Instructions to use midudev/text2emoji-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use midudev/text2emoji-tiny with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'midudev/text2emoji-tiny');
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
text2emoji-tiny
A 2.4M-parameter T5 trained from scratch by runonweb to turn an English sentence into a short emoji sequence. 3.9 MB as q8 ONNX (encoder 1.7 MB + merged decoder 2.1 MB), in the Transformers.js layout.
| Config | Params | val loss | emoji-set F1 |
|---|---|---|---|
| 3+3 layers, d_model 128, d_ff 512, 4 heads, BPE vocab 8192 | 2.43M | 3.72 | 0.38 |
Data: KomeijiForce/Text2Emoji, ~493k
ChatGPT-generated sentence → emoji pairs after cleaning. The dataset card lists no license; the weights are
MIT. Recipe: training/text2emoji in the runonweb repo.
Use
import { Emojifier } from 'runonweb/emoji'
const emojifier = new Emojifier()
await emojifier.load()
const { text } = await emojifier.emojify('I love pizza and my dog') // "🍕❤️🐶"
Or with Transformers.js directly: pipeline('text2text-generation', 'midudev/text2emoji-tiny', { dtype: 'q8' }),
decoding with no_repeat_ngram_size: 1; emojis come back space-separated.
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