metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_column_TQ
    results: []
language:
  - en
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ner_column_TQ

This model is a fine-tuned version of Gladiator/microsoft-deberta-v3-large_ner_conll2003 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2111
Precision: 0.8593
Recall: 0.8587
F1: 0.8590
Accuracy: 0.9163

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: 64
eval_batch_size: 64
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	702	0.1938	0.7778	0.7851	0.7815	0.8957
0.3587	2.0	1404	0.1562	0.8216	0.8219	0.8217	0.9098
0.1645	3.0	2106	0.1472	0.8161	0.8268	0.8214	0.9114
0.1645	4.0	2808	0.1528	0.8357	0.8195	0.8275	0.9097
0.1372	5.0	3510	0.1411	0.8301	0.8349	0.8325	0.9141
0.1259	6.0	4212	0.1396	0.8341	0.8431	0.8386	0.9149
0.1259	7.0	4914	0.1470	0.8178	0.8323	0.8250	0.9126
0.1205	8.0	5616	0.1413	0.8421	0.8480	0.8451	0.9156
0.1152	9.0	6318	0.1417	0.8342	0.8481	0.8411	0.9158
0.1126	10.0	7020	0.1475	0.8427	0.8493	0.8460	0.9154
0.1126	11.0	7722	0.1490	0.8477	0.8510	0.8493	0.9155
0.108	12.0	8424	0.1535	0.8511	0.8540	0.8526	0.9160
0.1035	13.0	9126	0.1569	0.8515	0.8552	0.8533	0.9160
0.1035	14.0	9828	0.1677	0.8530	0.8537	0.8534	0.9158
0.097	15.0	10530	0.1721	0.8549	0.8557	0.8553	0.9159
0.0912	16.0	11232	0.1822	0.8573	0.8574	0.8573	0.9165
0.0912	17.0	11934	0.1969	0.8577	0.8578	0.8577	0.9158
0.0854	18.0	12636	0.1969	0.8597	0.8587	0.8592	0.9165
0.08	19.0	13338	0.2035	0.8587	0.8587	0.8587	0.9165
0.0768	20.0	14040	0.2111	0.8593	0.8587	0.8590	0.9163

Framework versions

Transformers 4.30.2
Pytorch 1.13.1+cu116
Datasets 2.13.2
Tokenizers 0.13.3