scenario-TCR-XLMV-XCOPA-1_data-xcopa_all

This model is a fine-tuned version of facebook/xlm-v-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.6931
• Accuracy: 0.5592
• F1: 0.5289

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 500

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
No log	0.38	5	0.6932	0.4917	0.4383
No log	0.77	10	0.6931	0.5192	0.5064
No log	1.15	15	0.6931	0.5017	0.4613
No log	1.54	20	0.6932	0.4942	0.4576
No log	1.92	25	0.6931	0.505	0.4629
No log	2.31	30	0.6931	0.5	0.4643
No log	2.69	35	0.6931	0.4892	0.4580
No log	3.08	40	0.6931	0.4833	0.4552
No log	3.46	45	0.6932	0.4967	0.4588
No log	3.85	50	0.6931	0.5042	0.4711
No log	4.23	55	0.6931	0.5108	0.4846
No log	4.62	60	0.6932	0.4875	0.4591
No log	5.0	65	0.6931	0.4958	0.4641
No log	5.38	70	0.6931	0.4933	0.4777
No log	5.77	75	0.6931	0.5075	0.4901
No log	6.15	80	0.6931	0.4833	0.4464
No log	6.54	85	0.6931	0.5175	0.4917
No log	6.92	90	0.6931	0.4442	0.4225
No log	7.31	95	0.6931	0.4583	0.4377
No log	7.69	100	0.6931	0.5192	0.4978
No log	8.08	105	0.6931	0.5425	0.5230
No log	8.46	110	0.6931	0.535	0.5122
No log	8.85	115	0.6931	0.545	0.5194
No log	9.23	120	0.6931	0.5492	0.5259
No log	9.62	125	0.6931	0.535	0.5114
No log	10.0	130	0.6931	0.5475	0.5233
No log	10.38	135	0.6931	0.5525	0.5269
No log	10.77	140	0.6931	0.5458	0.5223
No log	11.15	145	0.6931	0.5392	0.5145
No log	11.54	150	0.6931	0.5483	0.5246
No log	11.92	155	0.6931	0.5342	0.5084
No log	12.31	160	0.6931	0.54	0.5158
No log	12.69	165	0.6931	0.5375	0.5084
No log	13.08	170	0.6931	0.5433	0.5133
No log	13.46	175	0.6931	0.5333	0.5096
No log	13.85	180	0.6931	0.5458	0.5215
No log	14.23	185	0.6931	0.5508	0.5259
No log	14.62	190	0.6931	0.5433	0.5168
No log	15.0	195	0.6931	0.55	0.5280
No log	15.38	200	0.6931	0.5442	0.5231
No log	15.77	205	0.6931	0.55	0.5280
No log	16.15	210	0.6931	0.5458	0.5257
No log	16.54	215	0.6931	0.5392	0.5195
No log	16.92	220	0.6931	0.5367	0.5165
No log	17.31	225	0.6931	0.5433	0.5235
No log	17.69	230	0.6931	0.55	0.5271
No log	18.08	235	0.6931	0.5425	0.5222
No log	18.46	240	0.6931	0.5417	0.5158
No log	18.85	245	0.6931	0.4983	0.4719
No log	19.23	250	0.6931	0.5483	0.5237
No log	19.62	255	0.6931	0.5425	0.5230
No log	20.0	260	0.6931	0.5467	0.5220
No log	20.38	265	0.6931	0.5467	0.5220
No log	20.77	270	0.6931	0.5508	0.5251
No log	21.15	275	0.6931	0.555	0.5283
No log	21.54	280	0.6931	0.5533	0.5257
No log	21.92	285	0.6931	0.555	0.5283
No log	22.31	290	0.6931	0.5533	0.5298
No log	22.69	295	0.6931	0.5517	0.5281
No log	23.08	300	0.6931	0.5567	0.5325
No log	23.46	305	0.6931	0.55	0.5288
No log	23.85	310	0.6931	0.5475	0.5233
No log	24.23	315	0.6931	0.5467	0.5220
No log	24.62	320	0.6931	0.55	0.5246
No log	25.0	325	0.6931	0.5483	0.5212
No log	25.38	330	0.6931	0.5467	0.5203
No log	25.77	335	0.6931	0.5483	0.5204
No log	26.15	340	0.6931	0.5492	0.5225
No log	26.54	345	0.6931	0.5492	0.5250
No log	26.92	350	0.6931	0.5542	0.5295
No log	27.31	355	0.6931	0.5567	0.5350
No log	27.69	360	0.6931	0.5533	0.5290
No log	28.08	365	0.6931	0.5558	0.5296
No log	28.46	370	0.6931	0.5542	0.5270
No log	28.85	375	0.6931	0.5383	0.5166
No log	29.23	380	0.6931	0.5483	0.5220
No log	29.62	385	0.6931	0.5475	0.5190
No log	30.0	390	0.6931	0.5483	0.5212
No log	30.38	395	0.6931	0.5208	0.4871
No log	30.77	400	0.6931	0.4867	0.4690
No log	31.15	405	0.6931	0.485	0.4663
No log	31.54	410	0.6931	0.455	0.4313
No log	31.92	415	0.6931	0.4608	0.4369
No log	32.31	420	0.6931	0.4617	0.4421
No log	32.69	425	0.6931	0.5258	0.4942
No log	33.08	430	0.6931	0.5608	0.5340
No log	33.46	435	0.6931	0.5583	0.5310
No log	33.85	440	0.6931	0.56	0.5352
No log	34.23	445	0.6931	0.5567	0.5325
No log	34.62	450	0.6931	0.5525	0.5277
No log	35.0	455	0.6931	0.5542	0.5303
No log	35.38	460	0.6931	0.5633	0.5379
No log	35.77	465	0.6931	0.5542	0.5295
No log	36.15	470	0.6931	0.5567	0.5309
No log	36.54	475	0.6931	0.555	0.5291
No log	36.92	480	0.6931	0.5575	0.5330
No log	37.31	485	0.6931	0.5517	0.5256
No log	37.69	490	0.6931	0.545	0.5168
No log	38.08	495	0.6931	0.54	0.5132
0.6936	38.46	500	0.6931	0.55	0.5238
0.6936	38.85	505	0.6931	0.5425	0.512
0.6936	39.23	510	0.6931	0.54	0.5106
0.6936	39.62	515	0.6931	0.5242	0.4906
0.6936	40.0	520	0.6931	0.5292	0.4978
0.6936	40.38	525	0.6931	0.53	0.5009
0.6936	40.77	530	0.6931	0.5308	0.5031
0.6936	41.15	535	0.6931	0.5425	0.5205
0.6936	41.54	540	0.6931	0.535	0.5088
0.6936	41.92	545	0.6931	0.5342	0.5084
0.6936	42.31	550	0.6931	0.5425	0.5205
0.6936	42.69	555	0.6931	0.5475	0.5241
0.6936	43.08	560	0.6931	0.5517	0.5264
0.6936	43.46	565	0.6931	0.5592	0.5339
0.6936	43.85	570	0.6931	0.5625	0.5350
0.6936	44.23	575	0.6931	0.5625	0.5358
0.6936	44.62	580	0.6931	0.5617	0.5337
0.6936	45.0	585	0.6931	0.5633	0.5355
0.6936	45.38	590	0.6931	0.56	0.5344
0.6936	45.77	595	0.6931	0.5625	0.5350
0.6936	46.15	600	0.6931	0.555	0.5258
0.6936	46.54	605	0.6931	0.5625	0.5350
0.6936	46.92	610	0.6931	0.5592	0.5289

Framework versions

• Transformers 4.33.3
• Pytorch 2.1.1+cu121
• Datasets 2.14.5
• Tokenizers 0.13.3