metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikiann
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: wikiann
          type: wikiann
          config: ace
          split: validation
          args: ace
        metrics:
          - name: Precision
            type: precision
            value: 0.34523809523809523
          - name: Recall
            type: recall
            value: 0.5420560747663551
          - name: F1
            type: f1
            value: 0.4218181818181818
          - name: Accuracy
            type: accuracy
            value: 0.8688172043010752

bert-finetuned-ner

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

Loss: 0.5677
Precision: 0.3452
Recall: 0.5421
F1: 0.4218
Accuracy: 0.8688

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	13	0.5728	0.2077	0.3551	0.2621	0.8199
No log	2.0	26	0.5687	0.2889	0.3645	0.3223	0.8312
No log	3.0	39	0.5447	0.2857	0.4486	0.3491	0.8425
No log	4.0	52	0.5509	0.2881	0.4766	0.3592	0.8489
No log	5.0	65	0.5751	0.3121	0.4579	0.3712	0.8511
No log	6.0	78	0.5358	0.3851	0.5794	0.4627	0.8667
No log	7.0	91	0.5484	0.3491	0.5514	0.4275	0.8645
No log	8.0	104	0.5671	0.3580	0.5421	0.4312	0.8672
No log	9.0	117	0.5666	0.3494	0.5421	0.4249	0.8688
No log	10.0	130	0.5677	0.3452	0.5421	0.4218	0.8688

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3