metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-multilingual-cased-WNUT-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: 0.5913669064748202
          - name: Recall
            type: recall
            value: 0.3809082483781279
          - name: F1
            type: f1
            value: 0.463359639233371
          - name: Accuracy
            type: accuracy
            value: 0.9500726682055228

bert-base-multilingual-cased-WNUT-ner

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

Loss: 0.3832
Precision: 0.5914
Recall: 0.3809
F1: 0.4634
Accuracy: 0.9501

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: 16
eval_batch_size: 16
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	213	0.2791	0.6008	0.2817	0.3836	0.9427
No log	2.0	426	0.2697	0.6520	0.3299	0.4382	0.9479
0.148	3.0	639	0.2846	0.5783	0.3661	0.4484	0.9492
0.148	4.0	852	0.3032	0.6248	0.3642	0.4602	0.9500
0.0413	5.0	1065	0.3355	0.5729	0.3568	0.4397	0.9495
0.0413	6.0	1278	0.3343	0.5714	0.3892	0.4631	0.9501
0.0413	7.0	1491	0.3522	0.5877	0.3818	0.4629	0.9500
0.0182	8.0	1704	0.3844	0.6120	0.3698	0.4610	0.9499
0.0182	9.0	1917	0.3847	0.5986	0.3828	0.4669	0.9504
0.008	10.0	2130	0.3832	0.5914	0.3809	0.4634	0.9501

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2