BERT Named Entity Recognition - n2c2 2018
Collection
8 items โข Updated
How to use pabRomero/BioClinicalBERT-full-finetuned-ner-pablo with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="pabRomero/BioClinicalBERT-full-finetuned-ner-pablo") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("pabRomero/BioClinicalBERT-full-finetuned-ner-pablo")
model = AutoModelForTokenClassification.from_pretrained("pabRomero/BioClinicalBERT-full-finetuned-ner-pablo")This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 231 | 0.0943 | 0.7464 | 0.7612 | 0.7537 | 0.9720 |
| No log | 2.0 | 462 | 0.0801 | 0.7861 | 0.7821 | 0.7841 | 0.9750 |
| 0.2571 | 3.0 | 693 | 0.0806 | 0.7900 | 0.7911 | 0.7906 | 0.9748 |
| 0.2571 | 4.0 | 924 | 0.0834 | 0.7938 | 0.7935 | 0.7936 | 0.9750 |
Base model
emilyalsentzer/Bio_ClinicalBERT