pranaydeeps/lettuce_pos_de_mono

pos_final_mono_de

This model is a fine-tuned version of dbmdz/bert-base-german-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1567
• Precision: 0.9771
• Recall: 0.9791
• F1: 0.9781
• Accuracy: 0.9810

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: 5e-05
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 1024
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 40.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.99	128	0.2357	0.9443	0.9413	0.9428	0.9475
No log	1.99	256	0.0513	0.9843	0.9842	0.9842	0.9853
No log	2.99	384	0.0406	0.9868	0.9866	0.9867	0.9875
0.6822	3.99	512	0.0365	0.9877	0.9877	0.9877	0.9885
0.6822	4.99	640	0.0352	0.9881	0.9882	0.9882	0.9890
0.6822	5.99	768	0.0345	0.9887	0.9887	0.9887	0.9895
0.6822	6.99	896	0.0353	0.9888	0.9888	0.9888	0.9896
0.024	7.99	1024	0.0371	0.9886	0.9888	0.9887	0.9895
0.024	8.99	1152	0.0387	0.9888	0.9888	0.9888	0.9896
0.024	9.99	1280	0.0402	0.9890	0.9889	0.9890	0.9898
0.024	10.99	1408	0.0429	0.9889	0.9890	0.9889	0.9897
0.0128	11.99	1536	0.0454	0.9889	0.9889	0.9889	0.9896
0.0128	12.99	1664	0.0461	0.9889	0.9889	0.9889	0.9897
0.0128	13.99	1792	0.0477	0.9892	0.9891	0.9891	0.9899
0.0128	14.99	1920	0.0507	0.9890	0.9891	0.9890	0.9898
0.0069	15.99	2048	0.0514	0.9893	0.9893	0.9893	0.9901
0.0069	16.99	2176	0.0530	0.9892	0.9892	0.9892	0.9899
0.0069	17.99	2304	0.0552	0.9890	0.9891	0.9891	0.9898
0.0069	18.99	2432	0.0567	0.9891	0.9892	0.9892	0.9898
0.0037	19.99	2560	0.0577	0.9892	0.9893	0.9892	0.9900
0.0037	20.99	2688	0.0592	0.9892	0.9893	0.9893	0.9899
0.0037	21.99	2816	0.0606	0.9893	0.9893	0.9893	0.9900
0.0037	22.99	2944	0.0628	0.9893	0.9893	0.9893	0.9900
0.0023	23.99	3072	0.0629	0.9892	0.9891	0.9891	0.9899
0.0023	24.99	3200	0.0625	0.9892	0.9893	0.9893	0.9900
0.0023	25.99	3328	0.0636	0.9893	0.9893	0.9893	0.9900
0.0023	26.99	3456	0.0650	0.9894	0.9894	0.9894	0.9901
0.0017	27.99	3584	0.0644	0.9894	0.9894	0.9894	0.9901
0.0017	28.99	3712	0.0656	0.9895	0.9895	0.9895	0.9901
0.0017	29.99	3840	0.0668	0.9895	0.9895	0.9895	0.9902
0.0017	30.99	3968	0.0666	0.9895	0.9894	0.9894	0.9901
0.0011	31.99	4096	0.0678	0.9894	0.9894	0.9894	0.9900
0.0011	32.99	4224	0.0685	0.9896	0.9896	0.9896	0.9902
0.0011	33.99	4352	0.0692	0.9894	0.9894	0.9894	0.9901
0.0011	34.99	4480	0.0698	0.9895	0.9895	0.9895	0.9902
0.0009	35.99	4608	0.0698	0.9894	0.9894	0.9894	0.9901
0.0009	36.99	4736	0.0695	0.9895	0.9895	0.9895	0.9902
0.0009	37.99	4864	0.0696	0.9894	0.9895	0.9894	0.9902
0.0009	38.99	4992	0.0699	0.9895	0.9895	0.9895	0.9902
0.0007	39.99	5120	0.0697	0.9894	0.9894	0.9894	0.9901

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.0
• Datasets 2.18.0
• Tokenizers 0.13.2