Get a weekly rundown of the latest AI models and research... subscribe! https://aimodels.substack.com/

Oliverguhr

Models by this creator

🔄

fullstop-punctuation-multilang-large

oliverguhr

Total Score

124

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.

Read more

Updated 5/16/2024

🤖

spelling-correction-english-base

oliverguhr

Total Score

62

The spelling-correction-english-base model is an experimental proof-of-concept spelling correction model for the English language, created by oliverguhr. It is designed to fix common typos and punctuation errors in text. This model is part of oliverguhr's research into developing models that can restore the punctuation of transcribed spoken language, as demonstrated by the fullstop-punctuation-multilang-large model. Model inputs and outputs Inputs English text with potential spelling and punctuation errors Outputs Corrected English text with improved spelling and punctuation Capabilities The spelling-correction-english-base model can detect and fix common spelling and punctuation mistakes in English text. For example, it can correct words like "comparsion" to "comparison" and add missing punctuation like periods and commas. What can I use it for? This model could be useful for various applications that require accurate spelling and punctuation, such as writing assistance tools, content editing, and language learning platforms. It could also be used as a starting point for fine-tuning on specific domains or languages. Things to try You can experiment with the spelling-correction-english-base model using the provided pipeline interface. Try running it on your own text samples to see how it performs, and consider ways you could integrate it into your projects or applications.

Read more

Updated 5/16/2024