README.md

---
language: en
tags:
- clinical-nlp
- summarization
- speech-language-pathology
- t5
license: mit
datasets:
- custom
metrics:
- rouge
---

# IPLC T5 Clinical Report Generator

This is a fine-tuned T5 model specialized in generating clinical report summaries for speech-language pathology evaluations. The model has been trained on a custom dataset of clinical reports and evaluation forms.

## Model Description

- **Model Type:** T5 (Text-to-Text Transfer Transformer)
- **Base Model:** t5-small
- **Task:** Clinical Report Summarization
- **Domain:** Speech-Language Pathology
- **Language:** English

## Intended Use

This model is designed to assist speech-language pathologists in generating clinical report summaries from structured evaluation data. It can process information about:

- Patient demographics
- Diagnostic information
- Language assessments
- Clinical observations
- Evaluation results

## Training Data

The model was fine-tuned on a custom dataset of speech-language pathology evaluation reports and clinical documentation.

## Usage

```python
from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained("pdarleyjr/iplc-t5-model")
tokenizer = T5Tokenizer.from_pretrained("pdarleyjr/iplc-t5-model")

text = "summarize: evaluation type: initial. primary diagnosis: F84.0. severity: mild. primary language: english"
input_ids = tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)

outputs = model.generate(
    input_ids,
    max_length=256,
    num_beams=4,
    no_repeat_ngram_size=3,
    length_penalty=2.0,
    early_stopping=True
)

summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(summary)
```

## Limitations

- The model is specifically trained for speech-language pathology evaluations
- Input should follow the expected format for optimal results
- Clinical judgment should always be used to verify generated summaries