Instructions to use Inria-CEDAR/FactSpotter-DeBERTaV3-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Inria-CEDAR/FactSpotter-DeBERTaV3-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Inria-CEDAR/FactSpotter-DeBERTaV3-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Large") model = AutoModelForSequenceClassification.from_pretrained("Inria-CEDAR/FactSpotter-DeBERTaV3-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- text: "Bourg-la-Reine is located in France and I love this town. I'm from People's Republic of China. [SEP] A Chinese, Loves, Bourg-la-Reine"
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# Model card for Inria-CEDAR/FactSpotter-DeBERTaV3-Large
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Given a triple of format "subject, predicate, object" and a text, the model determines if the triple is present in the text.
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Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3.
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We also provide Base and Small models
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- en
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widget:
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- text: "Bourg-la-Reine is located in France and I love this town. I'm from People's Republic of China. [SEP] A Chinese, Loves, Bourg-la-Reine"
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- text: "Bucharest is a city in Romania. [SEP] Romania | is located in | Bucharest"
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# Model card for Inria-CEDAR/FactSpotter-DeBERTaV3-Large
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Given a triple of format "subject, predicate, object" and a text, the model determines if the triple is present in the text.
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The delimiter can be ", " or " | ".
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Different from the paper using ELECTRA, this model is finetuned on DeBERTaV3.
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We also provide Base and Small models
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