Instructions to use Flaglab/SciBETO-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flaglab/SciBETO-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Flaglab/SciBETO-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Flaglab/SciBETO-base") model = AutoModelForMaskedLM.from_pretrained("Flaglab/SciBETO-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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@@ -42,11 +42,6 @@ The pretraining corpus includes over **11 billion tokens** from Spanish academic
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- Open-access repositories of Colombian universities (Universidad de los Andes, Universidad Nacional, Universidad Javeriana, Universidad del Rosario).
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- CORE API and institutional repositories (theses, dissertations, working papers).
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- Tax Statutes in Colombia
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- Filtered and cleaned with:
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- Language detection using FastText,
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- Deduplication,
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- Abstract and reference removal,
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- Normalization of encoding and special symbols.
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The final dataset covers multiple disciplines (economics, medicine, engineering, humanities), ensuring representation across scientific domains.
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- Open-access repositories of Colombian universities (Universidad de los Andes, Universidad Nacional, Universidad Javeriana, Universidad del Rosario).
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- CORE API and institutional repositories (theses, dissertations, working papers).
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- Tax Statutes in Colombia
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The final dataset covers multiple disciplines (economics, medicine, engineering, humanities), ensuring representation across scientific domains.
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