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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
# Load the fine-tuned BART tokenizer and model | |
tokenizer = AutoTokenizer.from_pretrained("EE21/BART-ToSSimplify") | |
model = AutoModelForSeq2SeqLM.from_pretrained("EE21/BART-ToSSimplify") | |
# Function to summarize using the fine-tuned BART model | |
def summarize_with_bart_ft(input_text): | |
inputs = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=1024, truncation=True) | |
summary_ids = model.generate(inputs, max_length=300, min_length=100, num_beams=1, early_stopping=False, length_penalty=1) | |
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=False) | |
return summary | |
# 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'] | |