detect-femicide-news-xlmr-nl-fft-freeze2

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: 0.4119
• Accuracy: 0.8571
• Precision Neg: 0.85
• Precision Pos: 0.875
• Recall Neg: 0.9444
• Recall Pos: 0.7
• F1 Score Neg: 0.8947
• F1 Score Pos: 0.7778

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: 1e-05
• train_batch_size: 24
• eval_batch_size: 8
• seed: 1996
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision Neg	Precision Pos	Recall Neg	Recall Pos	F1 Score Neg	F1 Score Pos
1.3215	1.0	23	1.0782	0.75	0.7391	0.8	0.9444	0.4	0.8293	0.5333
1.0955	2.0	46	0.9057	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.9344	3.0	69	0.7420	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.8303	4.0	92	0.5952	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.6728	5.0	115	0.5078	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.649	6.0	138	0.4546	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.6008	7.0	161	0.4454	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.5439	8.0	184	0.4495	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.5557	9.0	207	0.4479	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.5637	10.0	230	0.4470	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.5709	11.0	253	0.4500	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.5496	12.0	276	0.4456	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5586	13.0	299	0.4484	0.8214	0.8095	0.8571	0.9444	0.6	0.8718	0.7059
0.562	14.0	322	0.4435	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5555	15.0	345	0.4427	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5449	16.0	368	0.4404	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.555	17.0	391	0.4384	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5541	18.0	414	0.4383	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5463	19.0	437	0.4379	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5548	20.0	460	0.4357	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5365	21.0	483	0.4342	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5473	22.0	506	0.4308	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5467	23.0	529	0.4309	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.543	24.0	552	0.4312	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.543	25.0	575	0.4289	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5309	26.0	598	0.4290	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5406	27.0	621	0.4246	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5295	28.0	644	0.4248	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.535	29.0	667	0.4247	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5401	30.0	690	0.4265	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5285	31.0	713	0.4262	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5492	32.0	736	0.4247	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5473	33.0	759	0.4224	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.547	34.0	782	0.4250	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5394	35.0	805	0.4280	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5361	36.0	828	0.4247	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5294	37.0	851	0.4238	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5302	38.0	874	0.4236	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5384	39.0	897	0.4215	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5396	40.0	920	0.4209	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5305	41.0	943	0.4192	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5241	42.0	966	0.4204	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5433	43.0	989	0.4190	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5246	44.0	1012	0.4169	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.525	45.0	1035	0.4177	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5306	46.0	1058	0.4169	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5228	47.0	1081	0.4167	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5094	48.0	1104	0.4176	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5207	49.0	1127	0.4170	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5087	50.0	1150	0.4169	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5229	51.0	1173	0.4163	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5221	52.0	1196	0.4160	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5147	53.0	1219	0.4166	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.524	54.0	1242	0.4157	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5171	55.0	1265	0.4149	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5116	56.0	1288	0.4138	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5373	57.0	1311	0.4139	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5274	58.0	1334	0.4131	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5375	59.0	1357	0.4133	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.528	60.0	1380	0.4136	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5282	61.0	1403	0.4147	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.528	62.0	1426	0.4142	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5357	63.0	1449	0.4132	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5177	64.0	1472	0.4132	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5358	65.0	1495	0.4133	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5224	66.0	1518	0.4124	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5121	67.0	1541	0.4125	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5394	68.0	1564	0.4137	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.52	69.0	1587	0.4140	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5103	70.0	1610	0.4131	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5224	71.0	1633	0.4134	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5351	72.0	1656	0.4129	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5181	73.0	1679	0.4138	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.532	74.0	1702	0.4139	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5216	75.0	1725	0.4142	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5083	76.0	1748	0.4138	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.531	77.0	1771	0.4132	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5245	78.0	1794	0.4125	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5191	79.0	1817	0.4127	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.516	80.0	1840	0.4126	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5098	81.0	1863	0.4128	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5173	82.0	1886	0.4127	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5119	83.0	1909	0.4129	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5296	84.0	1932	0.4125	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5105	85.0	1955	0.4131	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5108	86.0	1978	0.4124	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5156	87.0	2001	0.4125	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5143	88.0	2024	0.4124	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5099	89.0	2047	0.4122	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5163	90.0	2070	0.4120	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5224	91.0	2093	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.4936	92.0	2116	0.4120	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5236	93.0	2139	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5261	94.0	2162	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5134	95.0	2185	0.4119	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5064	96.0	2208	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5072	97.0	2231	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5264	98.0	2254	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.5344	99.0	2277	0.4118	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778
0.522	100.0	2300	0.4119	0.8571	0.85	0.875	0.9444	0.7	0.8947	0.7778

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.2+cu113
• Datasets 1.18.3
• Tokenizers 0.11.0