Instructions to use LEIA/LEIA-LM-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LEIA/LEIA-LM-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="LEIA/LEIA-LM-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("LEIA/LEIA-LM-base") model = AutoModelForMaskedLM.from_pretrained("LEIA/LEIA-LM-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
language:
- en
library_name: transformers
widget:
- text: "--"
---
This is a BERTweet-base model that has been further pre-trained with preferential masking of emotion words for 100k steps on about 6.3M Vent posts.
This model is meant to be fine-tuned on labeled data or used as feature extractor for downstream tasks.
## Citation
Please cite the following paper if you find the model useful for your work:
```bibtex
@article{aroyehun2023leia,
title={LEIA: Linguistic Embeddings for the Identification of Affect},
author={Aroyehun, Segun Taofeek and Malik, Lukas and Metzler, Hannah and Haimerl, Nikolas and Di Natale, Anna and Garcia, David},
journal={EPJ Data Science},
volume={12},
year={2023},
publisher={Springer}
}
``` |