edyfjm07/distilbert-base-uncased-v7-finetuned-squad-es

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

• Train Loss: 0.0143
• Train End Logits Accuracy: 0.9966
• Train Start Logits Accuracy: 0.9932
• Validation Loss: 4.3715
• Validation End Logits Accuracy: 0.5654
• Validation Start Logits Accuracy: 0.5385
• Epoch: 49

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:

• optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 14650, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
• training_precision: float32

Training results

Train Loss	Train End Logits Accuracy	Train Start Logits Accuracy	Validation Loss	Validation End Logits Accuracy	Validation Start Logits Accuracy	Epoch
3.6949	0.1984	0.1779	2.8564	0.2769	0.2538	0
2.5459	0.3648	0.3635	2.2433	0.4436	0.4231	1
1.8293	0.5205	0.5107	2.1826	0.5013	0.4654	2
1.3619	0.6135	0.6067	2.2598	0.5090	0.4795	3
1.0012	0.7018	0.6826	2.4600	0.5308	0.4808	4
0.7736	0.7555	0.7624	2.4332	0.5410	0.5077	5
0.6124	0.7914	0.8131	2.7033	0.5410	0.5141	6
0.4781	0.8439	0.8498	2.8627	0.5462	0.5205	7
0.3867	0.8673	0.8660	3.0181	0.5449	0.5269	8
0.3473	0.8759	0.8882	3.0705	0.5410	0.5154	9
0.2735	0.9096	0.9083	3.1680	0.5590	0.5359	10
0.2354	0.9189	0.9206	3.2071	0.5705	0.5231	11
0.1955	0.9360	0.9300	3.4207	0.5449	0.5141	12
0.2068	0.9283	0.9296	3.2288	0.5551	0.5333	13
0.1852	0.9364	0.9381	3.5434	0.5385	0.5218	14
0.1522	0.9509	0.9471	3.5845	0.5487	0.5256	15
0.1404	0.9548	0.9582	3.6228	0.5628	0.5192	16
0.1255	0.9501	0.9578	3.6708	0.5628	0.5295	17
0.1253	0.9608	0.9578	3.7048	0.5564	0.5154	18
0.1120	0.9539	0.9633	3.6301	0.5628	0.5295	19
0.1043	0.9578	0.9642	3.6380	0.5474	0.5295	20
0.0999	0.9612	0.9659	3.8969	0.5449	0.5321	21
0.0845	0.9710	0.9757	3.9082	0.5590	0.5321	22
0.0874	0.9735	0.9689	3.7159	0.5603	0.5436	23
0.0700	0.9731	0.9748	3.9612	0.5564	0.5462	24
0.0572	0.9787	0.9774	4.0000	0.5590	0.5333	25
0.0628	0.9761	0.9778	3.8762	0.5551	0.5372	26
0.0550	0.9804	0.9821	3.9125	0.5590	0.5397	27
0.0586	0.9817	0.9808	3.9667	0.5603	0.5346	28
0.0465	0.9812	0.9838	3.9716	0.5603	0.5295	29
0.0438	0.9842	0.9825	4.0324	0.5577	0.5333	30
0.0422	0.9834	0.9872	4.2007	0.5603	0.5397	31
0.0427	0.9868	0.9846	4.1012	0.5513	0.5372	32
0.0395	0.9885	0.9855	4.0936	0.5487	0.5308	33
0.0355	0.9863	0.9872	4.1443	0.5667	0.5321	34
0.0366	0.9876	0.9881	4.2423	0.5551	0.5410	35
0.0323	0.9881	0.9872	4.3990	0.5513	0.5295	36
0.0252	0.9915	0.9893	4.2288	0.5551	0.5359	37
0.0285	0.9863	0.9906	4.3026	0.5654	0.5346	38
0.0251	0.9906	0.9915	4.2990	0.5628	0.5346	39
0.0313	0.9868	0.9885	4.2994	0.5679	0.5359	40
0.0208	0.9932	0.9932	4.2457	0.5603	0.5372	41
0.0225	0.9927	0.9910	4.4447	0.5628	0.5295	42
0.0194	0.9932	0.9919	4.3625	0.5603	0.5359	43
0.0189	0.9906	0.9923	4.3148	0.5679	0.5410	44
0.0182	0.9949	0.9927	4.3577	0.5628	0.5385	45
0.0160	0.9923	0.9949	4.3897	0.5615	0.5346	46
0.0146	0.9949	0.9936	4.3823	0.5679	0.5385	47
0.0163	0.9936	0.9945	4.3764	0.5667	0.5397	48
0.0143	0.9966	0.9932	4.3715	0.5654	0.5385	49

Framework versions

• Transformers 4.41.2
• TensorFlow 2.15.0
• Datasets 2.20.0
• Tokenizers 0.19.1