ncbi/ncbi_disease
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How to use chintagunta85/electramed-small-NCBI-ner with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="chintagunta85/electramed-small-NCBI-ner") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("chintagunta85/electramed-small-NCBI-ner")
model = AutoModelForTokenClassification.from_pretrained("chintagunta85/electramed-small-NCBI-ner")This model is a fine-tuned version of giacomomiolo/electramed_small_scivocab on the ncbi_disease 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 |
|---|---|---|---|---|---|---|---|
| 0.4787 | 1.0 | 340 | 0.5090 | 0.6090 | 0.5062 | 0.5529 | 0.9608 |
| 0.2029 | 2.0 | 680 | 0.1890 | 0.7643 | 0.8208 | 0.7916 | 0.9774 |
| 0.1402 | 3.0 | 1020 | 0.1106 | 0.7839 | 0.8802 | 0.8292 | 0.9807 |
| 0.075 | 4.0 | 1360 | 0.0876 | 0.8162 | 0.8698 | 0.8422 | 0.9817 |
| 0.0408 | 5.0 | 1700 | 0.0776 | 0.8090 | 0.8781 | 0.8422 | 0.9818 |
| 0.0308 | 6.0 | 2040 | 0.0697 | 0.8044 | 0.8823 | 0.8415 | 0.9825 |
| 0.0405 | 7.0 | 2380 | 0.0680 | 0.8118 | 0.8854 | 0.8470 | 0.9830 |
| 0.0138 | 8.0 | 2720 | 0.0665 | 0.8111 | 0.8854 | 0.8466 | 0.9826 |
| 0.0223 | 9.0 | 3060 | 0.0675 | 0.8064 | 0.8896 | 0.8460 | 0.9821 |
| 0.0395 | 10.0 | 3400 | 0.0664 | 0.8083 | 0.8875 | 0.8461 | 0.9821 |