| --- |
| license: other |
| license_name: desert-ant-labs-source-available-1.0 |
| license_link: https://license.desertant.ai/1.0 |
| language: |
| - multilingual |
| tags: |
| - text |
| - emoji |
| - text-classification |
| - on-device |
| - core-ml |
| - multilingual |
| pipeline_tag: text-classification |
| --- |
| |
| # Emo: on-device emoji suggestions from text |
|
|
| Takes a short text string and returns the best-matching emoji. Tuned for to-dos, |
| calendar entries, notes, and message drafts across **23 languages** (including |
| CJK, Arabic, Thai, Hindi, and more). The whole thing, model **and** tokenizer, |
| is **about 5 MB** and runs in well under 2 ms on device. |
|
|
| > `"Dentist appointment"` → 🦷 · `"réserver un vol pour Tokyo"` → ✈️ · `"犬の散歩"` → 🐕 · `"จองโรงแรม"` → 🏨 |
|
|
| ## Try it |
|
|
| - **Live demo:** [desert-ant-labs/emo-demo](https://huggingface.co/spaces/desert-ant-labs/emo-demo): type a phrase, get emojis, fully in your browser. |
| - **iOS / macOS:** [`emo-swift`](https://github.com/Desert-Ant-Labs/emo-swift): the Swift SDK with a built-in demo app. |
| - **Android / Kotlin / JVM:** [`emo-kotlin`](https://github.com/Desert-Ant-Labs/emo-kotlin): the Kotlin SDK (via JitPack), with an Android demo app. |
| - **JavaScript / TypeScript:** [`emo-js`](https://github.com/Desert-Ant-Labs/emo-js): the npm package (Node + browser). |
|
|
| ## Files |
|
|
| | File | Format | Size | Contents | |
| |---|---|---:|---| |
| | `Emo.mlmodelc` | Compiled Core ML | ~4.3 MB | 4-bit-palettized transformer model, ready to load on Apple platforms | |
| | `emo_tokenizer.bin` | Pruned unigram tokenizer | ~0.75 MB | 48k SentencePiece pieces + scores; token ids = semantic-table rows | |
| | `emo_meta.json` | JSON | tiny | emoji labels + n-gram hashing config the runtime needs | |
| | `emo.pt` | PyTorch checkpoint | ~48 MB | Full-precision weights + semantic table + tokenizer (for retraining / other runtimes) | |
|
|
| ## Architecture |
|
|
| A compact two-stream classifier - no large encoder, just a tiny transformer over the semantic tokens: |
|
|
| - **Lexical stream**: script-aware character/word n-grams (Latin, Han·Kana, Hangul |
| jamo, Devanagari clusters, SE-Asian, …) hashed into a fixed multi-hash signed |
| embedding table. Its size is independent of the number of languages. |
| - **Semantic stream**: a frozen multilingual static embedding (Model2Vec |
| [`potion-multilingual-128M`](https://huggingface.co/minishlab/potion-multilingual-128M), |
| distilled from BAAI `bge-m3`), PCA-reduced to 128 dims and **vocab-pruned to the |
| 48k tokens** that matter for the 22 target languages. Gives cross-lingual |
| generalization and handles out-of-vocabulary words. The matching ~0.75 MB unigram |
| tokenizer ships alongside (`emo_tokenizer.bin`). |
| - **Semantic pooling**: a small 2-layer transformer encoder runs over the semantic |
| token sequence, then an attention pool - order-aware, so it composes phrases and |
| idioms instead of averaging tokens. |
| - **Head**: a small MLP fusing the two streams into a softmax over a **curated vocabulary of ~800 everyday emojis** (the emojis that actually come up most across the |
| training phrases). Trained with n-gram dropout so the head relies on the semantic |
| stream, which is what makes it generalize across languages. |
|
|
| ## Inputs and outputs |
|
|
| - **Input:** a plain text string. Best on short, intent-oriented text. |
| - **Output:** a probability distribution over the ~800-emoji vocabulary; take the |
| top-1 (or top-k). Optimized for **top-1 relevance**. |
|
|
| ## Languages |
|
|
| English, Spanish, Portuguese, French, German, Italian, Dutch, Russian, Polish, |
| Turkish, Arabic, Chinese (Simplified & Traditional), Japanese, Korean, Hindi, |
| Indonesian, Thai, Vietnamese, Ukrainian, Swedish, Danish, Czech. |
|
|
| ## Limitations |
|
|
| - Tuned for short, intent-oriented text; long-form text produces noisier suggestions. |
| - Emoji semantics are imprecise; near-ties at the top of the ranking are expected. |
| - Per-language quality varies; lower-resource languages in the set are somewhat weaker. |
|
|
| ## Built on |
|
|
| - [`minishlab/potion-multilingual-128M`](https://huggingface.co/minishlab/potion-multilingual-128M) (MIT): semantic embedding stream (PCA-reduced, vocab-pruned derivative) + tokenizer lineage. |
| - [`BAAI/bge-m3`](https://huggingface.co/BAAI/bge-m3) (MIT): teacher the static embedding was distilled from. |
| - [Model2Vec](https://github.com/MinishLab/model2vec) (MIT): static-embedding distillation method. |
| - Unicode CLDR emoji annotations: multilingual keyword grounding in the training data. |
|
|
| See [`THIRD_PARTY_NOTICES.md`](THIRD_PARTY_NOTICES.md). |
|
|
| ## License |
|
|
| [Desert Ant Labs Source-Available License](https://license.desertant.ai/1.0). Free for |
| most apps; a commercial license is required at scale. Full terms are at the link. |
| Licensing: <licensing@desertant.ai>. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @software{emo_2026, |
| title = {Emo: on-device emoji suggestions from text}, |
| author = {Desert Ant Labs}, |
| year = {2026}, |
| url = {https://huggingface.co/desert-ant-labs/emo}, |
| } |
| ``` |
|
|
| --- |
|
|
| © 2026 Desert Ant Labs · <https://desertant.ai> |
|
|
