Spaces:
Running
Running
File size: 2,796 Bytes
935a660 eabf510 82413ee eabf510 4480f3c d0d62c4 4480f3c 09e6c30 eabf510 be07201 eabf510 09e6c30 4480f3c eabf510 4480f3c 935a660 eabf510 5b71e40 f5f665e ee4ac1e f5f665e eabf510 eedd478 eabf510 f414b62 b26ceaa d0d62c4 eedd478 eabf510 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
import streamlit as st
import plotly.express as px
import pandas as pd
import random
from umap import UMAP
from sentence_transformers import SentenceTransformer, util
from datasets import load_dataset
@st.cache_resource
def load_model():
return SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
@st.cache_data
def load_words_dataset():
dataset = load_dataset("marksverdhei/wordnet-definitions-en-2021", split="train")
return dataset["Word"]
@st.cache_resource
def prepare_umap():
all_enc = model.encode(all_words)
umap_3d = UMAP(n_components=3, init='random', random_state=0)
proj_3d = umap_3d.fit_transform(random.sample(all_enc.tolist(), k=2000))
return umap_3d
all_words = load_words_dataset()
model = load_model()
umap_3d = prepare_umap()
secret_word = random.choice(all_words)
secret_embedding = model.encode(secret_word)
if 'words' not in st.session_state:
st.session_state['words'] = []
if 'words_umap_df' not in st.session_state:
st.session_state['words_umap_df'] = pd.DataFrame({
"x": [],
"y": [],
"z": [],
"similarity": [],
"s": [],
"l": [],
})
words_umap_df = st.session_state['words_umap_df']
secret_embedding_3d = umap_3d.transform([secret_embedding])[0]
words_umap_df.loc[len(words_umap_df)] = {
"x": secret_embedding_3d[0],
"y": secret_embedding_3d[1],
"z": secret_embedding_3d[2],
"similarity": 1,
"s": 10,
"l": "Secret word"
}
st.write('Try to guess a secret word by semantic similarity')
word = st.text_input("Input a word")
used_words = [w for w, s in st.session_state['words']]
if st.button("Guess") or word:
if word not in used_words:
word_embedding = model.encode(word)
similarity = util.pytorch_cos_sim(
secret_embedding,
word_embedding
).cpu().numpy()[0][0]
st.session_state['words'].append((str(word), similarity))
pt = umap_3d.transform([word_embedding])[0]
st.session_state['words_umap_df'].loc[len(st.session_state['words_umap_df'])] = {
"x": pt[0],
"y": pt[1],
"z": pt[2],
"similarity": similarity,
"s": 3,
"l": str(word)
}
words_df = pd.DataFrame(
st.session_state['words'],
columns=["word", "similarity"]
).sort_values(by=["similarity"], ascending=False)
st.dataframe(words_df, use_container_width=True)
fig_3d = px.scatter_3d(st.session_state['words_umap_df'], x="x", y="y", z="z", color="similarity", hover_name="l", hover_data={"x": False, "y": False, "z": False, "s": False}, size="s", size_max=10, range_color=(0,1))
st.plotly_chart(fig_3d, theme="streamlit", use_container_width=True)
|