spyysalo/bc2gm_corpus
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How to use commanderstrife/bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="commanderstrife/bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("commanderstrife/bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("commanderstrife/bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner")This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the bc2gm_corpus dataset. 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 |
|---|---|---|---|---|---|---|---|
| 0.0981 | 1.0 | 782 | 0.0712 | 0.7228 | 0.7948 | 0.7571 | 0.9724 |
| 0.0509 | 2.0 | 1564 | 0.0687 | 0.7472 | 0.8199 | 0.7818 | 0.9746 |
| 0.0121 | 3.0 | 2346 | 0.0740 | 0.7725 | 0.8011 | 0.7866 | 0.9747 |
| 0.0001 | 4.0 | 3128 | 0.1009 | 0.7618 | 0.8251 | 0.7922 | 0.9741 |
| 0.0042 | 5.0 | 3910 | 0.1106 | 0.7757 | 0.8185 | 0.7965 | 0.9754 |
| 0.0015 | 6.0 | 4692 | 0.1182 | 0.7812 | 0.8111 | 0.7958 | 0.9758 |
| 0.0001 | 7.0 | 5474 | 0.1283 | 0.7693 | 0.8275 | 0.7973 | 0.9753 |
| 0.0072 | 8.0 | 6256 | 0.1376 | 0.7863 | 0.8158 | 0.8008 | 0.9762 |
| 0.0045 | 9.0 | 7038 | 0.1468 | 0.7856 | 0.8180 | 0.8015 | 0.9761 |
| 0.0 | 10.0 | 7820 | 0.1505 | 0.7854 | 0.8158 | 0.8003 | 0.9759 |