Update README.md

README.md

@@ -22,47 +22,33 @@ model-index:
       metrics:
            - name: Validation ROGUE-1
              type: rouge-1
-             value: 21.7291
+             value: 22.2046
              verified: true
            - name: Validation ROGUE-2
              type: rouge-2
-             value: 10.0209
+             value: 10.4332
              verified: true
            - name: Validation ROGUE-L
              type: rouge-l
-             value: 17.775
+             value: 18.1753
+             verified: true
+           - name: Validation ROGUE-L-SUM
+             type: rouge-l-sum
+             value: 20.846
              verified: true
 ---
 
 # bart-base-cnn-swe
+This model is a W.I.P
 
-WORK IN PROGRESS!
-- 1. Further fine-tune on checkpoint with cnn-daily-swe.
-- 2. Further fine-tune on xsum-swe.
-- 3. Lastly fine-tune on smaller domain dataset.
+## Model description
 
 This model is a fine-tuned version of [KBLab/bart-base-swedish-cased](https://huggingface.co/KBLab/bart-base-swedish-cased) on the [Gabriel/bart-base-cnn-swe](https://huggingface.co/datasets/Gabriel/cnn_daily_swe) dataset.
 
 
-
-# bart-base-cnn-swe
-
-This model is a fine-tuned version of [Gabriel/bart-base-cnn-swe](https://huggingface.co/Gabriel/bart-base-cnn-swe) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.9759
-- Rouge1: 22.2046
-- Rouge2: 10.4332
-- Rougel: 18.1753
-- Rougelsum: 20.846
-- Gen Len: 19.9971
-
-## Model description
-
-More information needed
-
 ## Intended uses & limitations
 
-More information needed
+This model should only be used to fine-tune further on.
 
 ## Training and evaluation data
 
@@ -88,8 +74,10 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
 |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
-| 1.8658        | 1.0   | 17944 | 2.0333          | 22.0871 | 10.2902 | 18.0577 | 20.7082   | 19.998  |
-| 1.8121        | 2.0   | 35888 | 1.9759          | 22.2046 | 10.4332 | 18.1753 | 20.846    | 19.9971 |
+| 2.2349        | 1.0   | 17944 | 2.0643          | 21.9564 | 10.2133 | 17.9958 | 20.6502   | 19.9992 |
+| 2.0726        | 2.0   | 35888 | 2.0253          | 22.0568 | 10.3302 | 18.0648 | 20.7482   | 19.9996 |
+| 1.8658        | 3.0   | 53832 | 2.0333          | 22.0871 | 10.2902 | 18.0577 | 20.7082   | 19.998  |
+| 1.8121        | 4.0   | 71776 | 1.9759          | 22.2046 | 10.4332 | 18.1753 | 20.846    | 19.9971 |
 
 
 ### Framework versions