metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - pv_dataset
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test_ner3
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: pv_dataset
          type: pv_dataset
          config: PVDatasetCorpus
          split: train
          args: PVDatasetCorpus
        metrics:
          - name: Precision
            type: precision
            value: 0.6698151950718686
          - name: Recall
            type: recall
            value: 0.6499117663801446
          - name: F1
            type: f1
            value: 0.6597133941985438
          - name: Accuracy
            type: accuracy
            value: 0.9606609586670052

test_ner3

This model is a fine-tuned version of distilbert-base-uncased on the pv_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.2983
Precision: 0.6698
Recall: 0.6499
F1: 0.6597
Accuracy: 0.9607

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1106	1.0	1813	0.1128	0.6050	0.5949	0.5999	0.9565
0.0705	2.0	3626	0.1190	0.6279	0.6122	0.6200	0.9585
0.0433	3.0	5439	0.1458	0.6342	0.5983	0.6157	0.9574
0.0301	4.0	7252	0.1453	0.6305	0.6818	0.6552	0.9594
0.0196	5.0	9065	0.1672	0.6358	0.6871	0.6605	0.9594
0.0133	6.0	10878	0.1931	0.6427	0.6138	0.6279	0.9587
0.0104	7.0	12691	0.1948	0.6657	0.6511	0.6583	0.9607
0.0081	8.0	14504	0.2243	0.6341	0.6574	0.6455	0.9586
0.0054	9.0	16317	0.2432	0.6547	0.6318	0.6431	0.9588
0.0041	10.0	18130	0.2422	0.6717	0.6397	0.6553	0.9605
0.0041	11.0	19943	0.2415	0.6571	0.6420	0.6495	0.9601
0.0027	12.0	21756	0.2567	0.6560	0.6590	0.6575	0.9601
0.0023	13.0	23569	0.2609	0.6640	0.6495	0.6566	0.9606
0.002	14.0	25382	0.2710	0.6542	0.6670	0.6606	0.9598
0.0012	15.0	27195	0.2766	0.6692	0.6539	0.6615	0.9610
0.001	16.0	29008	0.2938	0.6692	0.6415	0.6551	0.9603
0.0007	17.0	30821	0.2969	0.6654	0.6490	0.6571	0.9604
0.0007	18.0	32634	0.3035	0.6628	0.6456	0.6541	0.9601
0.0007	19.0	34447	0.2947	0.6730	0.6489	0.6607	0.9609
0.0004	20.0	36260	0.2983	0.6698	0.6499	0.6597	0.9607

Framework versions

Transformers 4.21.0
Pytorch 1.12.0+cu113
Datasets 2.4.0
Tokenizers 0.12.1