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