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gpu support #11

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12 changes: 7 additions & 5 deletions keyphrasetransformer/keyphrasetransformer.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
# import
import os
import sys
import nltk
import torch
from nltk.corpus import words
from nltk.tokenize import word_tokenize, sent_tokenize
from transformers import AutoTokenizer, T5ForConditionalGeneration, MT5ForConditionalGeneration
Expand All @@ -12,12 +12,14 @@
class KeyPhraseTransformer:
def __init__(self, model_type: str = "t5", model_name: str = "snrspeaks/KeyPhraseTransformer"):
self.model_name = model_name
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

if model_type == "t5":
self.model = T5ForConditionalGeneration.from_pretrained(self.model_name)
self.model = T5ForConditionalGeneration.from_pretrained(self.model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
if model_type == "mt5":
self.model = MT5ForConditionalGeneration.from_pretrained(self.model_name)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.model = MT5ForConditionalGeneration.from_pretrained(self.model_name).to(self.device)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)

def split_into_paragraphs(self, doc: str, max_tokens_per_para: int = 128):
sentences = sent_tokenize(doc.strip())
Expand Down Expand Up @@ -72,7 +74,7 @@ def filter_outputs(self, key_phrases, text):
def predict(self, doc: str):
input_ids = self.tokenizer.encode(
doc, return_tensors="pt", add_special_tokens=True
)
).to(self.device)
generated_ids = self.model.generate(
input_ids=input_ids,
num_beams=2,
Expand Down