library_name: transformers
tags:
- aphasia
- text-normalization
- seq2seq
- nlp
Model Card for Aphasia Text Normalization
This is a fine-tuned model designed to normalize aphasic speech patterns into standard English, providing better communication capabilities for individuals with speech difficulties.
Model Details
Model Description
- Developed by: Leif Rogers
- Shared by: Leif Rogers
- Model type: Seq2Seq Language Model
- Language(s): English (EN)
- License: Apache 2.0
- Finetuned from: T5-Small
The model was fine-tuned on a synthetic dataset generated to mimic aphasic speech patterns and their normalized counterparts. It is intended for applications in assistive technologies to aid individuals with speech impairments.
Model Sources
- Repository: GitHub Repo
- Paper: Not applicable
- Demo: Not available yet
Uses
Direct Use
The model can be used directly for text normalization tasks to convert aphasic speech into standard English.
Downstream Use
Potential downstream uses include integration into assistive communication applications, healthcare tools, or educational resources for speech therapy.
Out-of-Scope Use
The model is not designed for:
- Speech-to-text conversion
- Non-English languages
- Malicious applications (e.g., creating misleading outputs)
Bias, Risks, and Limitations
Bias
The model was trained on synthetic data, which may not represent real-world variations in aphasic speech patterns. It could produce biased outputs for certain dialects or speech patterns.
Risks
- Overgeneralization of input
- Misinterpretation of ambiguous input phrases
Recommendations
Users should evaluate the model’s performance in their specific use cases before deployment and provide manual oversight where necessary.
How to Get Started with the Model
Use the following code to load and use the model:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_name = "leifsternyc/aphasia-t5-normalization"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Example usage
input_text = "Want go food need"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))