320 lines
18 KiB
Markdown
320 lines
18 KiB
Markdown
---
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license: apache-2.0
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language:
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- ca
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- es
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- en
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base_model: BSC-LT/salamandra-2b
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tags:
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- valencian
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- catalan
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- spanish
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- english
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- text-generation
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- alia
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- gplsi
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datasets:
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- gplsi/alia_dogv
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- gplsi/alia_les_corts
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- gplsi/alia_amic
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- gplsi/alia_boua
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- gplsi/alia_tourism
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library_name: transformers
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pipeline_tag: text-generation
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---
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# Aitana-2B-S-base-1.0
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**Aitana-2B-S-base-1.0** is a generative language model from the **Aitana family**, developed by the [GPLSI (Language and Information System Group)](https://gplsi.dlsi.ua.es/) at the University of Alicante. This model is based on [BSC-LT/salamandra-2b](https://huggingface.co/BSC-LT/salamandra-2b) and has been continuously pre-trained on multilingual data (Valencian, Spanish, and English) to improve representation of Valencian and Catalan languages.
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## Table of Contents
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- [Model Description](#model-description)
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- [Evaluation](#evaluation)
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- [Training Data](#training-data)
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- [Intended Uses](#intended-uses)
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- [How to Use](#how-to-use)
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- [GGUF for LM Studio](#gguf-for-lm-studio)
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- [Additional Information](#additional-information)
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## Model Description
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| Property | Value |
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|----------|-------|
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| **Base Model** | [BSC-LT/salamandra-2b](https://huggingface.co/BSC-LT/salamandra-2b) |
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| **Architecture** | Transformer decoder-only |
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| **Parameters** | ~2.25B |
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| **Languages** | Valencian, Spanish, English |
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| **License** | Apache 2.0 |
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Aitana-2B-S-base-1.0 extends the multilingual Salamandra foundation with additional training on domain-specific Valencian, Spanish, and English data. The training emphasizes administrative, legal, and tourism domains.
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## Training Data
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This model was trained on the following ALIA datasets:
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| Dataset ID | Name | Language | Source |
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|------------|------|----------|--------|
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| dc8 | dogv_va_2025 | Valencian | [gplsi/alia_dogv](https://huggingface.co/datasets/gplsi/alia_dogv) |
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| dc9 | dogv_es_2025 | Spanish | [gplsi/alia_dogv](https://huggingface.co/datasets/gplsi/alia_dogv) |
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| dc10 | corts_es_va_2025 | Spanish/Valencian | [gplsi/alia_les_corts](https://huggingface.co/datasets/gplsi/alia_les_corts) |
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| dc11 | amic_va_2025 | Valencian | [gplsi/alia_amic](https://huggingface.co/datasets/gplsi/alia_amic) |
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| dc12 | boua_va_2025 | Valencian | [gplsi/alia_boua](https://huggingface.co/datasets/gplsi/alia_boua) |
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| dc13 | boua_es_2025 | Spanish | [gplsi/alia_boua](https://huggingface.co/datasets/gplsi/alia_boua) |
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| dc14 | tourism_va_2025 | Valencian | [gplsi/alia_tourism](https://huggingface.co/datasets/gplsi/alia_tourism) |
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| dc15 | tourism_es_2025 | Spanish | [gplsi/alia_tourism](https://huggingface.co/datasets/gplsi/alia_tourism) |
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| dc16 | tourism_en_2025 | English | [gplsi/alia_tourism](https://huggingface.co/datasets/gplsi/alia_tourism) |
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### Data Sources
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- **DOGV (Diari Oficial de la Generalitat Valenciana)**: Official communications of the Valencian Community including laws and public sector communications
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- **Les Corts Valencianes**: Transcripts from the Valencian Parliament plenary sessions and committee meetings
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- **AMIC**: Valencian language corpus
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- **BOUA (Butlletí Oficial de la Universitat d'Alacant)**: Official University of Alicante documents including grants, regulations, and resolutions
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- **Tourism**: Multilingual tourism domain content
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## Intended Uses
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This model can be used for:
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- **Text generation** in Valencian, Spanish, and English
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- **Fine-tuning** for specific downstream tasks
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- **Domain adaptation** for administrative, legal, or tourism applications
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> **Note**: Due to the formal register of training data (administrative and legal domains), generated text tends toward formal language.
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## How to Use
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### Transformers
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```python
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import torch
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from transformers import pipeline, AutoTokenizer
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model_id = "gplsi/Aitana-2B-S-base-1.0"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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generator = pipeline(
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"text-generation",
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model=model_id,
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tokenizer=tokenizer,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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# Valencian example
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text = "Les corts valencianes han pres la decisió de"
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result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
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print(result[0]['generated_text'])
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# Spanish example
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text = "El turismo en la Comunidad Valenciana"
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result = generator(text, do_sample=True, top_k=10, max_new_tokens=100)
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print(result[0]['generated_text'])
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```
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## GGUF for LM Studio
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This repository includes GGUF quantized versions for use with [LM Studio](https://lmstudio.ai/), [Ollama](https://ollama.ai/), and other llama.cpp-based tools.
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| File | Quantization | Size | Quality |
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|------|--------------|------|---------|
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| `Aitana-s2b-c0dc17-Q4_K_M.gguf` | Q4_K_M | ~1.3 GB | Good balance |
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| `Aitana-s2b-c0dc17-f16.gguf` | F16 | ~4.5 GB | Full precision |
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### Using with llama-cpp-python
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="gplsi/Aitana-2B-S-base-1.0",
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filename="Aitana-s2b-c0dc17-Q4_K_M.gguf",
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)
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output = llm("Les corts valencianes han decidit", max_tokens=100)
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print(output["choices"][0]["text"])
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```
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## Evaluation
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In the following table, we can see the results obtained with different benchmarks from [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) in comparison with the model used for continuous pre-training.
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The results have been obtained from the model pre-trained; no instruction tuning or fine-tuning of any kind has been performed.
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### Normalized score per language
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| Language |Salamandra 2B| Aitana-2B-S-base-1.0 |
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|----------|----------|----------|
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| Spanish | 0.150 |**0.163**|
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| Catalan | **0.224** | 0.220 |
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| English | **0.168** | 0.161 |
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| Valencian | 0.603 | **0.608**|
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### Valencian
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#### Classification Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
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| XNLI | va |Natural Language Inference | acc | **0.475** | 0.474 |
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#### Generation Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
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| Cocoteros | va |Reading Comprehension | bleu | 6.32 | **6.61** |
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| Phrases ca-va | va-ca |Translation - Adaptation | bleu | 79.82 | **81.57** |
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| Phrases va-ca | va-ca |Translation - Adaptation | bleu | **78.05** | 75.68 |
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| Phrases va-es | va-es |Translation | bleu | 76.04 | **76.31** |
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| Phrases es-va | es-va |Translation | bleu | 58.86 | **62.86** |
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### Catalan
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#### Classification Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
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| Belebele Cat_latn | ca | Reading Comprehension | acc | 0.231 | **0.257** |
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| COPA | ca | Commonsense Reasoning | acc | **0.700** | 0.690 |
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| XStoryCloze | ca | Commonsense Reasoning | acc | 0.655 | 0.655 |
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| OpenBookQA | ca | Question Answering | acc | 0.294 | **0.300** |
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| PAWS | ca | Paraphrasing | acc | 0.556 | **0.566** |
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| PiQA | ca | Question Answering | acc | **0.643** | 0.641 |
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| SiQA | ca | Question Answering | acc | **0.434** | 0.425 |
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| ARC Easy | ca | Question Answering | acc | 0.551 | **0.553** |
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| ARC Challenge | ca | Question Answering | acc | **0.290** | 0.282 |
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| XNLI | ca | Natural Language Inference| acc | **0.473** | 0.469 |
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| Teca | ca | Natural Language Inference| acc | **0.465** | 0.430 |
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| WNLI | ca | Natural Language Inference| acc | 0.577 | 0.577 |
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| Catcola | ca | Linguistic Acceptability | acc | 0.543 | **0.596** |
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| Catcola | ca | Linguistic Acceptability | mcc | 0.046 | -0.002 |
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| Catalanqa | ca | Question Answering | F1 | **0.668** | 0.643 |
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| Mgsm direct | ca | Math | exact match | 0.024 | 0.024 |
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| Catalanqa | ca | Question Answering | exact match | **0.437** | 0.405 |
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| Xquad | ca | Question Answering | exact match | **0.371** | 0.344 |
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| Xquad | ca | Question Answering | F1 | **0.579** | 0.568 |
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#### Generation Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|--------------------------|--------|----------------|-----------------------|
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| Cabreu abstractive | ca | Summarization | bleu | 5.78 | **6.52** |
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| Cabreu extractive | ca | Summarization | bleu | **42.89** | 41.61 |
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| Cabreu extreme | ca | Summarization | bleu | **3.29** | 3.01 |
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### Spanish
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#### Classification Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|---------------------------|-------------|---------------|-----------------------|
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| Belebele | es | Reading Comprehension | acc | 0.228 | **0.263** |
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| PAWS | es | Paraphrasing | acc | **0.561** | 0.553 |
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| XNLI | es | Natural Language Inference| acc | **0.439** | 0.422 |
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| WNLI | es | Natural Language Inference| acc | 0.563 | 0.563 |
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| XStoryCloze | es | Commonsense Reasoning | acc | 0.653 | **0.655** |
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| Escola | es | Linguistic Acceptability | acc | 0.593 | **0.618** |
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| Escola | es | Linguistic Acceptability | mcc | **0.031** | -0.020 |
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| OpenbookQA | es | Question Answering | acc | 0.308 | **0.316** |
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| MGSM Direct | es | Math | exact match | 0.020 | **0.032** |
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| XQUAD | es | Question Answering | exact match | **0.377** | 0.341 |
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| XQUAD | es | Question Answering | F1 | **0.584** | 0.559 |
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#### Generation Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|---------------------|---------|----------------|-----------------------|
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| Cocoteros | es |Reading Comprehension| bleu | **8.46** | 7.043 |
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| XLSum | es | Summarization | bleu | 0.801 | **1.622** |
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### English
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#### Classification Benchmarks
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| Dataset | Lang. | Task | Metric | Salamandra-2B | Aitana-2B-S-base-1.0 |
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|------------------------------|--------|----------------------------|-------------|---------------|-----------------------|
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| Arc Challenge | en | Question Answering | acc | **0.370** | 0.360 |
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| Arc Easy | en | Question Answering | acc | **0.722** | 0.712 |
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| Belebele | en | Reading Comprehension | acc | 0.216 | **0.252** |
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| PAWS | en | Paraphrasing | acc | 0.561 | **0.574** |
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| XNLI | en | Natural Language Inference | acc | **0.462** | 0.452 |
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| XStoryCloze | en | Commonsense Reasoning | acc | 0.711 | **0.713** |
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| OpenBookQA | en | Question Answering | acc | **0.300** | 0.270 |
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| PiQA | en | Question Answering | acc | 0.737 | **0.742** |
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| Social iqa | en | Question Answering | acc | **0.454** | 0.450 |
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| WNLI | en | Natural Language Inference | acc | 0.465 | **0.380** |
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| MGSM Direct | en | Math | exact match | **0.064** | 0.06 |
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| TriviaQA | en | Question Answering | exact match | **0.376** | 0.352 |
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## Additional Information
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### Author
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The model has been developed by the **Language and [Information Systems Group (GPLSI)](https://gplsi.dlsi.ua.es/)** and the **[Centro de Inteligencia Digital (CENID)](https://cenid.es)**, both part of the **[University of Alicante (UA)](https://www.ua.es/es/)**, as part of their ongoing research in **Natural Language Processing (NLP)**.
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### Part of the Aitana Family
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This model is part of the Aitana model family, which includes:
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- [gplsi/Aitana-2B-S](https://huggingface.co/gplsi/Aitana-2B-S) - Valencian-focused base model
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- [gplsi/Aitana-TA-2B-S](https://huggingface.co/gplsi/Aitana-TA-2B-S) - Translation model (Spanish ↔ Valencian)
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### Funding
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This work is funded by the **Ministerio para la Transformación Digital y de la Función Pública**, co-financed by the **EU – NextGenerationEU**, within the framework of the project *Desarrollo de Modelos ALIA*.
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### Acknowledgments
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We would like to express our gratitude to all individuals and institutions that have contributed to the development of this work.
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Special thanks to:
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- [Language Technologies Laboratory at Barcelona Supercomputing Center](https://www.bsc.es/es/discover-bsc/organisation/research-structure/language-technologies-laboratory)
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- [Centro Vasco de Tecnología de la Lengua (HiTZ)](https://www.hitz.eus/es)
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- [Centro Singular de Investigación en Tecnologías Inteligentes (CiTIUS)](https://citius.gal/)
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- [Sistemas Inteligentes de Acceso a la Información (SINAI)](https://www.ujaen.es/investigacion-y-transferencia/grupos-de-investigacion/sistemas-inteligentes-de-acceso-la-informacion-sinai)
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- [Instituto Universitario de Investigación Informática (IUII)](https://web.ua.es/es/iuii/)
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- [Leonardo HPC System](https://leonardo-supercomputer.cineca.eu/)
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- [European supercomputing ecosystem (EUROHPC)](https://www.eurohpc-ju.europa.eu/)
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We also acknowledge the financial, technical, and scientific support of the **Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA**, whose contribution has been essential to the completion of this research.
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### License
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[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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### Disclaimer
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This model is intended for general purposes and is available under a permissive Apache License 2.0. Be aware that the model may have biases and/or undesirable outputs. Users deploying systems based on this model are responsible for mitigating risks and complying with applicable AI regulations.
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### Reference
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```bibtex
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@misc{gplsi-aitana-2B-S-base-1.0,
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author = {Estevanell-Valladares, Ernesto L. and Yáñez-Romero, Fabio and Sepúlveda-Torres, Robiert and Consuegra-Ayala, Juan Pablo and Galeano, Santiago and Miró Maestre, María and Martínez-Murillo, Iván and Grande, Eduardo and Canal-Esteve, Miquel and Bonora, Mar and Gutierrez, Yoan and Abreu Salas, José Ignacio and Lloret, Elena and Montoyo, Andrés and Muñoz-Guillena and Palomar, Manuel},
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title = {Aitana 2B base: Continually pre-trained on Valencian},
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year = {2025},
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institution = {Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID), University of Alicante (UA)},
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howpublished = {\url{https://huggingface.co/gplsi/gplsi/Aitana-2B-S-base-1.0}},
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note = {Accessed: 2025-12-12}
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}
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```
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---
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**Copyright © 2025 Language and Information Systems Group (GPLSI) and Centro de Inteligencia Digital (CENID),
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University of Alicante (UA).
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Distributed under the Apache License 2.0.**
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