Add multilingual to the language tag

Hi! A PR to add multilingual to the language tag to improve the referencing.

README.md

@@ -3,49 +3,39 @@ language:
 - cs
 - sk
 - uk
+- multilingual
+license: cc-by-4.0
 tags:
 - translation
 - opus-mt-tc
-license: cc-by-4.0
 model-index:
 - name: opus-mt-tc-base-ces_slk-uk
   results:
   - task:
-      name: Translation ces-ukr
       type: translation
-      args: ces-ukr
+      name: Translation ces-ukr
     dataset:
       name: flores101-devtest
       type: flores_101
       args: ces ukr devtest
     metrics:
-    - name: BLEU
-      type: bleu
+    - type: bleu
       value: 21.8
-  - task:
-      name: Translation slk-ukr
-      type: translation
-      args: slk-ukr
-    dataset:
-      name: flores101-devtest
-      type: flores_101
-      args: slk ukr devtest
-    metrics:
-    - name: BLEU
-      type: bleu
+      name: BLEU
+    - type: bleu
       value: 21.4
+      name: BLEU
   - task:
-      name: Translation ces-ukr
       type: translation
-      args: ces-ukr
+      name: Translation ces-ukr
     dataset:
       name: tatoeba-test-v2021-08-07
       type: tatoeba_mt
       args: ces-ukr
     metrics:
-    - name: BLEU
-      type: bleu
+    - type: bleu
       value: 48.6
+      name: BLEU
 ---
 # opus-mt-tc-base-ces_slk-uk
 
@@ -53,7 +43,7 @@ Neural machine translation model for translating from Czech and Slovak (cs+sk) t
 
 This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
 
-* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
+* Publications: [OPUS-MT � Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge � Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)
 
 ```
 @inproceedings{tiedemann-thottingal-2020-opus,
@@ -136,7 +126,7 @@ print(pipe("Replace this with text in an accepted source language."))
 
 ## Acknowledgements
 
-The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
+The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union�s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union�s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.
 
 ## Model conversion info