metadata

dataset_info:
  features:
    - name: emoji
      dtype: string
    - name: message
      dtype: string
    - name: embed
      sequence: float64
  splits:
    - name: train
      num_bytes: 30665042
      num_examples: 3722
  download_size: 24682308
  dataset_size: 30665042
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
task_categories:
  - feature-extraction
language:
  - en
tags:
  - semantic-search
  - embeddings
  - emoji
size_categories:
  - 1K<n<10K

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}]