---
datasets:
- KomeijiForce/Text2Emoji
language:
- en
metrics:
- bertscore
pipeline_tag: text2text-generation
---

# EmojiLM

This is a [T5](https://huggingface.co/t5-base) model pre-trained on the [Text2Emoji](https://huggingface.co/datasets/KomeijiForce/Text2Emoji) dataset to translate setences into series of emojis.

For instance, "I love pizza" will be translated into "🍕😍".

An example implementation for translation:

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

path = "KomeijiForce/t5-base-emojilm"
tokenizer = T5Tokenizer.from_pretrained(path)
generator = T5ForConditionalGeneration.from_pretrained(path)

prefix = "translate into emojis:"
sentence = "I travel to enjoy the taste of sushi!"
inputs = tokenizer(prefix+" "+sentence, return_tensors="pt")
generated_ids = generator.generate(inputs["input_ids"], num_beams=4, do_sample=True, max_length=100)
decoded = tokenizer.decode(generated_ids[0], skip_special_tokens=True).replace(" ", "")
print(decoded)
```

You will probably get some output like "🇯🇵🍣🍱😋".

If you find this model & dataset resource useful, please consider cite our paper:

```
@article{DBLP:journals/corr/abs-2311-01751,
  author       = {Letian Peng and
                  Zilong Wang and
                  Hang Liu and
                  Zihan Wang and
                  Jingbo Shang},
  title        = {EmojiLM: Modeling the New Emoji Language},
  journal      = {CoRR},
  volume       = {abs/2311.01751},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2311.01751},
  doi          = {10.48550/ARXIV.2311.01751},
  eprinttype    = {arXiv},
  eprint       = {2311.01751},
  timestamp    = {Tue, 07 Nov 2023 18:17:14 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2311-01751.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```