Update README.md

README.md

@@ -1,42 +1,25 @@
 ---
-license: mit
-datasets:
-- davebulaval/CSMD
-language:
-- en
-model-index:
-- name: MeaningBERT
-  results:
-  - task:
-      type: assesing-meaning-preservation
-    dataset:
-      name: davebulaval/CSMD
-      type: regression
-    metrics:
-    - type: r_squared
-      value: 0.860
-    - type: pearsonr
-      value: 0.928
-    - type: rmse
-      value: 16.355
-    
-metrics:
-- r_squared
-- pearsonr
-- rmse
-
-tags:
-- text-simplification
-- meaning
-- assess
+title: MeaningBERT
+emoji: 🦀
+colorFrom: purple
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.2.0
+app_file: app.py
+pinned: false
 ---
 
 # Here is MeaningBERT
 
-MeaningBERT is an automatic and trainable model and [metric]() for assessing meaning preservation between sentences. MeaningBERT was
+MeaningBERT is an automatic and trainable metric for assessing meaning preservation between sentences. MeaningBERT was
 proposed in our
 article [MeaningBERT: assessing meaning preservation between sentences](https://www.frontiersin.org/articles/10.3389/frai.2023.1223924/full).
-Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity checks. For more details, refer to our publicly available article.
+Its goal is to assess meaning preservation between two sentences that correlate highly with human judgments and sanity
+checks. For more details, refer to our publicly available article.
+
+> This public version of our model uses the best model trained (where in our article, we present the performance results
+> of an average of 10 models) for a more extended period (1000 epochs instead of 250). We have observed later that the
+> model can further reduce dev loss and increase performance.
 
 ## Sanity Check
 
@@ -62,7 +45,8 @@ for computer floating-point inaccuracy, we round the ratings to the nearest inte
 
 Our second test evaluates meaning preservation between a source sentence and an unrelated sentence generated by a large
 language model.3 The idea is to verify that the metric finds a meaning preservation rating of 0 when given a completely
-irrelevant sentence mainly composed of irrelevant words (also known as word soup). Since this test's expected rating is 0, we check that the metric rating is lower or equal to a threshold value X∈[5, 1].
+irrelevant sentence mainly composed of irrelevant words (also known as word soup). Since this test's expected rating is
+0, we check that the metric rating is lower or equal to a threshold value X∈[5, 1].
 Again, to account for computer floating-point inaccuracy, we round the ratings to the nearest integer and do not use a
 a threshold value of 0%.