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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
# Function to summarize using the fine-tuned BART model
def summarize_with_bart_ft(input_text):
pipe_bart_ft = pipeline("summarization", model="EE21/BART-ToSSimplify")
summary = pipe_bart_ft(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
return summary[0]['summary_text']
# Function to summarize using BART-large-cnn
def summarize_with_bart_cnn(input_text):
pipe = pipeline("summarization", model="facebook/bart-large-cnn")
summary = pipe(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
return summary[0]['summary_text']
# Function to summarize using led-base-book-summary
def summarize_with_led(input_text):
pipe_led = pipeline("summarization", model="pszemraj/led-base-book-summary")
summary = pipe_led(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
return summary[0]['summary_text']
# Function to summarize using long-t5-tglobal-base-sci-simplify
def summarize_with_t5(input_text):
pipe_t5 = pipeline("summarization", model="pszemraj/long-t5-tglobal-base-sci-simplify")
summary = pipe_t5(input_text, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1)
return summary[0]['summary_text']
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