Initial commit

app.py

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+from transformers import PreTrainedTokenizerFast
+from tokenizers import SentencePieceBPETokenizer
+from transformers import BartForConditionalGeneration
+import streamlit as st
+import torch
+
+
+
+def tokenizer():
+    tokenizer = PreTrainedTokenizerFast.from_pretrained('Soyoung97/gec_kr')
+    return tokenizer
+
+
+@st.cache(allow_output_mutation=True)
+def get_model():
+    model = BartForConditionalGeneration.from_pretrained('Soyoung97/gec_kr')
+    model.eval()
+    return model
+
+
+default_text = '나느 오늘 지베 가써요'
+
+model = get_model()
+tokenizer = tokenizer()
+st.title("GEC_KR Model Test")
+text = st.text_area("Input corrputed sentence :", value=default_text)
+
+st.markdown("## Original sentence:")
+st.write(text)
+
+if text:
+    st.markdown("## Corrected output")
+    with st.spinner('processing..'):
+        raw_input_ids = tokenizer.encode(text)
+        input_ids = [tokenizer.bos_token_id] + \
+            raw_input_ids + [tokenizer.eos_token_id]
+        corrected_ids = model.generate(torch.tensor([input_ids]),
+                                     max_length=256,
+                                     eos_token_id=1,
+                                     num_beams=4,
+                                     early_stopping=True,
+                                     repetition_penalty=2.0)
+        summ = tokenizer.decode(corrected_ids.squeeze().tolist(), skip_special_tokens=True)
+    st.write(summ)

requirements.txt

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+torch
+transformers
+streamlit