Instructions to use mschwab/va_bert_tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mschwab/va_bert_tagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mschwab/va_bert_tagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mschwab/va_bert_tagger") model = AutoModelForTokenClassification.from_pretrained("mschwab/va_bert_tagger") - Notebooks
- Google Colab
- Kaggle
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## English Vossian Antonomasia Sequence Tagger
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This page presents a fine-tuned [BERT-base-cased](https://huggingface.co/bert-base-cased) language model for tagging Vossian Antonomasia expressions in text on word-level.
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### Dataset
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## English Vossian Antonomasia Sequence Tagger
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This page presents a fine-tuned [BERT-base-cased](https://huggingface.co/bert-base-cased) language model for tagging Vossian Antonomasia expressions in text on word-level.
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The tags {B,I}-SRC refers to the source chunks, {B,I}-MOD to the modifier chunks and {B,I}-TRG to the target chunks if existing. We used the IOB tagging format.
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### Dataset
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