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: 0.5180180180180181
          - name: Recall
            type: recall
            value: 0.31974050046339203
          - name: F1
            type: f1
            value: 0.39541547277936967
          - name: Accuracy
            type: accuracy
            value: 0.9357035175879397

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: 0.4235
Precision: 0.5180
Recall: 0.3197
F1: 0.3954
Accuracy: 0.9357
Corporation Precision: 0.2222
Corporation Recall: 0.2121
Corporation F1: 0.2171
Creative-work Precision: 0.4462
Creative-work Recall: 0.2042
Creative-work F1: 0.2802
Group Precision: 0.4030
Group Recall: 0.1636
Group F1: 0.2328
Location Precision: 0.5161
Location Recall: 0.4267
Location F1: 0.4672
Person Precision: 0.7747
Person Recall: 0.4569
Person F1: 0.5748
Product Precision: 0.1596
Product Recall: 0.1181
Product F1: 0.1357
B-corporation Precision: 0.3696
B-corporation Recall: 0.2576
B-corporation F1: 0.3036
B-creative-work Precision: 0.75
B-creative-work Recall: 0.2535
B-creative-work F1: 0.3789
B-group Precision: 0.5
B-group Recall: 0.1636
B-group F1: 0.2466
B-location Precision: 0.6293
B-location Recall: 0.4867
B-location F1: 0.5489
B-person Precision: 0.8608
B-person Recall: 0.4755
B-person F1: 0.6126
B-product Precision: 0.4545
B-product Recall: 0.1969
B-product F1: 0.2747
I-corporation Precision: 0.3333
I-corporation Recall: 0.2727
I-corporation F1: 0.3
I-creative-work Precision: 0.4262
I-creative-work Recall: 0.2016
I-creative-work F1: 0.2737
I-group Precision: 0.3478
I-group Recall: 0.1416
I-group F1: 0.2013
I-location Precision: 0.5932
I-location Recall: 0.3684
I-location F1: 0.4545
I-person Precision: 0.7625
I-person Recall: 0.3631
I-person F1: 0.4919
I-product Precision: 0.2222
I-product Recall: 0.1488
I-product F1: 0.1782

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	0.3858	0.4406	0.2576	0.3251	0.9303	0.0741	0.0606	0.0667	0.0667	0.0141	0.0233	0.1458	0.0848	0.1073	0.3829	0.4467	0.4123	0.7235	0.4452	0.5512	0.0	0.0	0.0	0.2391	0.1667	0.1964	0.0	0.0	0.0	0.375	0.0909	0.1463	0.5137	0.5	0.5068	0.8675	0.4732	0.6124	0.0	0.0	0.0	0.1923	0.0909	0.1235	0.3	0.0698	0.1132	0.1447	0.0973	0.1164	0.3636	0.3789	0.3711	0.7184	0.3720	0.4902	0.0	0.0	0.0
0.199	2.0	850	0.4265	0.5295	0.2743	0.3614	0.9326	0.1444	0.1970	0.1667	0.4583	0.1549	0.2316	0.4483	0.0788	0.1340	0.5263	0.4	0.4545	0.7839	0.4312	0.5564	0.0714	0.0236	0.0355	0.2969	0.2879	0.2923	0.7297	0.1901	0.3017	0.7368	0.0848	0.1522	0.6635	0.46	0.5433	0.8981	0.4522	0.6016	0.5	0.0630	0.1119	0.2090	0.2545	0.2295	0.5581	0.1860	0.2791	0.3	0.0531	0.0902	0.5536	0.3263	0.4106	0.7619	0.3333	0.4638	0.1538	0.0496	0.075
0.0799	3.0	1275	0.4235	0.5180	0.3197	0.3954	0.9357	0.2222	0.2121	0.2171	0.4462	0.2042	0.2802	0.4030	0.1636	0.2328	0.5161	0.4267	0.4672	0.7747	0.4569	0.5748	0.1596	0.1181	0.1357	0.3696	0.2576	0.3036	0.75	0.2535	0.3789	0.5	0.1636	0.2466	0.6293	0.4867	0.5489	0.8608	0.4755	0.6126	0.4545	0.1969	0.2747	0.3333	0.2727	0.3	0.4262	0.2016	0.2737	0.3478	0.1416	0.2013	0.5932	0.3684	0.4545	0.7625	0.3631	0.4919	0.2222	0.1488	0.1782

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1