BSC-LT / salamandraTA-7b-instruct

source = 'Catalan'
target = 'Galician'
source_sentence = "Als antics egipcis del període de l'Imperi Nou els fascinaven els monuments dels seus predecessors, que llavors tenien més de mil anys."

text = f"Translate the following text from {source} into {target}.\n{source}: {source_sentence} \n{target}:"
# Os antigos exipcios do período do Imperio Novo estaban fascinados polos monumentos dos seus predecesores, que entón tiñan máis de mil anos de antigüidade.

source = 'Catalan'
target = 'English'
source_sentence = 'Rafael Nadal i Maria Magdalena van inspirar a una generació sencera.'
machine_translation = 'Rafael Christmas and Maria the Muffin inspired an entire generation each in their own way.'

text = f"Please fix any mistakes in the following {source}-{target} machine translation or keep it unedited if it's correct.\nSource: {source_sentence} \nMT: {machine_translation} \nCorrected:"

# Rafael Nadal and Maria Magdalena inspired an entire generation.

source = 'English'
target = 'Asturian'

text = """Please translate this text from {} into {}.\n{}: President Donald Trump, who campaigned on promises to crack down on illegal immigration, has raised alarms in the U.S. dairy industry with his threat to impose 25% tariffs on Mexico and Canada by February 2025. This move is part of a broader strategy to declare a national emergency at the southern border to halt illegal migration completely.
However, the implications for the agriculture sector, particularly dairy, are significant. Approximately half of the U.S. dairy industry's workforce consists of immigrant labor, many of whom are undocumented. The National Milk Producers Federation estimates that removing immigrant workers could decimate the dairy herd by 2.1 million cows and slash milk production by nearly 50 billion pounds, leading to a dramatic 90.4% increase in milk prices.
The complex perspectives of Americans on undocumented workers were highlighted in a Pew Research Center study. While 64% of U.S. adults support legal pathways for undocumented immigrants, 35% oppose it—a gap that has been narrowing recently. Factors influencing public opinion include the belief that immigrants should have jobs and pass security checks, contrasted by concerns about lawbreakers being rewarded, fairness for legal migrants, and resource allocation.
According to Zach Rutledge, an agricultural economist at Michigan State University, as nations grow wealthier, their labor forces transition away from agriculture toward sectors like services and manufacturing. This shift has led to the U.S. relying heavily on immigrant labor for agricultural work. Domestic workers, even with employment taxes, may cost $15 to $25 an hour, while H-2A visa program workers might cost $25 to $30 an hour, accounting for additional housing expenses.
The National Milk Producers Federation has been vocal in advocating for changes to the H-2A visa program, which outside of its current seasonal limitations, does not support the dairy industry's year-round labor needs. Executive vice-president Jaime Castaneda reiterated the need for legislative clarity to address the undocumented workforce issues in dairy farming.
The Farm Workforce Modernization Act of 2023, which could grant legal status to certain undocumented farmworkers, has been stalled in Congress, despite acknowledgment of the sector's importance to feeding America. The need for coordinated legislative efforts to ensure both border security and labor market stability is imperative moving forward.
{}:""".format(source, target, source, target)

text = """Analyse the following tokenized text and mark the tokens containing named entities.
Use the following annotation guidelines with these tags for named entities: 
- ORG (Refers to named groups or organizations)
- PER (Refers to individual people or named groups of people)
- LOC (Refers to physical places or natural landmarks)
- MISC (Refers to entities that don't fit into standard categories).
Prepend B- to the first token of a given entity and I- to the remaining ones if they exist.
If a token is not a named entity, label it as O.
Input: ['La', 'defensa', 'del', 'antiguo', 'responsable', 'de', 'la', 'RFEF', 'confirma', 'que', 'interpondrá', 'un', 'recurso.']
Marked: """

# [('La', 'O'), ('defensa', 'O'), ('del', 'O'), ('antiguo', 'O'), ('responsable', 'O'), ('de', 'O'), ('la', 'O'), ('RFEF', 'B-ORG'), ('confirma', 'O'), ('que', 'O'), ('interpondrá', 'O'), ('un', 'O'), ('recurso.', 'O')]

Datasets with "-BSC" in their names (e.g., BOUA-SYNTH-BSC, DOGV-SYNTH-BSC) are synthetic datasets obtained by machine translating pre-existing monolingual corpora with our own seq-to-seq models. These datasets were generated internally for model training and are not published.

To consult the data summary document with the respective licences, please send an e-mail to [email protected] .

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• Eisele, A., & Chen, Y. (2010). MultiUN: A Multilingual Corpus from United Nation Documents. In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf
• El-Kishky, A., Chaudhary, V., Guzmán, F., & Koehn, P. (2020). CCAligned: A Massive Collection of Cross-Lingual Web-Document Pairs. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 5960–5969. https://doi.org/10.18653/v1/2020.emnlp-main.480
• El-Kishky, A., Renduchintala, A., Cross, J., Guzmán, F., & Koehn, P. (2021). XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 10424–10430. https://doi.org/10.18653/v1/2021.emnlp-main.814
• Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., Baines, M., Celebi, O., Wenzek, G., Chaudhary, V., Goyal, N., Birch, T., Liptchinsky, V., Edunov, S., Grave, E., Auli, M., & Joulin, A. (2020). Beyond English-Centric Multilingual Machine Translation (No. arXiv:2010.11125). arXiv. https://doi.org/10.48550/arXiv.2010.11125
• García-Martínez, M., Bié, L., Cerdà, A., Estela, A., Herranz, M., Krišlauks, R., Melero, M., O’Dowd, T., O’Gorman, S., Pinnis, M., Stafanovič, A., Superbo, R., & Vasiļevskis, A. (2021). Neural Translation for European Union (NTEU). 316–334. https://aclanthology.org/2021.mtsummit-up.23
• Gibert, O. de, Nail, G., Arefyev, N., Bañón, M., Linde, J. van der, Ji, S., Zaragoza-Bernabeu, J., Aulamo, M., Ramírez-Sánchez, G., Kutuzov, A., Pyysalo, S., Oepen, S., & Tiedemann, J. (2024). A New Massive Multilingual Dataset for High-Performance Language Technologies (No. arXiv:2403.14009). arXiv. http://arxiv.org/abs/2403.14009
• Koehn, P. (2005). Europarl: A Parallel Corpus for Statistical Machine Translation. Proceedings of Machine Translation Summit X: Papers, 79–86. https://aclanthology.org/2005.mtsummit-papers.11
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• Rozis, R.,Skadiņš, R (2017). Tilde MODEL - Multilingual Open Data for EU Languages. https://aclanthology.org/W17-0235
• Schwenk, H., Chaudhary, V., Sun, S., Gong, H., & Guzmán, F. (2019). WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (No. arXiv:1907.05791). arXiv. https://doi.org/10.48550/arXiv.1907.05791
• Schwenk, H., Wenzek, G., Edunov, S., Grave, E., & Joulin, A. (2020). CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB (No. arXiv:1911.04944). arXiv. https://doi.org/10.48550/arXiv.1911.04944
• Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufiş, D., & Varga, D. (n.d.). The JRC-Acquis: A Multilingual Aligned Parallel Corpus with 20+ Languages. http://www.lrec-conf.org/proceedings/lrec2006/pdf/340_pdf
• Subramani, N., Luccioni, S., Dodge, J., & Mitchell, M. (2023). Detecting Personal Information in Training Corpora: An Analysis. In A. Ovalle, K.-W. Chang, N. Mehrabi, Y. Pruksachatkun, A. Galystan, J. Dhamala, A. Verma, T. Cao, A. Kumar, & R. Gupta (Eds.), Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023) (pp. 208–220). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.trustnlp-1.18
• Tiedemann, J. (23-25). Parallel Data, Tools and Interfaces in OPUS. In N. C. (Conference Chair), K. Choukri, T. Declerck, M. U. Doğan, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12). European Language Resources Association (ELRA). http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper
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The non-public portion of this dataset was jointly created by the ILENIA partners: BSC-LT, HiTZ , and CiTIUS . For further information regarding the instruction-tuning data, please contact [email protected] .

• Alves, D. M., Pombal, J., Guerreiro, N. M., Martins, P. H., Alves, J., Farajian, A., Peters, B., Rei, R., Fernandes, P., Agrawal, S., Colombo, P., de Souza, J. G. C., & Martins, A. F. T. (2024). Tower: An open multilingual large language model for translation-related tasks (No. arXiv: 2402.17733). arXiv. https://arxiv.org/abs/2402.17733
• Armengol-Estapé, J., Carrino, C. P., Rodriguez-Penagos, C., de Gibert Bonet, O., Armentano-Oller, C., Gonzalez-Agirre, A., Melero, M., & Villegas, M. (2021). Are multilingual models the best choice for moderately under-resourced languages? A comprehensive assessment for Catalan. Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 4933–4946. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.437
• Currey, A., Nadejde, M., Pappagari, R. R., Mayer, M., Lauly, S., Niu, X., Hsu, B., & Dinu, G. (2022). MT-GenEval: A counterfactual and contextual dataset for evaluating gender accuracy in machine translation. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 4287–4299). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.288
• Federmann, C., Kocmi, T., & Xin, Y. (2022). NTREX-128 – News test references for MT evaluation of 128 languages. Proceedings of the First Workshop on Scaling Up Multilingual Evaluation, 21–24. Association for Computational Linguistics. https://aclanthology.org/2022.sumeval-1.4
• Ive, J., Specia, L., Szoc, S., Vanallemeersch, T., Van den Bogaert, J., Farah, E., Maroti, C., Ventura, A., & Khalilov, M. (2020). A post-editing dataset in the legal domain: Do we underestimate neural machine translation quality? In N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 3692–3697). European Language Resources Association. https://aclanthology.org/2020.lrec-1.455/
• Malmasi, S., Fang, A., Fetahu, B., Kar, S., & Rokhlenko, O. (2022). MultiCoNER: A large-scale multilingual dataset for complex named entity recognition. Proceedings of the 29th International Conference on Computational Linguistics, 3798–3809. International Committee on Computational Linguistics. https://aclanthology.org/2022.coling-1.334/
• NLLB Team, Costa-jussà, M. R., Cross, J., Çelebi, O., Elbayad, M., Heafield, K., Heffernan, K., Kalbassi, E., Lam, J., Licht, D., Maillard, J., Sun, A., Wang, S., Wenzek, G., Youngblood, A., Akula, B., Barrault, L., Mejia Gonzalez, G., Hansanti, P., Hoffman, J., Jarrett, S., Sadagopan, K. R., Rowe, D., Spruit, S., Tran, C., Andrews, P., Ayan, N. F., Bhosale, S., Edunov, S., Fan, A., Gao, C., Goswami, V., Guzmán, F., Koehn, P., Mourachko, A., Ropers, C., Saleem, S., Schwenk, H., & Wang, J. (2022). No language left behind: Scaling human-centered machine translation (No. arXiv: 2207.04672). arXiv. https://arxiv.org/abs/2207.04672
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• Urbizu, G., San Vicente, I., Saralegi, X., Agerri, R., & Soroa, A. (2022). BasqueGLUE: A natural language understanding benchmark for Basque. Proceedings of the Language Resources and Evaluation Conference, 1603–1612. European Language Resources Association. https://aclanthology.org/2022.lrec-1.172
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• BLEU : Sacrebleu implementation. Signature: nrefs:1— case:mixed— eff:no— tok:13a— smooth:exp—version:2.3.1
• TER : Sacrebleu implementation.
• ChrF : Sacrebleu implementation.
• Comet : Model checkpoint: "Unbabel/wmt22-comet-da".
• Comet-kiwi : Model checkpoint: "Unbabel/wmt22-cometkiwi-da".
• Bleurt : Model checkpoint: "lucadiliello/BLEURT-20".
• MetricX : Model checkpoint: "google/metricx-23-xl-v2p0".
• MetricX-QE : Model checkpoint: "google/metricx-23-qe-xl-v2p0".

English

This section presents the evaluation metrics for English translation tasks.

Spanish

This section presents the evaluation metrics for Spanish translation tasks.

Catalan

This section presents the evaluation metrics for Catalan translation tasks.

Galician

This section presents the evaluation metrics for Galician translation tasks.

Basque

This section presents the evaluation metrics for Basque translation tasks.

English-XX

Spanish-XX

Catalan-XX