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Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).
| Date | Stars |
|---|---|
| 2026-07-31 | 265 |
| 2026-08-04 | 265 |
| 2026-08-06 | 265 |
Today
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Momentum
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growth rate 0.00%/day
# Spanish Language Models 💃🏻
A repository part of the MarIA project.
## Corpora 📃
| Corpora | Number of documents | Number of tokens | Size (GB) |
|---------|---------------------|------------------|-----------|
| BNE | 201,080,084 | 135,733,450,668 | 570GB |
## Models 🤖
- ✨ <b>new</b> ✨ Ǎguila-7B: https://huggingface.co/projecte-aina/aguila-7b
A 7B parameters LLM that has been trained on a mixture of Spanish, Catalan and English data, adding up to a total of 26B tokens. It uses the [Falcon-7b](https://huggingface.co/tiiuae/falcon-7b) model as a starting point, a state-of-the-art English language model that was openly released just a few months ago by the Technology Innovation Institute. Read more [here](https://medium.com/@mpamies247/introducing-a%CC%8Cguila-a-new-open-source-llm-for-spanish-and-catalan-ee1ebc70bc79)
- RoBERTa-base BNE: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne
- RoBERTa-large BNE: https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne
Transformer-based masked language models for the Spanish language. They are based on the RoBERTa large model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
- longformer-base-4096-bne-es: https://huggingface.co/PlanTL-GOB-ES/longformer-base-4096-bne-es
The Longformer version of the roberta-base-ca-v2 masked language model for the Catalan language. The use of these models allows us to process larger contexts (up to 4096 tokens) as input without the need of additional aggregation strategies. The pretraining process of this model started from the roberta-base-ca-v2 checkpoint and was pretrained for MLM on both short and long documents in Catalan.
- GPT2-base BNE: https://huggingface.co/PlanTL-GOB-ES/gpt2-base-bne
- GPT2-large BNE: https://huggingface.co/PlanTL-GOB-ES/gpt2-large-bne
Transformer-based model for the Spanish language. They are based on the GPT-2 model and has been pre-trained using the largest Spanish corpus known to date, with a total of 570GB of clean and deduplicated text processed for this work, compiled from the web crawlings performed by the National Library of Spain (Biblioteca Nacional de España) from 2009 to 2019.
See results achieved on several tasks below. _Vegeu els resultats obtinguts en diverses tasques més avall_.
## Usage example ⚗️
For the RoBERTa-base
```python
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-base-bne')
model = AutoModelForMaskedLM.from_pretrained('PlanTL-GOB-ES/roberta-base-bne')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"¡Hola <mask>!"
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
```
For the RoBERTa-large
```python
from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, FillMaskPipeline
from pprint import pprint
tokenizer_hf = AutoTokenizer.from_pretrained('PlanTL-GOB-ES/roberta-large-bne')
model = AutoModelForMaskedLM.from_pretrained('PlanTL-GOB-ES/roberta-large-bne')
model.eval()
pipeline = FillMaskPipeline(model, tokenizer_hf)
text = f"¡Hola <mask>!"
res_hf = pipeline(text)
pprint([r['token_str'] for r in res_hf])
```
## Fine-tunned models 🧗🏼♀️🏇🏼🤽🏼♀️🏌🏼♂️🏄🏼♀️
- RoBERTa-base-BNE for Capitel-POS: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-pos
- RoBERTa-large-BNE for Capitel-POS: https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel-pos
- RoBERTa-base-BNE for Capitel-NER: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner
- RoBERTa-base-BNE for Capitel-NER: https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus (**very robust**)
- RoBERTa-larExcerpt of 11,884 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9bf1550e9d7d5e85, topic:language-model
matched fp:9bf1550e9d7d5e85, topic:embeddings
matched fp:9bf1550e9d7d5e85, topic:nlp