Instructions to use sara-m98/ECO_SPANBERT_3_FINAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sara-m98/ECO_SPANBERT_3_FINAL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sara-m98/ECO_SPANBERT_3_FINAL")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sara-m98/ECO_SPANBERT_3_FINAL") model = AutoModelForTokenClassification.from_pretrained("sara-m98/ECO_SPANBERT_3_FINAL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Epoch Training Loss Validation Loss Precision Recall F1 Accuracy 1 2.208200 2.119271 0.000000 0.000000 0.000000 0.980338 2 1.417800 1.337759 0.000000 0.000000 0.000000 0.980338 3 0.602800 0.555833 0.000000 0.000000 0.000000 0.980338 4 0.254100 0.233581 0.000000 0.000000 0.000000 0.980338 5 0.183300 0.186824 0.000000 0.000000 0.000000 0.980338 6 0.173600 0.176202 0.000000 0.000000 0.000000 0.980338 7 0.178000 0.171230 0.000000 0.000000 0.000000 0.980338 8 0.163400 0.164535 0.000000 0.000000 0.000000 0.980338 9 0.127000 0.114168 0.000000 0.000000 0.000000 0.980335 10 0.090600 0.103904 0.154052 0.093287 0.116205 0.981430 11 0.089800 0.100236 0.176934 0.163386 0.169891 0.981809 12 0.077900 0.092586 0.288121 0.267458 0.277405 0.983225 13 0.067800 0.089250 0.304672 0.297115 0.300846 0.983307 14 0.059400 0.086743 0.327498 0.327851 0.327674 0.983603 15 0.055400 0.088072 0.337137 0.350499 0.343688 0.983988 16 0.046800 0.086495 0.363776 0.384470 0.373837 0.984486 17 0.040500 0.086944 0.372215 0.382853 0.377459 0.984484 18 0.032100 0.085658 0.384933 0.402265 0.393408 0.984912 19 0.031100 0.088963 0.380247 0.415206 0.396958 0.985005 20 0.025200 0.088633 0.384929 0.407657 0.395967 0.984789 21 0.027400 0.087063 0.375304 0.416285 0.394733 0.984531 22 0.021900 0.087375 0.411872 0.424643 0.418160 0.985287 23 0.022700 0.087777 0.405183 0.438393 0.421134 0.985056 24 0.020800 0.086840 0.403841 0.436506 0.419539 0.985085
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