Instructions to use ArneBinder/sam-pointer-bart-base-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArneBinder/sam-pointer-bart-base-v0.3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ArneBinder/sam-pointer-bart-base-v0.3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,5 @@
|
|
| 1 |
-
---
|
| 2 |
-
license: cc-by-sa-4.0
|
| 3 |
-
---
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-sa-4.0
|
| 3 |
+
---
|
| 4 |
+
|
| 5 |
+
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).
|