Update README.md

README.md

@@ -23,31 +23,18 @@ thumbnail: https://github.com/Marcosdib/S2Query/Classification_Architecture_mode
 
 Disclaimer: The Brazilian Ministry of Science, Technology, and Innovation (MCTI) has partially supported this project.
 
-This project focus on a specific problem, creating a Research Financing Products Portfolio (FPP) outside
-of the Union budget, supported by the Brazilian Ministry of Science, Technology, and Innovation (MCTI), The problem 
-description and conceptual model of FPP/MCTI are shown in Figure 1.
-
-![Model](https://github.com/Marcosdib/S2Query/Classification_Architecture_model.png)
+The model [NLP MCTI Classification Multi](https://huggingface.co/spaces/unb-lamfo-nlp-mcti/NLP-W2V-CNN-Multi) is part of the project [Research Financing Product Portfolio (FPP)](https://huggingface.co/unb-lamfo-nlp-mcti) focuses 
+on the task of Text Classification and explores different machine learning strategies to classify a small amount 
+of long, unstructured, and uneven data to find a proper method with good performance. Pre-training and word embedding 
+solutions were used to learn word relationships from other datasets with considerable similarity and larger scale. 
+Then, using the acquired resources, based on the dataset available in the MCTI, transfer learning plus deep learning 
+models were applied to improve the understanding of each sentence. 
 
 ## According to the abstract,
 
-Text classification is a traditional problem in Natural Language Processing (NLP). Most of the state-of-the-art implementations
-require high-quality, voluminous, labeled data. Pre- trained models on large corpora have shown beneficial for text classification
-and other NLP tasks, but they can only take a limited amount of symbols as input. This is a real case study that explores 
-different machine learning strategies to classify a small amount of long, unstructured, and uneven data to find a proper method
-with good performance. The collected data includes texts of financing opportunities the international R&D funding organizations
-provided on theirwebsites. The main goal is to find international R&D funding eligible for Brazilian researchers, sponsored by
-the Ministry of Science, Technology and Innovation. We use pre-training and word embedding solutions to learn the relationship
-of the words from other datasets with considerable similarity and larger scale. Then, using the acquired features, based on the
-available dataset from MCTI, we apply transfer learning plus deep learning models to improve the comprehension of each sentence.
-Compared to the baseline accuracy rate of 81%, based on the available datasets, and the 85% accuracy rate achieved through a 
-Transformer-based approach, the Word2Vec-based approach improved the accuracy rate to 88%. The research results serve as 
-asuccessful case of artificial intelligence in a federal government application.
-
-This model focus on a more specific problem, creating a Research Financing Products Portfolio (FPP) outside ofthe Union budget,
-supported by the Brazilian Ministry of Science, Technology, and Innovation (MCTI). It was introduced in ["Using transfer learning to classify long unstructured texts with small amounts of labeled data"](https://www.scitepress.org/Link.aspx?doi=10.5220/0011527700003318) and first released in
-[this repository](https://huggingface.co/unb-lamfo-nlp-mcti). This model is uncased: it does not make a difference between english
-and English.
+Compared to the 81% baseline accuracy rate based on available datasets and the 85% accuracy rate achieved using a 
+Transformer-based approach, the Word2Vec-based approach improved the accuracy rate to 93%, according to 
+["Using transfer learning to classify long unstructured texts with small amounts of labeled data"](https://www.scitepress.org/Link.aspx?doi=10.5220/0011527700003318).
 
 ## Model description
 
@@ -159,6 +146,9 @@ output = model(encoded_input)
 
 ### Limitations and bias
 
+This model is uncased: it does not make a difference between english
+and English.
+
 Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
 predictions: