README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: hmBERT-CoNLL-cp2
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.8931730929727926
          - name: Recall
            type: recall
            value: 0.9005385392123864
          - name: F1
            type: f1
            value: 0.8968406938741306
          - name: Accuracy
            type: accuracy
            value: 0.983217164440637

hmBERT-CoNLL-cp2

This model is a fine-tuned version of dbmdz/bert-base-historic-multilingual-cased on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0666
• Precision: 0.8932
• Recall: 0.9005
• F1: 0.8968
• Accuracy: 0.9832

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.06	25	0.4116	0.3632	0.3718	0.3674	0.9005
No log	0.11	50	0.2247	0.6384	0.6902	0.6633	0.9459
No log	0.17	75	0.1624	0.7303	0.7627	0.7461	0.9580
No log	0.23	100	0.1541	0.7338	0.7688	0.7509	0.9588
No log	0.28	125	0.1349	0.7610	0.8095	0.7845	0.9643
No log	0.34	150	0.1230	0.7982	0.8253	0.8115	0.9694
No log	0.4	175	0.0997	0.8069	0.8406	0.8234	0.9727
No log	0.46	200	0.1044	0.8211	0.8410	0.8309	0.9732
No log	0.51	225	0.0871	0.8413	0.8603	0.8507	0.9760
No log	0.57	250	0.1066	0.8288	0.8465	0.8376	0.9733
No log	0.63	275	0.0872	0.8580	0.8667	0.8624	0.9766
No log	0.68	300	0.0834	0.8522	0.8706	0.8613	0.9773
No log	0.74	325	0.0832	0.8545	0.8834	0.8687	0.9783
No log	0.8	350	0.0776	0.8542	0.8834	0.8685	0.9787
No log	0.85	375	0.0760	0.8629	0.8896	0.8760	0.9801
No log	0.91	400	0.0673	0.8775	0.9004	0.8888	0.9824
No log	0.97	425	0.0681	0.8827	0.8938	0.8882	0.9817
No log	1.03	450	0.0659	0.8844	0.8950	0.8897	0.9824
No log	1.08	475	0.0690	0.8833	0.9015	0.8923	0.9832
0.1399	1.14	500	0.0666	0.8932	0.9005	0.8968	0.9832
0.1399	1.2	525	0.0667	0.8891	0.8997	0.8944	0.9825
0.1399	1.25	550	0.0699	0.8751	0.8953	0.8851	0.9820
0.1399	1.31	575	0.0617	0.8947	0.9068	0.9007	0.9840
0.1399	1.37	600	0.0633	0.9	0.9058	0.9029	0.9841
0.1399	1.42	625	0.0639	0.8966	0.9116	0.9040	0.9843
0.1399	1.48	650	0.0624	0.8972	0.9110	0.9041	0.9845
0.1399	1.54	675	0.0619	0.8980	0.9081	0.9030	0.9842
0.1399	1.59	700	0.0615	0.9002	0.9090	0.9045	0.9843
0.1399	1.65	725	0.0601	0.9037	0.9128	0.9082	0.9850
0.1399	1.71	750	0.0585	0.9031	0.9142	0.9086	0.9849
0.1399	1.77	775	0.0582	0.9035	0.9143	0.9089	0.9851
0.1399	1.82	800	0.0580	0.9044	0.9157	0.9100	0.9853
0.1399	1.88	825	0.0583	0.9034	0.9160	0.9097	0.9851
0.1399	1.94	850	0.0578	0.9058	0.9170	0.9114	0.9854
0.1399	1.99	875	0.0576	0.9060	0.9165	0.9112	0.9852

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0
• Datasets 2.4.0
• Tokenizers 0.12.1