DaviadiAF
/

T5-Small_AbsSumm_XSumCNN

@@ -8,199 +8,33 @@ metrics:
 - rouge
 pipeline_tag: summarization
 ---
-# Model Card for Model ID
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-## Model Details
 ### Model Description
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 ## Uses
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 ## How to Get Started with the Model
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ## Model Card Contact
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 - rouge
 pipeline_tag: summarization
 ---
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
+This is a fine-tuned T5-small model for abstractive summarization. This model is fine-tuned with XSUM dataset and CNN/DailyMail dataset where only 20% of the train split and test split are used due to the limited resources for fine-tuning the model (the fine-tuning is done via Google Colab freeplan). The way the datasets are used is by concatenating the the train split and the test split of each datasets. This model achieved 0.25 in ROUGE-1, 0.08 in ROUGE-2, and 0.20 in both ROUGE-L and ROUGE-LSum. The low scores achieved might be happened because the amount of data used for both training and testing, the limit put on the hyperparameters, and the method of preprocessing the data for the model to train and test with. A better resourceful environment might improve the performance of this model.
+- **Developed by:** Daviadi Auzan Fadhlillah
+- **Model type:** T5-small
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** T5-small
 ## Uses
+The aim of fine-tuning this model is to generate an abstractive summarization of online news articles, however this model can be used for other purposes where a summarization is needed.
 ## How to Get Started with the Model
+Use the code below to get started with the model or to improve the fine-tuning of the model.
+https://colab.research.google.com/drive/1pFUqp51uPdTIIeFUdrpKAYlWD-0VRnAC?usp=sharing
 ## Model Card Contact
+I open for any suggestions, therefore you can contact me via email.
+[email protected]
+Your suggestions are highly appreciated.
+Thank you for using this model.