scale.
I went to the cinema yesterday. It was great!
Here are the embeddings from the 3 above sentences (truncated for the sake of simplicity):
[[0.0927242711186409,-0.19866740703582764,-0.013638739474117756,-0.11876793205738068,0.011521861888468266,-0.03629707545042038, -0.030676838010549545,-0.03159608319401741,0.021390020847320557,0.03344911336898804,0.1698218137025833,-0.0009996045846492052, -0.07465217262506485,-0.21483412384986877,0.11283198744058609,0.03549865633249283,0.04985387250781059,-0.027558118104934692, 0.06297887861728668,0.09421529620885849,0.03700404614210129,0.06565431505441666,0.02284885197877884,0.06327767670154572, -0.09266531467437744,-0.014569456689059734,-0.06129194051027298,0.1818675994873047,0.09628438949584961,-0.09874546527862549, 0.030865425243973732, [...] ,-0.02097163535654545,0.021617714315652847,0.11045169830322266,0.01000999379903078,0.11451057344675064,0.18813028931617737, 0.007419265806674957,0.1630171686410904,0.21308083832263947,-0.03355317562818527,0.0778832957148552,0.2268853485584259,-0.13271427154541016, 0.005264544393867254,0.16081497073173523,0.09937280416488647,-0.12673905491828918,-0.12035898119211197,-0.06462062895298004, -0.0024213052820414305,0.08730605989694595,-0.04702030122280121,-0.03694896399974823,0.002265638206154108,-0.027780283242464066, -0.00017151003703474998,-0.20887477695941925,-0.2585527300834656,0.3124837279319763,0.05403835326433182,0.027094876393675804, -0.022925367578864098,0.038322173058986664]]
Embeddings are a core feature of Natural Language Processing because, once a machine is able to detect similarities between texts, it paves the ways for many interesting applications like semantic similarity, semantic search, paraphrase detection, clustering, and more.
Here are some examples where embeddings are extremely useful:
You might want to detect whether 2 sentences are talking about the same thing or not. That's useful for paraphrase (plagiarism) detection for example. It's also useful to understand if several persons are talking about the same topic or not.
Semantic search is the modern way of searching for information. Instead of naively searching for texts containing specific keywords, you can now search for texts talking about a topic you're interested in, even if keywords don't match (in case of synonyms for examples).
You might want to group things by categories (ideas, speeches, conversations...). Clustering is an old machine learning technique that can now be effectively applied to natural language processing.
Sentence Transformers is an amazing library that has been recently released in order to dramatically improve embeddings calculation between many pieces of text. This is useful for semantic similarity, semantic search, or paraphrase mining. It is much faster than standard Transformer-based models like Bert, Distilbert or Roberta because it uses the so called cosine-similarity technique.
Paraphrase Multilingual Mpnet Base v2 is a very good Natural Language Processing model based on Sentence Transformers, that is able to perform embeddings calculation and then detect semantic similarity in more than 50 languages with excellent performances.You can find it here.
Building an inference API for embeddings is interesting as soon a you want to use embeddings in production. But keep in mind that building such an API is not necessarily easy. First because you need to code the API (easy part) but also because you need to build a highly available, fast, and scalable infrastructure to serve your models behind the hood (hardest part). Machine learning models consume a lot of resources (memory, disk space, CPU, GPU...) which makes it hard to achieve high-availability and low latency at the same time.
Leveraging such an API is very interesting because it is completely decoupled from the rest of your stack (microservice architecture), so you can easily scale it independently and ensure high-availability of your models through redundancy. But an API is also the way to go in terms of language interoperability. Most machine learning frameworks are developed in Python, but it's likely that you want to access them from other languages like Javascript, Go, Ruby... In such situation, an API is a great solution.
NLP Cloud proposes an embeddings API that gives you the opportunity to extract embeddings
out of the box, based on Paraphrase Multilingual Mpnet Base v2 or on GPT-J.
The response time (latency) is very good for the first model, but much slower for GPT-J. You can either use the pre-trained
model, train your own model, or upload your own custom model!
For more details, see our documentation about embeddings here.
Testing embeddings locally is one thing, but using it reliably in production is another thing. With NLP Cloud you can just do both!