# PLOD: An Abbreviation Detection Dataset [](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/issues) [](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/stargazers) [](https://github.com/surrey-nlp/PLOD-AbbreviationDetection/network) [](https://github.com/surrey-nlp/PLOD-AbbreviationDetection) [](https://twitter.com/intent/tweet?text=AbbreviationDetectionDataset:&url=https%3A%2F%2Fgithub.com%2Fsurrey-nlp%2FPLOD-AbbreviationDetection) This is the repository for PLOD Dataset submitted to LREC 2022. The dataset can help build sequence labelling models for the task Abbreviation Detection. ### Dataset The dataset is present [here at this link](https://drive.google.com/drive/folders/1uI6V8-A1uoB05fUC2znrQLvHouMqUusK?usp=sharing).
### Installation We use the custom NER pipeline in the [spaCy transformers](https://spacy.io/universe/project/spacy-transformers) library to train our models. This library supports training via any pre-trained language models available at the :rocket: [HuggingFace repository](https://huggingface.co/).
Please see the instructions at these websites to setup your own custom training with our dataset. ### Model The working model is present [here at this link](https://huggingface.co/surrey-nlp/en_abbreviation_detection_roberta_lar).
On the link provided above, the model can be used with the help of the Inference API via the web-browser itself. We have placed some examples with the API for testing.
#### Usage (in Python) You can use the HuggingFace Model link above to find the instructions for using this model in Python locally.