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--- |
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datasets: |
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- KomeijiForce/Text2Emoji |
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language: |
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- en |
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metrics: |
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- bertscore |
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pipeline_tag: text2text-generation |
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--- |
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# EmojiLM |
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This is a [BART](https://huggingface.co/facebook/bart-large) model pre-trained on the [Text2Emoji](https://huggingface.co/datasets/KomeijiForce/Text2Emoji) dataset to translate setences into series of emojis. |
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For instance, "I love pizza" will be translated into "🍕😍". |
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An example implementation for translation: |
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```python |
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from transformers import BartTokenizer, BartForConditionalGeneration |
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def translate(sentence, **argv): |
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inputs = tokenizer(sentence, return_tensors="pt") |
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generated_ids = generator.generate(inputs["input_ids"], **argv) |
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decoded = tokenizer.decode(generated_ids[0], skip_special_tokens=True).replace(" ", "") |
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return decoded |
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path = "KomeijiForce/bart-large-emojilm" |
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tokenizer = BartTokenizer.from_pretrained(path) |
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generator = BartForConditionalGeneration.from_pretrained(path) |
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sentence = "I love the weather in Alaska!" |
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decoded = translate(sentence, num_beams=4, do_sample=True, max_length=100) |
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print(decoded) |
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``` |
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You will probably get some output like "❄️🏔️😍". |
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If you find this model & dataset resource useful, please consider cite our paper: |
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``` |
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@article{DBLP:journals/corr/abs-2311-01751, |
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author = {Letian Peng and |
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Zilong Wang and |
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Hang Liu and |
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Zihan Wang and |
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Jingbo Shang}, |
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title = {EmojiLM: Modeling the New Emoji Language}, |
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journal = {CoRR}, |
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volume = {abs/2311.01751}, |
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year = {2023}, |
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url = {https://doi.org/10.48550/arXiv.2311.01751}, |
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doi = {10.48550/ARXIV.2311.01751}, |
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eprinttype = {arXiv}, |
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eprint = {2311.01751}, |
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timestamp = {Tue, 07 Nov 2023 18:17:14 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2311-01751.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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``` |
