updating with better text

README.md

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 # Model Card for t5-small-spoken-typo
 
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
-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
+This model is a fine-tuned version of T5-small, adapted for correcting typographical errors and missing spaces in text. It has been trained on a combination of spoken corpora, including DailyDialog and BNC, with a focus on short utterances common in conversational English.
 
 ## 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 &#34;whatsup&#34; into &#34;what&#39;s up&#34;), 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](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
-- [Model Details](#model-details)
-  - [Model Description](#model-description)
-- [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
+The primary task of this model is **Text Correction**, with a focus on:
+- **Sentence Correction**: Enhancing readability by correcting sentences with missing spaces or typographical errors.
+- **Text Normalization**: Standardizing text by converting informal or irregular forms into more grammatically correct formats.
 
+This model is aimed to support processing user-generated content where informal language, abbreviations, and typos are prevalent, aiming to improve text clarity for further processing or human reading.
 
 # Model Details
 
 ## Model Description
+The `t5-small-spoken-typo` model is specifically designed to tackle the challenges of text correction within user-generated content, particularly in short, conversation-like sentences. It corrects for missing spaces, removes unnecessary punctuation, introduces and then corrects typos, and normalizes text by replacing informal contractions and abbreviations with their full forms.
 
-<!-- Provide a longer summary of what this model is/does. -->
-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
+## Developed by:
+- **Name**: Will Wade
+- **Affiliation**: Research & Innovation Manager, Occupational Therapist, Ace Centre, UK
+- **Contact Info**: wwade@acecentre.org.uk
 
-## 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 &#34;whatsup&#34; into &#34;what&#39;s up&#34;), fixing common misspellings, and ensuring consistent use of language.
+## Model type: 
+- Language model fine-tuned for text correction tasks.
 
-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.
+## Language(s) (NLP): 
+- English (`en`)
 
+## License:
+- apache-2.0
 
-- **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
-    - [GitHub Repo](https://github.com/willwade/dailyDialogCorrections/)
+## Parent Model:
+- The model is fine-tuned from `t5-small`.
 
+## Resources for more information:
+- [GitHub Repo](https://github.com/willwade/dailyDialogCorrections/)
 
 # Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ## Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
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-## Downstream Use [Optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
- 
-
-
+This model can be directly applied for correcting text in various applications, including but not limited to, enhancing the quality of user-generated content, preprocessing text for NLP tasks, and supporting assistive technologies.
 
 ## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
-
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+The model might not perform well on text significantly longer than the training examples (2-5 words), highly formal documents, or languages other than English. Use in sensitive contexts should be approached with caution due to potential biases. **Our typical use case here is AAC users - i.e. users using technology to communicate face to face to people**
 
 # Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-
+The model may inherit biases present in its training data, potentially reflecting or amplifying societal stereotypes. Given its training on conversational English, it may not generalize well to formal text or other dialects and languages.
 
 ## Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
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+Users are encouraged to critically assess the model's output, especially when used in sensitive or impactful contexts. Further fine-tuning with diverse and representative datasets could mitigate some limitations.
 
 # Training Details
 
 ## Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-More information on training data needed
-
+The model was trained on a curated subset of the DailyDialog and BNC corpora (2014 spoken), focusing on sentences 2-5 words in length, with manual introduction of typos and removal of spaces for robustness in text correction tasks.You can see the code to pre-process this [here](https://github.com/willwade/dailyDialogCorrections/tree/main)
 
 ## Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
 ### Preprocessing
-
-More information needed
+Sentences were stripped of apostrophes and commas, spaces were removed, and typos were introduced programmatically to simulate common errors in user-generated content.
 
 ### Speeds, Sizes, Times
+- Training was conducted on Google Colab, taking approximately 11 hrs to complete.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-More information needed
- 
 # Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ## Testing Data, Factors & Metrics
 
 ### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-More information needed
-
-
-### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-More information needed
+Evaluation was performed on a held-out test set derived from the same corpora and similar sentences, ensuring a diverse range of sentence structures and error types were represented.
 
 ### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-More information needed
+Performance was measured using the accuracy of space insertion and typo correction alongside qualitative assessments of text normalisation.
 
 ## Results 
-
-More information needed
-
-# Model Examination
-
-More information needed
+The model demonstrates high efficacy in correcting short, erroneous sentences, with particular strength in handling real-world, conversational text.
 
 # Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** More information needed
-- **Hours used:** More information needed
-- **Cloud Provider:** More information needed
-- **Compute Region:** More information needed
-- **Carbon Emitted:** 0.41
+The training was conducted with an emphasis on efficiency and minimising carbon emissions. Users leveraging cloud compute resources are encouraged to consider the environmental impact of large-scale model training and inference.
 
-# Technical Specifications [optional]
+# Technical Specifications
 
 ## Model Architecture and Objective
-
-More information needed
+The model follows the T5 architecture, fine-tuned for the specific task of text correction with a focus on typo correction and space insertion.
 
 ## Compute Infrastructure
-
-More information needed
-
-### Hardware
-
-More information needed
-
-### Software
-
-More information needed
+- **Hardware**: T4 GPU (Google Colab)
+- **Software**: PyTorch 1.8.1 with Transformers 4.8.2
 
 # Citation
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
 **BibTeX:**
 
-More information needed
-
-**APA:**
-
-More information needed
-
-# Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-More information needed
-
-# More Information [optional]
-
-More information needed
-
-# Model Card Authors [optional]
-
-<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
-
-Will Wade
-
-# Model Card Contact
-
-More information needed
-
-# How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-<details>
-<summary> Click to expand </summary>
-
-More information needed
-
-</details>
+```bibtex
+@misc{t5_small_spoken_typo_2021,
+  title={T5-small Spoken Typo Corrector},
+  author={Your Name},
+  year={2021},
+  howpublished={\url{https://huggingface.co/your-username/t5-small-spoken-typo}},
+}