fullstop-punctuation-multilang-large
Maintainer: oliverguhr
125
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The fullstop-punctuation-multilang-large model is a multilingual punctuation restoration model developed by Oliver Guhr. It can predict punctuation for English, Italian, French, and German text, making it useful for tasks like transcription of spoken language. The model was trained on the Europarl dataset provided by the SEPP-NLG Shared Task. It can restore common punctuation marks like periods, commas, question marks, hyphens, and colons. Similar models include bert-restore-punctuation and bert-base-multilingual-uncased-sentiment, which focus on punctuation restoration and multilingual sentiment analysis respectively.
Model inputs and outputs
Inputs
- Text: The model takes in raw text that may be missing punctuation.
Outputs
- Punctuated text: The model outputs the input text with punctuation marks restored at the appropriate locations.
Capabilities
The fullstop-punctuation-multilang-large model can effectively restore common punctuation in English, Italian, French, and German text. It performs best on restoring periods and commas, with F1 scores around 0.95 for those markers. The model struggles more with restoring less common punctuation like hyphens and colons, achieving F1 scores around 0.60 for those.
What can I use it for?
This model could be useful for any applications that involve transcribing or processing spoken language in the supported languages, such as automated captioning, meeting transcripts, or voice assistants. By automatically adding punctuation, the model can make the text more readable and natural. The multilingual aspect also makes it applicable across a range of international use cases. Companies could leverage this model to improve the quality of their speech-to-text pipelines or offer more polished text outputs to customers.
Things to try
One interesting aspect of this model is its ability to handle multiple languages. Practitioners could experiment with feeding it text in different languages and compare the punctuation restoration performance. It could also be fine-tuned on domain-specific datasets beyond the political speeches in Europarl to see if the model generalizes well. Additionally, combining this punctuation model with other NLP models like sentiment analysis or named entity recognition could lead to interesting applications for processing conversational data.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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