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README.md

@@ -1,106 +1,9 @@
----
-license: gpl-3.0
-language:
-- en
-metrics:
-- f1
-- recall
-- precision
----
-# GraphMatcher
-The GraphMatcher aims to find the correspondes between two ontologies and outputs the possible alignments between them.
-
-The GraphMatcher leverages Graph Attention Network[2] in its neural network structure.
-The project leverages a new neighborhood aggregation algorithm, so it examines contribution of neighboring terms which have not been used in the previous matchers before.
-
-The project has been submitted to The 17th International Workshop on Ontology Matching's OAEI 2022 (ISWC-2022) for conference track and obtained the highest F1-measure in uncertain reference alignments among other experts participating to this challenge. Its system paper has been published, and it was invited to the poster presentation session.
-
-## Set up
-* 1.) install requirements
-``` pip install -r requirements.txt```
-
-* 2.) set the parameters in the config.ini
-````
-[General]
-dataset =               ------> name of a dataset e.g., conference.
-K =                     ------> the parameter for K fold cross-validation
-ontology_split =        ------> True/False
-max_false_examples =    ------>
-
-[Paths]
-dataset_folder =        ------> a path to the ontologies
-alignment_folder =      ------> a path to the reference alignments
-save_model_path =       ------> save the model to the path
-load_model_path =       ------> model path
-output_folder =         ------> The output folder for the alignments
-
-[Parameters]
-max_paths =             ------>
-max_pathlen =           ------> ( number of neighboring concepts' types: Equivalent class, subclass of(general to specific or specific to general(2))...
-[Hyperparameters] 
-
-lr =                    ------> learning rate
-num_epochs =            ------> number of epochs
-weight_decay =          ------> Weight decay
-batch_size =            ------> Batch Size (8/16/32)
-
-````
-
-* 3.) train the model 
-```python
-python src/train_model.py
-
-```
-* 4.) test the model
-```python
-python src/test_model.py ${source.rdf} ${target.rdf}
-```
-### Sample Alignment:
-```xml
-  <map>
-    <Cell>
-      <entity1 rdf:resource='http://conference#has_the_last_name'/>
-      <entity2 rdf:resource='http://confof#hasSurname'/>
-      <relation>=</relation>
-      <measure rdf:datatype='http://www.w3.org/2001/XMLSchema#float'>0.972</measure>
-    </Cell>
-  </map>
-```
-
-* 5.) evaluate the model with the MELT
-
-
-Note: The codes in train_model.py and test_model.py are partially based on the VeeAlign[2] project with the permission of its main author. I would like to thank the main author.
-
-## References:
-[1] 
-````
-@inproceedings{iyer-etal-2021-veealign,
-    title = "{V}ee{A}lign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment",
-    author = "Iyer, Vivek  and
-      Agarwal, Arvind  and
-      Kumar, Harshit",
-    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
-    month = nov,
-    year = "2021",
-    address = "Online and Punta Cana, Dominican Republic",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.emnlp-main.842",
-    doi = "10.18653/v1/2021.emnlp-main.842",
-    pages = "10780--10792",
-   }
-````
-[2]
-````
-@misc{https://doi.org/10.48550/arxiv.1710.10903,
-  title = {Graph Attention Networks},
-  author = {Veličković, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Liò, Pietro and Bengio, Yoshua},
-  keywords = {Machine Learning (stat.ML), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), Social and Information Networks (cs.SI), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  url = {https://arxiv.org/abs/1710.10903},
-  publisher = {arXiv},
-  doi = {10.48550/ARXIV.1710.10903},
-  year = {2017},
-  copyright = {arXiv.org perpetual, non-exclusive license}
-}
-
-````
+# My Model
+    This is my model card.
+
+    ## Usage
+    ```python
+    from transformers import AutoModel, AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained("Sefika/GraphMatcher")
+    model = AutoModel.from_pretrained("Sefika/GraphMatcher")
+