electra-base-discriminator-finetuned-ner-cadec

csNoHug/electra-base-discriminator-finetuned-ner-cadec

electra-base-discriminator-finetuned-ner-cadec is a token classification model from csNoHug. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: electra
Access: Public
Created: Dec 7, 2023
Updated: Dec 13, 2023
SHA: 44cf8940

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Base models

google/electra-base-discriminator

Model card

From the Hugging Face model README

electra-base-discriminator-finetuned-ner-cadec

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

Loss: 0.4887
Precision: 0.6872
Recall: 0.7339
F1: 0.7097
Accuracy: 0.9230
Adr Precision: 0.6712
Adr Recall: 0.7483
Adr F1: 0.7076
Disease Precision: 0.1667
Disease Recall: 0.16
Disease F1: 0.1633
Drug Precision: 0.9509
Drug Recall: 0.9568
Drug F1: 0.9538
Finding Precision: 0.4074
Finding Recall: 0.3188
Finding F1: 0.3577
Symptom Precision: 0.5455
Symptom Recall: 0.6667
Symptom F1: 0.6
B-adr Precision: 0.7798
B-adr Recall: 0.8336
B-adr F1: 0.8058
B-disease Precision: 0.2174
B-disease Recall: 0.2
B-disease F1: 0.2083
B-drug Precision: 0.9753
B-drug Recall: 0.9753
B-drug F1: 0.9753
B-finding Precision: 0.5238
B-finding Recall: 0.3333
B-finding F1: 0.4074
B-symptom Precision: 0.6071
B-symptom Recall: 0.6296
B-symptom F1: 0.6182
I-adr Precision: 0.6587
I-adr Recall: 0.7267
I-adr F1: 0.6910
I-disease Precision: 0.1765
I-disease Recall: 0.15
I-disease F1: 0.1622
I-drug Precision: 0.9509
I-drug Recall: 0.9568
I-drug F1: 0.9538
I-finding Precision: 0.4565
I-finding Recall: 0.4038
I-finding F1: 0.4286
I-symptom Precision: 0.35
I-symptom Recall: 0.5385
I-symptom F1: 0.4242
Macro Avg F1: 0.5675
Weighted Avg F1: 0.7497

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 40

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Adr Precision	Adr Recall	Adr F1	Disease Precision	Disease Recall	Disease F1	Drug Precision	Drug Recall	Drug F1	Finding Precision	Finding Recall	Finding F1	Symptom Precision	Symptom Recall	Symptom F1	B-adr Precision	B-adr Recall	B-adr F1	B-disease Precision	B-disease Recall	B-disease F1	B-drug Precision	B-drug Recall	B-drug F1	B-finding Precision	B-finding Recall	B-finding F1	B-symptom Precision	B-symptom Recall	B-symptom F1	I-adr Precision	I-adr Recall	I-adr F1	I-disease Precision	I-disease Recall	I-disease F1	I-drug Precision	I-drug Recall	I-drug F1	I-finding Precision	I-finding Recall	I-finding F1	I-symptom Precision	I-symptom Recall	I-symptom F1	Macro Avg F1	Weighted Avg F1
No log	1.0	125	0.3085	0.5456	0.6433	0.5904	0.9018	0.4847	0.6883	0.5689	0.0	0.0	0.0	0.8245	0.9568	0.8857	0.0	0.0	0.0	0.0	0.0	0.0	0.6618	0.8071	0.7273	0.0	0.0	0.0	0.8791	0.9877	0.9302	0.0	0.0	0.0	0.0	0.0	0.0	0.4666	0.6498	0.5431	0.0	0.0	0.0	0.8564	0.9568	0.9038	0.0	0.0	0.0	0.0	0.0	0.0	0.3104	0.6156
No log	2.0	250	0.2624	0.5760	0.6784	0.6230	0.9132	0.5557	0.7317	0.6317	0.0294	0.04	0.0339	0.8851	0.9506	0.9167	0.1190	0.0725	0.0901	0.0	0.0	0.0	0.7120	0.8531	0.7762	0.1429	0.04	0.0625	0.9294	0.9753	0.9518	0.55	0.1667	0.2558	0.0	0.0	0.0	0.5670	0.7105	0.6307	0.1333	0.2	0.16	0.9006	0.9506	0.9249	0.25	0.1346	0.1750	0.0	0.0	0.0	0.3937	0.6840
No log	3.0	375	0.2659	0.6090	0.6931	0.6483	0.9140	0.6231	0.7383	0.6758	0.0189	0.04	0.0256	0.9070	0.9630	0.9341	0.1765	0.1739	0.1752	0.0	0.0	0.0	0.7648	0.8230	0.7928	0.0588	0.04	0.0476	0.9357	0.9877	0.9610	0.4340	0.3485	0.3866	0.0	0.0	0.0	0.6021	0.7105	0.6518	0.1522	0.35	0.2121	0.9123	0.9630	0.9369	0.4138	0.2308	0.2963	0.0	0.0	0.0	0.4285	0.7086
0.2971	4.0	500	0.2659	0.6313	0.6999	0.6638	0.9200	0.6259	0.7333	0.6754	0.0286	0.04	0.0333	0.9277	0.9506	0.9390	0.2687	0.2609	0.2647	0.625	0.1852	0.2857	0.7625	0.8354	0.7973	0.0769	0.04	0.0526	0.9691	0.9691	0.9691	0.4706	0.3636	0.4103	0.875	0.2593	0.4000	0.6173	0.6923	0.6527	0.1613	0.25	0.1961	0.9333	0.9506	0.9419	0.3721	0.3077	0.3368	0.0	0.0	0.0	0.4757	0.7208
0.2971	5.0	625	0.2827	0.6124	0.7157	0.6601	0.9156	0.5915	0.765	0.6672	0.0690	0.08	0.0741	0.9281	0.9568	0.9422	0.1957	0.1304	0.1565	0.5	0.2593	0.3415	0.7261	0.8726	0.7926	0.2308	0.12	0.1579	0.9578	0.9815	0.9695	0.5143	0.2727	0.3564	0.8333	0.3704	0.5128	0.5910	0.7429	0.6583	0.16	0.2	0.1778	0.9337	0.9568	0.9451	0.3077	0.1538	0.2051	0.0	0.0	0.0	0.4776	0.7180
0.2971	6.0	750	0.2900	0.6347	0.6965	0.6641	0.9196	0.6116	0.7217	0.6621	0.1190	0.2	0.1493	0.9394	0.9568	0.9480	0.3235	0.1594	0.2136	0.55	0.4074	0.4681	0.7581	0.8319	0.7932	0.2333	0.28	0.2545	0.9695	0.9815	0.9755	0.4783	0.1667	0.2472	0.8	0.4444	0.5714	0.6007	0.7065	0.6493	0.125	0.2	0.1538	0.9451	0.9568	0.9509	0.4074	0.2115	0.2785	0.4	0.3077	0.3478	0.5222	0.7195
0.2971	7.0	875	0.2994	0.6300	0.7078	0.6667	0.9193	0.6148	0.7317	0.6682	0.1389	0.2	0.1639	0.9398	0.9630	0.9512	0.2745	0.2029	0.2333	0.44	0.4074	0.4231	0.7465	0.8602	0.7993	0.2	0.24	0.2182	0.9639	0.9877	0.9756	0.4211	0.2424	0.3077	0.7	0.5185	0.5957	0.6237	0.7045	0.6616	0.1379	0.2	0.1633	0.9398	0.9630	0.9512	0.3256	0.2692	0.2947	0.3077	0.3077	0.3077	0.5275	0.7283
0.0963	8.0	1000	0.3197	0.6426	0.7067	0.6731	0.9190	0.6237	0.7267	0.6713	0.0952	0.08	0.0870	0.9390	0.9506	0.9448	0.3175	0.2899	0.3030	0.5	0.4444	0.4706	0.7568	0.8319	0.7926	0.1053	0.08	0.0909	0.9755	0.9815	0.9785	0.4167	0.3030	0.3509	0.5652	0.4815	0.52	0.6151	0.7085	0.6585	0.0714	0.05	0.0588	0.9448	0.9506	0.9477	0.3929	0.4231	0.4074	0.3846	0.3846	0.3846	0.5190	0.7263
0.0963	9.0	1125	0.3281	0.6512	0.6976	0.6736	0.9216	0.6354	0.7117	0.6714	0.1667	0.24	0.1967	0.9277	0.9506	0.9390	0.3019	0.2319	0.2623	0.6842	0.4815	0.5652	0.7780	0.8248	0.8007	0.2258	0.28	0.25	0.9636	0.9815	0.9725	0.4857	0.2576	0.3366	0.8125	0.4815	0.6047	0.6386	0.6903	0.6634	0.12	0.15	0.1333	0.9333	0.9506	0.9419	0.3542	0.3269	0.34	0.5556	0.3846	0.4545	0.5498	0.7322
0.0963	10.0	1250	0.3405	0.6563	0.7180	0.6858	0.9222	0.6241	0.7417	0.6778	0.0952	0.08	0.0870	0.9563	0.9444	0.9503	0.4043	0.2754	0.3276	0.6	0.5556	0.5769	0.7504	0.8513	0.7977	0.2222	0.16	0.1860	0.9811	0.9630	0.9720	0.5263	0.3030	0.3846	0.6087	0.5185	0.5600	0.6394	0.7287	0.6812	0.0667	0.05	0.0571	0.9563	0.9444	0.9503	0.45	0.3462	0.3913	0.4615	0.4615	0.4615	0.5442	0.7385
0.0963	11.0	1375	0.3641	0.6731	0.7112	0.6916	0.9184	0.6439	0.7233	0.6813	0.2083	0.2	0.2041	0.9509	0.9568	0.9538	0.4524	0.2754	0.3423	0.5	0.5556	0.5263	0.7732	0.8265	0.7990	0.2609	0.24	0.2500	0.9753	0.9753	0.9753	0.5714	0.3030	0.3960	0.52	0.4815	0.5	0.6360	0.7146	0.6730	0.1667	0.15	0.1579	0.9568	0.9568	0.9568	0.4857	0.3269	0.3908	0.3333	0.4615	0.3871	0.5486	0.7385
0.0455	12.0	1500	0.3604	0.6567	0.6931	0.6744	0.9163	0.6387	0.6983	0.6672	0.2222	0.24	0.2308	0.9118	0.9568	0.9337	0.3273	0.2609	0.2903	0.5833	0.5185	0.5490	0.7770	0.8142	0.7952	0.24	0.24	0.24	0.9467	0.9877	0.9668	0.4762	0.3030	0.3704	0.7143	0.5556	0.6250	0.6338	0.6761	0.6543	0.15	0.15	0.15	0.9226	0.9568	0.9394	0.3333	0.3077	0.32	0.3846	0.3846	0.3846	0.5446	0.7271
0.0455	13.0	1625	0.3913	0.6685	0.7033	0.6854	0.9226	0.6358	0.7217	0.6760	0.1429	0.08	0.1026	0.9451	0.9568	0.9509	0.3913	0.2609	0.3130	0.5417	0.4815	0.5098	0.7820	0.8319	0.8062	0.1429	0.08	0.1026	0.9755	0.9815	0.9785	0.4737	0.2727	0.3462	0.6190	0.4815	0.5417	0.6282	0.6943	0.6596	0.1	0.05	0.0667	0.9509	0.9568	0.9538	0.4146	0.3269	0.3656	0.375	0.4615	0.4138	0.5235	0.7315
0.0455	14.0	1750	0.3691	0.6739	0.7022	0.6877	0.9216	0.6509	0.7117	0.6799	0.2143	0.24	0.2264	0.9509	0.9568	0.9538	0.3636	0.2899	0.3226	0.6667	0.4444	0.5333	0.7752	0.8177	0.7959	0.28	0.28	0.28	0.9877	0.9877	0.9877	0.4889	0.3333	0.3964	0.8235	0.5185	0.6364	0.6467	0.7004	0.6725	0.1429	0.15	0.1463	0.9509	0.9568	0.9538	0.3529	0.3462	0.3495	0.625	0.3846	0.4762	0.5695	0.7403
0.0455	15.0	1875	0.3913	0.6597	0.7180	0.6876	0.9197	0.6457	0.735	0.6875	0.1667	0.16	0.1633	0.9226	0.9568	0.9394	0.3273	0.2609	0.2903	0.5161	0.5926	0.5517	0.7720	0.8389	0.8041	0.2273	0.2	0.2128	0.9639	0.9877	0.9756	0.4565	0.3182	0.375	0.6	0.5556	0.5769	0.6395	0.7146	0.6750	0.1111	0.1	0.1053	0.9281	0.9568	0.9422	0.3478	0.3077	0.3265	0.2857	0.4615	0.3529	0.5346	0.7363
0.0227	16.0	2000	0.3938	0.6649	0.7123	0.6878	0.9195	0.6454	0.725	0.6829	0.2333	0.28	0.2545	0.9563	0.9444	0.9503	0.3276	0.2754	0.2992	0.625	0.5556	0.5882	0.7763	0.8230	0.7990	0.2857	0.32	0.3019	0.9874	0.9691	0.9782	0.4375	0.3182	0.3684	0.6667	0.5185	0.5833	0.6304	0.7045	0.6654	0.1905	0.2	0.1951	0.9563	0.9444	0.9503	0.3462	0.3462	0.3462	0.3846	0.3846	0.3846	0.5572	0.7358
0.0227	17.0	2125	0.4021	0.6727	0.7169	0.6941	0.9216	0.6652	0.735	0.6983	0.2083	0.2	0.2041	0.9387	0.9444	0.9415	0.3333	0.2609	0.2927	0.4324	0.5926	0.5	0.7781	0.8319	0.8041	0.2609	0.24	0.2500	0.9752	0.9691	0.9721	0.4773	0.3182	0.3818	0.5517	0.5926	0.5714	0.6574	0.7186	0.6867	0.1875	0.15	0.1667	0.9503	0.9444	0.9474	0.375	0.3462	0.3600	0.2917	0.5385	0.3784	0.5519	0.7430
0.0227	18.0	2250	0.4107	0.6917	0.7089	0.7002	0.9227	0.6692	0.7217	0.6945	0.1905	0.16	0.1739	0.9451	0.9568	0.9509	0.4286	0.3043	0.3559	0.5417	0.4815	0.5098	0.7838	0.8212	0.8021	0.25	0.2	0.2222	0.9695	0.9815	0.9755	0.5	0.3333	0.4	0.5909	0.4815	0.5306	0.6567	0.7085	0.6816	0.1333	0.1	0.1143	0.9451	0.9568	0.9509	0.4419	0.3654	0.4	0.3333	0.3846	0.3571	0.5434	0.7415
0.0227	19.0	2375	0.4206	0.6551	0.7033	0.6783	0.9199	0.6205	0.7167	0.6651	0.1429	0.08	0.1026	0.9341	0.9630	0.9483	0.4151	0.3188	0.3607	0.5238	0.4074	0.4583	0.7585	0.8336	0.7943	0.1538	0.08	0.1053	0.9581	0.9877	0.9726	0.4583	0.3333	0.3860	0.6190	0.4815	0.5417	0.6191	0.6943	0.6546	0.1	0.05	0.0667	0.9398	0.9630	0.9512	0.4651	0.3846	0.4211	0.3846	0.3846	0.3846	0.5278	0.7281
0.0128	20.0	2500	0.4455	0.6458	0.7248	0.6830	0.9171	0.6080	0.7367	0.6662	0.1176	0.08	0.0952	0.9515	0.9691	0.9602	0.4211	0.3478	0.3810	0.6	0.5556	0.5769	0.7393	0.8531	0.7921	0.2	0.12	0.15	0.9697	0.9877	0.9786	0.5208	0.3788	0.4386	0.6364	0.5185	0.5714	0.6079	0.7186	0.6586	0.0909	0.05	0.0645	0.9515	0.9691	0.9602	0.4681	0.4231	0.4444	0.4	0.4615	0.4286	0.5487	0.7346
0.0128	21.0	2625	0.4208	0.6798	0.7214	0.7	0.9210	0.6587	0.7333	0.6940	0.2273	0.2	0.2128	0.9398	0.9630	0.9512	0.3929	0.3188	0.3520	0.56	0.5185	0.5385	0.7725	0.8354	0.8027	0.2727	0.24	0.2553	0.9636	0.9815	0.9725	0.4667	0.3182	0.3784	0.6087	0.5185	0.5600	0.6501	0.7146	0.6808	0.1538	0.1	0.1212	0.9398	0.9630	0.9512	0.4167	0.3846	0.4	0.3846	0.3846	0.3846	0.5507	0.7416
0.0128	22.0	2750	0.4410	0.6531	0.7248	0.6871	0.9182	0.6298	0.74	0.6805	0.1923	0.2	0.1961	0.9398	0.9630	0.9512	0.3636	0.2899	0.3226	0.5357	0.5556	0.5455	0.7536	0.8442	0.7963	0.25	0.24	0.2449	0.9695	0.9815	0.9755	0.5	0.3182	0.3889	0.6	0.5556	0.5769	0.6277	0.7166	0.6692	0.1579	0.15	0.1538	0.9398	0.9630	0.9512	0.3913	0.3462	0.3673	0.3333	0.3846	0.3571	0.5481	0.7357
0.0128	23.0	2875	0.4560	0.6790	0.7282	0.7027	0.9213	0.6544	0.7417	0.6953	0.2105	0.16	0.1818	0.9398	0.9630	0.9512	0.4	0.3188	0.3548	0.5926	0.5926	0.5926	0.7622	0.8283	0.7939	0.3333	0.24	0.2791	0.9636	0.9815	0.9725	0.5	0.3485	0.4107	0.6667	0.5926	0.6275	0.6461	0.7206	0.6813	0.1	0.05	0.0667	0.9398	0.9630	0.9512	0.4348	0.3846	0.4082	0.3571	0.3846	0.3704	0.5561	0.7410
0.0082	24.0	3000	0.4642	0.6646	0.7203	0.6913	0.9202	0.6421	0.7267	0.6818	0.2414	0.28	0.2593	0.9341	0.9630	0.9483	0.42	0.3043	0.3529	0.5	0.5926	0.5424	0.7651	0.8301	0.7963	0.2593	0.28	0.2692	0.9636	0.9815	0.9725	0.5238	0.3333	0.4074	0.6296	0.6296	0.6296	0.6309	0.7024	0.6648	0.1818	0.2	0.1905	0.9341	0.9630	0.9483	0.4390	0.3462	0.3871	0.2778	0.3846	0.3226	0.5588	0.7366
0.0082	25.0	3125	0.4606	0.6712	0.7214	0.6954	0.9187	0.6485	0.735	0.6891	0.1852	0.2	0.1923	0.9568	0.9568	0.9568	0.3704	0.2899	0.3252	0.6154	0.5926	0.6038	0.7687	0.8177	0.7925	0.24	0.24	0.24	0.9753	0.9753	0.9753	0.5116	0.3333	0.4037	0.625	0.5556	0.5882	0.6263	0.7126	0.6667	0.1579	0.15	0.1538	0.9568	0.9568	0.9568	0.3864	0.3269	0.3542	0.3846	0.3846	0.3846	0.5516	0.7346
0.0082	26.0	3250	0.4470	0.6767	0.7373	0.7057	0.9206	0.6556	0.755	0.7018	0.1875	0.12	0.1463	0.9341	0.9630	0.9483	0.3934	0.3478	0.3692	0.5556	0.5556	0.5556	0.7565	0.8301	0.7916	0.2	0.12	0.15	0.9639	0.9877	0.9756	0.4808	0.3788	0.4237	0.6	0.5556	0.5769	0.6430	0.7328	0.6850	0.1	0.05	0.0667	0.9341	0.9630	0.9483	0.4118	0.4038	0.4078	0.3846	0.3846	0.3846	0.5410	0.7390
0.0082	27.0	3375	0.4674	0.6727	0.7123	0.6920	0.9215	0.6473	0.725	0.6840	0.2222	0.24	0.2308	0.9394	0.9568	0.9480	0.3913	0.2609	0.3130	0.6	0.5556	0.5769	0.7655	0.8319	0.7973	0.28	0.28	0.28	0.9695	0.9815	0.9755	0.5143	0.2727	0.3564	0.6522	0.5556	0.6	0.6496	0.6943	0.6712	0.2	0.2	0.2000	0.9451	0.9568	0.9509	0.4	0.3077	0.3478	0.5	0.4615	0.4800	0.5659	0.7372
0.0062	28.0	3500	0.4693	0.6736	0.7361	0.7035	0.9203	0.6455	0.7617	0.6988	0.1818	0.16	0.1702	0.9509	0.9568	0.9538	0.4167	0.2899	0.3419	0.5833	0.5185	0.5490	0.7564	0.8407	0.7963	0.1905	0.16	0.1739	0.9755	0.9815	0.9785	0.4634	0.2879	0.3551	0.625	0.5556	0.5882	0.6354	0.7409	0.6841	0.125	0.1	0.1111	0.9509	0.9568	0.9538	0.4524	0.3654	0.4043	0.3846	0.3846	0.3846	0.5430	0.7395
0.0062	29.0	3625	0.4681	0.6905	0.7327	0.7110	0.9225	0.6682	0.7483	0.7060	0.1739	0.16	0.1667	0.9455	0.9630	0.9541	0.4630	0.3623	0.4065	0.5652	0.4815	0.52	0.7648	0.8230	0.7928	0.1905	0.16	0.1739	0.9695	0.9815	0.9755	0.5455	0.3636	0.4364	0.6087	0.5185	0.5600	0.6538	0.7186	0.6847	0.1176	0.1	0.1081	0.9455	0.9630	0.9541	0.4681	0.4231	0.4444	0.4286	0.4615	0.4444	0.5574	0.7428
0.0062	30.0	3750	0.4771	0.6765	0.7248	0.6998	0.9201	0.6562	0.735	0.6934	0.2273	0.2	0.2128	0.9451	0.9568	0.9509	0.3729	0.3188	0.3438	0.5862	0.6296	0.6071	0.7686	0.8230	0.7949	0.25	0.2	0.2222	0.9695	0.9815	0.9755	0.4583	0.3333	0.3860	0.6667	0.6667	0.6667	0.6383	0.7146	0.6743	0.1333	0.1	0.1143	0.9451	0.9568	0.9509	0.3922	0.3846	0.3883	0.375	0.4615	0.4138	0.5587	0.7384
0.0062	31.0	3875	0.4789	0.6874	0.7271	0.7067	0.9213	0.6702	0.745	0.7056	0.16	0.16	0.16	0.9341	0.9630	0.9483	0.4694	0.3333	0.3898	0.4615	0.4444	0.4528	0.7673	0.8230	0.7942	0.2273	0.2	0.2128	0.9639	0.9877	0.9756	0.5476	0.3485	0.4259	0.5833	0.5185	0.5490	0.6544	0.7206	0.6859	0.1667	0.15	0.1579	0.9341	0.9630	0.9483	0.4651	0.3846	0.4211	0.4	0.4615	0.4286	0.5599	0.7428
0.0043	32.0	4000	0.4845	0.6969	0.7316	0.7138	0.9225	0.6732	0.7417	0.7058	0.1923	0.2	0.1961	0.9571	0.9630	0.9600	0.4898	0.3478	0.4068	0.5714	0.5926	0.5818	0.7819	0.8248	0.8028	0.25	0.24	0.2449	0.9755	0.9815	0.9785	0.5349	0.3485	0.4220	0.6154	0.5926	0.6038	0.6568	0.7166	0.6854	0.1667	0.15	0.1579	0.9571	0.9630	0.9600	0.5116	0.4231	0.4632	0.4	0.4615	0.4286	0.5747	0.7498
0.0043	33.0	4125	0.4683	0.6809	0.7248	0.7021	0.9235	0.6577	0.7367	0.6950	0.15	0.12	0.1333	0.9394	0.9568	0.9480	0.4211	0.3478	0.3810	0.6154	0.5926	0.6038	0.7829	0.8425	0.8116	0.1579	0.12	0.1364	0.9695	0.9815	0.9755	0.5217	0.3636	0.4286	0.64	0.5926	0.6154	0.6487	0.7065	0.6764	0.1333	0.1	0.1143	0.9451	0.9568	0.9509	0.4490	0.4231	0.4356	0.5	0.5385	0.5185	0.5663	0.7470
0.0043	34.0	4250	0.4860	0.6896	0.7373	0.7126	0.9213	0.6657	0.7533	0.7068	0.15	0.12	0.1333	0.9337	0.9568	0.9451	0.4815	0.3768	0.4228	0.6	0.5556	0.5769	0.7634	0.8336	0.7970	0.1579	0.12	0.1364	0.9634	0.9753	0.9693	0.5208	0.3788	0.4386	0.64	0.5926	0.6154	0.6468	0.7267	0.6845	0.1875	0.15	0.1667	0.9337	0.9568	0.9451	0.5111	0.4423	0.4742	0.4167	0.3846	0.4	0.5627	0.7445
0.0043	35.0	4375	0.4857	0.6970	0.7373	0.7166	0.9220	0.6736	0.7533	0.7113	0.2	0.16	0.1778	0.9509	0.9568	0.9538	0.4808	0.3623	0.4132	0.5357	0.5556	0.5455	0.7761	0.8283	0.8014	0.2	0.16	0.1778	0.9753	0.9753	0.9753	0.5217	0.3636	0.4286	0.5926	0.5926	0.5926	0.6528	0.7308	0.6896	0.1875	0.15	0.1667	0.9509	0.9568	0.9538	0.4889	0.4231	0.4536	0.4	0.4615	0.4286	0.5668	0.7485
0.0037	36.0	4500	0.4816	0.6912	0.7350	0.7124	0.9228	0.6657	0.75	0.7053	0.1905	0.16	0.1739	0.9509	0.9568	0.9538	0.4706	0.3478	0.4000	0.5714	0.5926	0.5818	0.7745	0.8389	0.8054	0.1905	0.16	0.1739	0.9693	0.9753	0.9723	0.5111	0.3485	0.4144	0.6154	0.5926	0.6038	0.6557	0.7287	0.6903	0.1875	0.15	0.1667	0.9509	0.9568	0.9538	0.5	0.4231	0.4583	0.4667	0.5385	0.5	0.5739	0.7502
0.0037	37.0	4625	0.4878	0.6919	0.7350	0.7128	0.9229	0.6731	0.7517	0.7102	0.1667	0.16	0.1633	0.9568	0.9568	0.9568	0.4528	0.3478	0.3934	0.5172	0.5556	0.5357	0.7776	0.8354	0.8055	0.2174	0.2	0.2083	0.9753	0.9753	0.9753	0.5349	0.3485	0.4220	0.5926	0.5926	0.5926	0.6612	0.7308	0.6942	0.1765	0.15	0.1622	0.9568	0.9568	0.9568	0.4783	0.4231	0.4490	0.3889	0.5385	0.4516	0.5717	0.7519
0.0037	38.0	4750	0.4890	0.6876	0.7305	0.7084	0.9235	0.6652	0.745	0.7028	0.1429	0.12	0.1304	0.9568	0.9568	0.9568	0.4444	0.3478	0.3902	0.5517	0.5926	0.5714	0.7759	0.8336	0.8038	0.1579	0.12	0.1364	0.9753	0.9753	0.9753	0.5227	0.3485	0.4182	0.5926	0.5926	0.5926	0.6545	0.7247	0.6878	0.1765	0.15	0.1622	0.9568	0.9568	0.9568	0.4468	0.4038	0.4242	0.4118	0.5385	0.4667	0.5624	0.7473
0.0037	39.0	4875	0.4871	0.6869	0.7305	0.7080	0.9226	0.6722	0.745	0.7067	0.1667	0.16	0.1633	0.9509	0.9568	0.9538	0.4074	0.3188	0.3577	0.5152	0.6296	0.5667	0.7789	0.8354	0.8061	0.2174	0.2	0.2083	0.9693	0.9753	0.9723	0.5238	0.3333	0.4074	0.6071	0.6296	0.6182	0.6593	0.7206	0.6886	0.1765	0.15	0.1622	0.9509	0.9568	0.9538	0.4348	0.3846	0.4082	0.35	0.5385	0.4242	0.5649	0.7481
0.003	40.0	5000	0.4887	0.6872	0.7339	0.7097	0.9230	0.6712	0.7483	0.7076	0.1667	0.16	0.1633	0.9509	0.9568	0.9538	0.4074	0.3188	0.3577	0.5455	0.6667	0.6	0.7798	0.8336	0.8058	0.2174	0.2	0.2083	0.9753	0.9753	0.9753	0.5238	0.3333	0.4074	0.6071	0.6296	0.6182	0.6587	0.7267	0.6910	0.1765	0.15	0.1622	0.9509	0.9568	0.9538	0.4565	0.4038	0.4286	0.35	0.5385	0.4242	0.5675	0.7497

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: electra
License: apache-2.0
Created: Dec 7, 2023
Updated: Dec 13, 2023