twitter-roberta-base-dec2021-CoNLL

This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-dec2021 on the conll2003 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0412
• Precision: 0.9553
• Recall: 0.9628
• F1: 0.9590
• Accuracy: 0.9927

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: 64
• eval_batch_size: 1024
• 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	0.11	25	0.2126	0.5639	0.6067	0.5845	0.9349
No log	0.23	50	0.0849	0.8259	0.8612	0.8431	0.9765
No log	0.34	75	0.0640	0.8752	0.8957	0.8853	0.9820
No log	0.45	100	0.0572	0.8848	0.9034	0.8940	0.9832
No log	0.57	125	0.0469	0.9071	0.9239	0.9155	0.9866
No log	0.68	150	0.0442	0.9198	0.9278	0.9238	0.9877
No log	0.8	175	0.0424	0.9192	0.9322	0.9256	0.9881
No log	0.91	200	0.0407	0.9170	0.9414	0.9291	0.9891
No log	1.02	225	0.0402	0.9264	0.9403	0.9333	0.9894
No log	1.14	250	0.0399	0.9329	0.9446	0.9387	0.9897
No log	1.25	275	0.0384	0.9278	0.9413	0.9345	0.9897
No log	1.36	300	0.0363	0.9379	0.9477	0.9427	0.9906
No log	1.48	325	0.0362	0.9380	0.9493	0.9436	0.9905
No log	1.59	350	0.0364	0.9397	0.9497	0.9447	0.9905
No log	1.7	375	0.0367	0.9324	0.9475	0.9399	0.9899
No log	1.82	400	0.0372	0.9350	0.9460	0.9404	0.9899
No log	1.93	425	0.0339	0.9411	0.9514	0.9462	0.9909
No log	2.05	450	0.0336	0.9419	0.9529	0.9474	0.9911
No log	2.16	475	0.0336	0.9447	0.9537	0.9492	0.9914
0.079	2.27	500	0.0345	0.9420	0.9566	0.9492	0.9914
0.079	2.39	525	0.0364	0.9436	0.9522	0.9479	0.9913
0.079	2.5	550	0.0340	0.9479	0.9514	0.9496	0.9916
0.079	2.61	575	0.0339	0.9481	0.9559	0.9520	0.9917
0.079	2.73	600	0.0396	0.9326	0.9504	0.9414	0.9902
0.079	2.84	625	0.0348	0.9461	0.9544	0.9502	0.9915
0.079	2.95	650	0.0359	0.9419	0.9527	0.9473	0.9908
0.079	3.07	675	0.0347	0.9434	0.9573	0.9503	0.9916
0.079	3.18	700	0.0351	0.9464	0.9566	0.9515	0.9918
0.079	3.3	725	0.0370	0.9446	0.9536	0.9491	0.9911
0.079	3.41	750	0.0358	0.9462	0.9583	0.9522	0.9917
0.079	3.52	775	0.0353	0.9483	0.9564	0.9523	0.9920
0.079	3.64	800	0.0351	0.9469	0.9564	0.9516	0.9916
0.079	3.75	825	0.0361	0.9479	0.9579	0.9529	0.9919
0.079	3.86	850	0.0370	0.9498	0.9581	0.9539	0.9918
0.079	3.98	875	0.0374	0.9460	0.9574	0.9517	0.9915
0.079	4.09	900	0.0381	0.9506	0.9594	0.9550	0.9922
0.079	4.2	925	0.0415	0.9460	0.9557	0.9509	0.9912
0.079	4.32	950	0.0390	0.9493	0.9556	0.9524	0.9917
0.079	4.43	975	0.0389	0.9483	0.9591	0.9536	0.9919
0.0123	4.55	1000	0.0379	0.9464	0.9569	0.9516	0.9918
0.0123	4.66	1025	0.0376	0.9463	0.9579	0.9521	0.9920
0.0123	4.77	1050	0.0373	0.9499	0.9571	0.9535	0.9917
0.0123	4.89	1075	0.0366	0.9520	0.9584	0.9552	0.9923
0.0123	5.0	1100	0.0374	0.9488	0.9606	0.9547	0.9923
0.0123	5.11	1125	0.0393	0.9516	0.9589	0.9552	0.9920
0.0123	5.23	1150	0.0389	0.9539	0.9603	0.9571	0.9925
0.0123	5.34	1175	0.0397	0.9486	0.9576	0.9531	0.9917
0.0123	5.45	1200	0.0397	0.9478	0.9569	0.9523	0.9919
0.0123	5.57	1225	0.0388	0.9483	0.9593	0.9537	0.9920
0.0123	5.68	1250	0.0389	0.9502	0.9606	0.9554	0.9923
0.0123	5.8	1275	0.0380	0.9547	0.9616	0.9582	0.9925
0.0123	5.91	1300	0.0391	0.9496	0.9603	0.9549	0.9924
0.0123	6.02	1325	0.0381	0.9548	0.9603	0.9575	0.9924
0.0123	6.14	1350	0.0400	0.9529	0.9596	0.9562	0.9922
0.0123	6.25	1375	0.0393	0.9544	0.9616	0.9580	0.9927
0.0123	6.36	1400	0.0419	0.9514	0.9621	0.9567	0.9924
0.0123	6.48	1425	0.0415	0.9532	0.9626	0.9579	0.9925
0.0123	6.59	1450	0.0415	0.952	0.9613	0.9566	0.9923
0.0123	6.7	1475	0.0399	0.9542	0.9611	0.9577	0.9925
0.0052	6.82	1500	0.0416	0.9522	0.9591	0.9556	0.9921
0.0052	6.93	1525	0.0410	0.9502	0.9599	0.9550	0.9919
0.0052	7.05	1550	0.0406	0.9507	0.9613	0.9560	0.9921
0.0052	7.16	1575	0.0400	0.9508	0.9603	0.9555	0.9923
0.0052	7.27	1600	0.0402	0.9525	0.9618	0.9571	0.9924
0.0052	7.39	1625	0.0401	0.9550	0.9633	0.9591	0.9925
0.0052	7.5	1650	0.0397	0.9555	0.9647	0.9601	0.9927
0.0052	7.61	1675	0.0412	0.9526	0.9610	0.9568	0.9922
0.0052	7.73	1700	0.0419	0.9531	0.9616	0.9574	0.9923
0.0052	7.84	1725	0.0407	0.9555	0.9621	0.9588	0.9927
0.0052	7.95	1750	0.0409	0.9551	0.9628	0.9589	0.9927
0.0052	8.07	1775	0.0413	0.9520	0.9616	0.9568	0.9924
0.0052	8.18	1800	0.0414	0.9505	0.9605	0.9555	0.9923
0.0052	8.3	1825	0.0410	0.9542	0.9605	0.9573	0.9924
0.0052	8.41	1850	0.0417	0.9553	0.9599	0.9576	0.9924
0.0052	8.52	1875	0.0418	0.9545	0.9606	0.9576	0.9923
0.0052	8.64	1900	0.0414	0.9544	0.9616	0.9580	0.9924
0.0052	8.75	1925	0.0419	0.9555	0.9620	0.9587	0.9925
0.0052	8.86	1950	0.0415	0.9544	0.9611	0.9577	0.9926
0.0052	8.98	1975	0.0413	0.9542	0.9611	0.9577	0.9926
0.0027	9.09	2000	0.0412	0.9553	0.9628	0.9590	0.9927
0.0027	9.2	2025	0.0408	0.9554	0.9630	0.9592	0.9927
0.0027	9.32	2050	0.0404	0.9545	0.9613	0.9579	0.9926
0.0027	9.43	2075	0.0407	0.9557	0.9618	0.9587	0.9926
0.0027	9.55	2100	0.0410	0.9552	0.9618	0.9585	0.9926
0.0027	9.66	2125	0.0412	0.9552	0.9620	0.9586	0.9925
0.0027	9.77	2150	0.0413	0.9557	0.9621	0.9589	0.9925
0.0027	9.89	2175	0.0413	0.9557	0.9621	0.9589	0.9925
0.0027	10.0	2200	0.0413	0.9557	0.9621	0.9589	0.9925

Framework versions

• Transformers 4.20.1
• Pytorch 1.12.0
• Datasets 2.3.2
• Tokenizers 0.12.1