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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- lstm |
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- text-segmentation |
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- lightweight |
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- client-side |
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- web |
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- onnxruntime-web |
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- speech-to-text |
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- low-memory-footprint |
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--- |
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Check this [NPM package](https://github.com/orgs/the-vedantic-coder/packages/npm/stst/580080323) (built for Speech-To-Text usecase) implements the setup and inference for this model. It provides a [React app demo](https://sentence-splitter-poc.vercel.app/) and a `processDirectText` method to try direct inference on text. |
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The sentence splitter model is modification of the LSTM model with around 500 B input size taken from the repository: [NNSplit](https://github.com/kornelski/nnsplit) |
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The size of the model used here is **~4 MB**. |
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| | NNSplit | Spacy (Tagger) | Spacy (Sentencizer) | |
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|------------------------|------------|----------------|---------------------| |
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| Clean | 0.754371 | 0.853603 | 0.820934 | |
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| Partial punctuation | 0.485907 | 0.517829 | 0.249753 | |
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| Partial case | 0.761754 | 0.825119 | 0.819679 | |
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| Partial punctuation and case | 0.443704 | 0.458619 | 0.249873 | |
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| No punctuation and case| 0.166273 | 0.180859 | 0.00463281 | |
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### Example |
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No punctuation and no cases (~17% accuracy) <br> |
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**Input:** |
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```text |
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the difference between rest and graphql is explained as follows |
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rest is an architectural style that exposes resources via endpoints typically following crud operations each endpoint returns a fixed data structure graphql on the other hand allows clients to specify exactly what data they need in a single query often reducing overfetching and underfetching issues |
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``` |
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**Result: 28.90ms ✅** |
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