README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - f1
  - accuracy
model-index:
  - name: bert-base-uncased-test_16_107
    results: []

bert-base-uncased-test_16_107

This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:

• Loss: 0.6267
• F1: {'f1': 0.8174603174603174}
• Accuracy: {'accuracy': 0.6912751677852349}

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: 0.001
• 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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
No log	1.0	7	0.6347	{'f1': 0.7934978928356411}	{'accuracy': 0.6711409395973155}
No log	2.0	14	0.6336	{'f1': 0.8188529244747302}	{'accuracy': 0.6941514860977949}
No log	3.0	21	0.6697	{'f1': 0.8079161816065191}	{'accuracy': 0.6836049856184084}
No log	4.0	28	0.8194	{'f1': 0.8004807692307693}	{'accuracy': 0.6816874400767018}
No log	5.0	35	0.9298	{'f1': 0.7683322517845554}	{'accuracy': 0.6577181208053692}
No log	6.0	42	1.1747	{'f1': 0.806282722513089}	{'accuracy': 0.6807286673058485}
No log	7.0	49	1.4818	{'f1': 0.7477906186267844}	{'accuracy': 0.6442953020134228}
No log	8.0	56	1.3621	{'f1': 0.8078994614003591}	{'accuracy': 0.6922339405560882}
No log	9.0	63	1.3699	{'f1': 0.8139810426540284}	{'accuracy': 0.6989453499520614}
No log	10.0	70	1.5245	{'f1': 0.7581881533101044}	{'accuracy': 0.6673058485139022}
No log	11.0	77	1.8747	{'f1': 0.8120924718435093}	{'accuracy': 0.6960690316395014}
No log	12.0	84	2.1986	{'f1': 0.7953431372549019}	{'accuracy': 0.6797698945349953}
No log	13.0	91	2.1630	{'f1': 0.7586206896551725}	{'accuracy': 0.6510067114093959}
No log	14.0	98	2.2134	{'f1': 0.809214412285883}	{'accuracy': 0.6903163950143816}
No log	15.0	105	2.1405	{'f1': 0.7940813810110975}	{'accuracy': 0.6797698945349953}
No log	16.0	112	2.1033	{'f1': 0.7701375245579567}	{'accuracy': 0.663470757430489}
No log	17.0	119	2.0794	{'f1': 0.7461858529819695}	{'accuracy': 0.6490891658676894}
No log	18.0	126	2.0845	{'f1': 0.7980356046654389}	{'accuracy': 0.6845637583892618}
No log	19.0	133	2.0804	{'f1': 0.8069336521219366}	{'accuracy': 0.6903163950143816}
No log	20.0	140	1.8434	{'f1': 0.7755905511811024}	{'accuracy': 0.6720997123681688}
No log	21.0	147	2.2370	{'f1': 0.8142940831868777}	{'accuracy': 0.6960690316395014}
No log	22.0	154	2.1536	{'f1': 0.7385714285714285}	{'accuracy': 0.6490891658676894}
No log	23.0	161	2.3420	{'f1': 0.8100820633059789}	{'accuracy': 0.6893576222435283}
No log	24.0	168	2.2000	{'f1': 0.7918468190240889}	{'accuracy': 0.6768935762224353}
No log	25.0	175	2.3650	{'f1': 0.6687451286048324}	{'accuracy': 0.5925215723873442}
No log	26.0	182	2.0525	{'f1': 0.8165137614678899}	{'accuracy': 0.6931927133269415}
No log	27.0	189	1.5495	{'f1': 0.7929736511919698}	{'accuracy': 0.6836049856184084}
No log	28.0	196	1.5791	{'f1': 0.780550223928343}	{'accuracy': 0.6711409395973155}
No log	29.0	203	1.8794	{'f1': 0.8028759736369083}	{'accuracy': 0.6845637583892618}
No log	30.0	210	2.0042	{'f1': 0.7756703727926749}	{'accuracy': 0.6711409395973155}
No log	31.0	217	2.2522	{'f1': 0.7382920110192837}	{'accuracy': 0.6356663470757431}
No log	32.0	224	2.4130	{'f1': 0.7389502762430941}	{'accuracy': 0.6375838926174496}
No log	33.0	231	2.5367	{'f1': 0.7664041994750657}	{'accuracy': 0.6586768935762224}
No log	34.0	238	2.6368	{'f1': 0.773109243697479}	{'accuracy': 0.663470757430489}
No log	35.0	245	2.7019	{'f1': 0.7756410256410257}	{'accuracy': 0.6644295302013423}
No log	36.0	252	2.7419	{'f1': 0.7755102040816326}	{'accuracy': 0.6625119846596357}
No log	37.0	259	2.7663	{'f1': 0.7755102040816326}	{'accuracy': 0.6625119846596357}
No log	38.0	266	2.7834	{'f1': 0.7755102040816326}	{'accuracy': 0.6625119846596357}
No log	39.0	273	2.7963	{'f1': 0.7755102040816326}	{'accuracy': 0.6625119846596357}
No log	40.0	280	2.8087	{'f1': 0.7755102040816326}	{'accuracy': 0.6625119846596357}
No log	41.0	287	2.8192	{'f1': 0.774728781110402}	{'accuracy': 0.6615532118887824}
No log	42.0	294	2.8276	{'f1': 0.7757188498402556}	{'accuracy': 0.663470757430489}
No log	43.0	301	2.8378	{'f1': 0.7746478873239436}	{'accuracy': 0.6625119846596357}
No log	44.0	308	2.8475	{'f1': 0.7751441383728379}	{'accuracy': 0.663470757430489}
No log	45.0	315	2.8569	{'f1': 0.7756410256410257}	{'accuracy': 0.6644295302013423}
No log	46.0	322	2.8669	{'f1': 0.7761385503527902}	{'accuracy': 0.6653883029721956}
No log	47.0	329	2.8768	{'f1': 0.7761385503527902}	{'accuracy': 0.6653883029721956}
No log	48.0	336	2.8861	{'f1': 0.7766367137355583}	{'accuracy': 0.6663470757430489}
No log	49.0	343	2.8945	{'f1': 0.7758509955041747}	{'accuracy': 0.6653883029721956}
No log	50.0	350	2.9042	{'f1': 0.7757731958762887}	{'accuracy': 0.6663470757430489}
No log	51.0	357	2.9134	{'f1': 0.7755627009646302}	{'accuracy': 0.6653883029721956}
No log	52.0	364	2.9225	{'f1': 0.7771355170199101}	{'accuracy': 0.6673058485139022}
No log	53.0	371	2.9313	{'f1': 0.7766367137355583}	{'accuracy': 0.6663470757430489}
No log	54.0	378	2.9395	{'f1': 0.7763496143958868}	{'accuracy': 0.6663470757430489}
No log	55.0	385	2.9454	{'f1': 0.7755627009646302}	{'accuracy': 0.6653883029721956}
No log	56.0	392	2.9496	{'f1': 0.7744845360824743}	{'accuracy': 0.6644295302013423}
No log	57.0	399	2.9552	{'f1': 0.7734021949644931}	{'accuracy': 0.663470757430489}
No log	58.0	406	2.9611	{'f1': 0.7734021949644931}	{'accuracy': 0.663470757430489}
No log	59.0	413	2.9681	{'f1': 0.7736943907156673}	{'accuracy': 0.663470757430489}
No log	60.0	420	2.9750	{'f1': 0.7736943907156673}	{'accuracy': 0.663470757430489}
No log	61.0	427	2.9816	{'f1': 0.7736943907156673}	{'accuracy': 0.663470757430489}
No log	62.0	434	2.9875	{'f1': 0.7736943907156673}	{'accuracy': 0.663470757430489}
No log	63.0	441	2.9934	{'f1': 0.7736943907156673}	{'accuracy': 0.663470757430489}
No log	64.0	448	3.0007	{'f1': 0.7744845360824743}	{'accuracy': 0.6644295302013423}
No log	65.0	455	3.0093	{'f1': 0.772609819121447}	{'accuracy': 0.6625119846596357}
No log	66.0	462	3.0342	{'f1': 0.7872738615096695}	{'accuracy': 0.673058485139022}
No log	67.0	469	3.0762	{'f1': 0.77228992944195}	{'accuracy': 0.6596356663470757}
No log	68.0	476	3.0453	{'f1': 0.7932692307692308}	{'accuracy': 0.6701821668264621}
No log	69.0	483	2.6271	{'f1': 0.7821656050955414}	{'accuracy': 0.6720997123681688}
No log	70.0	490	2.4546	{'f1': 0.7858942065491183}	{'accuracy': 0.6740172579098753}
No log	71.0	497	2.2726	{'f1': 0.7861635220125784}	{'accuracy': 0.6740172579098753}
0.06	72.0	504	2.5214	{'f1': 0.8045563549160673}	{'accuracy': 0.6874400767018217}
0.06	73.0	511	2.0838	{'f1': 0.7878787878787878}	{'accuracy': 0.6845637583892618}
0.06	74.0	518	2.2489	{'f1': 0.8166465621230398}	{'accuracy': 0.7085330776605945}
0.06	75.0	525	1.9064	{'f1': 0.8097686375321336}	{'accuracy': 0.716203259827421}
0.06	76.0	532	2.3394	{'f1': 0.8191747572815534}	{'accuracy': 0.7142857142857143}
0.06	77.0	539	2.6605	{'f1': 0.8190476190476191}	{'accuracy': 0.7085330776605945}
0.06	78.0	546	2.8121	{'f1': 0.8196333530455352}	{'accuracy': 0.7075743048897412}
0.06	79.0	553	2.7504	{'f1': 0.817474566128067}	{'accuracy': 0.7075743048897412}
0.06	80.0	560	2.6757	{'f1': 0.8131466828971394}	{'accuracy': 0.7056567593480345}
0.06	81.0	567	2.6491	{'f1': 0.8080931943592886}	{'accuracy': 0.6999041227229147}
0.06	82.0	574	2.6431	{'f1': 0.8044554455445544}	{'accuracy': 0.6970278044103547}
0.06	83.0	581	2.6463	{'f1': 0.8039825762289983}	{'accuracy': 0.697986577181208}
0.06	84.0	588	2.6524	{'f1': 0.8024922118380062}	{'accuracy': 0.6960690316395014}
0.06	85.0	595	2.6633	{'f1': 0.8039825762289983}	{'accuracy': 0.697986577181208}
0.06	86.0	602	2.6732	{'f1': 0.8029832193909261}	{'accuracy': 0.6960690316395014}
0.06	87.0	609	2.6827	{'f1': 0.8039702233250621}	{'accuracy': 0.6970278044103547}
0.06	88.0	616	2.6952	{'f1': 0.8032178217821782}	{'accuracy': 0.6951102588686481}
0.06	89.0	623	2.7044	{'f1': 0.8029647930821494}	{'accuracy': 0.6941514860977949}
0.06	90.0	630	2.6813	{'f1': 0.8024922118380062}	{'accuracy': 0.6960690316395014}
0.06	91.0	637	2.6825	{'f1': 0.8039825762289983}	{'accuracy': 0.697986577181208}
0.06	92.0	644	2.6862	{'f1': 0.8054692355500311}	{'accuracy': 0.6999041227229147}
0.06	93.0	651	2.6907	{'f1': 0.8057107386716326}	{'accuracy': 0.6999041227229147}
0.06	94.0	658	2.6961	{'f1': 0.8057107386716326}	{'accuracy': 0.6999041227229147}
0.06	95.0	665	2.7058	{'f1': 0.8074303405572756}	{'accuracy': 0.7018216682646213}
0.06	96.0	672	2.7126	{'f1': 0.8091414453366277}	{'accuracy': 0.7037392138063279}
0.06	97.0	679	2.7181	{'f1': 0.8098765432098766}	{'accuracy': 0.7046979865771812}
0.06	98.0	686	2.7236	{'f1': 0.8098765432098766}	{'accuracy': 0.7046979865771812}
0.06	99.0	693	2.7287	{'f1': 0.810610734114744}	{'accuracy': 0.7056567593480345}
0.06	100.0	700	2.7363	{'f1': 0.81134401972873}	{'accuracy': 0.7066155321188878}
0.06	101.0	707	2.7417	{'f1': 0.81134401972873}	{'accuracy': 0.7066155321188878}
0.06	102.0	714	2.7474	{'f1': 0.81134401972873}	{'accuracy': 0.7066155321188878}
0.06	103.0	721	2.7536	{'f1': 0.8103448275862069}	{'accuracy': 0.7046979865771812}
0.06	104.0	728	2.7596	{'f1': 0.811076923076923}	{'accuracy': 0.7056567593480345}
0.06	105.0	735	2.7663	{'f1': 0.8105781057810578}	{'accuracy': 0.7046979865771812}
0.06	106.0	742	2.7725	{'f1': 0.810079901659496}	{'accuracy': 0.7037392138063279}
0.06	107.0	749	2.7777	{'f1': 0.810079901659496}	{'accuracy': 0.7037392138063279}
0.06	108.0	756	2.7820	{'f1': 0.810079901659496}	{'accuracy': 0.7037392138063279}
0.06	109.0	763	2.7861	{'f1': 0.810079901659496}	{'accuracy': 0.7037392138063279}
0.06	110.0	770	2.7901	{'f1': 0.810079901659496}	{'accuracy': 0.7037392138063279}
0.06	111.0	777	2.7939	{'f1': 0.8095823095823097}	{'accuracy': 0.7027804410354745}
0.06	112.0	784	2.7992	{'f1': 0.809085328422345}	{'accuracy': 0.7018216682646213}
0.06	113.0	791	2.8038	{'f1': 0.809085328422345}	{'accuracy': 0.7018216682646213}
0.06	114.0	798	2.8079	{'f1': 0.809085328422345}	{'accuracy': 0.7018216682646213}
0.06	115.0	805	2.8131	{'f1': 0.809085328422345}	{'accuracy': 0.7018216682646213}
0.06	116.0	812	2.8182	{'f1': 0.809085328422345}	{'accuracy': 0.7018216682646213}
0.06	117.0	819	2.8226	{'f1': 0.809085328422345}	{'accuracy': 0.7018216682646213}
0.06	118.0	826	2.8268	{'f1': 0.8085889570552146}	{'accuracy': 0.700862895493768}
0.06	119.0	833	2.8306	{'f1': 0.809319435928878}	{'accuracy': 0.7018216682646213}
0.06	120.0	840	2.8343	{'f1': 0.809319435928878}	{'accuracy': 0.7018216682646213}
0.06	121.0	847	2.8388	{'f1': 0.809319435928878}	{'accuracy': 0.7018216682646213}
0.06	122.0	854	2.8426	{'f1': 0.809319435928878}	{'accuracy': 0.7018216682646213}
0.06	123.0	861	2.8462	{'f1': 0.809319435928878}	{'accuracy': 0.7018216682646213}
0.06	124.0	868	2.8499	{'f1': 0.8088235294117647}	{'accuracy': 0.700862895493768}
0.06	125.0	875	2.8540	{'f1': 0.8088235294117647}	{'accuracy': 0.700862895493768}
0.06	126.0	882	2.8584	{'f1': 0.8088235294117647}	{'accuracy': 0.700862895493768}
0.06	127.0	889	2.8620	{'f1': 0.8088235294117647}	{'accuracy': 0.700862895493768}
0.06	128.0	896	2.8651	{'f1': 0.8088235294117647}	{'accuracy': 0.700862895493768}
0.06	129.0	903	2.8679	{'f1': 0.8088235294117647}	{'accuracy': 0.700862895493768}
0.06	130.0	910	2.8724	{'f1': 0.8095529699938763}	{'accuracy': 0.7018216682646213}
0.06	131.0	917	2.8759	{'f1': 0.8095529699938763}	{'accuracy': 0.7018216682646213}
0.06	132.0	924	2.8789	{'f1': 0.8095529699938763}	{'accuracy': 0.7018216682646213}
0.06	133.0	931	2.8821	{'f1': 0.8095529699938763}	{'accuracy': 0.7018216682646213}
0.06	134.0	938	2.8859	{'f1': 0.8095529699938763}	{'accuracy': 0.7018216682646213}
0.06	135.0	945	2.8891	{'f1': 0.8095529699938763}	{'accuracy': 0.7018216682646213}
0.06	136.0	952	2.8969	{'f1': 0.8097859327217125}	{'accuracy': 0.7018216682646213}
0.06	137.0	959	2.9240	{'f1': 0.8124238733252133}	{'accuracy': 0.7046979865771812}
0.06	138.0	966	2.9388	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.06	139.0	973	2.9474	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.06	140.0	980	2.9528	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.06	141.0	987	2.9566	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.06	142.0	994	2.9597	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	143.0	1001	2.9625	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	144.0	1008	2.9656	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	145.0	1015	2.9686	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	146.0	1022	2.9713	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	147.0	1029	2.9733	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	148.0	1036	2.9754	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	149.0	1043	2.9772	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	150.0	1050	2.9794	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	151.0	1057	2.9826	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	152.0	1064	2.9858	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	153.0	1071	2.9880	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	154.0	1078	2.9899	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	155.0	1085	2.9920	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	156.0	1092	2.9939	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	157.0	1099	2.9961	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	158.0	1106	2.9984	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	159.0	1113	3.0005	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	160.0	1120	3.0026	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	161.0	1127	3.0046	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	162.0	1134	3.0072	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	163.0	1141	3.0093	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	164.0	1148	3.0110	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	165.0	1155	3.0130	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	166.0	1162	3.0150	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	167.0	1169	3.0171	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	168.0	1176	3.0193	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	169.0	1183	3.0223	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	170.0	1190	3.0248	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	171.0	1197	3.0270	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	172.0	1204	3.0287	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	173.0	1211	3.0308	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	174.0	1218	3.0337	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	175.0	1225	3.0365	{'f1': 0.8138686131386861}	{'accuracy': 0.7066155321188878}
0.0002	176.0	1232	3.0402	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	177.0	1239	3.0431	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	178.0	1246	3.0455	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	179.0	1253	3.0478	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	180.0	1260	3.0501	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	181.0	1267	3.0524	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	182.0	1274	3.0543	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	183.0	1281	3.0561	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	184.0	1288	3.0581	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	185.0	1295	3.0599	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	186.0	1302	3.0620	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	187.0	1309	3.0641	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	188.0	1316	3.0660	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	189.0	1323	3.0677	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	190.0	1330	3.0694	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	191.0	1337	3.0711	{'f1': 0.8133738601823709}	{'accuracy': 0.7056567593480345}
0.0002	192.0	1344	3.0758	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	193.0	1351	3.0802	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	194.0	1358	3.0921	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	195.0	1365	3.0988	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	196.0	1372	3.1029	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	197.0	1379	3.1057	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	198.0	1386	3.1179	{'f1': 0.8140947752126367}	{'accuracy': 0.7066155321188878}
0.0002	199.0	1393	3.1298	{'f1': 0.8136004857316332}	{'accuracy': 0.7056567593480345}
0.0002	200.0	1400	3.1361	{'f1': 0.8138265615524559}	{'accuracy': 0.7056567593480345}
0.0002	201.0	1407	3.1397	{'f1': 0.8145454545454547}	{'accuracy': 0.7066155321188878}
0.0002	202.0	1414	3.1421	{'f1': 0.8145454545454547}	{'accuracy': 0.7066155321188878}
0.0002	203.0	1421	3.1440	{'f1': 0.8145454545454547}	{'accuracy': 0.7066155321188878}
0.0002	204.0	1428	3.1454	{'f1': 0.8145454545454547}	{'accuracy': 0.7066155321188878}
0.0002	205.0	1435	3.1469	{'f1': 0.8138265615524559}	{'accuracy': 0.7056567593480345}
0.0002	206.0	1442	3.1481	{'f1': 0.8138265615524559}	{'accuracy': 0.7056567593480345}
0.0002	207.0	1449	3.1495	{'f1': 0.8138265615524559}	{'accuracy': 0.7056567593480345}
0.0002	208.0	1456	3.1508	{'f1': 0.8143203883495146}	{'accuracy': 0.7066155321188878}
0.0002	209.0	1463	3.1522	{'f1': 0.8143203883495146}	{'accuracy': 0.7066155321188878}
0.0002	210.0	1470	3.1535	{'f1': 0.8143203883495146}	{'accuracy': 0.7066155321188878}
0.0002	211.0	1477	3.1484	{'f1': 0.8143203883495146}	{'accuracy': 0.7066155321188878}
0.0002	212.0	1484	3.1218	{'f1': 0.811965811965812}	{'accuracy': 0.7046979865771812}
0.0002	213.0	1491	3.1115	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0002	214.0	1498	3.1082	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
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0.0	216.0	1512	3.1098	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	217.0	1519	3.1112	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	218.0	1526	3.1127	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	219.0	1533	3.1157	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	220.0	1540	3.1183	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	221.0	1547	3.1205	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	222.0	1554	3.1225	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	223.0	1561	3.1242	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	224.0	1568	3.1265	{'f1': 0.8100490196078431}	{'accuracy': 0.7027804410354745}
0.0	225.0	1575	3.1292	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	226.0	1582	3.1319	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	227.0	1589	3.1339	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	228.0	1596	3.1360	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	229.0	1603	3.1378	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	230.0	1610	3.1394	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	231.0	1617	3.1412	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	232.0	1624	3.1432	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	233.0	1631	3.1450	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	234.0	1638	3.1466	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	235.0	1645	3.1481	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	236.0	1652	3.1495	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	237.0	1659	3.1509	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	238.0	1666	3.1522	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	239.0	1673	3.1534	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	240.0	1680	3.1547	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	241.0	1687	3.1563	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	242.0	1694	3.1578	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	243.0	1701	3.1593	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	244.0	1708	3.1611	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	245.0	1715	3.1633	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	246.0	1722	3.1655	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	247.0	1729	3.1673	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	248.0	1736	3.1689	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	249.0	1743	3.1704	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	250.0	1750	3.1720	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	251.0	1757	3.1736	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	252.0	1764	3.1750	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	253.0	1771	3.1767	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	254.0	1778	3.1781	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	255.0	1785	3.1796	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	256.0	1792	3.1812	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	257.0	1799	3.1828	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	258.0	1806	3.1844	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	259.0	1813	3.1865	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	260.0	1820	3.1887	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	261.0	1827	3.1907	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	262.0	1834	3.1924	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	263.0	1841	3.1962	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	264.0	1848	3.1992	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	265.0	1855	3.2013	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	266.0	1862	3.2030	{'f1': 0.810777709736681}	{'accuracy': 0.7037392138063279}
0.0	267.0	1869	3.2045	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	268.0	1876	3.2059	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	269.0	1883	3.2074	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	270.0	1890	3.2090	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	271.0	1897	3.2130	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	272.0	1904	3.2162	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	273.0	1911	3.2182	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	274.0	1918	3.2199	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	275.0	1925	3.2215	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	276.0	1932	3.2230	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	277.0	1939	3.2244	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	278.0	1946	3.2259	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	279.0	1953	3.2273	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	280.0	1960	3.2287	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	281.0	1967	3.2300	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	282.0	1974	3.2313	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	283.0	1981	3.2325	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	284.0	1988	3.2336	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	285.0	1995	3.2349	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	286.0	2002	3.2360	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	287.0	2009	3.2375	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	288.0	2016	3.2389	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
0.0	289.0	2023	3.2402	{'f1': 0.8102815177478581}	{'accuracy': 0.7027804410354745}
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0.0	292.0	2044	3.2450	{'f1': 0.8097859327217125}	{'accuracy': 0.7018216682646213}
0.0	293.0	2051	3.2471	{'f1': 0.8097859327217125}	{'accuracy': 0.7018216682646213}
0.0	294.0	2058	3.2489	{'f1': 0.8097859327217125}	{'accuracy': 0.7018216682646213}
0.0	295.0	2065	3.2503	{'f1': 0.8097859327217125}	{'accuracy': 0.7018216682646213}
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0.5783	1844.0	12908	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1845.0	12915	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1846.0	12922	0.6266	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1847.0	12929	0.6275	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1848.0	12936	0.6276	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1849.0	12943	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1850.0	12950	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1851.0	12957	0.6277	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1852.0	12964	0.6276	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1853.0	12971	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1854.0	12978	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1855.0	12985	0.6263	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1856.0	12992	0.6265	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5783	1857.0	12999	0.6260	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1858.0	13006	0.6261	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1859.0	13013	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1860.0	13020	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1861.0	13027	0.6266	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1862.0	13034	0.6264	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1863.0	13041	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1864.0	13048	0.6274	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1865.0	13055	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1866.0	13062	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1867.0	13069	0.6275	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1868.0	13076	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1869.0	13083	0.6284	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1870.0	13090	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1871.0	13097	0.6285	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1872.0	13104	0.6276	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1873.0	13111	0.6275	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1874.0	13118	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1875.0	13125	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1876.0	13132	0.6270	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1877.0	13139	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1878.0	13146	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1879.0	13153	0.6266	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1880.0	13160	0.6266	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1881.0	13167	0.6263	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1882.0	13174	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1883.0	13181	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1884.0	13188	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1885.0	13195	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1886.0	13202	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1887.0	13209	0.6278	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1888.0	13216	0.6277	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1889.0	13223	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1890.0	13230	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1891.0	13237	0.6274	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1892.0	13244	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1893.0	13251	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1894.0	13258	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1895.0	13265	0.6263	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1896.0	13272	0.6265	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1897.0	13279	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1898.0	13286	0.6266	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1899.0	13293	0.6274	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1900.0	13300	0.6270	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1901.0	13307	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1902.0	13314	0.6274	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1903.0	13321	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1904.0	13328	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1905.0	13335	0.6265	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1906.0	13342	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1907.0	13349	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1908.0	13356	0.6264	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1909.0	13363	0.6265	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1910.0	13370	0.6261	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1911.0	13377	0.6266	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1912.0	13384	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1913.0	13391	0.6276	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1914.0	13398	0.6280	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1915.0	13405	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1916.0	13412	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1917.0	13419	0.6280	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1918.0	13426	0.6276	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1919.0	13433	0.6275	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1920.0	13440	0.6277	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1921.0	13447	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1922.0	13454	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1923.0	13461	0.6286	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1924.0	13468	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1925.0	13475	0.6285	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1926.0	13482	0.6287	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1927.0	13489	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5763	1928.0	13496	0.6286	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1929.0	13503	0.6284	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1930.0	13510	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1931.0	13517	0.6285	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1932.0	13524	0.6280	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1933.0	13531	0.6284	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1934.0	13538	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1935.0	13545	0.6281	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1936.0	13552	0.6280	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1937.0	13559	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1938.0	13566	0.6290	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1939.0	13573	0.6290	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1940.0	13580	0.6288	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1941.0	13587	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1942.0	13594	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1943.0	13601	0.6287	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1944.0	13608	0.6287	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1945.0	13615	0.6287	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1946.0	13622	0.6288	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1947.0	13629	0.6286	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1948.0	13636	0.6286	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1949.0	13643	0.6286	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1950.0	13650	0.6283	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1951.0	13657	0.6284	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1952.0	13664	0.6282	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1953.0	13671	0.6280	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1954.0	13678	0.6280	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1955.0	13685	0.6279	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1956.0	13692	0.6276	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1957.0	13699	0.6277	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1958.0	13706	0.6275	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1959.0	13713	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1960.0	13720	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1961.0	13727	0.6273	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1962.0	13734	0.6274	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1963.0	13741	0.6274	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1964.0	13748	0.6272	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1965.0	13755	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1966.0	13762	0.6272	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1967.0	13769	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1968.0	13776	0.6270	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1969.0	13783	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1970.0	13790	0.6270	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1971.0	13797	0.6271	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1972.0	13804	0.6270	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1973.0	13811	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1974.0	13818	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1975.0	13825	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1976.0	13832	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1977.0	13839	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1978.0	13846	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1979.0	13853	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1980.0	13860	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1981.0	13867	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1982.0	13874	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1983.0	13881	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1984.0	13888	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1985.0	13895	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1986.0	13902	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1987.0	13909	0.6269	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1988.0	13916	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1989.0	13923	0.6268	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1990.0	13930	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1991.0	13937	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1992.0	13944	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1993.0	13951	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1994.0	13958	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1995.0	13965	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1996.0	13972	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1997.0	13979	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1998.0	13986	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5765	1999.0	13993	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
0.5759	2000.0	14000	0.6267	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3