Datasets:
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
text: string
source: string
-- schema metadata --
huggingface: '{"info": {"features": {"text": {"dtype": "string", "_type":' + 61
to
{'text': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1975, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 106, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 73, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
source: string
-- schema metadata --
huggingface: '{"info": {"features": {"text": {"dtype": "string", "_type":' + 61
to
{'text': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
NBR-500 Corpus
🇧🇷 Corpus de pré-treinamento para o modelo NBR-500 - Um Small Language Model de 500M parâmetros otimizado para Português Brasileiro.
📊 Estatísticas
| Métrica | Valor |
|---|---|
| Documentos | 3.89M |
| Tokens | ~1.5B |
| Idioma | Português Brasileiro |
| Formato | Parquet |
🔍 Pipeline de Processamento
O dataset passou por um rigoroso pipeline de qualidade baseado no SmolLM Training Playbook:
Filtragem de Qualidade
- Remoção de textos curtos (< 100 caracteres)
- Remoção de conteúdo repetitivo
- Filtragem de spam e baixa qualidade
Detecção de Idioma
- FastText LID para garantir 100% português
- Threshold de confiança > 0.8
Deduplicação
- MinHash LSH (datasketch)
- Remoção de near-duplicates
📁 Fontes
- Wikipedia PT-BR
- CulturaX Portuguese
- OSCAR Portuguese
- Outros corpora brasileiros
🚀 Uso
from datasets import load_dataset
dataset = load_dataset("limajr/nbr-500-corpus", split="train")
for example in dataset:
print(example["text"][:200])
break
🎯 Propósito
Este corpus foi criado especificamente para treinar o NBR-500, um modelo de linguagem pequeno e eficiente para:
- ✅ Execução em dispositivos de borda (Edge AI)
- ✅ Aplicações em Português Brasileiro
- ✅ Baixa latência e consumo de memória
- ✅ Quantização para GGUF (Q4, Q8)
📦 Modelo Relacionado
- Modelo: limajr/nbr-500
- Tokenizer: BPE nativo com 32k vocabulário (46% mais eficiente que GPT-2 para PT-BR)
📜 Licença
Apache 2.0
🙏 Créditos
Baseado nas práticas do SmolLM Training Playbook da HuggingFace.
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