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--- |
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language: |
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- pt |
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library_name: adapter-transformers |
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--- |
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# NER of medications |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model aims to demonstrate an extraction of entities from from medical texts. It gets the name of the doctor, his registration code (CRM), the substance and the dose |
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prescribed in Pt_BR. |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Nilton Seixas] |
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- **Language(s) (NLP):** [Brazilian portuguese] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [neuralmind/bert-large-portuguese-cased] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [niltonseixas/NER] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("niltonseixas/NER_tokenizer") |
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model = AutoModelForTokenClassification.from_pretrained("niltonseixas/NER") |
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nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy = "average") |
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example = "dra. Nayara Barbosa, CRM 12345 receitou Amoxilina 50 mg" |
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ner_results = nlp(example) |
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print(ner_results) |
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