Update README.md

README.md

@@ -51,7 +51,7 @@ base_model:
 
 SalamandraTA-7b-instruct is a translation LLM that has been instruction-tuned from SalamandraTA-7b-base. 
 The base model results from continually pre-training [Salamandra-7b](https://huggingface.co/BSC-LT/salamandra-7b) on parallel data and has not been published, but is reserved for internal use. 
-SalamandraTA-7b-instruct is proficent in 37 european languages and supportS translation-related tasks, namely: sentence-level-translation, paragraph-level-translation, document-level-translation, automatic post-editing, machine translation evaluation, multi-reference-translation, named-entity-recognition and context-aware translation.
+SalamandraTA-7b-instruct is proficent in 37 european languages and supports translation-related tasks, namely: sentence-level-translation, paragraph-level-translation, document-level-translation, automatic post-editing, machine translation evaluation, multi-reference-translation, named-entity-recognition and context-aware translation.
 
 > [!WARNING]
 > **DISCLAIMER:** This version of Salamandra is tailored exclusively for translation tasks. It lacks chat capabilities and has not been trained with any chat instructions.
@@ -135,7 +135,7 @@ Aragonese, Aranese, Asturian, Basque, Bulgarian, Croatian, Czech, Danish, Dutch,
 Irish, Italian, Latvian, Lithuanian, Maltese, Norwegian Bokmål, Norwegian Nynorsk, Occitan, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, 
 Slovenian, Spanish, Swedish, Ukrainian, Valencian, Welsh.
 
-The instruction-following model use the commonly adopted ChatML template:
+The instruction-following model uses the commonly adopted ChatML template:
 
 ```
 <|im_start|>system
@@ -322,7 +322,7 @@ including all of the official European languages plus Catalan, Basque, Galician,
 It amounts to 6,574,251,526 parallel sentence pairs. 
 
 This highly multilingual corpus is predominantly composed of data sourced from [OPUS](https://opus.nlpl.eu/), 
-with additional data taken from the [NTEU project](https://nteu.eu/), [Aina Project](https://projecteaina.cat/), and other sources 
+with additional data taken from the [NTEU Project](https://nteu.eu/), [Aina Project](https://projecteaina.cat/), and other sources 
 (see: [Data Sources](#pre-data-sources) and [References](#pre-references)). 
 Where little parallel Catalan <-> xx data could be found, synthetic Catalan data was generated from the Spanish side of the collected Spanish <-> xx corpora using 
 [Projecte Aina’s Spanish-Catalan model](https://huggingface.co/projecte-aina/aina-translator-es-ca). The final distribution of languages was as below:
@@ -475,7 +475,7 @@ Click the expand button below to see the full list of tasks included in the fine
 | Context-Aware Translation   | [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.2): [MT-GenEval](https://github.com/amazon-science/machine-translation-gender-eval)                     | en-de                                                          | 558    |
 |**Total**                  |                    |          |     **135,404**       |
 
-The non-public portion of this dataset was jointly created by the [ILENIA](https://proyectoilenia.es/) partners BSC, [HiTZ](http://hitz.ehu.eus/es), 
+The non-public portion of this dataset was jointly created by the [ILENIA](https://proyectoilenia.es/) partners: BSC-LT, [HiTZ](http://hitz.ehu.eus/es), 
 and [CiTIUS](https://citius.gal/es/). For further information regarding the instruction-tuning data, 
 please contact <langtech@bsc.es>.
 
@@ -506,9 +506,9 @@ please contact <langtech@bsc.es>.
 
 Below are the evaluation results on the [Flores+200 devtest set](https://huggingface.co/datasets/openlanguagedata/flores_plus), 
 compared against the state-of-the-art MADLAD400-7B model ([Kudugunta, S., et al.](https://arxiv.org/abs/2309.04662)) and SalamandraTA-7b-base model. 
-These results cover translation directions between CA-XX, ES-XX, EN-XX, as well as XX-CA, XX-ES, and XX-EN. 
+These results cover the translation directions CA-XX, ES-XX, EN-XX, as well as XX-CA, XX-ES, and XX-EN. 
 The metrics have been computed excluding Asturian, Aranese, and Aragonese, as we report them separately. 
-The evaluation was conducted using [MT Lens](https://github.com/langtech-bsc/mt-evaluation) following the standard setting (beam search with beam size 5, limiting the translation length to 500 tokens). We report the following metrics:
+The evaluation was conducted using [MT Lens](https://github.com/langtech-bsc/mt-evaluation), following the standard setting (beam search with beam size 5, limiting the translation length to 500 tokens). We report the following metrics:
 
 <details>
 <summary>Click to show metrics details</summary>
@@ -651,7 +651,7 @@ This section presents the evaluation metrics for Basque translation tasks.
 
 The tables below summarize the performance metrics for English, Spanish, and Catalan to Asturian, Aranese and Aragonese compared 
 against [Transducens/IbRo-nllb](https://huggingface.co/Transducens/IbRo-nllb) [(Galiano Jimenez, et al.)](https://aclanthology.org/2024.wmt-1.85/), 
-NLLB-3.3 ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)) and [SalamandraTA-2B](https://huggingface.co/BSC-LT/salamandraTA-2B).
+[NLLB-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)) and [SalamandraTA-2B](https://huggingface.co/BSC-LT/salamandraTA-2B).
 
 <details>
 <summary>English evaluation</summary>
@@ -662,7 +662,7 @@ NLLB-3.3 ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)) and [Sa
 |:---------------------------------|:---------|:---------|-------:|-------:|-------:|
 | SalamandraTA-7b-instruct               | en       | ast      |  **31.49** |  **54.01** |  **60.65** |
 | SalamandraTA-7b-base                   | en       | ast      |  26.4      |  64.02 |  57.35 |
-| nllb-3.3B                              | en       | ast      |  22.02     |  77.26 |  51.4  |
+| nllb-200-3.3B                              | en       | ast      |  22.02     |  77.26 |  51.4  |
 | Transducens/IbRo-nllb                  | en       | ast      |  20.56 |  63.92 |  53.32 |
 | | | | | | |
 | SalamandraTA-7b-instruct | en       | arn      |  **13.04** |  **87.13** |  **37.56** |
@@ -687,7 +687,7 @@ NLLB-3.3 ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)) and [Sa
 | SalamandraTA-7b-base             | es       | ast      |  17.65 |  75.78 |  51.05 |
 | Transducens/IbRo-nllb            | es       | ast      |  16.79	 |  76.36 |  50.89 |
 | SalamandraTA-2B                   | es       | ast      |  16.68 |  77.29 |  49.46 |
-| nllb-3.3B                        | es       | ast      |  11.85 | 100.86 |  40.27 |
+| nllb-200-3.3B                        | es       | ast      |  11.85 | 100.86 |  40.27 |
 | | | | | | |
 | SalamandraTA-7b-base             | es       | arn      |  **29.19** |  **71.85** |  **49.42** |
 | Transducens/IbRo-nllb             | es       | arn      |  28.45 |  72.56 | 49.28  |
@@ -715,7 +715,7 @@ NLLB-3.3 ([Costa-jussà et al., 2022](https://arxiv.org/abs/2207.04672)) and [Sa
 | SalamandraTA-7b-base                   | ca       | ast      |  26.11 |  63.63 |  **58.08** |
 | SalamandraTA-2B                   | ca       | ast      |  25.32 |  62.59 |  55.98 |
 | Transducens/IbRo-nllb            | ca       | ast      |  24.77 |  61.60 |  57.49 |
-| nllb-3.3B                        | ca       | ast      |  17.17 |  91.47 |  45.83 |
+| nllb-200-3.3B                        | ca       | ast      |  17.17 |  91.47 |  45.83 |
 | | | | | | |
 | SalamandraTA-7b-base                   | ca       | arn      |  **17.77** |  **80.88** |  **42.12** |
 | Transducens/IbRo-nllb                   | ca       | arn      |  17.51 |  81.18 |  41.91 |
@@ -805,13 +805,15 @@ within the framework of [ILENIA Project](https://proyectoilenia.es/) with refere
 
 ### Acknowledgements
 
-The success of this project has been made possible thanks to the invaluable contributions of numerous research centers, teams, and projects that provided access to their data. 
-Their efforts have been instrumental in advancing our work, and we sincerely appreciate their support. 
-We would like to thank, among others:
-[CENID](https://cenid.es/), [CiTIUS](https://citius.gal/es/), [Gaitu proiektua](https://gaitu.eus/), [Helsinki NLP](https://github.com/Helsinki-NLP), [HiTZ](http://hitz.ehu.eus/es), [Institut d’Estudis Aranesi](http://www.institutestudisaranesi.cat/), [MaCoCu Project](https://macocu.eu/), [Machine Translate Foundation](https://machinetranslate.org/about), [NTEU Project](https://nteu.eu/), [Orai NLP technologies](https://huggingface.co/orai-nlp), [Proxecto Nós](https://nos.gal/es/proxecto-nos), [Softcatalà](https://www.softcatala.org/), [Tatoeba Project](https://tatoeba.org/), [TILDE Project](https://tilde.ai/tildelm/), [Transducens - Departament de Llenguatges i Sistemes Informàtics Universitat d’Alacant](https://transducens.dlsi.ua.es/), [Unbabel](https://huggingface.co/Unbabel).
+The success of this project has been made possible thanks to the invaluable contributions of our partners in the [ILENIA Project](https://proyectoilenia.es/): 
+[HiTZ](http://hitz.ehu.eus/es), and [CiTIUS](https://citius.gal/es/).
+Their efforts have been instrumental in advancing our work, and we sincerely appreciate their help and support. 
 
 
 
+### Disclaimer
+
+
 ### Disclaimer
 Be aware that the model may contain biases or other unintended distortions. 
 When third parties deploy systems or provide services based on this model, or use the model themselves,