Instructions to use rwillh11/base-mdbertav3-token-classification-groups-bilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rwillh11/base-mdbertav3-token-classification-groups-bilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rwillh11/base-mdbertav3-token-classification-groups-bilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rwillh11/base-mdbertav3-token-classification-groups-bilingual") model = AutoModelForTokenClassification.from_pretrained("rwillh11/base-mdbertav3-token-classification-groups-bilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Accuracy (token level) . 988
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Balacned Accuracy (token level) .937
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A model to detect the mention of social groups in political texts and speech. Finetuned by Will Horne, Alona Dolinsky and Lena Huber.
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Model Type: MdeBERTa-V3
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Metrics:
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Accuracy (token level) . 988
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Balacned Accuracy (token level) .937
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