Instructions to use aksw/text2sparql-L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aksw/text2sparql-L with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aksw/text2sparql-L", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use aksw/text2sparql-L with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aksw/text2sparql-L to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aksw/text2sparql-L to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aksw/text2sparql-L to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="aksw/text2sparql-L", max_seq_length=2048, )
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README.md
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### 馃И Evaluation
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The model was evaluated using F1-score on a hand-crafted dataset for the First
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```
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@misc{text2sparql2025,
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author = {Marcos G么lo, Paulo do Carmo, Edgard Marx, Ricardo Marcacini},
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title = {
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year = {2025},
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howpublished = {\url{https://huggingface.co/aksw/text2sparql-L}},
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}
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### 馃И Evaluation
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The model was evaluated using F1-score on a hand-crafted dataset for the First Text2SPARQL Challenge, a Co-Located with Text2KG at ESWC25.
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---
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```
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@misc{text2sparql2025,
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author = {Marcos G么lo, Paulo do Carmo, Edgard Marx, Ricardo Marcacini},
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title = {text2SPARQL-L: Natural Language Text to SPARQL for DBpedia},
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year = {2025},
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howpublished = {\url{https://huggingface.co/aksw/text2sparql-L}},
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}
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