eriktks/conll2003
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How to use Gladiator/funnel-transformer-xlarge_ner_conll2003 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("token-classification", model="Gladiator/funnel-transformer-xlarge_ner_conll2003") # Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Gladiator/funnel-transformer-xlarge_ner_conll2003")
model = AutoModelForTokenClassification.from_pretrained("Gladiator/funnel-transformer-xlarge_ner_conll2003")This model is a fine-tuned version of funnel-transformer/xlarge 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 |
|---|---|---|---|---|---|---|---|
| 0.1349 | 1.0 | 878 | 0.0441 | 0.9328 | 0.9438 | 0.9383 | 0.9881 |
| 0.0308 | 2.0 | 1756 | 0.0377 | 0.9457 | 0.9561 | 0.9509 | 0.9901 |
| 0.0144 | 3.0 | 2634 | 0.0432 | 0.9512 | 0.9578 | 0.9545 | 0.9906 |
| 0.007 | 4.0 | 3512 | 0.0419 | 0.9551 | 0.9584 | 0.9567 | 0.9913 |
| 0.0041 | 5.0 | 4390 | 0.0436 | 0.9565 | 0.9593 | 0.9579 | 0.9915 |