metadata

tags:
  - generated_from_trainer
datasets:
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: twitter-roberta-base-dec2021-CoNLL
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
          args: conll2003
        metrics:
          - name: Precision
            type: precision
            value: 0.9552512940390716
          - name: Recall
            type: recall
            value: 0.9628071356445641
          - name: F1
            type: f1
            value: 0.9590143324113654
          - name: Accuracy
            type: accuracy
            value: 0.9926599431486313

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