Model Card for t5-small-spoken-typo
This is a finetuned t5-small model using Spoken corpora (DailyDialog and BNC). We have done a number of things to the data though
- Only used sentences of 2-5 words long
- Removed apostrophes, commas etc
- Added in typos across the data set
- And most importantly - removed spaces
Task
The primary task of this model is Text Correction, specifically designed for:
Sentence Correction: Correcting sentences with missing spaces or typographical errors to enhance readability and understanding. This task is crucial for applications like assistive technology tools, text preprocessing in NLP pipelines, and improving user-generated content.
Text Normalization: Converting informal or irregular text forms into a more standard, grammatically correct format. This includes expanding contractions (e.g., turning "whatsup" into "what's up"), fixing common misspellings, and ensuring consistent use of language.
This model is particularly suited for processing user-generated content where informal language, abbreviations, and typos are common. It aims to improve the clarity and quality of text inputs, making them more accessible for subsequent NLP tasks or human readers.
Table of Contents
- Model Card for t5-small-spoken-typo
- Table of Contents
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Environmental Impact
- Technical Specifications [optional]
- Citation
- Glossary [optional]
- More Information [optional]
- Model Card Authors [optional]
- Model Card Contact
- How to Get Started with the Model
Model Details
Model Description
This is a finetuned t5-small model using Spoken corpora (DailyDialog and BNC). We have done a number of things to the data though
- Only used sentences of 2-5 words long
- Removed apostrophes, commas etc
- Added in typos across the data set
- And most importantly - removed spaces
Task
The primary task of this model is Text Correction, specifically designed for:
Sentence Correction: Correcting sentences with missing spaces or typographical errors to enhance readability and understanding. This task is crucial for applications like assistive technology tools, text preprocessing in NLP pipelines, and improving user-generated content.
Text Normalization: Converting informal or irregular text forms into a more standard, grammatically correct format. This includes expanding contractions (e.g., turning "whatsup" into "what's up"), fixing common misspellings, and ensuring consistent use of language.
This model is particularly suited for processing user-generated content where informal language, abbreviations, and typos are common. It aims to improve the clarity and quality of text inputs, making them more accessible for subsequent NLP tasks or human readers.
- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): en
- License: apache-2.0
- Parent Model: More information needed
- Resources for more information: More information needed
Uses
Direct Use
Downstream Use [Optional]
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Training Details
Training Data
More information on training data needed
Training Procedure
Preprocessing
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Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Model Examination
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: 0.41
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation
BibTeX:
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APA:
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Glossary [optional]
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More Information [optional]
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Model Card Authors [optional]
Will Wade
Model Card Contact
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How to Get Started with the Model
Use the code below to get started with the model.
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