t5_bg_1B_uncased

Model Card for Model ID

T5 model trained on Bulgarian literature, Web, Parallel English-Bulgarian texts, Bulgarian and English Wikipedia, and other datasets - uncased.

Model Details

1.1B parameter T5 model trained on 35B (41B depending on tokenization) tokens for 3 epochs with T5 Span Corruption objective.

• Tokenizer vocabulary size is 50176.

• Model hidden dimension is 11536.

• Feed-Forward dimension is 6144.

• Hidden layer count is 16 for both the encoder and the decoder.

• Developed by: Artificial Inteligence and Language Technologies Department at Institute of Information and Communication Technologies - Bulgarian Academy of Sciences.

• Funded by: The model is pretrained within the CLaDA-BG: National Interdisciplinary Research E-Infrastructure for Bulgarian Language and Cultural heritage - member of the pan-European research consortia CLARIN-ERIC & DARIAH-ERIC, funded by the Ministry of Education and Science of Bulgaria (support for the Bulgarian National Roadmap for Research Infrastructure). The training was performed at the supercomputer HEMUS at IICT-BAS, part of the RIs of the CoE on Informatics and ICT, financed by the OP SESG (2014–2020), and co-financed by the European Union through the ESIF.

• Model type: T5

• Language(s) (NLP): Bulgarian.

• License: MIT

Uses

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The model is intended to be used as a base model for fine-tuning tasks in NLP.

Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

>>> import torch
>>> from transformers import (
>>>     T5ForConditionalGeneration,
>>>     PreTrainedTokenizerFast
>>> )

>>> model = T5ForConditionalGeneration.from_pretrained('AIaLT-IICT/t5_bg_1B_uncased')
>>> tokenizer = PreTrainedTokenizerFast.from_pretrained('AIaLT-IICT/t5_bg_1B_uncased')

>>> prompt = "Събудих се след[SEN_0] и отидох да си купя[SEN_1]."

>>> model_inputs = tokenizer([prompt], return_tensors="pt", add_special_tokens=True, return_token_type_ids=False)
>>> generated_ids = model.generate(**model_inputs)
>>> tokenizer.decode(generated_ids[0])

'[CLS][SEN_0] тежък ден[SEN_1] сладолед[SEN_2][SEP]'

Out-of-Scope Use

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The model is trained on span corruption task. If you want to use the model for any other type of text generation it is recommended to fine-tune it.

Recommendations

It is recommended to use the model for text generation fine-tuning tasks. The encoder of the model alone can be used for text and token classification.

Training Details

Training Data

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Trained on 29B tokens consisting of deduplicated union of:

• uonlp/CulturaX
• MaCoCu-bg 2.0
• HPLT 2.0 Bulgarian (Cyrillic) cleaned
• Literature
• Wikipedia
• others

Training Procedure

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Trained with the T5 Span Corruption objective with 25% noise density, 3 tokens mean noise span length for 3 epochs with bf16 mixed precision, 512 tokens input length and batch size of 256*512 tokens.

Evaluation

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The model is evaluated on the T5 Span Corruption objective that it was trained on. It achieves test loss of 1.38 and test accuracy of 71.50%

Cite

@inproceedings{simov-etal-2026-small,
  title = {Small Can Be Beautiful in LLMs for SSH: a Case for Bulgarian },
  author = {Simov, Kiril and Paev, Nikolay and Osenova, Petya and Valchev, Teodor and Marinov, Stefan},
  booktitle = {Proceedings of Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities (LLMs4SSH) @ LREC 2026},
  month = {May},
  year = {2026},
  pages = {187--197},
  address = {Palma, Mallorca, Spain},
  publisher = {European Language Resources Association (ELRA)},
  editor = {Montejo-Raez, Arturo and Grisot, Cristina and Blochowiak, Joanna and Ljubešić, Nikola and Battaner, Elena and Rigau, German},
  doi = {10.63317/5n9bq7g3it2s},
  abstract = {In the paper we present a set of small LLM-based models for solving the basic NLP tasks for Bulgarian - POS tagging, Lemmatization, Dependency parsing, Named Entity Recognition, Named Entity Linking, Event Annotation, among others. In order to create fine-tuned models for these tasks, we first pre-train models using architectures like BERT, Modern-BERT, and T5 with different sizes, over Bulgarian data only. For each of the tasks we report our approach towards the fine-tuning, the results from the experiments and also the evaluation. Then we define a way to visualize the results over HTML documents which contain the analyzed texts. Our rationale are as follows: most, if not all SSH research scenarios, need a reliable processing chains that can be customized with respect to the specific needs. These scenarios also would need proper visualization for human observation. We aim to provide such a basic LLM-based toolkit.}
}

Model Card Authors

Nikolay Paev, Kiril Simov

Model Card Contact

[email protected]