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### Imports
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
from transformers import BartForConditionalGeneration, BartTokenizer
from transformers import T5ForConditionalGeneration, T5Tokenizer
from transformers import ProphetNetForConditionalGeneration, ProphetNetTokenizer
import torch
from config import config
### Classes and functions
##==========================================================================================================
class SummarizationUtilities:
##==========================================================================================================
"""
Definition of attributes
"""
model_name = None
device = None
tokenizer = None
model = None
##==========================================================================================================
"""
Function: __init__
Arguments:
- model_name
- device
"""
def __init__(self, model_name="google/pegasus-xsum", device=None, model_path=config.pegasus_model_path):
self.model_name = model_name
if device == None:
self.device = self.detect_available_cuda_device()
else:
self.device = device
self.tokenizer = PegasusTokenizer.from_pretrained(model_path)
self.model = PegasusForConditionalGeneration.from_pretrained(model_path).to(device)
##=========================================================================================================
"""
Function: detect_available_cuda_device
Arguments: NA
"""
def detect_available_cuda_device(self):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
##=========================================================================================================
"""
Function: detect_available_cuda_device
Arguments: NA
"""
def tokenize(self, src_text, truncation = True, padding="longest", return_tensors="pt"):
return self.tokenizer(src_text, truncation=truncation, padding=padding, return_tensors=return_tensors).to(self.device)
##=========================================================================================================
"""
Function: generate
Arguments:
- batch
"""
def generate(self, batch):
text_generated = self.model.generate(**batch)
return text_generated
##=========================================================================================================
"""
Function: decode_generated_text
Arguments:
- batch
"""
def decode_generated_text(self, generated_text, skip_special_tokens=True):
return self.tokenizer.batch_decode(generated_text, skip_special_tokens=skip_special_tokens)
##=========================================================================================================
"""
Function: get_summary
Arguments:
- src_text
"""
def get_summary(self, src_text):
summary = None
batch = self.tokenize(src_text)
generated_text = self.generate(batch)
target_text = self.decode_generated_text(generated_text)
#print("target_text", target_text)
summary = target_text
return summary
def summarize(self, src_text):
summary = None
batch = self.tokenize(src_text)
generated_text = self.generate(batch)
target_text = self.decode_generated_text(generated_text)
#print("target_text", target_text)
summary = target_text
return summary
##=========================================================================================================
##==========================================================================================================
class BARTSummarizer:
def __init__(self, device=None, model_path=config.bart_model_path):
# https://stackoverflow.com/questions/66639722/why-does-huggingfaces-bart-summarizer-replicate-the-given-input-text
self.device = device if device else torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.tokenizer = BartTokenizer.from_pretrained("sshleifer/distilbart-xsum-6-6") #facebook/bart-large-cnn
# self.model = BartForConditionalGeneration.from_pretrained("sshleifer/distilbart-xsum-6-6").to(self.device)
self.tokenizer = BartTokenizer.from_pretrained(model_path)
self.model = BartForConditionalGeneration.from_pretrained(model_path)
def summarize(self, text):
inputs = self.tokenizer([text], truncation=True, padding="longest", return_tensors="pt").to(self.device)
summary_ids = self.model.generate(inputs["input_ids"], num_beams=4, max_length=200, early_stopping=True)
summary = self.tokenizer.decode(summary_ids.squeeze(), skip_special_tokens=True)
return summary
class T5Summarizer:
def __init__(self, device=None, model_path=config.t5_model_path):
self.device = device if device else torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.tokenizer = T5Tokenizer.from_pretrained("t5-base")
# self.model = T5ForConditionalGeneration.from_pretrained("t5-base").to(self.device)
self.tokenizer = T5Tokenizer.from_pretrained(model_path)
self.model = T5ForConditionalGeneration.from_pretrained(model_path).to(self.device)
def summarize(self, text):
inputs = self.tokenizer.encode_plus(text, return_tensors="pt", truncation=True, padding="longest").to(self.device)
summary_ids = self.model.generate(inputs.input_ids)
summary = self.tokenizer.decode(summary_ids.squeeze(), skip_special_tokens=True)
return summary
class ProphetNetSummarizer:
def __init__(self, device=None, model_path=config.prophetnet_model_path):
self.device = device if device else torch.device("cuda" if torch.cuda.is_available() else "cpu")
# self.tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
# self.model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased").to(self.device)
self.tokenizer = ProphetNetTokenizer.from_pretrained(model_path)
self.model = ProphetNetForConditionalGeneration.from_pretrained(model_path).to(self.device)
def summarize(self, text):
inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding="longest").to(self.device)
summary_ids = self.model.generate(inputs.input_ids)
summary = self.tokenizer.decode(summary_ids.squeeze(), skip_special_tokens=True)
return summary |