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README.md

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 license: cc-by-sa-4.0
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-This a argument structure prediction model for the scientific domain. It is a pointer network based on [A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (Bao et al., EMNLP 2022)](https://aclanthology.org/2022.emnlp-main.713/), but as a full reimplementation within the [PyTorch-IE](https://github.com/ArneBinder/pytorch-ie) framework. The actual source model code can be found in the [pie-modules](https://github.com/ArneBinder/pie-modules) repository. The model was trained with the [PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) on the [SciArg dataset](https://aclanthology.org/W18-5206/), see [here](https://huggingface.co/datasets/pie/sciarg) for further information and an integration into [pie-datasets](https://github.com/ArneBinder/pie-datasets). 
+This is an argument structure prediction model for the scientific domain. It is a pointer network based on 
+[A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism 
+(Bao et al., EMNLP 2022)](https://aclanthology.org/2022.emnlp-main.713/). Given a plain input text, the model generates 
+in one go tuples that represent argumentative relations, e.g. of type `supports` or `attacks`, between a pair of 
+Argumentative Discourse Units (ADUs). Each ADU is defined by start- and end-offsets and a is also typed (`background_claim`, 
+`own_claim`, or `data`). 
+
+However, this is a full reimplementation of the model
+within the [PyTorch-IE](https://github.com/ArneBinder/pytorch-ie) framework. The model source code can be 
+found in the [pie-modules](https://github.com/ArneBinder/pie-modules) repository. The model was trained with the 
+[PyTorch-IE-Hydra-Template](https://github.com/ArneBinder/pytorch-ie-hydra-template-1) on the 
+[SciArg dataset](https://aclanthology.org/W18-5206/), see [here](https://huggingface.co/datasets/pie/sciarg) for 
+further information and an integration into [pie-datasets](https://github.com/ArneBinder/pie-datasets). Further 
+information regarding the training setup and model performance can be found in the [config.yaml](config.yaml), 
+in the [wandb-metadata.json](wandb-metadata.json), and in [wandb-summary.json](wandb-summary.json).
+
+You can try out the model in [this HF space](https://huggingface.co/spaces/ArneBinder/sam-pointer-bart-base-v0.3).