eriktks/conll2003
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How to use kevinl2046/hfcourse-bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="kevinl2046/hfcourse-bert-finetuned-ner") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("kevinl2046/hfcourse-bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("kevinl2046/hfcourse-bert-finetuned-ner")This model is a fine-tuned version of bert-base-cased on the conll2003 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 |
|---|---|---|---|---|---|---|---|
| 0.0518 | 1.0 | 1756 | 0.0405 | 0.9319 | 0.9416 | 0.9367 | 0.9899 |
| 0.0231 | 2.0 | 3512 | 0.0450 | 0.9493 | 0.9515 | 0.9504 | 0.9910 |
| 0.0136 | 3.0 | 5268 | 0.0422 | 0.9507 | 0.9549 | 0.9528 | 0.9918 |
Base model
google-bert/bert-base-cased