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README.md

@@ -5,9 +5,9 @@ tags:
 - abstractive
 - hybrid
 - multistep
+base_model: LongT5
 datasets: dennlinger/eur-lex-sum
 pipeline_tag: summarization
-base_model: LongT5
 model-index:
 - name: BART
   results:
@@ -27,7 +27,7 @@ model-index:
     - type: BERTScore
       value: 0.8496733237911203
     - type: BARTScore
-      value: -1.6461917861213897
+      value: -2.2194945887200546
     - type: BLANC
       value: 0.17446891570320744
 ---
@@ -38,7 +38,7 @@ model-index:
 ---
 ### Model Description
 
-This model is a fine-tuned version of LongT5. The research involves a multi-step summarization approach to long, legal documents. Many decisions in the renewables energy space are heavily dependent on regulations. But these regulations are often long and complicated. The proposed architecture first uses one or more extractive summarization steps to compress the source text, before the final summary is created by the abstractive summarization model. This fine-tuned abstractive model has been trained on a dataset, pre-processed through extractive summarization by RoBERTa with dependent ratio. The research has used multiple extractive-abstractive model combinations, which can be found on https://huggingface.co/MikaSie. To obtain optimal results, feed the model an extractive summary as input as it was designed this way!
+This model is a fine-tuned version of LongT5. The research involves a multi-step summarization approach to long, legal documents. Many decisions in the renewables energy space are heavily dependent on regulations. But these regulations are often long and complicated. The proposed architecture first uses one or more extractive summarization steps to compress the source text, before the final summary is created by the abstractive summarization model. This fine-tuned abstractive model has been trained on a dataset, pre-processed through extractive summarization by No extractive model with No ratio ratio. The research has used multiple extractive-abstractive model combinations, which can be found on https://huggingface.co/MikaSie. To obtain optimal results, feed the model an extractive summary as input as it was designed this way!
 
 The dataset used by this model is the [EUR-lex-sum](https://huggingface.co/datasets/dennlinger/eur-lex-sum) dataset. The evaluation metrics can be found in the metadata of this model card.
 This paper was introduced by the master thesis of Mika Sie at the University Utrecht in collaboration with Power2x. More information can be found in PAPER_LINK. 
@@ -59,7 +59,7 @@ This paper was introduced by the master thesis of Mika Sie at the University Utr
 ---
 ### Direct Use
 
-This model can be directly used for summarizing long, legal documents. However, it is recommended to first use an extractive summarization tool, such as RoBERTa, to compress the source text before feeding it to this model. This model has been specifically designed to work with extractive summaries.
+This model can be directly used for summarizing long, legal documents. However, it is recommended to first use an extractive summarization tool, such as No extractive model, to compress the source text before feeding it to this model. This model has been specifically designed to work with extractive summaries.
 An example using the Huggingface pipeline could be:
 
 ```python