distilgpt2-finetuned-ner

antoineedy-old/distilgpt2-finetuned-ner

distilgpt2-finetuned-ner is a token classification model from antoineedy-old. 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: gpt2
Access: Public
Created: Apr 11, 2024
Updated: Apr 11, 2024
SHA: 61c9af85

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Base models

distilbert/distilgpt2

Model card

From the Hugging Face model README

distilgpt2-finetuned-ner

This model is a fine-tuned version of distilbert/distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.4074
0 Precision: 0.9557
0 Recall: 0.8738
0 F1-score: 0.9129
1 Precision: 0.6128
1 Recall: 0.8971
1 F1-score: 0.7282
2 Precision: 0.4667
2 Recall: 0.6272
2 F1-score: 0.5352
3 Precision: 0.6606
3 Recall: 0.7742
3 F1-score: 0.7129
Accuracy: 0.8577
Macro avg Precision: 0.6739
Macro avg Recall: 0.7931
Macro avg F1-score: 0.7223
Weighted avg Precision: 0.8827
Weighted avg Recall: 0.8577
Weighted avg F1-score: 0.8656

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	0.7625	0.9807	0.6141	0.7553	0.4507	0.9242	0.6059	0.1833	0.7455	0.2942	0.3184	0.6280	0.4226	0.6479	0.4833	0.7279	0.5195	0.8510	0.6479	0.6977
No log	2.0	134	0.5560	0.9828	0.6673	0.7949	0.4835	0.9278	0.6357	0.2298	0.7634	0.3532	0.3942	0.7892	0.5258	0.7032	0.5226	0.7870	0.5774	0.8631	0.7032	0.7419
No log	3.0	201	0.5080	0.9905	0.6402	0.7778	0.4718	0.9531	0.6312	0.2308	0.8100	0.3593	0.3957	0.8409	0.5382	0.6896	0.5222	0.8110	0.5766	0.8684	0.6896	0.7291
No log	4.0	268	0.4785	0.9840	0.7067	0.8226	0.4939	0.9549	0.6511	0.2580	0.8100	0.3913	0.4923	0.8215	0.6156	0.7413	0.5571	0.8233	0.6202	0.8734	0.7413	0.7736
No log	5.0	335	0.4872	0.9877	0.6839	0.8082	0.5178	0.9440	0.6688	0.2810	0.7885	0.4143	0.3887	0.8860	0.5403	0.7260	0.5438	0.8256	0.6079	0.8719	0.7260	0.7592
No log	6.0	402	0.4646	0.9873	0.7051	0.8227	0.5303	0.9477	0.6801	0.2845	0.8136	0.4215	0.4304	0.8839	0.5789	0.7441	0.5581	0.8376	0.6258	0.8758	0.7441	0.7748
No log	7.0	469	0.4540	0.9866	0.7006	0.8193	0.5122	0.9495	0.6654	0.2659	0.8387	0.4038	0.4656	0.8581	0.6036	0.7398	0.5576	0.8367	0.6230	0.8755	0.7398	0.7719
0.6085	8.0	536	0.4723	0.9839	0.7255	0.8352	0.5019	0.9549	0.6580	0.2909	0.8029	0.4271	0.4913	0.8473	0.6219	0.7578	0.5670	0.8326	0.6355	0.8754	0.7578	0.7862
0.6085	9.0	603	0.4998	0.9777	0.7716	0.8625	0.5286	0.9495	0.6791	0.3279	0.7921	0.4638	0.5496	0.8215	0.6586	0.7916	0.5960	0.8337	0.6660	0.8787	0.7916	0.8141
0.6085	10.0	670	0.4960	0.9867	0.7176	0.8309	0.5358	0.9458	0.6841	0.2789	0.8136	0.4154	0.4576	0.8710	0.6	0.7529	0.5647	0.8370	0.6326	0.8776	0.7529	0.7829
0.6085	11.0	737	0.5292	0.9760	0.7870	0.8713	0.5425	0.9440	0.6891	0.3302	0.7563	0.4597	0.5712	0.8280	0.6760	0.8023	0.6050	0.8288	0.6740	0.8802	0.8023	0.8231
0.6085	12.0	804	0.5283	0.9778	0.7666	0.8594	0.5438	0.9296	0.6862	0.3135	0.7563	0.4433	0.5130	0.8495	0.6397	0.7864	0.5870	0.8255	0.6571	0.8768	0.7864	0.8100
0.6085	13.0	871	0.5510	0.9793	0.7678	0.8608	0.5409	0.9422	0.6873	0.3107	0.7706	0.4428	0.5307	0.8366	0.6494	0.7882	0.5904	0.8293	0.6601	0.8789	0.7882	0.8118
0.6085	14.0	938	0.5859	0.9751	0.7913	0.8737	0.5610	0.9296	0.6997	0.3303	0.7742	0.4630	0.5641	0.8237	0.6696	0.8050	0.6076	0.8297	0.6765	0.8806	0.8050	0.8255
0.2627	15.0	1005	0.6114	0.9770	0.7811	0.8681	0.5360	0.9404	0.6828	0.3392	0.7563	0.4684	0.5418	0.8366	0.6577	0.7979	0.5985	0.8286	0.6692	0.8787	0.7979	0.8190
0.2627	16.0	1072	0.6434	0.9761	0.7747	0.8638	0.5407	0.9350	0.6852	0.3518	0.7276	0.4743	0.4913	0.8516	0.6231	0.7923	0.5900	0.8222	0.6616	0.8752	0.7923	0.8135
0.2627	17.0	1139	0.6766	0.9729	0.7939	0.8743	0.5558	0.9350	0.6972	0.3577	0.7204	0.4780	0.5313	0.8387	0.6505	0.8063	0.6044	0.8220	0.6750	0.8772	0.8063	0.8251
0.2627	18.0	1206	0.7459	0.9672	0.8159	0.8851	0.5644	0.9260	0.7013	0.3752	0.6953	0.4874	0.5762	0.8215	0.6773	0.8206	0.6207	0.8147	0.6878	0.8774	0.8206	0.8364
0.2627	19.0	1273	0.7824	0.9682	0.8301	0.8938	0.5644	0.9332	0.7034	0.3808	0.7097	0.4956	0.6364	0.7978	0.7080	0.8314	0.6374	0.8177	0.7002	0.8829	0.8314	0.8461
0.2627	20.0	1340	0.7763	0.9667	0.8261	0.8909	0.5615	0.9314	0.7006	0.3845	0.6918	0.4942	0.6127	0.8065	0.6964	0.8280	0.6313	0.8139	0.6955	0.8799	0.8280	0.8427
0.2627	21.0	1407	0.7584	0.9694	0.8155	0.8858	0.5683	0.9242	0.7038	0.3736	0.7312	0.4945	0.5826	0.8194	0.6810	0.8215	0.6235	0.8225	0.6913	0.8800	0.8215	0.8378
0.2627	22.0	1474	0.8247	0.9684	0.8232	0.8899	0.5617	0.9278	0.6998	0.3886	0.7061	0.5013	0.5962	0.8129	0.6879	0.8264	0.6287	0.8175	0.6947	0.8802	0.8264	0.8415
0.1736	23.0	1541	0.7862	0.9692	0.8163	0.8862	0.5709	0.9224	0.7053	0.3704	0.7168	0.4884	0.5794	0.8237	0.6803	0.8217	0.6225	0.8198	0.6900	0.8797	0.8217	0.8379
0.1736	24.0	1608	0.9049	0.9655	0.8356	0.8959	0.5781	0.9224	0.7107	0.3987	0.6774	0.5020	0.6097	0.8129	0.6968	0.8346	0.6380	0.8121	0.7013	0.8808	0.8346	0.8479
0.1736	25.0	1675	0.9058	0.9677	0.8293	0.8932	0.5872	0.9242	0.7181	0.3962	0.6703	0.4980	0.5672	0.8258	0.6725	0.8303	0.6296	0.8124	0.6954	0.8801	0.8303	0.8444
0.1736	26.0	1742	0.9049	0.9667	0.8258	0.8907	0.5548	0.9314	0.6954	0.384	0.6882	0.4929	0.6215	0.8086	0.7028	0.8277	0.6317	0.8135	0.6955	0.8799	0.8277	0.8424
0.1736	27.0	1809	1.0230	0.9629	0.8477	0.9017	0.584	0.9224	0.7152	0.4114	0.6487	0.5035	0.6361	0.8043	0.7104	0.8423	0.6486	0.8058	0.7077	0.8818	0.8423	0.8539
0.1736	28.0	1876	0.9487	0.9654	0.8398	0.8982	0.5746	0.9242	0.7087	0.4043	0.6667	0.5034	0.6231	0.8108	0.7047	0.8374	0.6419	0.8104	0.7037	0.8817	0.8374	0.8502
0.1736	29.0	1943	0.9729	0.9661	0.8347	0.8956	0.5805	0.9242	0.7131	0.4021	0.6774	0.5047	0.5978	0.8151	0.6897	0.8341	0.6366	0.8128	0.7008	0.8808	0.8341	0.8474
0.1305	30.0	2010	1.0135	0.9636	0.8487	0.9025	0.5939	0.9188	0.7215	0.4102	0.6631	0.5068	0.6324	0.8065	0.7089	0.8436	0.6500	0.8093	0.7099	0.8828	0.8436	0.8552
0.1305	31.0	2077	1.0109	0.9618	0.8455	0.8999	0.5670	0.9242	0.7028	0.4184	0.6523	0.5098	0.6462	0.7935	0.7124	0.8401	0.6483	0.8039	0.7062	0.8804	0.8401	0.8519
0.1305	32.0	2144	1.0659	0.9627	0.8517	0.9038	0.5995	0.9134	0.7239	0.4201	0.6595	0.5132	0.6210	0.8	0.6992	0.8448	0.6508	0.8061	0.7100	0.8822	0.8448	0.8560
0.1305	33.0	2211	1.0779	0.9600	0.8554	0.9047	0.6002	0.9079	0.7227	0.4293	0.6416	0.5144	0.6229	0.7957	0.6988	0.8462	0.6531	0.8002	0.7101	0.8807	0.8462	0.8566
0.1305	34.0	2278	1.2438	0.9552	0.8732	0.9124	0.6053	0.9079	0.7264	0.4898	0.6022	0.5402	0.6505	0.7806	0.7097	0.8576	0.6752	0.7910	0.7222	0.8820	0.8576	0.8650
0.1305	35.0	2345	1.1222	0.9609	0.8602	0.9077	0.5955	0.9116	0.7204	0.4312	0.6631	0.5226	0.6588	0.7806	0.7146	0.8502	0.6616	0.8039	0.7163	0.8837	0.8502	0.8604
0.1305	36.0	2412	1.2303	0.9577	0.8687	0.9110	0.5946	0.9188	0.7220	0.4699	0.6165	0.5333	0.6648	0.7806	0.7181	0.8555	0.6718	0.7961	0.7211	0.8832	0.8555	0.8638
0.1305	37.0	2479	1.1387	0.9603	0.8507	0.9021	0.5886	0.9116	0.7153	0.4189	0.6667	0.5145	0.6353	0.7828	0.7013	0.8429	0.6508	0.8029	0.7083	0.8803	0.8429	0.8541
0.1036	38.0	2546	1.2204	0.9585	0.8623	0.9079	0.5988	0.9025	0.7199	0.4458	0.6344	0.5237	0.6387	0.7871	0.7052	0.8503	0.6605	0.7966	0.7142	0.8812	0.8503	0.8598
0.1036	39.0	2613	1.2219	0.9594	0.8594	0.9066	0.5950	0.9097	0.7195	0.4512	0.6129	0.5198	0.6210	0.8	0.6992	0.8486	0.6567	0.7955	0.7113	0.8805	0.8486	0.8581
0.1036	40.0	2680	1.2658	0.9567	0.8691	0.9108	0.5948	0.9061	0.7182	0.4534	0.6272	0.5263	0.6761	0.7720	0.7209	0.8546	0.6702	0.7936	0.7190	0.8825	0.8546	0.8632
0.1036	41.0	2747	1.1926	0.9607	0.8560	0.9053	0.6017	0.9079	0.7237	0.4180	0.6487	0.5084	0.6397	0.7978	0.7100	0.8472	0.6550	0.8026	0.7119	0.8821	0.8472	0.8578
0.1036	42.0	2814	1.2121	0.9590	0.8602	0.9069	0.5986	0.8989	0.7186	0.4414	0.6344	0.5206	0.6314	0.7957	0.7041	0.8489	0.6576	0.7973	0.7125	0.8809	0.8489	0.8587
0.1036	43.0	2881	1.2253	0.9597	0.8572	0.9056	0.5899	0.9061	0.7146	0.4490	0.6308	0.5246	0.6252	0.8	0.7019	0.8473	0.6559	0.7985	0.7117	0.8806	0.8473	0.8573
0.1036	44.0	2948	1.2930	0.9572	0.8705	0.9117	0.6098	0.8971	0.7261	0.4534	0.6272	0.5263	0.6595	0.7871	0.7176	0.8560	0.6699	0.7955	0.7204	0.8830	0.8560	0.8644
0.0887	45.0	3015	1.2525	0.9584	0.8612	0.9072	0.5969	0.9116	0.7214	0.4456	0.6308	0.5223	0.6386	0.7828	0.7034	0.8497	0.6599	0.7966	0.7136	0.8810	0.8497	0.8592
0.0887	46.0	3082	1.3334	0.9556	0.8720	0.9119	0.6014	0.9043	0.7224	0.4751	0.6165	0.5367	0.6606	0.7742	0.7129	0.8565	0.6732	0.7918	0.7210	0.8820	0.8565	0.8643
0.0887	47.0	3149	1.3047	0.9584	0.8667	0.9103	0.6031	0.9025	0.7231	0.4490	0.6308	0.5246	0.6542	0.7892	0.7154	0.8538	0.6662	0.7973	0.7183	0.8828	0.8538	0.8627
0.0887	48.0	3216	1.3094	0.9581	0.8677	0.9106	0.6022	0.9043	0.7229	0.4492	0.6344	0.5260	0.6594	0.7785	0.7140	0.8541	0.6672	0.7962	0.7184	0.8828	0.8541	0.8630
0.0887	49.0	3283	1.3583	0.9574	0.8712	0.9123	0.6121	0.8971	0.7277	0.4504	0.6344	0.5268	0.6624	0.7806	0.7167	0.8565	0.6706	0.7959	0.7209	0.8834	0.8565	0.8650
0.0887	50.0	3350	1.3429	0.9579	0.8643	0.9087	0.6105	0.9025	0.7283	0.4447	0.6344	0.5229	0.6330	0.7828	0.7000	0.8516	0.6615	0.7960	0.7150	0.8813	0.8516	0.8608
0.0887	51.0	3417	1.3133	0.9598	0.8633	0.9090	0.6043	0.9097	0.7262	0.4403	0.6344	0.5198	0.6421	0.7871	0.7072	0.8517	0.6616	0.7986	0.7156	0.8827	0.8517	0.8612
0.0887	52.0	3484	1.3570	0.9562	0.8722	0.9123	0.6116	0.8953	0.7267	0.4531	0.6237	0.5249	0.6600	0.7763	0.7134	0.8563	0.6702	0.7919	0.7193	0.8824	0.8563	0.8645
0.0787	53.0	3551	1.4055	0.9560	0.8768	0.9147	0.6091	0.8971	0.7255	0.4822	0.6308	0.5466	0.6716	0.7742	0.7193	0.8602	0.6797	0.7947	0.7265	0.8841	0.8602	0.8677
0.0787	54.0	3618	1.3776	0.9576	0.8718	0.9127	0.6148	0.8989	0.7302	0.4571	0.6308	0.5301	0.6565	0.7849	0.7150	0.8573	0.6715	0.7966	0.7220	0.8837	0.8573	0.8655
0.0787	55.0	3685	1.3579	0.9590	0.8655	0.9099	0.6065	0.9043	0.7261	0.4552	0.6380	0.5313	0.6376	0.7871	0.7045	0.8532	0.6646	0.7987	0.7180	0.8826	0.8532	0.8622
0.0787	56.0	3752	1.4072	0.9563	0.8746	0.9136	0.6151	0.8971	0.7298	0.4684	0.6380	0.5402	0.6661	0.7763	0.7170	0.8590	0.6765	0.7965	0.7252	0.8839	0.8590	0.8668
0.0787	57.0	3819	1.3831	0.9570	0.8718	0.9124	0.6141	0.8989	0.7297	0.4524	0.6308	0.5269	0.6606	0.7785	0.7147	0.8568	0.6710	0.7950	0.7209	0.8833	0.8568	0.8651
0.0787	58.0	3886	1.4017	0.9565	0.8734	0.9131	0.6159	0.8971	0.7303	0.4607	0.6308	0.5325	0.6588	0.7763	0.7127	0.8577	0.6730	0.7944	0.7222	0.8832	0.8577	0.8658
0.0787	59.0	3953	1.3982	0.9564	0.8728	0.9127	0.6133	0.8989	0.7291	0.4630	0.6272	0.5327	0.6582	0.7785	0.7133	0.8574	0.6727	0.7944	0.7220	0.8830	0.8574	0.8654
0.0739	60.0	4020	1.4074	0.9557	0.8738	0.9129	0.6128	0.8971	0.7282	0.4667	0.6272	0.5352	0.6606	0.7742	0.7129	0.8577	0.6739	0.7931	0.7223	0.8827	0.8577	0.8656

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Task: Token Classification
Library: transformers
Type: gpt2
License: apache-2.0
Created: Apr 11, 2024
Updated: Apr 11, 2024