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🥇 | 1st place medal | [
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🥈 | 2nd place medal | [
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🇩🇿 | flag for algeria | [-0.5171316862106323,0.016471445560455322,1.1334940195083618,-0.6998956203460693,0.7440917491912842,(...TRUNCATED) |
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local emoji semantic search
Emoji, their text descriptions and precomputed text embeddings with Alibaba-NLP/gte-large-en-v1.5 for use in emoji semantic search.
This work is largely inspired by the original emoji-semantic-search repo and aims to provide the data for fully local use, as the demo is not working as of a few days ago.
- This repo only contains a precomputed embedding "database", equivalent to server/emoji-embeddings.jsonl.gz in the original repo, to be used as the database for semantic search,
- If working with the original repo, the inference class also needs to be updated to use SentenceTransformers instead of OpenAI calls (see below example)
- The provided inference code is almost instant even on CPU 🔥
basic inference example
since the dataset is tiny, just load with pandas:
import pandas as pd
df = pd.read_parquet("hf://datasets/pszemraj/local-emoji-search-gte/data/train-00000-of-00001.parquet")
print(df.info())
load the sentence-transformers model:
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
define a minimal semantic search inference function:
Click me to expand the inference function code
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import semantic_search
def get_top_emojis(
query: str,
emoji_df: pd.DataFrame,
model,
top_k: int = 5,
num_digits: int = 4,
) -> list:
"""
Performs semantic search to find the most relevant emojis for a given query.
Args:
query (str): The search query.
emoji_df (pd.DataFrame): DataFrame containing emoji metadata and embeddings.
model (SentenceTransformer): The sentence transformer model for encoding.
top_k (int): Number of top results to return.
num_digits (int): Number of digits to round scores to
Returns:
list: A list of dicts, where each dict represents a top match. Each dict has keys 'emoji', 'message', and 'score'
"""
query_embed = model.encode(query)
embeddings_array = np.vstack(emoji_df.embed.values, dtype=np.float32)
hits = semantic_search(query_embed, embeddings_array, top_k=top_k)[0]
# Extract the top hits + metadata
results = [
{
"emoji": emoji_df.loc[hit["corpus_id"], "emoji"],
"message": emoji_df.loc[hit["corpus_id"], "message"],
"score": round(hit["score"], num_digits),
}
for hit in hits
]
return results
run inference!
import pprint as pp
query_text = "that is flames"
top_emojis = get_top_emojis(query_text, df, model, top_k=5)
pp.pprint(top_emojis, indent=2)
# [ {'emoji': '❤\u200d🔥', 'message': 'heart on fire', 'score': 0.7043},
# {'emoji': '🥵', 'message': 'hot face', 'score': 0.694},
# {'emoji': '😳', 'message': 'flushed face', 'score': 0.6794},
# {'emoji': '🔥', 'message': 'fire', 'score': 0.6744},
# {'emoji': '🧨', 'message': 'firecracker', 'score': 0.663}]
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