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antoineedy-old/
stanford-deidentifier-base-finetuned-ner

Name: stanford-deidentifier-base-finetuned-ner
Availability: InStock

generated_from_trainer

StanfordAIMI/stanford-deidentifier-base

finetune

mit

endpoints_compatible

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stanford-deidentifier-base-finetuned-ner

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

Loss: 0.6522
0 Precision: 0.9766
0 Recall: 0.9646
0 F1-score: 0.9706
1 Precision: 0.8268
1 Recall: 0.8689
1 F1-score: 0.8473
2 Precision: 0.8419
2 Recall: 0.8916
2 F1-score: 0.8660
3 Precision: 0.8394
3 Recall: 0.8975
3 F1-score: 0.8675
Accuracy: 0.9507
Macro avg Precision: 0.8712
Macro avg Recall: 0.9057
Macro avg F1-score: 0.8878
Weighted avg Precision: 0.9521
Weighted avg Recall: 0.9507
Weighted avg F1-score: 0.9513

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 60

Training results

Training Loss	Epoch	Step	Validation Loss	0 Precision	0 Recall	0 F1-score	1 Precision	1 Recall	1 F1-score	2 Precision	2 Recall	2 F1-score	3 Precision	3 Recall	3 F1-score	Accuracy	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score
No log	1.0	67	0.2686	0.9927	0.8966	0.9422	0.5702	0.9369	0.7089	0.5731	0.9458	0.7138	0.7626	0.8809	0.8175	0.9003	0.7246	0.9151	0.7956	0.9322	0.9003	0.9091
No log	2.0	134	0.2344	0.9922	0.9195	0.9544	0.6492	0.9296	0.7645	0.6989	0.9261	0.7966	0.7478	0.9529	0.8380	0.9226	0.7720	0.9320	0.8384	0.9411	0.9226	0.9275
No log	3.0	201	0.2085	0.9944	0.9139	0.9525	0.6088	0.9709	0.7484	0.7197	0.9360	0.8137	0.7834	0.9418	0.8553	0.9207	0.7766	0.9407	0.8425	0.9430	0.9207	0.9265
No log	4.0	268	0.2154	0.9925	0.9342	0.9625	0.6791	0.9709	0.7992	0.7966	0.9261	0.8565	0.8028	0.9474	0.8691	0.9374	0.8178	0.9446	0.8718	0.9506	0.9374	0.9408
No log	5.0	335	0.2524	0.9892	0.9381	0.9630	0.6934	0.9442	0.7996	0.7602	0.9212	0.8330	0.8200	0.9335	0.8731	0.9376	0.8157	0.9342	0.8671	0.9486	0.9376	0.9407
No log	6.0	402	0.2449	0.9908	0.9383	0.9638	0.7013	0.9515	0.8074	0.7705	0.9261	0.8412	0.8052	0.9391	0.8670	0.9389	0.8169	0.9387	0.8698	0.9499	0.9389	0.9418
No log	7.0	469	0.2695	0.9877	0.9426	0.9646	0.7282	0.9102	0.8091	0.7519	0.9557	0.8416	0.8153	0.9418	0.8740	0.9406	0.8208	0.9376	0.8723	0.9493	0.9406	0.9431
0.196	8.0	536	0.3949	0.9802	0.9596	0.9698	0.7948	0.8835	0.8368	0.8190	0.8916	0.8538	0.8430	0.9224	0.8810	0.9493	0.8592	0.9143	0.8853	0.9521	0.9493	0.9503
0.196	9.0	603	0.3717	0.9810	0.9581	0.9694	0.7918	0.8859	0.8362	0.8097	0.9015	0.8531	0.8291	0.9141	0.8696	0.9480	0.8529	0.9149	0.8821	0.9514	0.9480	0.9492
0.196	10.0	670	0.3790	0.9808	0.9509	0.9656	0.7426	0.9102	0.8179	0.7895	0.8867	0.8353	0.8264	0.8837	0.8541	0.9413	0.8348	0.9079	0.8682	0.9468	0.9413	0.9431
0.196	11.0	737	0.4844	0.9738	0.9671	0.9704	0.8519	0.8519	0.8519	0.8389	0.8719	0.8551	0.8286	0.8837	0.8552	0.9500	0.8733	0.8937	0.8832	0.9508	0.9500	0.9503
0.196	12.0	804	0.3634	0.9858	0.9515	0.9684	0.7669	0.9102	0.8324	0.8161	0.8966	0.8545	0.8103	0.9584	0.8782	0.9470	0.8448	0.9292	0.8834	0.9526	0.9470	0.9486
0.196	13.0	871	0.5154	0.9773	0.9663	0.9718	0.8460	0.8665	0.8561	0.8411	0.8867	0.8633	0.8372	0.9114	0.8727	0.9526	0.8754	0.9077	0.8910	0.9539	0.9526	0.9531
0.196	14.0	938	0.4340	0.9827	0.9605	0.9715	0.8125	0.8835	0.8465	0.8251	0.9064	0.8638	0.8297	0.9446	0.8834	0.9519	0.8625	0.9237	0.8913	0.9549	0.9519	0.9529
0.0268	15.0	1005	0.4523	0.9818	0.9588	0.9702	0.7996	0.8811	0.8383	0.8153	0.8916	0.8518	0.8293	0.9418	0.8820	0.9496	0.8565	0.9183	0.8856	0.9529	0.9496	0.9507
0.0268	16.0	1072	0.4877	0.9777	0.9628	0.9702	0.8129	0.8859	0.8479	0.8219	0.8867	0.8531	0.8417	0.8837	0.8622	0.9495	0.8635	0.9048	0.8833	0.9514	0.9495	0.9502
0.0268	17.0	1139	0.5068	0.9817	0.9609	0.9712	0.8118	0.8689	0.8394	0.8311	0.8966	0.8626	0.8313	0.9557	0.8892	0.9516	0.8640	0.9205	0.8906	0.9543	0.9516	0.9525
0.0268	18.0	1206	0.5995	0.9725	0.9684	0.9705	0.8430	0.8471	0.8450	0.8429	0.8719	0.8571	0.8387	0.8643	0.8513	0.9495	0.8743	0.8879	0.8810	0.9499	0.9495	0.9497
0.0268	19.0	1273	0.6119	0.9750	0.9663	0.9706	0.8401	0.8544	0.8472	0.8436	0.8768	0.8599	0.8299	0.8920	0.8598	0.9502	0.8721	0.8974	0.8844	0.9512	0.9502	0.9506
0.0268	20.0	1340	0.6114	0.9740	0.9667	0.9703	0.8353	0.8617	0.8483	0.8585	0.8670	0.8627	0.8346	0.8809	0.8571	0.9500	0.8756	0.8941	0.8846	0.9508	0.9500	0.9504
0.0268	21.0	1407	0.5467	0.9800	0.9641	0.9720	0.8284	0.8908	0.8585	0.8436	0.8768	0.8599	0.8392	0.9252	0.8801	0.9532	0.8728	0.9142	0.8926	0.9551	0.9532	0.9539
0.0268	22.0	1474	0.5370	0.9779	0.9618	0.9697	0.8266	0.8908	0.8575	0.8257	0.8867	0.8551	0.8235	0.8920	0.8564	0.9495	0.8634	0.9078	0.8847	0.9515	0.9495	0.9502
0.007	23.0	1541	0.6169	0.9739	0.9658	0.9699	0.8480	0.8665	0.8571	0.8429	0.8719	0.8571	0.8203	0.8726	0.8456	0.9493	0.8713	0.8942	0.8824	0.9503	0.9493	0.9497
0.007	24.0	1608	0.6113	0.9744	0.9658	0.9701	0.8269	0.8811	0.8531	0.8725	0.8768	0.8747	0.8289	0.8587	0.8435	0.9496	0.8757	0.8956	0.8854	0.9507	0.9496	0.9501
0.007	25.0	1675	0.5969	0.9775	0.9656	0.9715	0.8440	0.8665	0.8551	0.8161	0.8966	0.8545	0.8398	0.9003	0.8690	0.9518	0.8694	0.9072	0.8875	0.9532	0.9518	0.9523
0.007	26.0	1742	0.5509	0.9811	0.9620	0.9714	0.8288	0.8811	0.8541	0.8249	0.8818	0.8524	0.8180	0.9335	0.8719	0.9514	0.8632	0.9146	0.8875	0.9540	0.9514	0.9523
0.007	27.0	1809	0.5003	0.9818	0.9567	0.9691	0.7780	0.8932	0.8316	0.8243	0.9015	0.8612	0.8304	0.9224	0.8740	0.9479	0.8536	0.9184	0.8840	0.9517	0.9479	0.9491
0.007	28.0	1876	0.5675	0.9806	0.9611	0.9708	0.8039	0.8956	0.8473	0.8429	0.8719	0.8571	0.8275	0.9169	0.8699	0.9503	0.8637	0.9114	0.8863	0.9530	0.9503	0.9513
0.007	29.0	1943	0.6114	0.9806	0.9616	0.9710	0.8053	0.8835	0.8426	0.8551	0.8719	0.8634	0.8260	0.9335	0.8765	0.9509	0.8667	0.9126	0.8884	0.9535	0.9509	0.9518
0.0025	30.0	2010	0.6773	0.9741	0.9654	0.9698	0.8172	0.8786	0.8468	0.8737	0.8522	0.8628	0.8329	0.8698	0.8509	0.9489	0.8745	0.8915	0.8826	0.9501	0.9489	0.9494
0.0025	31.0	2077	0.5380	0.9791	0.9601	0.9695	0.7996	0.8908	0.8427	0.8326	0.8818	0.8565	0.8321	0.9058	0.8674	0.9488	0.8608	0.9096	0.8840	0.9514	0.9488	0.9497
0.0025	32.0	2144	0.5114	0.9825	0.9601	0.9712	0.8102	0.8908	0.8486	0.8296	0.9113	0.8685	0.8280	0.9335	0.8776	0.9516	0.8626	0.9239	0.8915	0.9546	0.9516	0.9526
0.0025	33.0	2211	0.5792	0.9785	0.9611	0.9697	0.8013	0.8908	0.8437	0.8429	0.8719	0.8571	0.8286	0.8975	0.8617	0.9488	0.8628	0.9053	0.8831	0.9512	0.9488	0.9496
0.0025	34.0	2278	0.6516	0.9748	0.9658	0.9703	0.8361	0.8665	0.8510	0.8396	0.8768	0.8578	0.8342	0.8781	0.8556	0.9498	0.8712	0.8968	0.8837	0.9509	0.9498	0.9503
0.0025	35.0	2345	0.5294	0.9823	0.9599	0.9709	0.8071	0.8835	0.8436	0.8227	0.8916	0.8558	0.8301	0.9474	0.8849	0.9511	0.8606	0.9206	0.8888	0.9541	0.9511	0.9521
0.0025	36.0	2412	0.6674	0.9759	0.9665	0.9711	0.8469	0.8592	0.8530	0.8364	0.8818	0.8585	0.8303	0.8947	0.8613	0.9511	0.8724	0.9006	0.8860	0.9522	0.9511	0.9515
0.0025	37.0	2479	0.6354	0.9753	0.9626	0.9689	0.8141	0.8714	0.8417	0.8310	0.8719	0.8510	0.8329	0.8837	0.8575	0.9477	0.8633	0.8974	0.8798	0.9493	0.9477	0.9483
0.0014	38.0	2546	0.6787	0.9746	0.9658	0.9702	0.8526	0.8422	0.8474	0.8241	0.8768	0.8496	0.8253	0.9030	0.8624	0.9496	0.8691	0.8970	0.8824	0.9508	0.9496	0.9500
0.0014	39.0	2613	0.7050	0.9739	0.9646	0.9692	0.8279	0.8641	0.8456	0.8462	0.8670	0.8564	0.8303	0.8809	0.8548	0.9484	0.8696	0.8941	0.8815	0.9495	0.9484	0.9489
0.0014	40.0	2680	0.6279	0.9781	0.9618	0.9699	0.8205	0.8762	0.8474	0.8174	0.8818	0.8483	0.8278	0.9058	0.8651	0.9491	0.8609	0.9064	0.8827	0.9512	0.9491	0.9499
0.0014	41.0	2747	0.6812	0.9779	0.9658	0.9719	0.8436	0.8641	0.8537	0.8333	0.8867	0.8592	0.8333	0.9141	0.8719	0.9523	0.8721	0.9077	0.8892	0.9538	0.9523	0.9528
0.0014	42.0	2814	0.6036	0.9804	0.9594	0.9698	0.7898	0.9029	0.8426	0.8443	0.8818	0.8627	0.8295	0.9030	0.8647	0.9489	0.8610	0.9118	0.8849	0.9520	0.9489	0.9500
0.0014	43.0	2881	0.6358	0.9779	0.9622	0.9700	0.8075	0.8859	0.8449	0.8524	0.8818	0.8668	0.8329	0.8975	0.864	0.9496	0.8677	0.9069	0.8864	0.9517	0.9496	0.9504
0.0014	44.0	2948	0.6128	0.98	0.9626	0.9712	0.8121	0.8811	0.8452	0.8426	0.8966	0.8687	0.8384	0.9197	0.8771	0.9516	0.8683	0.9150	0.8906	0.9538	0.9516	0.9524
0.001	45.0	3015	0.7238	0.9744	0.9663	0.9703	0.8318	0.8641	0.8476	0.8689	0.8818	0.8753	0.8373	0.8837	0.8598	0.9505	0.8781	0.8989	0.8883	0.9515	0.9505	0.9509
0.001	46.0	3082	0.5885	0.9811	0.9620	0.9714	0.8157	0.8811	0.8471	0.8161	0.8966	0.8545	0.8421	0.9307	0.8842	0.9518	0.8638	0.9176	0.8893	0.9542	0.9518	0.9526
0.001	47.0	3149	0.6924	0.9744	0.9654	0.9699	0.8245	0.8665	0.8450	0.8436	0.8768	0.8599	0.8373	0.8698	0.8533	0.9489	0.8699	0.8946	0.8820	0.9500	0.9489	0.9494
0.001	48.0	3216	0.5978	0.9798	0.9609	0.9702	0.8040	0.8859	0.8430	0.8287	0.8818	0.8544	0.8333	0.9141	0.8719	0.9496	0.8614	0.9107	0.8849	0.9522	0.9496	0.9505
0.001	49.0	3283	0.5363	0.9823	0.9592	0.9706	0.8013	0.8908	0.8437	0.8222	0.9113	0.8645	0.8313	0.9280	0.8770	0.9505	0.8593	0.9223	0.8889	0.9537	0.9505	0.9516
0.001	50.0	3350	0.5867	0.9793	0.9616	0.9704	0.8093	0.8859	0.8459	0.8265	0.8916	0.8578	0.8363	0.9058	0.8697	0.9500	0.8629	0.9112	0.8859	0.9524	0.9500	0.9508
0.001	51.0	3417	0.6383	0.9775	0.9643	0.9709	0.8238	0.8738	0.8481	0.8404	0.8818	0.8606	0.8355	0.9003	0.8667	0.9507	0.8693	0.9050	0.8865	0.9523	0.9507	0.9513
0.001	52.0	3484	0.6261	0.9779	0.9626	0.9702	0.8071	0.8835	0.8436	0.8436	0.8768	0.8599	0.8346	0.8947	0.8636	0.9495	0.8658	0.9044	0.8843	0.9515	0.9495	0.9502
0.0008	53.0	3551	0.6686	0.9763	0.9663	0.9712	0.8361	0.8665	0.8510	0.8647	0.8818	0.8732	0.8308	0.8975	0.8628	0.9516	0.8770	0.9030	0.8896	0.9528	0.9516	0.9521
0.0008	54.0	3618	0.6354	0.9785	0.9641	0.9713	0.8291	0.8714	0.8497	0.8333	0.8867	0.8592	0.8333	0.9141	0.8719	0.9514	0.8686	0.9091	0.8880	0.9532	0.9514	0.9521
0.0008	55.0	3685	0.6611	0.9758	0.9650	0.9704	0.8287	0.8689	0.8483	0.8483	0.8818	0.8647	0.8338	0.8892	0.8606	0.9502	0.8717	0.9012	0.8860	0.9515	0.9502	0.9507
0.0008	56.0	3752	0.6380	0.9766	0.9637	0.9701	0.8200	0.8738	0.8461	0.8451	0.8867	0.8654	0.8342	0.8920	0.8621	0.9498	0.8690	0.9040	0.8859	0.9514	0.9498	0.9504
0.0008	57.0	3819	0.6418	0.9768	0.9635	0.9701	0.8182	0.8738	0.8451	0.8419	0.8916	0.8660	0.8390	0.8947	0.8660	0.9500	0.8690	0.9059	0.8868	0.9516	0.9500	0.9506
0.0008	58.0	3886	0.6478	0.9770	0.9635	0.9702	0.8182	0.8738	0.8451	0.8419	0.8916	0.8660	0.8394	0.8975	0.8675	0.9502	0.8691	0.9066	0.8872	0.9518	0.9502	0.9508
0.0008	59.0	3953	0.6498	0.9766	0.9643	0.9705	0.8249	0.8689	0.8463	0.8419	0.8916	0.8660	0.8394	0.8975	0.8675	0.9505	0.8707	0.9056	0.8876	0.9520	0.9505	0.9511
0.0006	60.0	4020	0.6522	0.9766	0.9646	0.9706	0.8268	0.8689	0.8473	0.8419	0.8916	0.8660	0.8394	0.8975	0.8675	0.9507	0.8712	0.9057	0.8878	0.9521	0.9507	0.9513

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

antoineedy-old/stanford-deidentifier-base-finetuned-ner

stanford-deidentifier-base-finetuned-ner

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

antoineedy-old/
stanford-deidentifier-base-finetuned-ner