Bram Vanroy commited on
Commit
e10ccfa
β€’
1 Parent(s): 05de9a6

update typos

Browse files
Files changed (2) hide show
  1. app.py +3 -3
  2. utils.py +1 -1
app.py CHANGED
@@ -10,11 +10,11 @@ from utils import get_resources, LANGUAGES, translate
10
  import streamlit as st
11
 
12
  st.set_page_config(
13
- page_title="Text-to-AMR demo by Bram Vanroy",
14
  page_icon="πŸ‘©β€πŸ’»"
15
  )
16
 
17
- st.title("πŸ‘©β€πŸ’» Multilingual text to AMR")
18
 
19
  if "text" not in st.session_state:
20
  st.session_state["text"] = ""
@@ -107,7 +107,7 @@ st.markdown("""
107
  st.markdown("""[Abstract meaning representation](https://aclanthology.org/W13-2322/) (AMR)
108
  is a semantic framework to describe meaning relations of sentences as graphs. In the SignON project, AMR is used as
109
  an interlingua to translate between modalities and languages. To this end, I built MBART models for the task of
110
- generating linearized AMR representations from an input sentence, which is show-cased in this demo.
111
  """)
112
 
113
 
 
10
  import streamlit as st
11
 
12
  st.set_page_config(
13
+ page_title="Multilingual text-to-AMR demo by Bram Vanroy",
14
  page_icon="πŸ‘©β€πŸ’»"
15
  )
16
 
17
+ st.title("πŸ‘©β€πŸ’» Multilingual text-to-AMR")
18
 
19
  if "text" not in st.session_state:
20
  st.session_state["text"] = ""
 
107
  st.markdown("""[Abstract meaning representation](https://aclanthology.org/W13-2322/) (AMR)
108
  is a semantic framework to describe meaning relations of sentences as graphs. In the SignON project, AMR is used as
109
  an interlingua to translate between modalities and languages. To this end, I built MBART models for the task of
110
+ generating AMR representations from an input sentence, which is show-cased in this demo.
111
  """)
112
 
113
 
utils.py CHANGED
@@ -3,12 +3,12 @@ from typing import Tuple, Union, Dict, List
3
  from multi_amr.data.postprocessing_graph import ParsedStatus
4
  from multi_amr.data.tokenization import AMRTokenizerWrapper
5
  from optimum.bettertransformer import BetterTransformer
 
6
  import streamlit as st
7
  import torch
8
  from torch.quantization import quantize_dynamic
9
  from torch import nn, qint8
10
  from transformers import MBartForConditionalGeneration, AutoConfig
11
- import penman
12
 
13
 
14
  @st.cache_resource(show_spinner=False)
 
3
  from multi_amr.data.postprocessing_graph import ParsedStatus
4
  from multi_amr.data.tokenization import AMRTokenizerWrapper
5
  from optimum.bettertransformer import BetterTransformer
6
+ import penman
7
  import streamlit as st
8
  import torch
9
  from torch.quantization import quantize_dynamic
10
  from torch import nn, qint8
11
  from transformers import MBartForConditionalGeneration, AutoConfig
 
12
 
13
 
14
  @st.cache_resource(show_spinner=False)