InfoDeskGenericNer

InfoDeskInc/InfoDeskGenericNer

InfoDeskGenericNer is a machine learning model from InfoDeskInc. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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At a glance

License: mit
Model type: xlm-roberta
Access: Public
Created: Aug 14, 2024
Updated: Aug 14, 2024
SHA: 480dfe31

Base models

Model card

From the Hugging Face model README

xlm-roberta-base-finetuned-generic_ner_ontonotes-ner-2024_08_14

This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0851
Precision: 0.8634
Recall: 0.8879
F1: 0.8755
Accuracy: 0.9783
O Precision: 0.9952
O Recall: 0.9917
O F1: 0.9934
B-cardinal Precision: 0.8585
B-cardinal Recall: 0.8994
B-cardinal F1: 0.8784
B-date Precision: 0.8627
B-date Recall: 0.8796
B-date F1: 0.8711
I-date Precision: 0.8742
I-date Recall: 0.9023
I-date F1: 0.8880
B-person Precision: 0.9204
B-person Recall: 0.9596
B-person F1: 0.9396
I-person Precision: 0.9452
I-person Recall: 0.9818
I-person F1: 0.9632
B-norp Precision: 0.8898
B-norp Recall: 0.9311
B-norp F1: 0.9100
B-gpe Precision: 0.9471
B-gpe Recall: 0.9395
B-gpe F1: 0.9433
I-gpe Precision: 0.9119
I-gpe Recall: 0.8846
I-gpe F1: 0.8980
B-law Precision: 0.5909
B-law Recall: 0.8667
B-law F1: 0.7027
I-law Precision: 0.5170
I-law Recall: 0.7982
I-law F1: 0.6276
B-org Precision: 0.9061
B-org Recall: 0.8716
B-org F1: 0.8885
I-org Precision: 0.9212
I-org Recall: 0.9075
I-org F1: 0.9143
B-percent Precision: 0.9321
B-percent Recall: 0.8996
B-percent F1: 0.9156
I-percent Precision: 0.8822
I-percent Recall: 0.9887
I-percent F1: 0.9324
B-ordinal Precision: 0.8356
B-ordinal Recall: 0.8356
B-ordinal F1: 0.8356
B-money Precision: 0.9051
B-money Recall: 0.9304
B-money F1: 0.9176
I-money Precision: 0.9372
I-money Recall: 0.9753
I-money F1: 0.9558
B-work Of Art Precision: 0.5354
B-work Of Art Recall: 0.6355
B-work Of Art F1: 0.5812
I-work Of Art Precision: 0.5849
I-work Of Art Recall: 0.6998
I-work Of Art F1: 0.6372
B-fac Precision: 0.4833
B-fac Recall: 0.6312
B-fac F1: 0.5474
B-time Precision: 0.7782
B-time Recall: 0.8299
B-time F1: 0.8032
I-cardinal Precision: 0.7683
I-cardinal Recall: 0.8892
I-cardinal F1: 0.8243
B-loc Precision: 0.8206
B-loc Recall: 0.7530
B-loc F1: 0.7854
B-quantity Precision: 0.8731
B-quantity Recall: 0.9
B-quantity F1: 0.8864
I-quantity Precision: 0.8889
I-quantity Recall: 0.9706
I-quantity F1: 0.9279
I-norp Precision: 0.6792
I-norp Recall: 0.5373
I-norp F1: 0.6000
I-loc Precision: 0.7721
I-loc Recall: 0.7692
I-loc F1: 0.7706
B-product Precision: 0.5447
B-product Recall: 0.6979
B-product F1: 0.6119
I-time Precision: 0.7694
I-time Recall: 0.8766
I-time F1: 0.8195
B-event Precision: 0.7308
B-event Recall: 0.5733
B-event F1: 0.6425
I-event Precision: 0.7951
I-event Recall: 0.6198
I-event F1: 0.6966
I-fac Precision: 0.6463
I-fac Recall: 0.6909
I-fac F1: 0.6678
B-language Precision: 0.8387
B-language Recall: 0.65
B-language F1: 0.7324
I-product Precision: 0.8480
I-product Recall: 0.8192
I-product F1: 0.8333
I-ordinal Precision: 1.0
I-ordinal Recall: 0.0
I-ordinal F1: 0.0
I-language Precision: 1.0
I-language Recall: 1.0
I-language F1: 1.0

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
lr_scheduler_warmup_steps: 500
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	O Precision	O Recall	O F1	B-cardinal Precision	B-cardinal Recall	B-cardinal F1	B-date Precision	B-date Recall	B-date F1	I-date Precision	I-date Recall	I-date F1	B-person Precision	B-person Recall	B-person F1	I-person Precision	I-person Recall	I-person F1	B-norp Precision	B-norp Recall	B-norp F1	B-gpe Precision	B-gpe Recall	B-gpe F1	I-gpe Precision	I-gpe Recall	I-gpe F1	B-law Precision	B-law Recall	B-law F1	I-law Precision	I-law Recall	I-law F1	B-org Precision	B-org Recall	B-org F1	I-org Precision	I-org Recall	I-org F1	B-percent Precision	B-percent Recall	B-percent F1	I-percent Precision	I-percent Recall	I-percent F1	B-ordinal Precision	B-ordinal Recall	B-ordinal F1	B-money Precision	B-money Recall	B-money F1	I-money Precision	I-money Recall	I-money F1	B-work Of Art Precision	B-work Of Art Recall	B-work Of Art F1	I-work Of Art Precision	I-work Of Art Recall	I-work Of Art F1	B-fac Precision	B-fac Recall	B-fac F1	B-time Precision	B-time Recall	B-time F1	I-cardinal Precision	I-cardinal Recall	I-cardinal F1	B-loc Precision	B-loc Recall	B-loc F1	B-quantity Precision	B-quantity Recall	B-quantity F1	I-quantity Precision	I-quantity Recall	I-quantity F1	I-norp Precision	I-norp Recall	I-norp F1	I-loc Precision	I-loc Recall	I-loc F1	B-product Precision	B-product Recall	B-product F1	I-time Precision	I-time Recall	I-time F1	B-event Precision	B-event Recall	B-event F1	I-event Precision	I-event Recall	I-event F1	I-fac Precision	I-fac Recall	I-fac F1	B-language Precision	B-language Recall	B-language F1	I-product Precision	I-product Recall	I-product F1	I-ordinal Precision	I-language Precision	I-language Recall	I-language F1
0.43	0.3332	1248	0.1141	0.7842	0.8162	0.7999	0.9680	0.9940	0.9886	0.9913	0.8180	0.8605	0.8387	0.8343	0.8244	0.8294	0.8103	0.9023	0.8538	0.8776	0.9537	0.9141	0.9234	0.9693	0.9458	0.8485	0.9146	0.8803	0.9480	0.8583	0.9009	0.8588	0.7931	0.8246	1.0	0.0	0.0	0.3333	0.6754	0.4464	0.8220	0.8229	0.8224	0.8696	0.8734	0.8715	0.8902	0.9563	0.9221	0.9170	0.9170	0.9170	0.825	0.7911	0.8077	0.7884	0.8719	0.8280	0.9129	0.9593	0.9355	0.3367	0.1542	0.2115	0.4216	0.6862	0.5223	0.4031	0.325	0.3599	0.6897	0.4149	0.5181	0.6894	0.9125	0.7854	0.4729	0.6128	0.5339	0.8872	0.9077	0.8973	0.8442	0.9559	0.8966	0.75	0.0448	0.0845	0.5819	0.6374	0.6084	0.2260	0.6875	0.3402	0.7253	0.7437	0.7344	0.6857	0.2069	0.3179	0.5475	0.6832	0.6078	0.4843	0.6727	0.5632	1.0	0.025	0.0488	0.5455	0.1695	0.2586	1.0	1.0	1.0	1.0
0.105	0.6663	2496	0.0947	0.8324	0.8524	0.8423	0.9745	0.9920	0.9929	0.9924	0.8393	0.8623	0.8507	0.8746	0.8149	0.8437	0.8787	0.8613	0.8699	0.9319	0.9185	0.9252	0.9546	0.9673	0.9609	0.8904	0.9124	0.9012	0.8901	0.9615	0.9244	0.8396	0.9025	0.8699	0.6	0.4	0.48	0.4565	0.5526	0.5	0.8549	0.8488	0.8519	0.9132	0.8534	0.8823	0.9579	0.8952	0.9255	0.9203	0.9585	0.9390	0.8270	0.8185	0.8227	0.8616	0.9192	0.8895	0.9241	0.9738	0.9483	0.5193	0.5654	0.5414	0.5861	0.6682	0.6245	0.4261	0.4688	0.4464	0.7593	0.6805	0.7177	0.8645	0.8367	0.8504	0.8719	0.5396	0.6667	0.8769	0.8769	0.8769	0.8629	0.9485	0.9037	0.7179	0.4179	0.5283	0.8259	0.6081	0.7004	0.4889	0.6875	0.5714	0.74	0.8196	0.7778	0.7566	0.4957	0.5990	0.7438	0.6556	0.6969	0.6076	0.6364	0.6217	0.5246	0.8	0.6337	0.8489	0.6667	0.7468	1.0	1.0	1.0	1.0
0.0911	0.9995	3744	0.0930	0.8390	0.8678	0.8531	0.9746	0.9946	0.9902	0.9924	0.8656	0.8358	0.8505	0.8726	0.8452	0.8587	0.8527	0.9116	0.8812	0.8653	0.9684	0.9140	0.9111	0.9855	0.9468	0.8994	0.9176	0.9084	0.9277	0.9374	0.9325	0.8923	0.8696	0.8808	0.5667	0.7556	0.6476	0.4024	0.5789	0.4748	0.8818	0.8469	0.8640	0.8967	0.8967	0.8967	0.9327	0.9083	0.9204	0.8680	0.9925	0.9261	0.8854	0.7671	0.8220	0.8770	0.9331	0.9042	0.9266	0.9724	0.9489	0.4681	0.6168	0.5323	0.5297	0.6637	0.5892	0.5475	0.6125	0.5782	0.72	0.7469	0.7332	0.7570	0.8717	0.8103	0.8699	0.6524	0.7456	0.8188	0.8692	0.8433	0.8248	0.9522	0.8840	0.6364	0.5224	0.5738	0.8806	0.6484	0.7468	0.5138	0.5833	0.5463	0.7733	0.8418	0.8061	0.7319	0.4353	0.5459	0.7147	0.6556	0.6839	0.6151	0.6218	0.6184	0.7429	0.65	0.6933	0.7669	0.7062	0.7353	1.0	1.0	1.0	1.0
0.0687	1.3326	4992	0.0859	0.8593	0.8739	0.8665	0.9773	0.9938	0.9923	0.9930	0.8537	0.8959	0.8742	0.8901	0.8505	0.8699	0.8793	0.8914	0.8853	0.9347	0.9473	0.9410	0.9343	0.9822	0.9577	0.9099	0.9154	0.9126	0.9510	0.9254	0.9380	0.8698	0.8816	0.8757	0.6383	0.6667	0.6522	0.6562	0.5526	0.6	0.8763	0.8787	0.8775	0.8960	0.9126	0.9042	0.9298	0.9258	0.9278	0.8931	0.9774	0.9333	0.8322	0.8151	0.8235	0.9126	0.9304	0.9214	0.9372	0.9753	0.9558	0.5642	0.5748	0.5694	0.5895	0.6614	0.6234	0.4837	0.5563	0.5174	0.7592	0.7718	0.7654	0.7927	0.8805	0.8343	0.7432	0.75	0.7466	0.8551	0.9077	0.8806	0.8961	0.9191	0.9074	0.75	0.4925	0.5946	0.7807	0.7692	0.7749	0.5285	0.6771	0.5936	0.7326	0.8671	0.7942	0.7314	0.5517	0.6290	0.7697	0.6722	0.7176	0.6778	0.6655	0.6716	0.7368	0.7	0.7179	0.8531	0.6893	0.7625	1.0	1.0	1.0	1.0
0.0638	1.6658	6240	0.0855	0.8512	0.8825	0.8665	0.9769	0.9947	0.9912	0.9930	0.8476	0.8985	0.8723	0.8660	0.8737	0.8698	0.8713	0.8919	0.8815	0.9208	0.9577	0.9389	0.9413	0.9786	0.9596	0.9022	0.9258	0.9139	0.9359	0.9535	0.9446	0.9280	0.8891	0.9081	0.5818	0.7111	0.64	0.5780	0.5526	0.5650	0.8955	0.8685	0.8818	0.9273	0.8887	0.9076	0.9185	0.9345	0.9264	0.9231	0.9509	0.9368	0.8530	0.8151	0.8336	0.9187	0.9443	0.9313	0.9579	0.9593	0.9586	0.4462	0.6776	0.5380	0.4534	0.7472	0.5644	0.4554	0.6375	0.5312	0.7578	0.8050	0.7807	0.7677	0.8863	0.8227	0.8517	0.6829	0.7580	0.8369	0.9077	0.8708	0.8854	0.9375	0.9107	0.8919	0.4925	0.6346	0.8918	0.6337	0.7409	0.5349	0.7188	0.6133	0.7778	0.8418	0.8085	0.7590	0.5431	0.6332	0.7812	0.6198	0.6912	0.5287	0.6691	0.5907	0.8214	0.575	0.6765	0.8447	0.7684	0.8047	1.0	1.0	1.0	1.0
0.059	1.9989	7488	0.0828	0.8576	0.8837	0.8705	0.9778	0.9945	0.9919	0.9932	0.8352	0.9038	0.8682	0.8651	0.8749	0.8699	0.8784	0.8875	0.8829	0.9139	0.9589	0.9359	0.9361	0.9826	0.9588	0.8975	0.9311	0.9140	0.9323	0.9481	0.9401	0.8806	0.9175	0.8987	0.6	0.6667	0.6316	0.576	0.6316	0.6025	0.8991	0.8673	0.8829	0.9162	0.9005	0.9083	0.9330	0.9127	0.9227	0.8966	0.9811	0.9369	0.7907	0.8151	0.8027	0.8939	0.9387	0.9158	0.9448	0.9709	0.9577	0.5774	0.6449	0.6093	0.6834	0.6772	0.6803	0.5024	0.6438	0.5644	0.7870	0.7510	0.7686	0.7655	0.8659	0.8126	0.8710	0.6585	0.75	0.8992	0.8923	0.8958	0.8969	0.9596	0.9272	0.8571	0.5373	0.6606	0.8517	0.6520	0.7386	0.5610	0.7188	0.6301	0.8185	0.8133	0.8159	0.7396	0.6121	0.6698	0.8033	0.6749	0.7335	0.5574	0.7418	0.6365	0.9259	0.625	0.7463	0.8333	0.7627	0.7965	1.0	1.0	1.0	1.0
0.0469	2.3321	8736	0.0843	0.8674	0.8854	0.8763	0.9785	0.9944	0.9921	0.9932	0.8763	0.8817	0.8790	0.8636	0.8826	0.8730	0.8583	0.9116	0.8842	0.9310	0.9556	0.9432	0.9548	0.9818	0.9681	0.8977	0.9266	0.9119	0.9468	0.9398	0.9433	0.9367	0.8876	0.9115	0.6	0.8	0.6857	0.6083	0.6404	0.6239	0.9025	0.8724	0.8872	0.9201	0.9043	0.9121	0.9361	0.8952	0.9152	0.8680	0.9925	0.9261	0.8339	0.8253	0.8296	0.9066	0.9192	0.9129	0.9385	0.9767	0.9573	0.5744	0.6495	0.6096	0.6268	0.6975	0.6603	0.4928	0.6375	0.5559	0.8	0.7801	0.7899	0.7943	0.9009	0.8443	0.7980	0.7226	0.7584	0.8992	0.8923	0.8958	0.8859	0.9706	0.9263	0.8182	0.5373	0.6486	0.8016	0.7399	0.7695	0.5185	0.7292	0.6061	0.7604	0.8639	0.8089	0.7273	0.6207	0.6698	0.7753	0.6749	0.7216	0.6168	0.72	0.6644	0.9259	0.625	0.7463	0.8114	0.8023	0.8068	1.0	1.0	1.0	1.0
0.0439	2.6652	9984	0.0868	0.8635	0.8846	0.8739	0.9781	0.9953	0.9914	0.9934	0.8517	0.8923	0.8716	0.8660	0.8778	0.8719	0.8604	0.9097	0.8844	0.9214	0.9623	0.9414	0.9377	0.9842	0.9604	0.8797	0.9258	0.9022	0.9402	0.9323	0.9362	0.8920	0.9040	0.8980	0.6066	0.8222	0.6981	0.4802	0.7456	0.5842	0.9044	0.8693	0.8865	0.9143	0.9122	0.9133	0.9321	0.8996	0.9156	0.8763	0.9887	0.9291	0.8385	0.8356	0.8370	0.9046	0.9248	0.9146	0.9321	0.9782	0.9546	0.6207	0.5888	0.6043	0.6463	0.6930	0.6688	0.5575	0.6062	0.5808	0.7787	0.8174	0.7976	0.7440	0.9067	0.8173	0.7508	0.7530	0.7519	0.8712	0.8846	0.8779	0.88	0.9706	0.9231	0.6792	0.5373	0.6000	0.7948	0.7802	0.7874	0.5378	0.6667	0.5953	0.7744	0.8797	0.8237	0.7366	0.5905	0.6555	0.7912	0.6474	0.7121	0.6809	0.6982	0.6894	0.6744	0.725	0.6988	0.7967	0.8192	0.8078	1.0	1.0	1.0	1.0
0.0435	2.9984	11232	0.0851	0.8634	0.8879	0.8755	0.9783	0.9952	0.9917	0.9934	0.8585	0.8994	0.8784	0.8627	0.8796	0.8711	0.8742	0.9023	0.8880	0.9204	0.9596	0.9396	0.9452	0.9818	0.9632	0.8898	0.9311	0.9100	0.9471	0.9395	0.9433	0.9119	0.8846	0.8980	0.5909	0.8667	0.7027	0.5170	0.7982	0.6276	0.9061	0.8716	0.8885	0.9212	0.9075	0.9143	0.9321	0.8996	0.9156	0.8822	0.9887	0.9324	0.8356	0.8356	0.8356	0.9051	0.9304	0.9176	0.9372	0.9753	0.9558	0.5354	0.6355	0.5812	0.5849	0.6998	0.6372	0.4833	0.6312	0.5474	0.7782	0.8299	0.8032	0.7683	0.8892	0.8243	0.8206	0.7530	0.7854	0.8731	0.9	0.8864	0.8889	0.9706	0.9279	0.6792	0.5373	0.6000	0.7721	0.7692	0.7706	0.5447	0.6979	0.6119	0.7694	0.8766	0.8195	0.7308	0.5733	0.6425	0.7951	0.6198	0.6966	0.6463	0.6909	0.6678	0.8387	0.65	0.7324	0.8480	0.8192	0.8333	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Type: xlm-roberta
License: mit
Created: Aug 14, 2024
Updated: Aug 14, 2024