bert-base-NER-finetuned-ner

antoineedy-old/bert-base-NER-finetuned-ner

bert-base-NER-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 mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: bert
Access: Public
Created: Apr 11, 2024
Updated: Apr 11, 2024
SHA: 0efea1bb

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Base models

dslim/bert-base-NER

Model card

From the Hugging Face model README

bert-base-NER-finetuned-ner

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

Loss: 0.9704
0 Precision: 0.9706
0 Recall: 0.9413
0 F1-score: 0.9558
1 Precision: 0.8027
1 Recall: 0.9205
1 F1-score: 0.8575
2 Precision: 0.7853
2 Recall: 0.8165
2 F1-score: 0.8006
3 Precision: 0.7817
3 Recall: 0.8603
3 F1-score: 0.8191
Accuracy: 0.9272
Macro avg Precision: 0.8351
Macro avg Recall: 0.8847
Macro avg F1-score: 0.8583
Weighted avg Precision: 0.9313
Weighted avg Recall: 0.9272
Weighted avg F1-score: 0.9285

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	0.3241	0.9901	0.8116	0.8920	0.5586	0.9694	0.7088	0.4424	0.8807	0.5890	0.6615	0.8696	0.7514	0.8343	0.6631	0.8829	0.7353	0.8979	0.8343	0.8495
No log	2.0	134	0.3219	0.9882	0.8544	0.9164	0.6282	0.9480	0.7556	0.5375	0.8318	0.6531	0.6460	0.9106	0.7558	0.8665	0.7000	0.8862	0.7702	0.9064	0.8665	0.8763
No log	3.0	201	0.3126	0.9927	0.8353	0.9072	0.5873	0.9725	0.7323	0.5213	0.8624	0.6498	0.6578	0.9199	0.7671	0.8561	0.6898	0.8975	0.7641	0.9062	0.8561	0.8677
No log	4.0	268	0.3805	0.9851	0.8936	0.9371	0.7105	0.9419	0.8100	0.6166	0.8410	0.7115	0.7001	0.9218	0.7958	0.8979	0.7531	0.8996	0.8136	0.9196	0.8979	0.9035
No log	5.0	335	0.4058	0.9839	0.9028	0.9416	0.6786	0.9587	0.7947	0.6887	0.8593	0.7646	0.7718	0.9069	0.8339	0.9064	0.7807	0.9069	0.8337	0.9246	0.9064	0.9110
No log	6.0	402	0.4349	0.9833	0.9130	0.9468	0.7246	0.9373	0.8173	0.6786	0.8716	0.7631	0.7649	0.9088	0.8306	0.9130	0.7878	0.9077	0.8395	0.9275	0.9130	0.9169
No log	7.0	469	0.4379	0.9839	0.9184	0.9500	0.7308	0.9465	0.8248	0.7072	0.8716	0.7808	0.7755	0.9069	0.8361	0.9179	0.7994	0.9108	0.8479	0.9308	0.9179	0.9214
0.2085	8.0	536	0.4750	0.9862	0.8964	0.9391	0.6702	0.9694	0.7925	0.7038	0.8502	0.7701	0.7369	0.9181	0.8176	0.9028	0.7743	0.9085	0.8298	0.9236	0.9028	0.9079
0.2085	9.0	603	0.5353	0.9817	0.9225	0.9512	0.7443	0.9526	0.8357	0.7342	0.8532	0.7893	0.7751	0.9050	0.8351	0.9207	0.8088	0.9083	0.8528	0.9315	0.9207	0.9236
0.2085	10.0	670	0.5730	0.9786	0.9325	0.9550	0.7920	0.9434	0.8611	0.7413	0.8502	0.7920	0.7722	0.8901	0.8270	0.9263	0.8211	0.9040	0.8588	0.9338	0.9263	0.9285
0.2085	11.0	737	0.5801	0.9787	0.9199	0.9484	0.7404	0.9419	0.8291	0.7199	0.8410	0.7757	0.7679	0.8994	0.8285	0.9166	0.8017	0.9005	0.8454	0.9276	0.9166	0.9197
0.2085	12.0	804	0.7227	0.9675	0.9526	0.96	0.8496	0.8807	0.8649	0.8170	0.7920	0.8043	0.7921	0.8939	0.8399	0.9337	0.8565	0.8798	0.8673	0.9356	0.9337	0.9343
0.2085	13.0	871	0.6296	0.9744	0.9421	0.9579	0.8331	0.9159	0.8725	0.7568	0.8471	0.7994	0.7794	0.8883	0.8303	0.9309	0.8359	0.8983	0.8650	0.9356	0.9309	0.9325
0.2085	14.0	938	0.7074	0.9728	0.9397	0.9559	0.8070	0.9205	0.8600	0.7690	0.8349	0.8006	0.7804	0.8734	0.8243	0.9278	0.8323	0.8921	0.8602	0.9326	0.9278	0.9293
0.0385	15.0	1005	0.7392	0.9714	0.9441	0.9576	0.8425	0.8914	0.8663	0.7634	0.8287	0.7947	0.7721	0.8957	0.8293	0.9299	0.8373	0.8900	0.8620	0.9340	0.9299	0.9313
0.0385	16.0	1072	0.7589	0.9741	0.9399	0.9567	0.8003	0.9190	0.8555	0.7604	0.8349	0.7959	0.7876	0.8771	0.8300	0.9281	0.8306	0.8927	0.8595	0.9331	0.9281	0.9297
0.0385	17.0	1139	0.7045	0.9724	0.9380	0.9549	0.7847	0.9251	0.8491	0.7624	0.8440	0.8012	0.8056	0.8641	0.8338	0.9266	0.8313	0.8928	0.8597	0.9318	0.9266	0.9282
0.0385	18.0	1206	0.7735	0.9698	0.9437	0.9566	0.8043	0.9174	0.8571	0.7721	0.8287	0.7994	0.8074	0.8510	0.8286	0.9286	0.8384	0.8852	0.8604	0.9322	0.9286	0.9298
0.0385	19.0	1273	0.7184	0.9735	0.9399	0.9564	0.8150	0.9159	0.8625	0.7439	0.8440	0.7908	0.7863	0.8771	0.8292	0.9282	0.8297	0.8942	0.8597	0.9332	0.9282	0.9298
0.0385	20.0	1340	0.7814	0.9741	0.9341	0.9537	0.7875	0.9235	0.8501	0.7535	0.8226	0.7865	0.7581	0.8696	0.8101	0.9229	0.8183	0.8875	0.8501	0.9293	0.9229	0.9249
0.0385	21.0	1407	0.8279	0.9696	0.9445	0.9569	0.8201	0.9128	0.8640	0.7768	0.8196	0.7976	0.7880	0.8585	0.8217	0.9289	0.8386	0.8838	0.8601	0.9323	0.9289	0.9301
0.0385	22.0	1474	0.7268	0.9724	0.9332	0.9524	0.7704	0.9388	0.8463	0.7647	0.8349	0.7982	0.7818	0.8473	0.8132	0.9224	0.8223	0.8885	0.8525	0.9287	0.9224	0.9243
0.0127	23.0	1541	0.8197	0.9698	0.9445	0.9570	0.8078	0.9190	0.8598	0.7928	0.8073	0.8	0.7973	0.8641	0.8293	0.9294	0.8419	0.8837	0.8615	0.9327	0.9294	0.9305
0.0127	24.0	1608	0.8221	0.9722	0.9447	0.9582	0.8197	0.9037	0.8596	0.7718	0.8379	0.8035	0.7933	0.8790	0.8339	0.9307	0.8392	0.8913	0.8638	0.9344	0.9307	0.9320
0.0127	25.0	1675	0.8098	0.9735	0.9373	0.9550	0.7766	0.9358	0.8488	0.7928	0.8073	0.8	0.7809	0.8696	0.8229	0.9257	0.8310	0.8875	0.8567	0.9314	0.9257	0.9274
0.0127	26.0	1742	0.8023	0.9710	0.9404	0.9554	0.7897	0.9358	0.8565	0.7813	0.8196	0.8	0.8035	0.8529	0.8275	0.9275	0.8364	0.8872	0.8599	0.9319	0.9275	0.9288
0.0127	27.0	1809	0.7750	0.9748	0.9373	0.9557	0.7897	0.9358	0.8565	0.7591	0.8287	0.7924	0.7963	0.8808	0.8364	0.9276	0.8300	0.8957	0.8603	0.9333	0.9276	0.9293
0.0127	28.0	1876	0.9205	0.9673	0.9465	0.9568	0.8220	0.9037	0.8609	0.7861	0.7982	0.7921	0.7925	0.8603	0.8250	0.9288	0.8420	0.8772	0.8587	0.9314	0.9288	0.9297
0.0127	29.0	1943	0.7887	0.9726	0.9376	0.9548	0.7695	0.9343	0.8439	0.7756	0.8349	0.8041	0.8057	0.8492	0.8268	0.9256	0.8308	0.8890	0.8574	0.9311	0.9256	0.9273
0.0052	30.0	2010	0.8106	0.9778	0.9371	0.9570	0.7861	0.9327	0.8531	0.7658	0.8502	0.8058	0.7897	0.8883	0.8361	0.9288	0.8299	0.9021	0.8630	0.9351	0.9288	0.9307
0.0052	31.0	2077	0.8659	0.9699	0.9421	0.9558	0.8022	0.9113	0.8533	0.7929	0.8196	0.8060	0.7922	0.8734	0.8308	0.9281	0.8393	0.8866	0.8615	0.9319	0.9281	0.9293
0.0052	32.0	2144	0.8154	0.9722	0.9389	0.9553	0.7878	0.9251	0.8509	0.7768	0.8410	0.8076	0.7986	0.8641	0.8301	0.9272	0.8339	0.8923	0.8610	0.9321	0.9272	0.9287
0.0052	33.0	2211	0.8569	0.9727	0.9432	0.9577	0.8086	0.9174	0.8596	0.7878	0.8287	0.8077	0.7953	0.8827	0.8367	0.9307	0.8411	0.8930	0.8654	0.9347	0.9307	0.9320
0.0052	34.0	2278	0.8868	0.9705	0.9432	0.9566	0.8011	0.9113	0.8526	0.7843	0.8226	0.8030	0.7976	0.8659	0.8304	0.9285	0.8384	0.8858	0.8607	0.9323	0.9285	0.9298
0.0052	35.0	2345	0.8586	0.9745	0.9412	0.9575	0.8021	0.9235	0.8586	0.7771	0.8318	0.8035	0.79	0.8827	0.8338	0.9298	0.8359	0.8948	0.8634	0.9346	0.9298	0.9313
0.0052	36.0	2412	0.9288	0.9698	0.9449	0.9572	0.8157	0.9067	0.8588	0.7864	0.8104	0.7982	0.7825	0.8641	0.8212	0.9286	0.8386	0.8815	0.8588	0.9320	0.9286	0.9298
0.0052	37.0	2479	0.9396	0.9684	0.9460	0.9570	0.8186	0.9037	0.8590	0.7824	0.8135	0.7976	0.7917	0.8566	0.8229	0.9288	0.8403	0.8799	0.8591	0.9317	0.9288	0.9298
0.0032	38.0	2546	0.9108	0.9706	0.9408	0.9555	0.8014	0.9067	0.8508	0.7743	0.8287	0.8006	0.7862	0.8696	0.8258	0.9268	0.8331	0.8865	0.8582	0.9310	0.9268	0.9282
0.0032	39.0	2613	0.8132	0.9757	0.9306	0.9526	0.7853	0.9174	0.8463	0.7249	0.8379	0.7773	0.7700	0.8976	0.8289	0.9224	0.8140	0.8959	0.8513	0.9299	0.9224	0.9247
0.0032	40.0	2680	0.9634	0.9692	0.9421	0.9554	0.8033	0.9052	0.8512	0.7876	0.8165	0.8018	0.7825	0.8641	0.8212	0.9266	0.8356	0.8820	0.8574	0.9304	0.9266	0.9279
0.0032	41.0	2747	0.9024	0.9711	0.9387	0.9546	0.7937	0.9174	0.8511	0.7655	0.8287	0.7959	0.7840	0.8585	0.8196	0.9253	0.8286	0.8858	0.8553	0.9301	0.9253	0.9269
0.0032	42.0	2814	0.9623	0.9682	0.9456	0.9567	0.8217	0.9021	0.8601	0.7922	0.8043	0.7982	0.7795	0.8622	0.8187	0.9283	0.8404	0.8786	0.8584	0.9314	0.9283	0.9294
0.0032	43.0	2881	0.9335	0.9692	0.9441	0.9565	0.8148	0.9083	0.8590	0.7811	0.8073	0.7940	0.7817	0.8603	0.8191	0.9278	0.8367	0.8800	0.8572	0.9312	0.9278	0.9290
0.0032	44.0	2948	0.8909	0.9714	0.9380	0.9544	0.7924	0.9220	0.8523	0.7642	0.8226	0.7923	0.7817	0.8603	0.8191	0.9250	0.8274	0.8857	0.8546	0.9300	0.9250	0.9266
0.0026	45.0	3015	0.9011	0.9711	0.9393	0.9549	0.7900	0.9205	0.8503	0.7876	0.8165	0.8018	0.7811	0.8641	0.8205	0.9259	0.8325	0.8851	0.8569	0.9306	0.9259	0.9274
0.0026	46.0	3082	0.9105	0.9709	0.9387	0.9546	0.7921	0.9205	0.8515	0.7801	0.8135	0.7964	0.7785	0.8641	0.8191	0.9253	0.8304	0.8842	0.8554	0.9301	0.9253	0.9268
0.0026	47.0	3149	0.9380	0.9698	0.9404	0.9549	0.7936	0.9113	0.8484	0.7811	0.8073	0.7940	0.7808	0.8622	0.8195	0.9253	0.8313	0.8803	0.8542	0.9296	0.9253	0.9267
0.0026	48.0	3216	0.9258	0.9702	0.9393	0.9545	0.7846	0.9190	0.8465	0.7843	0.8226	0.8030	0.7849	0.8492	0.8157	0.9249	0.8310	0.8825	0.8549	0.9295	0.9249	0.9264
0.0026	49.0	3283	0.9463	0.9697	0.9404	0.9548	0.7918	0.9128	0.8480	0.7836	0.8196	0.8012	0.7880	0.8585	0.8217	0.9257	0.8333	0.8828	0.8564	0.9300	0.9257	0.9271
0.0026	50.0	3350	0.9205	0.9708	0.9406	0.9555	0.7939	0.9190	0.8519	0.7895	0.8257	0.8072	0.7836	0.8566	0.8185	0.9266	0.8345	0.8855	0.8583	0.9310	0.9266	0.9280
0.0026	51.0	3417	0.9339	0.9702	0.9412	0.9555	0.8024	0.9128	0.8541	0.7872	0.8257	0.8060	0.7808	0.8622	0.8195	0.9269	0.8352	0.8855	0.8587	0.9310	0.9269	0.9283
0.0026	52.0	3484	0.9439	0.9712	0.9413	0.9560	0.7995	0.9205	0.8557	0.7959	0.8226	0.8090	0.7808	0.8622	0.8195	0.9276	0.8368	0.8867	0.8601	0.9319	0.9276	0.9290
0.0013	53.0	3551	0.9354	0.9715	0.9406	0.9558	0.7974	0.9266	0.8571	0.7855	0.8287	0.8065	0.7863	0.8566	0.8200	0.9275	0.8352	0.8881	0.8599	0.9319	0.9275	0.9289
0.0013	54.0	3618	0.9541	0.9715	0.9404	0.9557	0.7992	0.9251	0.8575	0.7832	0.8287	0.8053	0.7840	0.8585	0.8196	0.9273	0.8345	0.8882	0.8595	0.9318	0.9273	0.9288
0.0013	55.0	3685	0.9586	0.9715	0.9402	0.9556	0.7984	0.9266	0.8577	0.7820	0.8226	0.8018	0.7810	0.8566	0.8171	0.9269	0.8332	0.8865	0.8581	0.9314	0.9269	0.9284
0.0013	56.0	3752	0.9737	0.9690	0.9413	0.9549	0.8005	0.9083	0.8510	0.7853	0.8165	0.8006	0.7814	0.8585	0.8181	0.9259	0.8340	0.8811	0.8562	0.9298	0.9259	0.9272
0.0013	57.0	3819	0.9620	0.9695	0.9404	0.9547	0.7997	0.9098	0.8512	0.7807	0.8165	0.7982	0.7795	0.8622	0.8187	0.9256	0.8323	0.8822	0.8557	0.9298	0.9256	0.9270
0.0013	58.0	3886	0.9616	0.9697	0.9404	0.9548	0.7997	0.9159	0.8539	0.7853	0.8165	0.8006	0.7787	0.8585	0.8167	0.9259	0.8334	0.8828	0.8565	0.9301	0.9259	0.9273
0.0013	59.0	3953	0.9692	0.9701	0.9412	0.9554	0.8021	0.9174	0.8559	0.7830	0.8165	0.7994	0.7814	0.8585	0.8181	0.9266	0.8341	0.8834	0.8572	0.9307	0.9266	0.9280
0.001	60.0	4020	0.9704	0.9706	0.9413	0.9558	0.8027	0.9205	0.8575	0.7853	0.8165	0.8006	0.7817	0.8603	0.8191	0.9272	0.8351	0.8847	0.8583	0.9313	0.9272	0.9285

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: bert
License: mit
Created: Apr 11, 2024
Updated: Apr 11, 2024