update README

README.md

@@ -1,24 +1,24 @@
 ---
 language:
   - en
-  - sq
+  - zh
 tags:
 - translation
 license: cc-by-4.0
 ---
 
-## HPLT MT release v1.0
+### HPLT MT release v1.0
 
-This repository contains the translation model for en-sq trained with OPUS and HPLT data. For usage instructions, evaluation scripts, and inference scripts, please refer to the [HPLT-MT-Models v1.0](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0) GitHub repository.
+This repository contains the translation model for en-zh_hant trained with HPLT data only. For usage instructions, evaluation scripts, and inference scripts, please refer to the [HPLT-MT-Models v1.0](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0) GitHub repository.
 
 ### Model Info
 
 * Source language: English
-* Target language: Albanian
-* Dataset: OPUS and HPLT data
+* Target language: Traditional Chinese
+* Data: HPLT data only
 * Model architecture: Transformer-base
 * Tokenizer: SentencePiece (Unigram)
-* Cleaning: We used OpusCleaner with a set of basic rules. Details can be found in the filter files in [Github](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0/data/en-sq/raw/v2)
+* Cleaning: We used OpusCleaner with a set of basic rules. Details can be found in the filter files in [Github](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0/data/en-zh_hant/raw/v0)
 
 You can also read our deliverable report [here](https://hplt-project.org/HPLT_D5_1___Translation_models_for_select_language_pairs.pdf) for more details.
 
@@ -29,12 +29,12 @@ The model has been trained with Marian. To run inference, refer to the [Inferenc
 
 The model can be used with the Hugging Face framework if the weights are converted to the Hugging Face format. We might provide this in the future; contributions are also welcome.
 
-### Benchmarks
+## Benchmarks
 
 | testset                                | BLEU | chrF++ | COMET22 |
 | -------------------------------------- | ---- | ----- | ----- |
-| flores200     | 30.7 | 56.6  | 0.8761  |
-| ntrex | 32.7   | 56.1  | 0.8517  |
+| flores200     | 25.4 | 18.9  | 0.8017  |
+| ntrex | 21.3   | 21.6  | 0.7492  |
 
 ### Acknowledgements