Update README.md

README.md

@@ -7,7 +7,7 @@ datasets:
 metrics:
 - rouge
 model-index:
-- name: eval-mt5-base-aggressive
+- name: mt5-base-turkish-sum
   results:
   - task:
       name: Summarization
@@ -22,33 +22,21 @@ model-index:
       value: 47.4222
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
-# eval-mt5-base-aggressive
+# [Mukayese: Turkish NLP Strikes Back](https://arxiv.org/abs/2203.01215)
+
+## Summarization: mukayese/mbart-large-turkish-sum
+
+This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum/tu dataset.
 
-This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum tu dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.7801
+
 - Rouge1: 47.4222
 - Rouge2: 34.8624
 - Rougel: 42.2487
 - Rougelsum: 43.9494
-- Gen Len: 51.3525
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
 
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+Check [this](https://arxiv.org/abs/2203.01215) paper for more details on the model and the dataset.
 
 ### Training hyperparameters
 
@@ -67,25 +55,22 @@ The following hyperparameters were used during training:
 - num_epochs: 10.0
 - label_smoothing_factor: 0.1
 
-### Training results
-
-| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
-|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
-| 3.084         | 1.0   | 3895  | 2.9282          | 31.6872 | 22.1113 | 29.2851 | 29.7608   | 18.9861 |
-| 2.9162        | 2.0   | 7790  | 2.8552          | 32.1716 | 22.5001 | 29.6845 | 30.1887   | 18.9938 |
-| 2.8149        | 3.0   | 11685 | 2.8089          | 32.5681 | 22.689  | 30.0409 | 30.5507   | 18.9959 |
-| 2.7325        | 4.0   | 15580 | 2.7948          | 33.1236 | 23.1775 | 30.5156 | 31.0461   | 18.9958 |
-| 2.6679        | 5.0   | 19475 | 2.7810          | 33.1766 | 23.162  | 30.4802 | 31.0527   | 18.9967 |
-| 2.6237        | 6.0   | 23370 | 2.7790          | 33.1118 | 23.2043 | 30.5064 | 31.0096   | 18.9978 |
-| 2.5711        | 7.0   | 27265 | 2.7801          | 33.2033 | 23.2957 | 30.59   | 31.1504   | 18.9979 |
-| 2.538         | 8.0   | 31160 | 2.7777          | 33.0256 | 23.0621 | 30.3818 | 30.978    | 18.998  |
-| 2.5           | 9.0   | 35055 | 2.7839          | 33.2288 | 23.2361 | 30.5421 | 31.1573   | 18.998  |
-| 2.4719        | 10.0  | 38950 | 2.7832          | 33.2098 | 23.2274 | 30.5164 | 31.1094   | 18.9981 |
-
-
 ### Framework versions
 
 - Transformers 4.11.3
 - Pytorch 1.8.2+cu111
 - Datasets 1.14.0
 - Tokenizers 0.10.3
+
+### Citation
+
+```
+@misc{safaya-etal-2022-mukayese,
+    title={Mukayese: Turkish NLP Strikes Back},
+    author={Ali Safaya and Emirhan Kurtuluş and Arda Göktoğan and Deniz Yuret},
+    year={2022},
+    eprint={2203.01215},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```