metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - peoples_daily_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_peoples_daily
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: peoples_daily_ner
          type: peoples_daily_ner
          config: peoples_daily_ner
          split: train
          args: peoples_daily_ner
        metrics:
          - name: Precision
            type: precision
            value: 0.9205354599829109
          - name: Recall
            type: recall
            value: 0.9365401332946972
          - name: F1
            type: f1
            value: 0.9284688307957485
          - name: Accuracy
            type: accuracy
            value: 0.9929549534505072

ner_peoples_daily

This model is a fine-tuned version of hfl/rbt6 on the peoples_daily_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.0249
Precision: 0.9205
Recall: 0.9365
F1: 0.9285
Accuracy: 0.9930

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: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3154	1.0	164	0.0410	0.8258	0.8684	0.8466	0.9868
0.0394	2.0	328	0.0287	0.8842	0.9070	0.8954	0.9905
0.0293	3.0	492	0.0264	0.8978	0.9168	0.9072	0.9916
0.02	4.0	656	0.0254	0.9149	0.9226	0.9188	0.9923
0.016	5.0	820	0.0250	0.9167	0.9281	0.9224	0.9927
0.0124	6.0	984	0.0252	0.9114	0.9328	0.9220	0.9928
0.0108	7.0	1148	0.0249	0.9169	0.9339	0.9254	0.9928
0.0097	8.0	1312	0.0249	0.9205	0.9365	0.9285	0.9930

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.5.2
Tokenizers 0.13.1