metadata
base_model: microsoft/mdeberta-v3-base
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-pre-ner-full-mdeberta_data-univner_en66
results: []
scenario-non-kd-pre-ner-full-mdeberta_data-univner_en66
This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1822
- Precision: 0.7036
- Recall: 0.7226
- F1: 0.7130
- Accuracy: 0.9771
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.1469 | 1.2755 | 500 | 0.1352 | 0.4213 | 0.4658 | 0.4425 | 0.9588 |
0.066 | 2.5510 | 1000 | 0.1071 | 0.5530 | 0.6429 | 0.5945 | 0.9700 |
0.0349 | 3.8265 | 1500 | 0.1015 | 0.6313 | 0.7267 | 0.6756 | 0.9738 |
0.0222 | 5.1020 | 2000 | 0.1179 | 0.6442 | 0.7516 | 0.6937 | 0.9748 |
0.0137 | 6.3776 | 2500 | 0.1166 | 0.6736 | 0.7350 | 0.7030 | 0.9760 |
0.009 | 7.6531 | 3000 | 0.1178 | 0.6533 | 0.7236 | 0.6866 | 0.9753 |
0.0068 | 8.9286 | 3500 | 0.1312 | 0.6976 | 0.7091 | 0.7033 | 0.9763 |
0.0048 | 10.2041 | 4000 | 0.1438 | 0.6793 | 0.7215 | 0.6998 | 0.9754 |
0.0038 | 11.4796 | 4500 | 0.1479 | 0.6906 | 0.7371 | 0.7131 | 0.9761 |
0.0031 | 12.7551 | 5000 | 0.1503 | 0.6745 | 0.7422 | 0.7068 | 0.9760 |
0.0025 | 14.0306 | 5500 | 0.1489 | 0.6886 | 0.7143 | 0.7012 | 0.9768 |
0.0019 | 15.3061 | 6000 | 0.1565 | 0.6755 | 0.7350 | 0.7040 | 0.9757 |
0.0018 | 16.5816 | 6500 | 0.1564 | 0.7004 | 0.7453 | 0.7222 | 0.9771 |
0.0014 | 17.8571 | 7000 | 0.1661 | 0.7066 | 0.7381 | 0.7220 | 0.9771 |
0.0013 | 19.1327 | 7500 | 0.1724 | 0.6811 | 0.7164 | 0.6983 | 0.9757 |
0.0008 | 20.4082 | 8000 | 0.1760 | 0.6788 | 0.7329 | 0.7048 | 0.9763 |
0.0007 | 21.6837 | 8500 | 0.1777 | 0.6900 | 0.7350 | 0.7118 | 0.9768 |
0.0008 | 22.9592 | 9000 | 0.1821 | 0.6795 | 0.7464 | 0.7114 | 0.9761 |
0.0006 | 24.2347 | 9500 | 0.1888 | 0.6715 | 0.7660 | 0.7157 | 0.9763 |
0.0005 | 25.5102 | 10000 | 0.1830 | 0.6973 | 0.7298 | 0.7132 | 0.9767 |
0.0005 | 26.7857 | 10500 | 0.1805 | 0.7045 | 0.7257 | 0.7149 | 0.9769 |
0.0004 | 28.0612 | 11000 | 0.1812 | 0.6963 | 0.7215 | 0.7087 | 0.9765 |
0.0003 | 29.3367 | 11500 | 0.1822 | 0.7036 | 0.7226 | 0.7130 | 0.9771 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1