LeifsterNYC
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aphasia-t5-normalization

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 ---
 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](https://github.com/leifsternyc/aphasiaprediction)
+- **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:
+```python
+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))