Instructions to use sara-m98/MAMO_DEBERTA_3_FINAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sara-m98/MAMO_DEBERTA_3_FINAL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sara-m98/MAMO_DEBERTA_3_FINAL")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sara-m98/MAMO_DEBERTA_3_FINAL") model = AutoModelForTokenClassification.from_pretrained("sara-m98/MAMO_DEBERTA_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 4.193900 3.519788 0.000127 0.003115 0.000244 0.590811 2 1.017600 0.463222 0.000000 0.000000 0.000000 0.958212 3 0.252900 0.254212 0.000000 0.000000 0.000000 0.958212 4 0.181400 0.193040 0.116047 0.079751 0.094535 0.960686 5 0.126900 0.142126 0.300626 0.179439 0.224737 0.972061 6 0.105200 0.122365 0.534498 0.381308 0.445091 0.974879 7 0.089100 0.122182 0.426747 0.395639 0.410605 0.973475 8 0.077900 0.119754 0.512744 0.426168 0.465464 0.975112 9 0.070100 0.125701 0.494024 0.463551 0.478303 0.975437 10 0.061100 0.124493 0.409744 0.408723 0.409233 0.973354 11 0.038600 0.131724 0.472663 0.500935 0.486388 0.975149 12 0.037700 0.137880 0.459763 0.459190 0.459476 0.973921 13 0.030900 0.144064 0.476932 0.495950 0.486255 0.974749 14 0.027400 0.141285 0.471942 0.523988 0.496605 0.974656
- Downloads last month
- 3