Salamandra Model Card

SalamandraTA-2B is a machine translation model that has been continually pre-trained on Salamandra 2B on 70 billion tokens of parallel data in 30 different languages: Catalan, Italian, Portuguese, German, English, Spanish, Euskera, Galician, French, Bulgarian, Czech, Lithuanian, Croatian, Dutch, Romanian, Danish, Greek, Finnish, Hungarian, Slovak, Slovenian, Estonian, Polish, Latvian, Swedish, Maltese, Irish, Aranese, Aragonese, Asturian. SalamandraTA-2B is the first model in SalamandraTA series and is trained to handle sentence- and paragraph- level machine translation.

Developed by: The Language Technologies Unit from Barcelona Supercomputing Center (BSC).
Model type: A 2B parameter model continually pre-trained on 70 billion tokens.
Languages: Catalan, Italian, Portuguese, German, English, Spanish, Euskera, Galician, French, Bulgarian, Czech, Lithuanian, Croatian, Dutch, Romanian, Danish, Greek, Finnish, Hungarian, Slovak, Slovenian, Estonian, Polish, Latvian, Swedish, Maltese, Irish, Aranese, Aragonese, Asturian.
License: Apache License, Version 2.0

Model Details

Description

This machine translation model is built upon the foundation of Salamandra 2B. By leveraging the knowledge of the base Salamandra 2B model, this model is able to perform high quality translations between almost 900 translation directions.

Key Features:

Continual Pretraining: The model is trained on 70 Billion tokens of parallel data. All data employed is open-sourced or generated from open-source
data using the Machine Translation models at BSC
Large Language Model Foundation: Built on Salamandra 2B, providing a strong language understanding and generation capability.
Multilingual Support: Capable of translating between 30 european languages, including low-resource languages.
High-Quality Translations: Delivers accurate and fluent translations, thanks to its continual pretraining and large-scale dataset.
Efficient Inference: 2 Billion parameters allow for a trade-off between performance and hardware requirements by most systems.

Hyperparameters

The full list of hyperparameters for each model can be found here.

Architecture


Total Parameters	2,253,490,176
Embedding Parameters	524,288,000
Layers	24
Hidden size	2,048
Attention heads	16
Context length	8,192
Vocabulary size	256,000
Precision	bfloat16
Embedding type	RoPE
Activation Function	SwiGLU
Layer normalization	RMS Norm
Flash attention	✅
Grouped Query Attention	❌
Num. query groups	N/A

Intended Use

Direct Use

The models are intended for both research and commercial use in any of the languages included in the training data. The base models are intended for general machine translation tasks.

Out-of-scope Use

The model is not intended for malicious activities, such as harming others or violating human rights. Any downstream application must comply with current laws and regulations. Irresponsible usage in production environments without proper risk assessment and mitigation is also discouraged.

Hardware and Software

Training Framework

Continual pre-training was conducted using LLaMA-Factory framework.

Compute Infrastructure

All models were trained on MareNostrum 5, a pre-exascale EuroHPC supercomputer hosted and operated by Barcelona Supercomputing Center.

The accelerated partition is composed of 1,120 nodes with the following specifications:

4x Nvidia Hopper GPUs with 64 HBM2 memory
2x Intel Sapphire Rapids 8460Y+ at 2.3Ghz and 32c each (64 cores)
4x NDR200 (BW per node 800Gb/s)
512 GB of Main memory (DDR5)
460GB on NVMe storage

How to use

To translate with the salamandraTA-2B model, first you need to create a prompt that specifies the source and target languages in this format:

[source_language] sentence \n[target_language]

You can translate between these languages by using their names directly:

Italian, Portuguese, German, English, Spanish, Euskera, Galician, French, Bulgarian, Czech, Lithuanian, Croatian, Dutch, Romanian, Danish, Greek, Finnish, Hungarian, Slovak, Slovenian, Estonian, Polish, Latvian, Swedish, Maltese, Irish, Aranese, Aragonese, Asturian.

Inference

To translate from Spanish to Catalan using Huggingface's AutoModel class on a single sentence you can use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = 'BSC-LT/salamandraTA-2b'

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Move model to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

src_lang_code = 'Spanish'
tgt_lang_code = 'Catalan'
sentence = 'Ayer se fue, tomó sus cosas y se puso a navegar.'

prompt = f'[{src_lang_code}] {sentence} \n[{tgt_lang_code}]'

# Tokenize and move inputs to the same device as the model
input_ids = tokenizer(prompt, return_tensors='pt').input_ids.to(device)
output_ids = model.generate(input_ids, max_length=500, num_beams=5)
input_length = input_ids.shape[1]

generated_text = tokenizer.decode(output_ids[0, input_length:], skip_special_tokens=True).strip()
print(generated_text)
#Ahir se'n va anar, va agafar les seves coses i es va posar a navegar.

To run batch inference using Huggingface's AutoModel class you can use the following code.

Show code

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = 'BSC-LT/salamandraTA-2b'

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation='eager')

# Move the model to GPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# List of sentences to translate
sentences = [
  'Ayer se fue, tomó sus cosas y se puso a navegar.',
  'Se despidió y decidió batirse en duelo con el mar, y recorrer el mundo en su velero',
  'Su corazón buscó una forma diferente de vivir, pero las olas le gritaron: Vete con los demás',
  'Y se durmió y la noche le gritó: Dónde vas, y en sus sueños dibujó gaviotas, y pensó: Hoy debo regresar.'
]

src_lang_code = 'Spanish'
tgt_lang_code = 'Catalan'

prompt = lambda x: f'[{src_lang_code}] {x} \n[{tgt_lang_code}]'
prompts = [prompt(x) for x in sentences]


encodings = tokenizer(prompts, return_tensors='pt', padding=True, add_special_tokens=True)

input_ids = encodings['input_ids'].to(model.device)
attention_mask = encodings['attention_mask'].to(model.device)

with torch.no_grad(): 
    outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, num_beams=5,max_length=256,early_stopping=True)

results_detokenized = []
for i, output in enumerate(outputs):
    input_length = input_ids[i].shape[0]
    generated_text = tokenizer.decode(output[input_length:], skip_special_tokens=True).strip()
    results_detokenized.append(generated_text)

print("Generated Translations:", results_detokenized)

#Generated Translations: ["Ahir se'n va anar, va agafar les seves coses i es va posar a navegar.", 
#"Es va acomiadar i va decidir batre's en duel amb el mar, i recórrer el món en el seu veler", 
#"El seu cor va buscar una forma diferent de viure, però les onades li van cridar: Vés amb els altres", 
#"I es va adormir i la nit li va cridar: On vas, i en els seus somnis va dibuixar gavines, i va pensar: Avui he de tornar."]

Data

Pretraining Data

The training corpus consists of 70 billion tokens of Catalan- and Spanish-centric parallel data, including all of the official European languages plus Catalan, Basque, Galician, Asturian, Aragonese and Aranese. It amounts to 3,157,965,012 parallel sentence pairs.

This highly multilingual corpus is predominantly composed of data sourced from OPUS, with additional data taken from the NTEU project and Project Aina’s existing corpora. 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. The final distribution of languages was as below:

Click the expand button below to see the full list of corpora included in the training data.

Data Sources

Dataset	Ca-xx Languages	Es-xx Langugages
CCMatrix	eu
DGT		bg,cs,da,de,el ,et,fi,fr,ga,hr,hu,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv
ELRC-EMEA		bg,cs,da,hu,lt,lv,mt,pl,ro,sk,sl
EMEA		bg,cs,da,el,fi,hu,lt,mt,nl,pl,ro,sk,sl,sv
EUBookshop	lt,pl,pt	cs,da,de,el,fi,fr,ga,it,lv,mt,nl,pl,pt,ro,sk,sl,sv
Europarl		bg,cs,da,el,fi,fr,hu,lt,lv,nl,pl,pt ,ro,sk,sl,sv
Europat		hr
KDE4	bg,cs,da,de,el ,et,eu,fi,fr,ga,gl,hr,it,lt,lv,nl,pl,pt,ro,sk,sl,sv	bg,ga,hr
GlobalVoices	bg,de,fr,it,nl,pl,pt	bg,de,fr,pt
GNOME	eu,fr,ga,gl,pt	ga
JRC-Arquis		cs,da,et,fr,lt,lv,mt,nl,pl ,ro,sv
MultiCCAligned	bg,cs,de,el,et,fi,fr,hr,hu,it,lt,lv,nl,pl,ro,sk,sv	bg,fi,fr,hr,it,lv,nl,pt
MultiHPLT	et,fi,ga,hr,mt
MultiParaCrawl	bg,da	de,fr,ga,hr,hu,it,mt,pt
MultiUN		fr
News-Commentary		fr
NLLB	bg,da,el,et,fi,fr,gl,hu,it ,lt,lv,pt,ro,sk,sl	bg,cs,da,de,el ,et,fi,fr,hu,it,lt,lv,nl,pl,pt ,ro,sk,sl,sv
NTEU		bg,cs,da,de,el ,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv
OpenSubtitles	bg,cs,da,de,el ,et,eu,fi,gl,hr,hu,lt,lv,nl,pl,pt,ro,sk,sl,sv	da,de,fi,fr,hr,hu,it,lv,nl
Tatoeba	de,pt	pt
TildeModel		bg
UNPC		fr
WikiMatrix	bg,cs,da,de,el ,et,eu,fi,fr,gl,hr,hu,it,lt,nl,pl,pt,ro,sk,sl,sv	bg,fr,hr,it,pt
XLENT	eu,ga,gl	ga

We provide an extense Datasheet section following the best practices defined by (Gebru et al., 2021).

Datasheet

Motivation

For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description.

The purpose of creating this dataset is to pre-train multilingual models on parallel data in a large number of European languages, with Spanish and Catalan as the pivot languages. We have found that there is a lack of high quality parallel data in the scale necessary for training models, particularly between mid to low resource languages, and so in this dataset we have attempted to compile all publicly available resources for the included smaller languages, in addition to creating additional resources for Catalan as the pivot language.

Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)?

The dataset has been created by the Machine Translation sub-group of the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS), which aims to advance the field of natural language processing through cutting-edge research and development and the use of HPC. In particular, the main contributors were Audrey Mash and Francesca De Luca Fornaciari.

Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.

This work/research has been promoted and financed by the Government of Catalonia through the Aina project.

Composition

What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description.

The dataset consists entirely of parallel text separated at sentence level. Specifically, data was mainly sourced from the following databases and repositories:

Opus: Repository which aims to provide freely available parallel datasets in order to advance work in computational linguistics and automatic translation.
ELRC-SHARE: Repository used for documenting, storing, browsing and accessing Language Resources that are collected through the European Language Resource Coordination.

How many instances are there in total (of each type, if appropriate)?

The dataset contains a diverse range of sentence pairs across multiple languages. 36.02% of the data is parallel with Catalan, 27.59% is parallel with Spanish and 0.37% is parallel with English.

Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).

The dataset is a sample from various sources. Language pairs which had fewer than 100 million parallel sentence pairs after filtering and cleaning were taken in their entirety. A sample of 100 million sentence pairs was taken from language pairs which had more data than this after preprocessing. All sampling was random. Where very little data existed between Catalan and the target language, synthetic Catalan data was created in order to increase the sample size. This was done using Projecte Aina’s Spanish-Catalan model.

What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description.

Each instance consists of a parallel sentence pair processed for deduplication, language identification, and language alignment.

Is there a label or target associated with each instance? If so, please provide a description.

Each instance is labelled with the two languages present in the sentence pair.

Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.

No significant information is missing from the instances.

Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.

Instances are related through shared language identifiers.

Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.

The dataset is split randomly into training, validation, and test sets.

Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.

Despite filtering for alignment and language identification, a small number of misaligned sentence pairs and incorrectly labelled languages may remain present in the data. The thresholds chosen for this task aim to achieve an optimal balance, prioritising higher accuracy.

Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.

The dataset is self-contained and does not rely on external resources.

Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor–patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description.

The dataset does not contain confidential data.

Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. If the dataset does not relate to people, you may skip the remaining questions in this section.

The dataset includes web-crawled content, which may overrepresent pornographic material across languages (Kreutzer et al., 2022). We have performed no filtering for toxic material.

Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.

The dataset does not explicitly identify any subpopulations.

Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how.

Web-sourced instances in the dataset may contain personally identifiable information (PII) that is publicly available on the Web, such as names, IP addresses, email addresses, and phone numbers. While it would be possible to indirectly identify individuals through the combination of multiple data points, the nature and scale of web data makes it difficult to parse such information.

Does the dataset contain data that might be considered sensitive in any way? If so, please provide a description.

Given that the dataset includes web-sourced content and other publicly available documents, instances may inadvertently reveal financial information, health-related details, or forms of government identification, such as social security numbers (Subramani et al., 2023), especially if the content originates from less-regulated sources or user-generated platforms.

Collection Process

How was the data collected?

This dataset is constituted by combining several sources, all of which take the form of web-sourced datasets with some preprocessing available under permissive license (p.e. Common Crawl).

What mechanisms or procedures were used to collect the data? How were these mechanisms or procedures validated?

All datasets were acquired through open direct download and validated with data integrity tests.

If the dataset is a sample from a larger set, what was the sampling strategy?

The sampling strategy was to use the whole dataset resulting from the filtering explained in the ‘preprocessing/cleaning/labelling’ section, with the particularity that language pairs consisting of over 100 million sentence pairs were randomly sampled down to 100 million.

Who was involved in the data collection process and how were they compensated?

This data is generally extracted, filtered and sampled by automated processes. The code required to run these processes has been developed entirely by members of the LangTech data team, or otherwise obtained from open-source software. Furthermore, there has been no monetary consideration for acquiring data from suppliers.

Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? If not, please describe the timeframe in which the data associated with the instances was created.

Data were acquired and processed from April 2023 to August 2024. However, as mentioned, much data has been obtained from open projects such as Common Crawl, which contains data from 2014, so it is the end date (04/2024) rather than the start date that is important.

Were any ethical review processes conducted? If so, please provide a description of these review processes, including the outcomes, as well as a link or other access point to any supporting documentation.

No particular ethical review process has been carried out as the data is mostly open and not particularly sensitive. However, we have an internal evaluation team and a bias team to monitor ethical issues. In addition, we work closely with ‘Observatori d'Ètica en Intel·ligència Artificial’ (OEIAC) and ‘Agencia Española de Supervisión de la Inteligencia Artificial’ (AESIA) to audit the processes we carry out from an ethical and legal point of view, respectively.

Preprocessing

Was any preprocessing/cleaning/labeling of the data done? If so, please provide a description. If not, you may skip the remaining questions in this section.

All data was filtered according to two specific criteria:

Alignment - sentence level alignments were calculated using LaBSE and sentence pairs with a score below 0.75 were discarded.
Language identification - The probability of being the target language was calculated using either Idiomata Cognitor or Lingua.py and sentences identified as unlikely to be the correct language were filtered out. Thresholds varied by language.

Was the “raw” data saved in addition to the preprocessed/cleaned/labeled data? If so, please provide a link or other access point to the “raw” data.

The original raw data was kept on the BSC servers but is not publicly available.

Is the software that was used to preprocess/clean/label the data available? If so, please provide a link or other access point.

No, our internal cleaning pipeline for parallel data has not been made publicly available.

Uses

Has the dataset been used for any tasks already? If so, please provide a description.

Pre-train the SalamandraTA model family.

What (other) tasks could the dataset be used for?

The data can be used primarily to pre-train other Machine Translation models.

Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? Is there anything a dataset consumer could do to mitigate these risks or harms?

Web-crawled content is over-represented with standard language varieties, impacting language model performance for minority languages. Language diversity in data is crucial to avoid bias, especially in encoding non-standard dialects, preventing the exclusion of demographic groups. Moreover, despite legal uncertainties in web-scraped data, we prioritize permissive licenses and privacy protection measures, acknowledging the challenges posed by personally identifiable information (PII) within large-scale datasets. Our ongoing efforts aim to address privacy concerns and contribute to a more inclusive linguistic dataset.

Are there tasks for which the dataset should not be used?

Distribution

Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? If so, please provide a description.

The dataset will not be released or distributed to third parties. Any related question to distribution is omitted in this section.

Maintenance

Who will be supporting/hosting/maintaining the dataset?

The dataset will be hosted by the Language Technologies unit (LangTech) of the Barcelona Supercomputing Center (BSC). The team will ensure regular updates and monitor the dataset for any issues related to content integrity, legal compliance, and bias for the sources they are responsible for.

How can the owner/curator/manager of the dataset be contacted?

The data owner may be contacted with the email address [email protected].

Will the dataset be updated?

The dataset will not be updated.

If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances? If so, please describe these limits and explain how they will be enforced.

The dataset does not keep sensitive data that could allow direct identification of individuals, apart from the data that is publicly available in web-sourced content. Due to the sheer volume and diversity of web data, it is not feasible to notify individuals or manage data retention on an individual basis. However, efforts are made to mitigate the risks associated with sensitive information through pre-processing and filtering to remove identifiable or harmful content. Despite these measures, vigilance is maintained to address potential privacy and ethical issues.

Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers.

Since the dataset will not be updated, only the final version will be kept.

If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so?

The dataset does not allow for external contributions.

References

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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
Ziemski, M., Junczys-Dowmunt, M., & Pouliquen, B. (n.d.). The United Nations Parallel Corpus v1.0. https://aclanthology.org/L16-1561

Evaluation

Below are the evaluation results on Flores-200 dev and devtest compared to NLLB-3.3 (Costa-jussà et al., 2022) for CA-XX and XX-CA directions. The metrics have been computed excluding Asturian, Aranese, and Aragonese as we report them separately. The evaluation was conducted using MT Lens following the standard setting (beam search with beam size 5, limiting the translation length to 250 tokens). We report the following metrics:

Click to show metrics details

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".

Flores200-dev

	Bleu ↑	Ter ↓	ChrF ↑	Comet ↑	Comet-kiwi ↑	Bleurt ↑
CA-XX
SalamandraTA-2B	27.41	60.88	56.27	0.86	0.82	0.76
nllb 3.3B	26.84	61.75	55.7	0.86	0.82	0.76
XX-CA
SalamandraTA-2B	30.75	57.66	57.6	0.85	0.81	0.73
nllb 3.3B	29.76	58.25	56.75	0.85	0.82	0.73

Click to show full table CA-XX Flores-dev

	source	target	Bleu ↑	Ter ↓	ChrF ↑	Comet ↑	Comet-kiwi ↑	Bleurt ↑
nllb 3.3B	ca	sv	33.05	53.98	60.09	0.88	0.83	0.79
SalamandraTA-2B	ca	sv	30.62	55.4	57.77	0.87	0.81	0.78

SalamandraTA-2B	ca	sl	25.74	63.78	54.29	0.88	0.83	0.81
nllb 3.3B	ca	sl	25.04	65.02	53.08	0.88	0.83	0.82

SalamandraTA-2B	ca	sk	26.03	62.58	53.53	0.89	0.84	0.8
nllb 3.3B	ca	sk	25.59	63.17	53.28	0.89	0.84	0.8

SalamandraTA-2B	ca	ro	33.08	54.36	59.18	0.89	0.85	0.8
nllb 3.3B	ca	ro	31.91	55.46	58.36	0.89	0.85	0.81

SalamandraTA-2B	ca	pt	37.6	48.82	62.73	0.88	0.84	0.76
nllb 3.3B	ca	pt	36.85	49.56	62.02	0.88	0.85	0.76

nllb 3.3B	ca	pl	17.97	73.06	47.94	0.88	0.84	0.78
SalamandraTA-2B	ca	pl	17.85	72.67	47.77	0.88	0.84	0.78

SalamandraTA-2B	ca	nl	23.88	64.95	54.46	0.85	0.84	0.75
nllb 3.3B	ca	nl	23.26	66.46	54.17	0.85	0.85	0.75

SalamandraTA-2B	ca	mt	25.62	59.08	60.83	0.69	0.61	0.43
nllb 3.3B	ca	mt	25.37	59.47	60.1	0.69	0.63	0.39

SalamandraTA-2B	ca	lv	21.23	71.48	49.47	0.82	0.79	0.73
nllb 3.3B	ca	lv	20.56	70.88	50.07	0.85	0.78	0.77

SalamandraTA-2B	ca	lt	19.92	71.02	50.88	0.87	0.8	0.81
nllb 3.3B	ca	lt	18.82	71.8	51.84	0.87	0.82	0.82

SalamandraTA-2B	ca	it	26.76	60.67	56.3	0.88	0.85	0.77
nllb 3.3B	ca	it	26.42	61.47	55.66	0.87	0.86	0.77

SalamandraTA-2B	ca	hu	22.8	66.41	53.41	0.86	0.82	0.85
nllb 3.3B	ca	hu	21.2	68.54	51.99	0.87	0.83	0.87

SalamandraTA-2B	ca	hr	26.24	61.83	55.87	0.89	0.84	0.81
nllb 3.3B	ca	hr	24.04	64.25	53.79	0.89	0.85	0.82

nllb 3.3B	ca	gl	32.85	51.69	59.33	0.87	0.85	0.72
SalamandraTA-2B	ca	gl	31.84	52.52	59.16	0.87	0.84	0.71

SalamandraTA-2B	ca	ga	25.24	63.36	53.24	0.78	0.64	0.62
nllb 3.3B	ca	ga	23.51	66.54	51.53	0.77	0.66	0.62

SalamandraTA-2B	ca	fr	40.14	48.34	64.24	0.86	0.84	0.73
nllb 3.3B	ca	fr	39.8	48.96	63.97	0.86	0.85	0.74

nllb 3.3B	ca	fi	18.63	71.42	52.71	0.89	0.82	0.82
SalamandraTA-2B	ca	fi	18.49	71.46	52.09	0.88	0.8	0.8

SalamandraTA-2B	ca	eu	18.75	71.09	57.05	0.87	0.81	0.8
nllb 3.3B	ca	eu	13.15	77.69	50.35	0.83	0.75	0.75

SalamandraTA-2B	ca	et	22.03	67.55	54.87	0.88	0.8	0.79
nllb 3.3B	ca	et	20.07	70.66	53.19	0.88	0.81	0.8

nllb 3.3B	ca	es	25.59	60.39	53.7	0.86	0.86	0.74
SalamandraTA-2B	ca	es	24.46	61.54	53.02	0.86	0.86	0.74

nllb 3.3B	ca	en	49.62	37.33	71.65	0.89	0.86	0.8
SalamandraTA-2B	ca	en	46.62	40.03	70.23	0.88	0.86	0.79

SalamandraTA-2B	ca	el	23.38	63	50.03	0.87	0.84	0.74
nllb 3.3B	ca	el	22.62	63.73	49.5	0.87	0.84	0.74

SalamandraTA-2B	ca	de	31.89	57.12	59.07	0.84	0.83	0.75
nllb 3.3B	ca	de	31.19	57.87	58.47	0.85	0.84	0.76

SalamandraTA-2B	ca	da	34.69	53.31	61.11	0.87	0.82	0.75
nllb 3.3B	ca	da	34.32	54.2	60.2	0.88	0.83	0.77

SalamandraTA-2B	ca	cs	25.67	63.37	53.07	0.89	0.85	0.79
nllb 3.3B	ca	cs	25.02	63.59	52.43	0.89	0.85	0.79

SalamandraTA-2B	ca	bg	32.09	57.01	59.4	0.89	0.85	0.84
nllb 3.3B	ca	bg	31.24	58.41	58.81	0.89	0.86	0.85

Click to show full table XX-CA Flores-dev

	source	target	Bleu ↑	Ter ↓	ChrF ↑	Comet ↑	Comet-kiwi ↑	Bleurt ↑
SalamandraTA-2B	sv	ca	34.21	53	59.52	0.86	0.83	0.74
nllb 3.3B	sv	ca	33.03	53.42	59.02	0.86	0.84	0.75

SalamandraTA-2B	sl	ca	28.98	59.95	56.24	0.85	0.82	0.72
nllb 3.3B	sl	ca	27.51	61.23	54.96	0.85	0.83	0.72

SalamandraTA-2B	sk	ca	30.61	58.1	57.53	0.86	0.81	0.73
nllb 3.3B	sk	ca	29.24	58.93	56.29	0.86	0.83	0.73

SalamandraTA-2B	ro	ca	33.73	54.23	60.11	0.87	0.83	0.75
nllb 3.3B	ro	ca	32.9	54.71	59.56	0.87	0.84	0.75

SalamandraTA-2B	pt	ca	35.99	50.64	61.52	0.87	0.84	0.76
nllb 3.3B	pt	ca	34.63	51.15	60.68	0.87	0.84	0.76

SalamandraTA-2B	pl	ca	25.77	64.99	53.46	0.84	0.82	0.71
nllb 3.3B	pl	ca	24.41	65.69	52.45	0.85	0.83	0.71

SalamandraTA-2B	nl	ca	26.04	64.09	53.64	0.84	0.84	0.71
nllb 3.3B	nl	ca	25.35	64.64	53.15	0.84	0.85	0.71

SalamandraTA-2B	mt	ca	37.51	50.18	62.42	0.79	0.69	0.75
nllb 3.3B	mt	ca	36.29	51.01	61.24	0.79	0.7	0.75

SalamandraTA-2B	lv	ca	27.14	62.61	55.6	0.84	0.78	0.7
nllb 3.3B	lv	ca	27.02	61.12	54.28	0.84	0.79	0.71

SalamandraTA-2B	lt	ca	27.76	61.3	54.52	0.84	0.76	0.71
nllb 3.3B	lt	ca	26.05	62.75	53.4	0.84	0.77	0.71

SalamandraTA-2B	it	ca	28.44	61.09	57.12	0.87	0.85	0.74
nllb 3.3B	it	ca	27.79	61.42	56.62	0.87	0.86	0.74

SalamandraTA-2B	hu	ca	28.15	60.01	55.29	0.85	0.81	0.72
nllb 3.3B	hu	ca	27.06	60.44	54.38	0.85	0.83	0.72

SalamandraTA-2B	hr	ca	29.89	58.61	56.62	0.85	0.82	0.72
nllb 3.3B	hr	ca	28.23	59.55	55.37	0.86	0.84	0.73

nllb 3.3B	gl	ca	34.28	52.34	60.86	0.87	0.85	0.76
SalamandraTA-2B	gl	ca	32.14	54.03	60.3	0.87	0.84	0.75

SalamandraTA-2B	ga	ca	28.59	61.13	55.61	0.8	0.69	0.68
nllb 3.3B	ga	ca	28.09	61.12	54.55	0.8	0.7	0.68

SalamandraTA-2B	fr	ca	34.53	52.9	60.38	0.87	0.83	0.76
nllb 3.3B	fr	ca	33.61	53.57	59.73	0.87	0.84	0.76

SalamandraTA-2B	fi	ca	26.71	62.19	54.09	0.86	0.8	0.71
nllb 3.3B	fi	ca	26.31	62.6	54.06	0.86	0.82	0.71

SalamandraTA-2B	eu	ca	27.93	60.26	55.27	0.87	0.83	0.73
nllb 3.3B	eu	ca	26.43	63.76	53.75	0.86	0.82	0.72

SalamandraTA-2B	et	ca	30.03	58.25	56.88	0.86	0.79	0.72
nllb 3.3B	et	ca	27.56	59.95	54.92	0.86	0.8	0.72

nllb 3.3B	es	ca	25.33	64.23	55.1	0.86	0.84	0.73
SalamandraTA-2B	es	ca	22.95	67.1	53.67	0.86	0.84	0.72

SalamandraTA-2B	en	ca	43.55	42.62	67.03	0.88	0.85	0.78
nllb 3.3B	en	ca	42.21	43.63	65.95	0.88	0.85	0.78

SalamandraTA-2B	el	ca	28.52	60.34	54.99	0.85	0.83	0.71
nllb 3.3B	el	ca	27.36	60.49	54.76	0.85	0.85	0.72

SalamandraTA-2B	de	ca	33.07	54.46	59.06	0.85	0.84	0.74
nllb 3.3B	de	ca	31.43	56.05	57.95	0.86	0.85	0.74

SalamandraTA-2B	da	ca	34.6	53.22	60.43	0.86	0.83	0.75
nllb 3.3B	da	ca	32.71	54.2	58.9	0.86	0.84	0.75

SalamandraTA-2B	cs	ca	30.92	57.54	57.71	0.86	0.82	0.73
nllb 3.3B	cs	ca	29.02	58.78	56.44	0.86	0.83	0.73

SalamandraTA-2B	bg	ca	31.68	56.32	58.61	0.85	0.84	0.73
nllb 3.3B	bg	ca	29.87	57.75	57.26	0.85	0.85	0.73

Flores200-devtest

	Bleu ↑	Ter ↓	ChrF ↑	Comet ↑	Comet-kiwi ↑	Bleurt ↑
CA-XX
SalamandraTA-2B	27.09	61.06	56.41	0.86	0.81	0.75
nllb 3.3B	26.7	61.74	55.85	0.86	0.82	0.76
XX-CA
SalamandraTA-2B	31	57.46	57.96	0.85	0.81	0.73
nllb 3.3B	30.31	58.26	57.12	0.85	0.82	0.73

Click to show full table CA-XX Flores-devtest

	source	target	Bleu ↑	Ter ↓	ChrF ↑	Comet ↑	Comet-kiwi ↑	Bleurt ↑
nllb 3.3B	ca	sv	32.49	55.11	59.93	0.88	0.82	0.79
SalamandraTA-2B	ca	sv	30.53	56.24	58.05	0.87	0.8	0.77

SalamandraTA-2B	ca	sl	25.16	64.25	53.88	0.87	0.82	0.8
nllb 3.3B	ca	sl	24.64	66.02	52.71	0.88	0.82	0.81

SalamandraTA-2B	ca	sk	25.64	63.03	53.55	0.88	0.83	0.79
nllb 3.3B	ca	sk	25.44	63.29	53.37	0.89	0.84	0.79

SalamandraTA-2B	ca	ro	33.21	54.27	59.53	0.89	0.84	0.8
nllb 3.3B	ca	ro	31.29	56.44	58.16	0.89	0.85	0.8

SalamandraTA-2B	ca	pt	37.9	48.95	63.15	0.88	0.84	0.75
nllb 3.3B	ca	pt	37.31	49.31	62.7	0.88	0.85	0.75

SalamandraTA-2B	ca	pl	18.62	71.88	48.44	0.88	0.83	0.77
nllb 3.3B	ca	pl	18.01	72.23	48.26	0.88	0.83	0.77

SalamandraTA-2B	ca	nl	23.4	65.66	54.55	0.85	0.84	0.74
nllb 3.3B	ca	nl	22.99	66.68	53.95	0.85	0.84	0.75

nllb 3.3B	ca	mt	24.78	59.97	59.58	0.68	0.62	0.36
SalamandraTA-2B	ca	mt	24.35	60.1	60.51	0.69	0.6	0.4

SalamandraTA-2B	ca	lv	20.55	71.85	50.24	0.82	0.78	0.74
nllb 3.3B	ca	lv	20.16	70.37	50.3	0.85	0.78	0.78

SalamandraTA-2B	ca	lt	20.37	70.15	51.61	0.88	0.79	0.82
nllb 3.3B	ca	lt	19.95	70.47	52.49	0.88	0.81	0.81

SalamandraTA-2B	ca	it	27.18	60.37	56.65	0.88	0.85	0.77
nllb 3.3B	ca	it	26.83	60.96	56.33	0.88	0.85	0.77

SalamandraTA-2B	ca	hu	21.76	66.96	53.45	0.86	0.81	0.85
nllb 3.3B	ca	hu	20.54	68.28	52.2	0.87	0.82	0.87

SalamandraTA-2B	ca	hr	25.41	62.55	55.65	0.89	0.84	0.81
nllb 3.3B	ca	hr	24.01	64.39	53.95	0.89	0.84	0.82

nllb 3.3B	ca	gl	32.33	52.64	59.3	0.87	0.85	0.71
SalamandraTA-2B	ca	gl	31.97	52.76	59.48	0.87	0.84	0.7

SalamandraTA-2B	ca	ga	23.19	66.3	51.99	0.77	0.64	0.6
nllb 3.3B	ca	ga	22.38	67.76	50.92	0.77	0.66	0.6

nllb 3.3B	ca	fr	40.82	47.72	64.82	0.86	0.85	0.74
SalamandraTA-2B	ca	fr	40.35	47.79	64.56	0.86	0.84	0.73

nllb 3.3B	ca	fi	18.93	70.8	53.03	0.89	0.81	0.82
SalamandraTA-2B	ca	fi	18.92	70.69	52.85	0.88	0.8	0.8

SalamandraTA-2B	ca	eu	18.33	72	56.65	0.86	0.81	0.79
nllb 3.3B	ca	eu	12.79	78.69	50.19	0.83	0.75	0.75

SalamandraTA-2B	ca	et	21.45	67.08	55.01	0.88	0.8	0.79
nllb 3.3B	ca	et	19.84	70.08	53.48	0.88	0.8	0.79

nllb 3.3B	ca	es	25.87	59.66	54.06	0.86	0.86	0.74
SalamandraTA-2B	ca	es	24.73	60.79	53.48	0.86	0.86	0.73

nllb 3.3B	ca	en	48.41	38.1	71.29	0.89	0.86	0.8
SalamandraTA-2B	ca	en	45.19	41.18	69.46	0.88	0.85	0.78

SalamandraTA-2B	ca	el	22.78	63.17	49.97	0.87	0.83	0.73
nllb 3.3B	ca	el	22.59	63.8	49.33	0.87	0.83	0.73

SalamandraTA-2B	ca	de	31.31	57.16	59.42	0.85	0.83	0.75
nllb 3.3B	ca	de	31.25	57.87	59.05	0.85	0.83	0.75

SalamandraTA-2B	ca	da	34.83	53.16	61.44	0.88	0.82	0.75
nllb 3.3B	ca	da	34.43	53.82	60.73	0.88	0.83	0.76

SalamandraTA-2B	ca	cs	24.98	63.45	53.11	0.89	0.84	0.77
nllb 3.3B	ca	cs	24.73	63.94	52.66	0.89	0.85	0.78

SalamandraTA-2B	ca	bg	32.25	55.76	59.85	0.89	0.85	0.84
nllb 3.3B	ca	bg	31.45	56.93	59.29	0.89	0.85	0.85

Click to show full table XX-CA Flores-devtest

	source	target	Bleu ↑	Ter ↓	ChrF ↑	Comet ↑	Comet-kiwi ↑	Bleurt ↑
SalamandraTA-2B	sv	ca	34.4	52.6	59.96	0.86	0.82	0.73
nllb 3.3B	sv	ca	33.4	53.19	59.29	0.86	0.83	0.74

SalamandraTA-2B	sl	ca	29.12	59.26	56.56	0.85	0.8	0.71
nllb 3.3B	sl	ca	28.23	60.61	55.34	0.85	0.82	0.72

SalamandraTA-2B	sk	ca	30.71	57.99	57.81	0.85	0.8	0.72
nllb 3.3B	sk	ca	29.79	58.99	56.61	0.85	0.82	0.73

SalamandraTA-2B	ro	ca	34.79	53.37	61.22	0.87	0.83	0.75
nllb 3.3B	ro	ca	33.53	54.36	60.18	0.87	0.84	0.75

SalamandraTA-2B	pt	ca	36.72	50.64	62.08	0.87	0.84	0.76
nllb 3.3B	pt	ca	36.11	50.96	61.33	0.87	0.84	0.76

SalamandraTA-2B	pl	ca	25.62	64.15	53.55	0.85	0.81	0.71
nllb 3.3B	pl	ca	25.14	64.43	53.09	0.85	0.83	0.71

SalamandraTA-2B	nl	ca	26.17	63.88	54.01	0.84	0.83	0.7
nllb 3.3B	nl	ca	25.61	64.26	53.43	0.84	0.85	0.71

SalamandraTA-2B	mt	ca	36.97	50.43	62.69	0.79	0.68	0.75
nllb 3.3B	mt	ca	36.03	51.51	61.46	0.79	0.69	0.74

SalamandraTA-2B	lv	ca	27.81	61.96	56.12	0.84	0.77	0.7
nllb 3.3B	lv	ca	26.83	63.33	53.93	0.84	0.78	0.7

SalamandraTA-2B	lt	ca	27.29	61.15	54.14	0.84	0.75	0.7
nllb 3.3B	lt	ca	26.13	62.2	53.17	0.84	0.77	0.7

SalamandraTA-2B	it	ca	29.12	60.95	57.85	0.87	0.85	0.74
nllb 3.3B	it	ca	28.06	61.81	57.06	0.87	0.85	0.74

SalamandraTA-2B	hu	ca	28.21	60.54	55.38	0.85	0.81	0.71
nllb 3.3B	hu	ca	27.58	60.77	54.76	0.85	0.83	0.72

SalamandraTA-2B	hr	ca	30.13	57.59	57.25	0.86	0.81	0.72
nllb 3.3B	hr	ca	29.15	62.59	56.04	0.86	0.83	0.72

nllb 3.3B	gl	ca	34.23	53.25	61.28	0.88	0.85	0.76
SalamandraTA-2B	gl	ca	32.09	54.77	60.42	0.87	0.84	0.75

SalamandraTA-2B	ga	ca	28.11	62.93	55.28	0.8	0.68	0.67
nllb 3.3B	ga	ca	27.73	62.91	53.93	0.79	0.69	0.66

SalamandraTA-2B	fr	ca	35.87	52.28	61.2	0.87	0.83	0.75
nllb 3.3B	fr	ca	34.42	53.05	60.31	0.87	0.84	0.76

SalamandraTA-2B	fi	ca	27.35	61.33	54.95	0.86	0.8	0.7
nllb 3.3B	fi	ca	27.04	62.35	54.48	0.86	0.81	0.71

SalamandraTA-2B	eu	ca	28.02	60.45	55.44	0.87	0.82	0.73
nllb 3.3B	eu	ca	26.68	62.62	54.22	0.86	0.82	0.71

SalamandraTA-2B	et	ca	29.84	58.79	56.74	0.86	0.78	0.72
nllb 3.3B	et	ca	28.43	60.01	55.48	0.86	0.79	0.72

nllb 3.3B	es	ca	25.64	64.21	55.18	0.87	0.85	0.73
SalamandraTA-2B	es	ca	23.47	66.71	54.05	0.86	0.84	0.72

SalamandraTA-2B	en	ca	43.98	42.35	67.3	0.87	0.85	0.77
nllb 3.3B	en	ca	43.24	43.37	66.58	0.88	0.85	0.78

SalamandraTA-2B	el	ca	28.91	59.86	55.26	0.85	0.83	0.71
nllb 3.3B	el	ca	28.46	60.28	55.13	0.85	0.84	0.72

SalamandraTA-2B	de	ca	33.71	54.06	59.79	0.86	0.83	0.74
nllb 3.3B	de	ca	32.71	54.91	58.91	0.86	0.84	0.74

SalamandraTA-2B	da	ca	35.14	52.51	60.81	0.86	0.82	0.74
nllb 3.3B	da	ca	34.03	53.41	59.46	0.86	0.83	0.75

SalamandraTA-2B	cs	ca	31.12	56.71	58.22	0.86	0.81	0.73
nllb 3.3B	cs	ca	29.26	58.38	56.53	0.86	0.82	0.73

SalamandraTA-2B	bg	ca	31.33	56.72	58.75	0.85	0.84	0.73
nllb 3.3B	bg	ca	30.5	57.03	57.92	0.85	0.85	0.73

Evaluation Aranese, Aragonese, Asturian

Using MT Lens we evaluate Spanish-Asturian (ast), Spanish-Aragonese (an) and Spanish-Aranese (arn) on BLEU and ChrF scores on the Flores+ dev evaluation dataset. We also report BLEU and ChrF scores for catalan directions.

Asturian Flores+ dev

Below are the evaluation results compared to Apertium, Eslema and NLLB (Costa-jussà et al., 2022).

	source	target	Bleu	ChrF
nllb 3.3B	es	ast	18.78	50.5
Eslema	es	ast	17.30	50.77
nllb 600M	es	ast	17.23	49.72
SalamandraTA-2B	es	ast	17.11	49.49
Apertium	es	ast	16.66	50.57


nllb 3.3B	ca	ast	25.87	54.9
SalamandraTA-2B	ca	ast	25.17	55.17

Aragonese Flores+ dev

Below are the evaluation results on compared to Apertium, Softcatalà and Traduze.

	source	target	Bleu	ChrF
Apertium	es	an	65.34	82.00
Softcatalà	es	an	50.21	73.97
SalamandraTA-2B	es	an	49.13	74.22
Traduze	es	an	37.43	69.51


SalamandraTA-2B	ca	an	17.06	49.12

Aranese Flores+ dev

Below are the evaluation results on compared to Apertium and Softcatalà.

	source	target	Bleu	ChrF
Apertium	es	arn	48.96	72.63
Softcatalà	es	arn	34.43	58.61
SalamandraTA-2B	es	arn	34.35	57.78


SalamandraTA-2B	ca	arn	21.95	48.67

Ethical Considerations and Limitations

Detailed information on the work done to examine the presence of unwanted social and cognitive biases in the base model can be found at Salamandra-2B model card. With regard to MT models, no specific analysis has yet been carried out in order to evaluate potential biases or limitations in translation accuracy across different languages, dialects, or domains. However, we recognize the importance of identifying and addressing any harmful stereotypes, cultural inaccuracies, or systematic performance discrepancies that may arise in Machine Translation. As such, we plan to perform more analyses as soon as we have implemented the necessary metrics and methods within our evaluation framework MT Lens.

Additional information

Author

The Language Technologies Unit from Barcelona Supercomputing Center.

Contact

For further information, please send an email to [email protected].

Copyright

Funding

This work has been promoted and financed by the Government of Catalonia through the Aina Project.

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of ILENIA Project with reference 2022/TL22/00215337.

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, they bear the responsibility for mitigating any associated risks and ensuring compliance with applicable regulations, including those governing the use of Artificial Intelligence.

The Barcelona Supercomputing Center, as the owner and creator of the model, shall not be held liable for any outcomes resulting from third-party use.

License

Apache License, Version 2.0