Spaces:
Running
Running
Bram Vanroy
commited on
Commit
Β·
f3fd096
1
Parent(s):
50faa6a
init space
Browse files- .gitignore +237 -0
- README.md +10 -7
- app.py +113 -0
- packages.txt +1 -0
- requirements.txt +6 -0
.gitignore
ADDED
@@ -0,0 +1,237 @@
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1 |
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Pipfile*
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data/*
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*config.json
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
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.idea/
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MANIFEST
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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db.sqlite3-journal
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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*.sage.py
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# Environments
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.env
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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README.md
CHANGED
@@ -1,13 +1,16 @@
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---
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-
title: Text To
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-
emoji:
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colorFrom:
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colorTo:
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sdk: streamlit
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sdk_version: 1.17.0
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app_file: app.py
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pinned:
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license: gpl-3.0
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---
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-
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Text To AMR
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emoji: π¦
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colorFrom: green
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colorTo: gray
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sdk: streamlit
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sdk_version: 1.17.0
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app_file: app.py
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pinned: true
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license: gpl-3.0
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tags:
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- natural language processing
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- semantic parsing
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- abstract meaning representation
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- amr
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---
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app.py
ADDED
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from collections import Counter
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import graphviz
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from optimum.bettertransformer import BetterTransformer
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import penman
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from penman.models.noop import NoOpModel
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from mbart_amr.constraints.constraints import AMRLogitsProcessor
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from mbart_amr.data.linearization import linearized2penmanstr
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from mbart_amr.data.tokenization import AMRMBartTokenizer
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from transformers import MBartForConditionalGeneration, LogitsProcessorList
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import streamlit as st
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if "logits_processor" not in st.session_state:
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st.session_state["logits_processor"] = None
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if "tokenizer" not in st.session_state:
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st.session_state["tokenizer"] = None
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if "model" not in st.session_state:
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st.session_state["tokenizer"] = AMRMBartTokenizer.from_pretrained("BramVanroy/mbart-en-to-amr", src_lang="en_XX")
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st.session_state["model"] = MBartForConditionalGeneration.from_pretrained("BramVanroy/mbart-en-to-amr")
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st.session_state["model"] = BetterTransformer.transform(st.session_state["model"], keep_original_model=False)
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st.session_state["model"].resize_token_embeddings(len(st.session_state["tokenizer"]))
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st.session_state["logits_processor"] = AMRLogitsProcessor(st.session_state["tokenizer"],
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st.session_state["model"].config.max_length)
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st.title("π Parse text into AMR")
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text = st.text_input(label="Text to transform (en)")
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if text and "model" in st.session_state:
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gen_kwargs = {
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"max_length": st.session_state["model"].config.max_length,
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"num_beams": st.session_state["model"].config.num_beams,
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"logits_processor": LogitsProcessorList([st.session_state["logits_processor"]]) if st.session_state[
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"logits_processor"] else None
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}
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encoded = st.session_state["tokenizer"](text, return_tensors="pt")
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generated = st.session_state["model"].generate(**encoded, **gen_kwargs)
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linearized = st.session_state["tokenizer"].decode_and_fix(generated)[0]
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penman_str = linearized2penmanstr(linearized)
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try:
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graph = penman.decode(penman_str, model=NoOpModel())
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except Exception as exc:
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st.write(f"The generated graph is not valid so it cannot be visualized correctly. Below is the closest attempt"
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f" to a valid graph but note that this is invalid Penman.")
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st.code(penman_str)
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with st.expander("Error trace"):
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st.write(exc)
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else:
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visualized = graphviz.Digraph(node_attr={"color": "#3aafa9", "style": "rounded,filled", "shape": "box",
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"fontcolor": "white"})
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# Count which names occur multiple times, e.g. t/talk-01 t2/talk-01
|
59 |
+
nodename_c = Counter([item[2] for item in graph.triples if item[1] == ":instance"])
|
60 |
+
# Generated initial nodenames for each variable, e.g. {"t": "talk-01", "t2": "talk-01"}
|
61 |
+
nodenames = {item[0]: item[2] for item in graph.triples if item[1] == ":instance"}
|
62 |
+
|
63 |
+
# Modify nodenames, so that the values are unique, e.g. {"t": "talk-01 (1)", "t2": "talk-01 (2)"}
|
64 |
+
# but only the value occurs more than once
|
65 |
+
nodename_str_c = Counter()
|
66 |
+
for varname in nodenames:
|
67 |
+
nodename = nodenames[varname]
|
68 |
+
if nodename_c[nodename] > 1:
|
69 |
+
nodename_str_c[nodename] += 1
|
70 |
+
nodenames[varname] = f"{nodename} ({nodename_str_c[nodename]})"
|
71 |
+
|
72 |
+
def get_node_name(item: str):
|
73 |
+
return nodenames[item] if item in nodenames else item
|
74 |
+
|
75 |
+
try:
|
76 |
+
for triple in graph.triples:
|
77 |
+
if triple[1] == ":instance":
|
78 |
+
continue
|
79 |
+
else:
|
80 |
+
visualized.edge(get_node_name(triple[0]), get_node_name(triple[2]), label=triple[1])
|
81 |
+
except Exception as exc:
|
82 |
+
st.write("The generated graph is not valid so it cannot be visualized correctly. Below is the closest attempt"
|
83 |
+
" to a valid graph but note that this is probably invalid Penman.")
|
84 |
+
st.code(penman_str)
|
85 |
+
st.write("The initial linearized output of the model was:")
|
86 |
+
st.code(linearized)
|
87 |
+
|
88 |
+
with st.expander("Error trace"):
|
89 |
+
st.write(exc)
|
90 |
+
else:
|
91 |
+
st.subheader("Graph visualization")
|
92 |
+
st.graphviz_chart(visualized, use_container_width=True)
|
93 |
+
|
94 |
+
# Download
|
95 |
+
img = visualized.pipe(format="png")
|
96 |
+
st.download_button("Download graph", img, mime="image/png")
|
97 |
+
|
98 |
+
# Additional info
|
99 |
+
st.subheader("Model output and Penman graph")
|
100 |
+
st.write("The linearized output of the model (after some post-processing) is:")
|
101 |
+
st.code(linearized)
|
102 |
+
st.write("When converted into Penman, it looks like this:")
|
103 |
+
st.code(penman.encode(graph))
|
104 |
+
|
105 |
+
|
106 |
+
########################
|
107 |
+
# Information, socials #
|
108 |
+
########################
|
109 |
+
st.markdown("## Contact βοΈ")
|
110 |
+
|
111 |
+
st.markdown("Would you like additional functionality in the demo? Or just want to get in touch?"
|
112 |
+
" Give me a shout on [Twitter](https://twitter.com/BramVanroy)"
|
113 |
+
" or add me on [LinkedIn](https://www.linkedin.com/in/bramvanroy/)!")
|
packages.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
graphviz
|
requirements.txt
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
graphviz
|
2 |
+
optimum>=1.6.0
|
3 |
+
penman
|
4 |
+
streamlit==1.17.0
|
5 |
+
torch>=1.13
|
6 |
+
git+https://github.com/BramVanroy/multilingual-text-to-amr@5859af0d870acd2f76d71e5a7d12fa35a7a2059b#egg=mbart-amr
|