metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test4
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ner
          type: ner
          config: default
          split: train
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.594855305466238
          - name: Recall
            type: recall
            value: 0.6423611111111112
          - name: F1
            type: f1
            value: 0.6176961602671119
          - name: Accuracy
            type: accuracy
            value: 0.9579571605593911

test4

This model is a fine-tuned version of bert-base-cased on the ner dataset. It achieves the following results on the evaluation set:

Loss: 0.3100
Precision: 0.5949
Recall: 0.6424
F1: 0.6177
Accuracy: 0.9580

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
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
No log	1.0	418	0.2052	0.2415	0.2465	0.2440	0.9423
0.3341	2.0	836	0.1816	0.4286	0.4792	0.4525	0.9513
0.1296	3.0	1254	0.2039	0.4589	0.5035	0.4801	0.9526
0.0727	4.0	1672	0.2130	0.5237	0.5764	0.5488	0.9566
0.0553	5.0	2090	0.2290	0.5171	0.5764	0.5452	0.9551
0.0412	6.0	2508	0.2351	0.5390	0.5521	0.5455	0.9555
0.0412	7.0	2926	0.2431	0.5280	0.5903	0.5574	0.9542
0.0321	8.0	3344	0.2490	0.5825	0.625	0.6030	0.9570
0.0249	9.0	3762	0.2679	0.5764	0.5764	0.5764	0.9573
0.0192	10.0	4180	0.2574	0.5506	0.6042	0.5762	0.9558
0.0206	11.0	4598	0.2857	0.5498	0.5938	0.5710	0.9559
0.0147	12.0	5016	0.2638	0.5548	0.5972	0.5753	0.9550
0.0147	13.0	5434	0.2771	0.5677	0.5972	0.5821	0.9577
0.0129	14.0	5852	0.3016	0.5761	0.6181	0.5963	0.9549
0.0118	15.0	6270	0.3055	0.5587	0.6111	0.5837	0.9570
0.0099	16.0	6688	0.2937	0.5682	0.6076	0.5872	0.9564
0.0099	17.0	7106	0.3075	0.5313	0.6181	0.5714	0.9531
0.0085	18.0	7524	0.3079	0.6026	0.6424	0.6218	0.9580
0.0085	19.0	7942	0.3082	0.5833	0.6319	0.6067	0.9572
0.0074	20.0	8360	0.3100	0.5949	0.6424	0.6177	0.9580

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1