README.md

metadata

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

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