Update README.md

README.md

@@ -18,4 +18,115 @@ configs:
   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](https://hf.co/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](https://archive.md/ikcze) and aims to provide the data for fully local use/alternative, as the [demo](https://www.emojisearch.app/) is [not working](https://github.com/lilianweng/emoji-semantic-search/issues/6#issue-2724936875) as of a few days ago.
+
+- this repo contains only pre-computed embedding "database", equivalent to [server/emoji-embeddings.jsonl.gz](https://github.com/lilianweng/emoji-semantic-search/blob/6a6f351852b99e7b899437fa31309595a9008cd1/server/emoji-embeddings.jsonl.gz) in the original repo, to use as the database for semantic search and replacing OpenAI calls with local compute
+  - if working with the original repo, the [inference class](https://github.com/lilianweng/emoji-semantic-search/blob/6a6f351852b99e7b899437fa31309595a9008cd1/server/app.py#L18) also needs to be updated to use SentenceTransformers instead of OpenAI calls
+- the provided basic code is near-instant even on CPU 🔥
+
+## basic inference example 
+
+
+since the dataset is tiny, just load with pandas:
+
+```py
+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:
+
+```py
+# 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:
+
+<details>
+  <summary>Click me to expand the inference function code</summary>
+  
+
+```py
+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
+
+```
+
+</details>
+
+run inference!
+
+```py
+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}]
+```