pos-ner-tagging-v2

This model is a fine-tuned version of om-ashish-soni/pos-ner-tagging-v2 on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6442
• Precision: 0.9394
• Recall: 0.9408
• F1: 0.9401
• Accuracy: 0.9270

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: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3297	1.0	1756	0.4190	0.9189	0.9231	0.9210	0.9051
0.2521	2.0	3512	0.3836	0.9210	0.9300	0.9255	0.9114
0.1932	3.0	5268	0.4155	0.9295	0.9338	0.9316	0.9183
0.1325	4.0	7024	0.3969	0.9328	0.9356	0.9342	0.9211
0.0973	5.0	8780	0.4247	0.9332	0.9367	0.9349	0.9222
0.0799	6.0	10536	0.4606	0.9338	0.9374	0.9356	0.9229
0.0554	7.0	12292	0.4836	0.9333	0.9379	0.9356	0.9239
0.0415	8.0	14048	0.5271	0.9361	0.9391	0.9376	0.9245
0.0285	9.0	15804	0.5363	0.9366	0.9397	0.9381	0.9253
0.022	10.0	17560	0.5653	0.9377	0.9396	0.9387	0.9258
0.0146	11.0	19316	0.5962	0.9374	0.9400	0.9387	0.9259
0.0121	12.0	21072	0.6061	0.9385	0.9401	0.9393	0.9266
0.0085	13.0	22828	0.6263	0.9384	0.9403	0.9394	0.9261
0.0062	14.0	24584	0.6365	0.9381	0.9399	0.9390	0.9259
0.0053	15.0	26340	0.6386	0.9384	0.9402	0.9393	0.9264
0.0042	16.0	28096	0.6442	0.9394	0.9408	0.9401	0.9270

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3