beto-finetuned-token-reqadjzar

mnavas/beto-finetuned-token-reqadjzar

beto-finetuned-token-reqadjzar is a token classification model from mnavas. Use it when you need labels on individual words, such as names. It is set up for transformers.

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

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At a glance

Task: Token Classification
Library: transformers
Model type: bert
Access: Public
Created: Jun 7, 2023
Updated: Jun 27, 2023
SHA: f42ec538

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Model card

From the Hugging Face model README

beto-finetuned-token-reqadjzar

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

Loss: 1.1061
Precision: 0.2533
Recall: 0.3333
F1: 0.2879
Accuracy: 0.8498

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: 0.0001
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: 200

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7331	1.0	24	0.5920	0.0	0.0	0.0	0.7532
0.4759	2.0	48	0.3954	0.0085	0.0175	0.0115	0.8321
0.3186	3.0	72	0.5127	0.0188	0.0702	0.0296	0.8159
0.1906	4.0	96	0.4865	0.1190	0.2632	0.1639	0.8509
0.145	5.0	120	0.4650	0.1597	0.3333	0.2159	0.8760
0.1107	6.0	144	0.5465	0.1062	0.2105	0.1412	0.8514
0.0903	7.0	168	0.5441	0.1359	0.2456	0.175	0.8796
0.0698	8.0	192	0.4353	0.1204	0.2281	0.1576	0.8842
0.0505	9.0	216	0.7170	0.19	0.3333	0.2420	0.8432
0.0687	10.0	240	0.5893	0.1963	0.3684	0.2561	0.8860
0.039	11.0	264	0.5877	0.1951	0.4211	0.2667	0.8780
0.0278	12.0	288	0.5715	0.2237	0.2982	0.2556	0.8577
0.0354	13.0	312	0.9535	0.2283	0.3684	0.2819	0.8532
0.024	14.0	336	0.6500	0.2169	0.3158	0.2571	0.8674
0.0223	15.0	360	0.7513	0.1855	0.4035	0.2541	0.8722
0.0156	16.0	384	0.6566	0.3	0.4737	0.3673	0.9012
0.0156	17.0	408	0.8436	0.2292	0.3860	0.2876	0.8696
0.0189	18.0	432	0.8043	0.1711	0.2281	0.1955	0.8181
0.0128	19.0	456	0.6518	0.1619	0.2982	0.2099	0.8814
0.0122	20.0	480	0.8418	0.2347	0.4035	0.2968	0.8793
0.0242	21.0	504	0.7948	0.2292	0.3860	0.2876	0.8814
0.0124	22.0	528	0.8059	0.2037	0.3860	0.2667	0.8842
0.0098	23.0	552	0.9458	0.1765	0.2632	0.2113	0.8584
0.0287	24.0	576	0.7110	0.1488	0.3158	0.2022	0.8825
0.0253	25.0	600	0.6823	0.2021	0.3333	0.2517	0.8781
0.0151	26.0	624	0.7382	0.2022	0.3158	0.2466	0.8791
0.0118	27.0	648	0.6036	0.2360	0.3684	0.2877	0.8965
0.0102	28.0	672	0.9152	0.1765	0.3158	0.2264	0.8446
0.0229	29.0	696	0.6878	0.2584	0.4035	0.3151	0.8982
0.0168	30.0	720	0.7333	0.2784	0.4737	0.3506	0.8937
0.0145	31.0	744	0.6051	0.1864	0.3860	0.2514	0.9
0.0207	32.0	768	0.9083	0.3279	0.3509	0.3390	0.8894
0.0191	33.0	792	0.6983	0.2222	0.3509	0.2721	0.8884
0.0103	34.0	816	0.7287	0.2449	0.4211	0.3097	0.8840
0.0091	35.0	840	0.5929	0.2184	0.3333	0.2639	0.8851
0.0059	36.0	864	0.7604	0.2421	0.4035	0.3026	0.8810
0.0035	37.0	888	0.9380	0.2143	0.3684	0.2710	0.8622
0.0025	38.0	912	0.9824	0.2	0.3509	0.2548	0.8704
0.0059	39.0	936	1.0658	0.2796	0.4561	0.3467	0.8669
0.0199	40.0	960	0.9755	0.1705	0.3860	0.2366	0.8449
0.0034	41.0	984	0.9697	0.2619	0.3860	0.3121	0.8656
0.0035	42.0	1008	1.0582	0.1959	0.3333	0.2468	0.8461
0.0088	43.0	1032	0.8500	0.1849	0.3860	0.25	0.8515
0.0263	44.0	1056	1.2832	0.2	0.3509	0.2548	0.8255
0.0088	45.0	1080	0.9282	0.2308	0.4211	0.2981	0.8534
0.0343	46.0	1104	0.7165	0.2222	0.3158	0.2609	0.8594
0.0024	47.0	1128	0.7355	0.2308	0.4737	0.3103	0.8782
0.0019	48.0	1152	0.6493	0.2165	0.3684	0.2727	0.8779
0.0009	49.0	1176	0.6999	0.1964	0.3860	0.2604	0.8766
0.0008	50.0	1200	0.7496	0.2062	0.3509	0.2597	0.8709
0.0009	51.0	1224	0.7670	0.2019	0.3684	0.2609	0.8750
0.0006	52.0	1248	0.7549	0.24	0.4211	0.3057	0.8832
0.0007	53.0	1272	0.7556	0.2706	0.4035	0.3239	0.8870
0.0007	54.0	1296	0.7188	0.1695	0.3509	0.2286	0.8833
0.0005	55.0	1320	0.7120	0.1927	0.3684	0.2530	0.8822
0.0009	56.0	1344	0.7377	0.2245	0.3860	0.2839	0.8819
0.0008	57.0	1368	0.7295	0.2277	0.4035	0.2911	0.8859
0.0009	58.0	1392	0.7158	0.2340	0.3860	0.2914	0.8900
0.0013	59.0	1416	0.6715	0.1897	0.3860	0.2543	0.8941
0.0006	60.0	1440	0.6787	0.21	0.3684	0.2675	0.8861
0.0007	61.0	1464	0.6794	0.2584	0.4035	0.3151	0.8940
0.0012	62.0	1488	0.6823	0.2273	0.3509	0.2759	0.8778
0.0008	63.0	1512	0.7189	0.2588	0.3860	0.3099	0.8791
0.0008	64.0	1536	0.7077	0.2371	0.4035	0.2987	0.8905
0.0007	65.0	1560	0.7201	0.2738	0.4035	0.3262	0.8860
0.0005	66.0	1584	0.7339	0.2584	0.4035	0.3151	0.8894
0.0005	67.0	1608	0.7490	0.2157	0.3860	0.2767	0.8845
0.0006	68.0	1632	0.7342	0.2162	0.4211	0.2857	0.8833
0.0012	69.0	1656	0.7287	0.3108	0.4035	0.3511	0.8895
0.0012	70.0	1680	0.8877	0.2079	0.3684	0.2658	0.8615
0.0007	71.0	1704	0.9370	0.2095	0.3860	0.2716	0.8644
0.002	72.0	1728	0.7715	0.2391	0.3860	0.2953	0.8677
0.0007	73.0	1752	0.8765	0.22	0.3860	0.2803	0.8628
0.0006	74.0	1776	0.8515	0.2371	0.4035	0.2987	0.8639
0.0007	75.0	1800	0.8448	0.2286	0.4211	0.2963	0.8633
0.0009	76.0	1824	0.8501	0.2232	0.4386	0.2959	0.8650
0.0007	77.0	1848	0.8550	0.2198	0.3509	0.2703	0.8657
0.0005	78.0	1872	0.7445	0.25	0.4035	0.3087	0.8780
0.0007	79.0	1896	0.8889	0.26	0.4561	0.3312	0.8630
0.0005	80.0	1920	0.8930	0.2812	0.4737	0.3529	0.8650
0.0004	81.0	1944	0.8678	0.26	0.4561	0.3312	0.8745
0.0005	82.0	1968	0.8747	0.2784	0.4737	0.3506	0.8746
0.0005	83.0	1992	0.8726	0.2872	0.4737	0.3576	0.8687
0.001	84.0	2016	0.8887	0.2857	0.4211	0.3404	0.8693
0.0006	85.0	2040	0.7915	0.2963	0.4211	0.3478	0.8821
0.0007	86.0	2064	1.0194	0.2857	0.4211	0.3404	0.8606
0.0009	87.0	2088	0.7594	0.2366	0.3860	0.2933	0.8777
0.0021	88.0	2112	0.9788	0.25	0.3333	0.2857	0.8539
0.0012	89.0	2136	0.8719	0.2093	0.3158	0.2517	0.8697
0.0019	90.0	2160	1.1859	0.1810	0.3684	0.2428	0.8111
0.001	91.0	2184	0.9690	0.2118	0.3158	0.2535	0.8421
0.0007	92.0	2208	0.9863	0.1880	0.3860	0.2529	0.8495
0.0006	93.0	2232	0.9942	0.1868	0.2982	0.2297	0.8641
0.0007	94.0	2256	1.0118	0.2159	0.3333	0.2621	0.8637
0.0007	95.0	2280	1.0435	0.2754	0.3333	0.3016	0.8615
0.0008	96.0	2304	0.9795	0.2471	0.3684	0.2958	0.8657
0.0007	97.0	2328	0.9189	0.2020	0.3509	0.2564	0.8807
0.0009	98.0	2352	0.9240	0.2273	0.3509	0.2759	0.8762
0.0005	99.0	2376	0.8891	0.2561	0.3684	0.3022	0.8821
0.0004	100.0	2400	0.9028	0.2469	0.3509	0.2899	0.8818
0.0004	101.0	2424	0.9228	0.2410	0.3509	0.2857	0.8830
0.0004	102.0	2448	0.9409	0.2278	0.3158	0.2647	0.8795
0.0006	103.0	2472	0.9777	0.24	0.3158	0.2727	0.8796
0.0005	104.0	2496	0.9872	0.2432	0.3158	0.2748	0.8791
0.0006	105.0	2520	0.9820	0.2329	0.2982	0.2615	0.8746
0.0006	106.0	2544	1.0301	0.2879	0.3333	0.3089	0.8702
0.0006	107.0	2568	1.0468	0.3226	0.3509	0.3361	0.8637
0.0004	108.0	2592	1.0155	0.2941	0.3509	0.3200	0.8683
0.0005	109.0	2616	0.9970	0.2821	0.3860	0.3259	0.8678
0.0004	110.0	2640	1.0453	0.28	0.3684	0.3182	0.8687
0.0009	111.0	2664	0.9247	0.2278	0.3158	0.2647	0.8747
0.0006	112.0	2688	0.8811	0.2785	0.3860	0.3235	0.8921
0.0005	113.0	2712	0.9462	0.1905	0.2807	0.2270	0.8817
0.0005	114.0	2736	0.9685	0.2078	0.2807	0.2388	0.8792
0.0006	115.0	2760	1.0339	0.2712	0.2807	0.2759	0.8672
0.0004	116.0	2784	1.0155	0.2571	0.3158	0.2835	0.8687
0.0005	117.0	2808	0.9998	0.25	0.3509	0.2920	0.8768
0.0006	118.0	2832	0.9849	0.2473	0.4035	0.3067	0.8715
0.0033	119.0	2856	0.7929	0.2376	0.4211	0.3038	0.8832
0.0485	120.0	2880	0.9585	0.2	0.2807	0.2336	0.8585
0.0114	121.0	2904	0.7619	0.2472	0.3860	0.3014	0.8831
0.0177	122.0	2928	0.7737	0.2881	0.2982	0.2931	0.8688
0.02	123.0	2952	1.1362	0.1959	0.3333	0.2468	0.8214
0.0056	124.0	2976	1.2073	0.3659	0.2632	0.3061	0.8277
0.0208	125.0	3000	0.8549	0.2162	0.2807	0.2443	0.8430
0.0066	126.0	3024	0.9482	0.2667	0.2807	0.2735	0.8383
0.0155	127.0	3048	0.7532	0.2289	0.3333	0.2714	0.8629
0.0091	128.0	3072	0.7973	0.2368	0.3158	0.2707	0.8524
0.0029	129.0	3096	0.8988	0.25	0.3684	0.2979	0.8621
0.0054	130.0	3120	0.9882	0.2299	0.3509	0.2778	0.8362
0.0037	131.0	3144	1.0792	0.2093	0.3158	0.2517	0.8468
0.0012	132.0	3168	0.9729	0.2632	0.3509	0.3008	0.8427
0.0009	133.0	3192	0.9521	0.2043	0.3333	0.2533	0.8416
0.0011	134.0	3216	0.9539	0.1978	0.3158	0.2432	0.8401
0.0006	135.0	3240	0.9692	0.2754	0.3333	0.3016	0.8504
0.0007	136.0	3264	0.9811	0.2603	0.3333	0.2923	0.8526
0.0007	137.0	3288	0.9732	0.25	0.3333	0.2857	0.8444
0.0004	138.0	3312	0.9955	0.2278	0.3158	0.2647	0.8373
0.0005	139.0	3336	0.9939	0.2466	0.3158	0.2769	0.8389
0.001	140.0	3360	1.0081	0.2432	0.3158	0.2748	0.8377
0.0006	141.0	3384	1.0216	0.2308	0.3158	0.2667	0.8404
0.0005	142.0	3408	1.0364	0.25	0.3158	0.2791	0.8332
0.0004	143.0	3432	1.0185	0.2571	0.3158	0.2835	0.8426
0.0006	144.0	3456	1.0168	0.2603	0.3333	0.2923	0.8458
0.0005	145.0	3480	1.0079	0.2754	0.3333	0.3016	0.8476
0.0006	146.0	3504	1.0080	0.25	0.3333	0.2857	0.8438
0.0004	147.0	3528	1.0194	0.2346	0.3333	0.2754	0.8396
0.0004	148.0	3552	1.0299	0.2262	0.3333	0.2695	0.8373
0.0005	149.0	3576	1.0331	0.2289	0.3333	0.2714	0.8387
0.0004	150.0	3600	1.0294	0.2436	0.3333	0.2815	0.8412
0.0004	151.0	3624	1.0366	0.2405	0.3333	0.2794	0.8410
0.0004	152.0	3648	1.0533	0.2468	0.3333	0.2836	0.8448
0.0005	153.0	3672	1.0379	0.2879	0.3333	0.3089	0.8458
0.0005	154.0	3696	1.0395	0.2836	0.3333	0.3065	0.8454
0.0004	155.0	3720	1.0438	0.2836	0.3333	0.3065	0.8453
0.0004	156.0	3744	1.0475	0.2879	0.3333	0.3089	0.8453
0.0004	157.0	3768	1.0558	0.2794	0.3333	0.304	0.8450
0.0004	158.0	3792	1.0596	0.2754	0.3333	0.3016	0.8444
0.0004	159.0	3816	1.0633	0.2836	0.3333	0.3065	0.8445
0.0004	160.0	3840	1.0653	0.2836	0.3333	0.3065	0.8445
0.0004	161.0	3864	1.0687	0.2754	0.3333	0.3016	0.8446
0.0004	162.0	3888	1.0732	0.2714	0.3333	0.2992	0.8448
0.0005	163.0	3912	1.0729	0.2568	0.3333	0.2901	0.8444
0.0004	164.0	3936	1.0764	0.2533	0.3333	0.2879	0.8436
0.0005	165.0	3960	1.0737	0.2794	0.3333	0.304	0.8465
0.0005	166.0	3984	1.0700	0.2754	0.3333	0.3016	0.8482
0.0004	167.0	4008	1.0679	0.2794	0.3333	0.304	0.8496
0.0005	168.0	4032	1.0695	0.2676	0.3333	0.2969	0.8498
0.0004	169.0	4056	1.0704	0.2714	0.3333	0.2992	0.8498
0.0005	170.0	4080	1.0716	0.2794	0.3333	0.304	0.8495
0.0004	171.0	4104	1.0702	0.2639	0.3333	0.2946	0.8498
0.0005	172.0	4128	1.0713	0.25	0.3333	0.2857	0.8491
0.0004	173.0	4152	1.0736	0.2436	0.3333	0.2815	0.8491
0.0005	174.0	4176	1.0808	0.2568	0.3333	0.2901	0.8486
0.0004	175.0	4200	1.0867	0.2639	0.3333	0.2946	0.8486
0.0004	176.0	4224	1.0899	0.2754	0.3333	0.3016	0.8486
0.0004	177.0	4248	1.0900	0.2603	0.3333	0.2923	0.8486
0.0005	178.0	4272	1.0871	0.2754	0.3333	0.3016	0.8489
0.0004	179.0	4296	1.0863	0.2794	0.3333	0.304	0.8492
0.0004	180.0	4320	1.0892	0.2754	0.3333	0.3016	0.8493
0.0004	181.0	4344	1.0919	0.2639	0.3333	0.2946	0.8489
0.0004	182.0	4368	1.0933	0.2639	0.3333	0.2946	0.8490
0.0004	183.0	4392	1.0949	0.2639	0.3333	0.2946	0.8489
0.0004	184.0	4416	1.0953	0.2639	0.3333	0.2946	0.8489
0.0004	185.0	4440	1.1031	0.2714	0.3333	0.2992	0.8496
0.0004	186.0	4464	1.1049	0.2639	0.3333	0.2946	0.8494
0.0004	187.0	4488	1.1082	0.2676	0.3333	0.2969	0.8495
0.0004	188.0	4512	1.1091	0.2639	0.3333	0.2946	0.8494
0.0004	189.0	4536	1.1109	0.2639	0.3333	0.2946	0.8494
0.0004	190.0	4560	1.1119	0.2639	0.3333	0.2946	0.8494
0.0004	191.0	4584	1.1129	0.2603	0.3333	0.2923	0.8494
0.0004	192.0	4608	1.1139	0.2639	0.3333	0.2946	0.8494
0.0005	193.0	4632	1.1051	0.2676	0.3333	0.2969	0.8497
0.0004	194.0	4656	1.1037	0.2639	0.3333	0.2946	0.8495
0.0004	195.0	4680	1.1045	0.2568	0.3333	0.2901	0.8496
0.0004	196.0	4704	1.1052	0.2568	0.3333	0.2901	0.8496
0.0004	197.0	4728	1.1057	0.2568	0.3333	0.2901	0.8496
0.0004	198.0	4752	1.1057	0.2533	0.3333	0.2879	0.8497
0.0004	199.0	4776	1.1061	0.2533	0.3333	0.2879	0.8497
0.0004	200.0	4800	1.1061	0.2533	0.3333	0.2879	0.8498

Framework versions

Transformers 4.31.0.dev0
Pytorch 2.0.0
Datasets 2.1.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: bert
Created: Jun 7, 2023
Updated: Jun 27, 2023