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--- |
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language: |
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- en |
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tags: |
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- part-of-speech |
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- finetuned |
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license: cc-by-nc-3.0 |
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--- |
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# BERT-base-multilingual-cased finetuned for Part-of-Speech tagging |
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This is a multilingual BERT model fine tuned for part-of-speech tagging for English. It is trained using the Penn TreeBank (Marcus et al., 1993) and achieves an F1-score of 96.69. |
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## Usage |
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A *transformers* pipeline can be used to run the model: |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline |
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model_name = "QCRI/bert-base-multilingual-cased-pos-english" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForTokenClassification.from_pretrained(model_name) |
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pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer) |
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outputs = pipeline("A test example") |
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print(outputs) |
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``` |
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## Citation |
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This model was used for all the part-of-speech tagging based results in *Analyzing Encoded Concepts in Transformer Language Models*, published at NAACL'22. If you find this model useful for your own work, please use the following citation: |
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```bib |
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@inproceedings{sajjad-NAACL, |
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title={Analyzing Encoded Concepts in Transformer Language Models}, |
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author={Hassan Sajjad, Nadir Durrani, Fahim Dalvi, Firoj Alam, Abdul Rafae Khan and Jia Xu}, |
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booktitle={North American Chapter of the Association of Computational Linguistics: Human Language Technologies (NAACL)}, |
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series={NAACL~'22}, |
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year={2022}, |
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address={Seattle} |
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} |
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``` |
