metadata

license: mit
base_model: roberta-base
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: roberta-base-finetuned-ner
    results: []

roberta-base-finetuned-ner

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1185
Precision: 0.7791
Recall: 0.8034
F1: 0.7910
Accuracy: 0.7680

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	0.19	50	1.2727	0.6213	0.4935	0.5501	0.4934
No log	0.37	100	1.2623	0.6398	0.5312	0.5805	0.5263
No log	0.56	150	1.2519	0.6609	0.5693	0.6117	0.5593
No log	0.75	200	1.2423	0.6713	0.5940	0.6303	0.5815
No log	0.93	250	1.2330	0.6828	0.6167	0.6481	0.6014
No log	1.12	300	1.2241	0.6914	0.6388	0.6640	0.6219
No log	1.31	350	1.2158	0.6962	0.6540	0.6744	0.6350
No log	1.49	400	1.2076	0.6995	0.6637	0.6811	0.6434
No log	1.68	450	1.2000	0.7048	0.6767	0.6905	0.6545
1.2539	1.87	500	1.1926	0.7093	0.6880	0.6985	0.6645
1.2539	2.05	550	1.1859	0.7148	0.6990	0.7068	0.6736
1.2539	2.24	600	1.1793	0.7206	0.7092	0.7148	0.6824
1.2539	2.43	650	1.1733	0.7269	0.7209	0.7239	0.6935
1.2539	2.61	700	1.1676	0.7340	0.7306	0.7323	0.7025
1.2539	2.8	750	1.1620	0.7385	0.7380	0.7382	0.7091
1.2539	2.99	800	1.1569	0.7429	0.7451	0.744	0.7160
1.2539	3.17	850	1.1521	0.7496	0.7560	0.7528	0.7265
1.2539	3.36	900	1.1476	0.7539	0.7622	0.7580	0.7325
1.2539	3.54	950	1.1435	0.7552	0.7657	0.7604	0.7349
1.1751	3.73	1000	1.1399	0.7585	0.7718	0.7651	0.7405
1.1751	3.92	1050	1.1364	0.7626	0.7789	0.7706	0.7470
1.1751	4.1	1100	1.1332	0.7657	0.7835	0.7745	0.7513
1.1751	4.29	1150	1.1303	0.7700	0.7895	0.7796	0.7561
1.1751	4.48	1200	1.1278	0.7727	0.7934	0.7829	0.7589
1.1751	4.66	1250	1.1256	0.7732	0.7945	0.7837	0.7600
1.1751	4.85	1300	1.1237	0.7744	0.7960	0.7851	0.7614
1.1751	5.04	1350	1.1221	0.7748	0.7973	0.7859	0.7622
1.1751	5.22	1400	1.1208	0.7766	0.7995	0.7879	0.7643
1.1751	5.41	1450	1.1198	0.7783	0.8021	0.7900	0.7665
1.1363	5.6	1500	1.1191	0.7789	0.8032	0.7908	0.7675
1.1363	5.78	1550	1.1187	0.7791	0.8034	0.7910	0.7680
1.1363	5.97	1600	1.1185	0.7791	0.8034	0.7910	0.7680

Framework versions

Transformers 4.36.2
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.15.2