metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wnut_17
          type: wnut_17
          config: wnut_17
          split: test
          args: wnut_17
        metrics:
          - name: Precision
            type: precision
            value: 0.5254237288135594
          - name: Recall
            type: recall
            value: 0.3160333642261353
          - name: F1
            type: f1
            value: 0.3946759259259259
          - name: Accuracy
            type: accuracy
            value: 0.9350753768844221

bert-finetuned-ner

This model is a fine-tuned version of bert-base-cased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.4362
Precision: 0.5254
Recall: 0.3160
F1: 0.3947
Accuracy: 0.9351
Corporation Precision: 0.1833
Corporation Recall: 0.1667
Corporation F1: 0.1746
Creative-work Precision: 0.4308
Creative-work Recall: 0.1972
Creative-work F1: 0.2705
Group Precision: 0.3467
Group Recall: 0.1576
Group F1: 0.2167
Location Precision: 0.55
Location Recall: 0.44
Location F1: 0.4889
Person Precision: 0.8008
Person Recall: 0.4592
Person F1: 0.5837
Product Precision: 0.1566
Product Recall: 0.1024
Product F1: 0.1238
B-corporation Precision: 0.3256
B-corporation Recall: 0.2121
B-corporation F1: 0.2569
B-creative-work Precision: 0.76
B-creative-work Recall: 0.2676
B-creative-work F1: 0.3958
B-group Precision: 0.5179
B-group Recall: 0.1758
B-group F1: 0.2624
B-location Precision: 0.6792
B-location Recall: 0.48
B-location F1: 0.5625
B-person Precision: 0.8615
B-person Recall: 0.4639
B-person F1: 0.6030
B-product Precision: 0.4468
B-product Recall: 0.1654
B-product F1: 0.2414
I-corporation Precision: 0.2889
I-corporation Recall: 0.2364
I-corporation F1: 0.26
I-creative-work Precision: 0.45
I-creative-work Recall: 0.2093
I-creative-work F1: 0.2857
I-group Precision: 0.2549
I-group Recall: 0.1150
I-group F1: 0.1585
I-location Precision: 0.5606
I-location Recall: 0.3895
I-location F1: 0.4596
I-person Precision: 0.7564
I-person Recall: 0.3512
I-person F1: 0.4797
I-product Precision: 0.1972
I-product Recall: 0.1157
I-product F1: 0.1458

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: 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Corporation Precision	Corporation Recall	Corporation F1	Creative-work Precision	Creative-work Recall	Creative-work F1	Group Precision	Group Recall	Group F1	Location Precision	Location Recall	Location F1	Person Precision	Person Recall	Person F1	Product Precision	Product Recall	Product F1	B-corporation Precision	B-corporation Recall	B-corporation F1	B-creative-work Precision	B-creative-work Recall	B-creative-work F1	B-group Precision	B-group Recall	B-group F1	B-location Precision	B-location Recall	B-location F1	B-person Precision	B-person Recall	B-person F1	B-product Precision	B-product Recall	B-product F1	I-corporation Precision	I-corporation Recall	I-corporation F1	I-creative-work Precision	I-creative-work Recall	I-creative-work F1	I-group Precision	I-group Recall	I-group F1	I-location Precision	I-location Recall	I-location F1	I-person Precision	I-person Recall	I-person F1	I-product Precision	I-product Recall	I-product F1
No log	1.0	425	0.3879	0.5038	0.2484	0.3327	0.9296	0.0714	0.0455	0.0556	0.1429	0.0070	0.0134	0.1667	0.0909	0.1176	0.4583	0.3667	0.4074	0.7569	0.4499	0.5643	0.0556	0.0079	0.0138	0.3333	0.1364	0.1935	1.0	0.0282	0.0548	0.4722	0.1030	0.1692	0.6162	0.4067	0.4900	0.9037	0.4592	0.6090	0.5	0.0157	0.0305	0.1111	0.0545	0.0732	0.5	0.0155	0.0301	0.12	0.0796	0.0957	0.4595	0.3579	0.4024	0.7108	0.3512	0.4701	0.125	0.0165	0.0292
0.196	2.0	850	0.4338	0.5712	0.2864	0.3815	0.9328	0.2174	0.2273	0.2222	0.4762	0.1408	0.2174	0.35	0.0848	0.1366	0.5727	0.42	0.4846	0.7992	0.4452	0.5719	0.1463	0.0472	0.0714	0.3208	0.2576	0.2857	0.8065	0.1761	0.2890	0.6	0.0909	0.1579	0.7216	0.4667	0.5668	0.8807	0.4476	0.5935	0.6522	0.1181	0.2	0.2917	0.2545	0.2718	0.6	0.1860	0.2840	0.2857	0.0708	0.1135	0.5625	0.3789	0.4528	0.7566	0.3423	0.4713	0.1765	0.0496	0.0774
0.0785	3.0	1275	0.4362	0.5254	0.3160	0.3947	0.9351	0.1833	0.1667	0.1746	0.4308	0.1972	0.2705	0.3467	0.1576	0.2167	0.55	0.44	0.4889	0.8008	0.4592	0.5837	0.1566	0.1024	0.1238	0.3256	0.2121	0.2569	0.76	0.2676	0.3958	0.5179	0.1758	0.2624	0.6792	0.48	0.5625	0.8615	0.4639	0.6030	0.4468	0.1654	0.2414	0.2889	0.2364	0.26	0.45	0.2093	0.2857	0.2549	0.1150	0.1585	0.5606	0.3895	0.4596	0.7564	0.3512	0.4797	0.1972	0.1157	0.1458

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1