StanzaSharp 0.1.0

StanzaSharp

Stanza's English NLP pipeline in .NET:

• tokenization and sentence splitting
• multi-word token expansion ("don't" → "do" + "n't")
• part-of-speech tags and morphological features
• lemmas
• dependency parsing
• named entities (OntoNotes types: PERSON, ORG, GPE, DATE, ...)
• constituency parsing
• sentence sentiment (negative, neutral, positive)

It runs Stanza's own pretrained models through TorchSharp, with no Python needed. The output is identical to Python Stanza 1.15.0 on its golden test data.

Install

dotnet add package StanzaSharp
dotnet add package TorchSharp-cpu

Add TorchSharp-cpu (or a TorchSharp-cuda-* package for GPU) for the native libtorch. Its version must match the TorchSharp version StanzaSharp depends on.

Download the models

The models (about 600 MB) come from Stanza's Hugging Face repository. Download them once:

await ModelDownloader.DownloadAsync("models/stanza/en");                            // all, ~600 MB
await ModelDownloader.DownloadAsync("models/stanza/en", "tokenize,mwt,pos,lemma");  // only what these need

Each file is checked against its published MD5, and files already present are kept. Pipeline.Load itself never touches the network.

Use

using StanzaSharp;

using var nlp = Pipeline.Load("models/stanza/en");
var doc = nlp.Process("Barack Obama was born in Hawaii. He was elected president in 2008.");

foreach (var sentence in doc.Sentences)
{
    foreach (var word in sentence.Words)
        Console.WriteLine($"{word.Id}\t{word.Text}\t{word.Lemma}\t{word.Upos}\t{word.Feats}\t{word.Head}\t{word.Deprel}");
    Console.WriteLine(sentence.Constituency); // (ROOT (S (NP (NNP Barack) (NNP Obama)) ...))
}

• Pipeline.Load(dir, new PipelineOptions { Processors = "tokenize,mwt" }) runs only the listed processors; each needs the ones before it. The default is all eight, like Stanza's English default: tokenize,mwt,pos,lemma,constituency,depparse,sentiment,ner.
• Add ner to the list for named entities: doc.Entities (text, type, character offsets) and a BIOES tag per token (Token.Ner).
• Add sentiment to the list for sentence.Sentiment: 0 negative, 1 neutral, 2 positive.
• Conllu.Write(doc) gives CoNLL-U in Stanza's format.

GPU

Reference TorchSharp-cuda-windows (or TorchSharp-cuda-linux) instead of TorchSharp-cpu, then Pipeline.Load(dir, new PipelineOptions { Device = torch.CUDA, DisableTf32 = true }). DisableTf32 gives output identical to the CPU by turning TF32 off process-wide; with TF32 on (libtorch's default), a few near-tie decisions can differ. See the repository's docs/gpu.md for measurements.

License

Apache 2.0, like Stanza, whose code this ports (see NOTICE). Stanza's models are not included. Their licenses vary with the training data.

Source, issues and docs: https://github.com/bisforboman/StanzaSharp