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pdarleyjr/
iplc-t5-model

Name: iplc-t5-model
Availability: InStock

safetensors

clinical-nlp

summarization

speech-language-pathology

custom

mit

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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