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--- |
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inference: False |
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license: apache-2.0 |
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language: |
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- pt |
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metrics: |
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- f1 |
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pipeline_tag: token-classification |
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datasets: |
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- harem |
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--- |
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# Portuguese NER BERT-CRF HAREM Default |
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This model is a fine-tuned BERT model adapted for Named Entity Recognition (NER) tasks. It utilizes Conditional Random Fields (CRF) as the decoder. |
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The model follows the HAREM Selective labeling scheme for NER. Additionally, it provides options for HAREM Default and Conll-2003 labeling schemes. |
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## How to Use |
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You can employ this model using the Transformers library's *pipeline* for NER, or incorporate it as a conventional Transformer in the HuggingFace ecosystem. |
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```python |
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from transformers import pipeline |
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import torch |
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import nltk |
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ner_classifier = pipeline( |
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"ner", |
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model="arubenruben/NER-PT-BERT-CRF-HAREM-Selective", |
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device=torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu"), |
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trust_remote_code=True |
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) |
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text = "FCPorto vence o Benfica por 5-0 no Estádio do Dragão" |
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tokens = nltk.wordpunct_tokenize(text) |
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result = ner_classifier(tokens) |
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``` |
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## Demo |
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There is a [Notebook](https://github.com/arubenruben/PT-Pump-Up/blob/master/BERT-CRF.ipynb) available to test our code. |
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## PT-Pump-Up |
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This model is integrated in the project [PT-Pump-Up](https://github.com/arubenruben/PT-Pump-Up) |
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## Evaluation |
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#### Testing Data |
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The model was tested on the Miniharem Testset. |
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### Results |
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F1-Score: 0.832 |
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## Citation |
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Citation will be made available soon. |
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**BibTeX:** |
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:( |
