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6 changes: 4 additions & 2 deletions tutorials/text/README.md
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Expand Up @@ -11,11 +11,13 @@ Hands-on tutorials for curating text data with NeMo Curator. Complete working ex
| Tutorial | Description | Files |
|----------|-------------|-------|
| **[Download & Extract](download-and-extract/)** | Data acquisition workflows | `download_extract_tutorial.ipynb` |
| **[Deduplication](deduplication/)** | Remove duplicate content | `semantic_e2e.ipynb`, `semantic_step_by_step.ipynb` |
| **[Classification](distributed-data-classification/)** | Quality assessment and categorization | `quality-classification.ipynb`, `domain-classification.ipynb`, `aegis-classification.ipynb`, `fineweb-edu-classification.ipynb` |
| **[Deduplication](deduplication/)** | Remove duplicate content | Fuzzy and semantic deduplication notebooks |
| **[Classification](distributed-data-classification/)** | Quality assessment and categorization | `quality-classification.ipynb`, `domain-classification.ipynb`, `fineweb-edu-classification.ipynb`, and more |
| **[PEFT Curation](peft-curation/)** | Instruction-tuning data preparation | `main.py`, `stages.py` |
| **[TinyStories](tinystories/)** | End-to-end processing pipeline | `main.py`, `stages.py` |
| **[Megatron Tokenizing](megatron-tokenizer/)** | Tokenization pipeline that produces Megatron-LM ready files | `main.py` |
| **[Llama Nemotron Data Curation](llama-nemotron-data-curation/)** | Data curation on the Llama Nemotron Post-Training Dataset | `main.py` and helper files |
| **[GLiNER-based PII Redaction](gliner-pii-redaction/)** | Redacting personally identifiable information with NVIDIA's GLiNER-PII model | `gliner_pii_redaction.ipynb` |

## Documentation Links

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2 changes: 2 additions & 0 deletions tutorials/text/gliner-pii-redaction/README.md
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Expand Up @@ -13,3 +13,5 @@ Additionally, the tutorial uses the [GLiNER](https://github.com/urchade/GLiNER)
```bash
uv pip install gliner
```

For reference, this tutorial was tested with `gliner==0.2.24`.
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Expand Up @@ -26,6 +26,13 @@
"# !uv pip install gliner"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For reference, this tutorial was tested with `gliner==0.2.24`."
]
},
{
"cell_type": "code",
"execution_count": 2,
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2 changes: 1 addition & 1 deletion tutorials/text/gliner-pii-redaction/gliner_pii_redactor.py
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Expand Up @@ -164,7 +164,7 @@ def process(self, batch: DocumentBatch) -> DocumentBatch | None:

# Run model inference via the GLiNER library
# This returns a list of dictionaries per document, each containing the entities and their confidence scores
entities = self.model.run(
entities = self.model.inference(
df[self.text_field].tolist(),
self.labels,
threshold=self.threshold,
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