Spaces:
Sleeping
Sleeping
File size: 1,486 Bytes
23975dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Load the fine-tuned model and tokenizer
model_name = "C:\\fine-tuned-model"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
# Prompt
prompt = """Write a medical summary in detailed way with patient details like Sex, Age and medical details in a paragraph format from the below data
{
"Sex": "M",
"ID": 585248,
"DateOfBirth": "08/10/1995",
"Age": "28 years",
"VisitDate": "09/25/2023",
"LogNumber": 6418481,
"Historian": "Self",
"TriageNotes": ["fever"],
"HistoryOfPresentIllness": {
"Complaint": [
"The patient presents with a chief complaint of chills.",
"The problem is made better by exercise and rest.",
"The patient also reports change in appetite and chest pain/pressure as abnormal symptoms related to the complaint."
]
}
}"""
# Tokenize and generate text with sampling and different decoding parameters
input_ids = tokenizer.encode(prompt, return_tensors="pt", max_length=512)
generated_text = model.generate(
input_ids,
max_length=200,
num_beams=5,
temperature=0.9, # Adjust the temperature for more randomness
no_repeat_ngram_size=2,
top_k=50,
top_p=0.95,
early_stopping=True,
do_sample=True,
)
# Decode and print the generated text
decoded_text = tokenizer.decode(generated_text[0], skip_special_tokens=True)
print(f"Generated Text: {decoded_text}")
|