metadata

license: apache-2.0
base_model: ethanyt/guwenbert-large
tags:
  - generated_from_trainer
datasets:
  - ched_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: guwenbert-large-CHED-Event Detection
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ched_ner
          type: ched_ner
          config: ched_ner
          split: validation
          args: ched_ner
        metrics:
          - name: Precision
            type: precision
            value: 0.7442799461641992
          - name: Recall
            type: recall
            value: 0.8069066147859922
          - name: F1
            type: f1
            value: 0.7743290548424737
          - name: Accuracy
            type: accuracy
            value: 0.9666064635130461

guwenbert-large-CHED-Event Detection

This model is a fine-tuned version of ethanyt/guwenbert-large on the ched_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.1905
Precision: 0.7443
Recall: 0.8069
F1: 0.7743
Accuracy: 0.9666

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	356	0.1420	0.6862	0.7573	0.72	0.9609
0.2304	2.0	712	0.1324	0.6907	0.7972	0.7401	0.9624
0.095	3.0	1068	0.1314	0.7268	0.7918	0.7579	0.9656
0.095	4.0	1424	0.1348	0.7248	0.7967	0.7590	0.9659
0.0613	5.0	1780	0.1525	0.7088	0.8147	0.7581	0.9635
0.0397	6.0	2136	0.1635	0.7224	0.8127	0.7649	0.9648
0.0397	7.0	2492	0.1693	0.7416	0.7986	0.7691	0.9662
0.0261	8.0	2848	0.1809	0.7338	0.8059	0.7682	0.9657
0.0164	9.0	3204	0.1904	0.7291	0.8127	0.7686	0.9655
0.0124	10.0	3560	0.1905	0.7443	0.8069	0.7743	0.9666

Framework versions

Transformers 4.43.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1