Update README.md

README.md

@@ -34,17 +34,12 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization
 
-## Intended uses & limitations
+### Datasets
 
-More information needed
+All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/
 
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
 
 ### Training hyperparameters
 
@@ -73,3 +68,23 @@ The following hyperparameters were used during training:
 - Pytorch 1.9.0+cu111
 - Datasets 1.10.2
 - Tokenizers 0.10.3
+### Citation
+
+@InProceedings{10.1007/978-3-031-04819-7_33,
+author="da Costa Cunha, Lu{\'i}s Filipe
+and Ramalho, Jos{\'e} Carlos",
+editor="Rocha, Alvaro
+and Adeli, Hojjat
+and Dzemyda, Gintautas
+and Moreira, Fernando",
+title="NER in Archival Finding Aids: Next Level",
+booktitle="Information Systems and Technologies",
+year="2022",
+publisher="Springer International Publishing",
+address="Cham",
+pages="333--342",
+abstract="Currently, there is a vast amount of archival finding aids in Portuguese archives, however, these documents lack structure (are not annotated) making them hard to process and work with. In this way, we intend to extract and classify entities of interest, like geographicallocations, people's names, dates, etc. For this, we will use an architecture that has been revolutionizing several NLP tasks, Transformers, presenting several models in order to achieve high results. It is also intended to understand what will be the degree of improvement that this new mechanism will present in comparison with previous architectures. Can Transformer-based models replace the LSTMs in NER? We intend to answer this question along this paper.",
+isbn="978-3-031-04819-7"
+}
+
+