electricidad-small-finetuned-restaurant-sentiment-analysis-usElectionTweets1Jul11Nov-spanish

This model is a fine-tuned version of mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.3534
• Accuracy: 0.7585
• F1: 0.7585

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: 2e-05
• 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: 60

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.8145	1.0	1222	0.7033	0.7168	0.7168
0.7016	2.0	2444	0.5936	0.7731	0.7731
0.6183	3.0	3666	0.5190	0.8046	0.8046
0.5516	4.0	4888	0.4678	0.8301	0.8301
0.4885	5.0	6110	0.3670	0.8713	0.8713
0.4353	6.0	7332	0.3119	0.8987	0.8987
0.3957	7.0	8554	0.2908	0.9084	0.9084
0.3386	8.0	9776	0.2108	0.9348	0.9348
0.2976	9.0	10998	0.1912	0.9422	0.9422
0.2828	10.0	12220	0.1496	0.9591	0.9591
0.243	11.0	13442	0.1326	0.9639	0.9639
0.2049	12.0	14664	0.1249	0.9693	0.9693
0.2041	13.0	15886	0.1049	0.9752	0.9752
0.1855	14.0	17108	0.0816	0.9798	0.9798
0.1637	15.0	18330	0.0733	0.9836	0.9836
0.1531	16.0	19552	0.0577	0.9880	0.9880
0.1221	17.0	20774	0.0581	0.9895	0.9895
0.1207	18.0	21996	0.0463	0.9903	0.9903
0.1152	19.0	23218	0.0472	0.9908	0.9908
0.1028	20.0	24440	0.0356	0.9936	0.9936
0.1027	21.0	25662	0.0278	0.9957	0.9957
0.0915	22.0	26884	0.0344	0.9946	0.9946
0.0887	23.0	28106	0.0243	0.9954	0.9954
0.0713	24.0	29328	0.0208	0.9969	0.9969
0.0749	25.0	30550	0.0198	0.9964	0.9964
0.0699	26.0	31772	0.0153	0.9969	0.9969
0.0567	27.0	32994	0.0144	0.9972	0.9972
0.0613	28.0	34216	0.0105	0.9982	0.9982
0.0567	29.0	35438	0.0117	0.9982	0.9982
0.0483	30.0	36660	0.0072	0.9985	0.9985
0.0469	31.0	37882	0.0063	0.9987	0.9987
0.0485	32.0	39104	0.0067	0.9985	0.9985
0.0464	33.0	40326	0.0020	0.9995	0.9995
0.0472	34.0	41548	0.0036	0.9995	0.9995
0.0388	35.0	42770	0.0016	0.9995	0.9995
0.0248	36.0	43992	0.0047	0.9990	0.9990
0.0396	37.0	45214	0.0004	0.9997	0.9997
0.0331	38.0	46436	0.0020	0.9995	0.9995
0.0292	39.0	47658	0.0000	1.0	1.0
0.0253	40.0	48880	0.0001	1.0	1.0
0.0285	41.0	50102	0.0000	1.0	1.0
0.0319	42.0	51324	0.0000	1.0	1.0
0.0244	43.0	52546	0.0000	1.0	1.0
0.0261	44.0	53768	0.0001	1.0	1.0
0.0256	45.0	54990	0.0000	1.0	1.0
0.0258	46.0	56212	0.0000	1.0	1.0
0.0173	47.0	57434	0.0000	1.0	1.0
0.0253	48.0	58656	0.0000	1.0	1.0
0.0241	49.0	59878	0.0000	1.0	1.0
0.019	50.0	61100	0.0000	1.0	1.0
0.0184	51.0	62322	0.0000	1.0	1.0
0.0139	52.0	63544	0.0000	1.0	1.0
0.0159	53.0	64766	0.0000	1.0	1.0
0.0119	54.0	65988	0.0000	1.0	1.0
0.0253	55.0	67210	0.0000	1.0	1.0
0.0166	56.0	68432	0.0000	1.0	1.0
0.0125	57.0	69654	0.0000	1.0	1.0
0.0155	58.0	70876	0.0000	1.0	1.0
0.0106	59.0	72098	0.0000	1.0	1.0
0.0083	60.0	73320	0.0000	1.0	1.0

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1