mi-super-modelo

omegaodin/mi-super-modelo

mi-super-modelo is a text classification model from omegaodin. Use it when you need a label for a piece of text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
Model type: distilbert
Access: Public
Created: Sep 8, 2024
Updated: Sep 8, 2024
SHA: 68775986

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Base models

Model card

From the Hugging Face model README

mi-super-modelo

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

Loss: 0.3218
Accuracy: 0.87

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7166	0.04	5	0.6899	0.5
0.6804	0.08	10	0.6806	0.505
0.6756	0.12	15	0.6625	0.565
0.6527	0.16	20	0.6229	0.67
0.6245	0.2	25	0.6537	0.585
0.6079	0.24	30	0.5368	0.76
0.5977	0.28	35	0.4603	0.83
0.3683	0.32	40	0.5971	0.71
0.3948	0.36	45	0.4346	0.815
0.4459	0.4	50	0.4177	0.81
0.5583	0.44	55	0.3364	0.855
0.5495	0.48	60	0.3367	0.865
0.1608	0.52	65	0.3992	0.825
0.4232	0.56	70	0.3484	0.835
0.6385	0.6	75	0.3930	0.86
0.2918	0.64	80	0.3389	0.86
0.2137	0.68	85	0.3272	0.865
0.419	0.72	90	0.3188	0.885
0.2032	0.76	95	0.3158	0.87
0.226	0.8	100	0.3204	0.87
0.2525	0.84	105	0.3398	0.83
0.2573	0.88	110	0.3494	0.85
0.3895	0.92	115	0.3368	0.835
0.2776	0.96	120	0.3241	0.87
0.2487	1.0	125	0.3218	0.87

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cpu
Datasets 2.21.0
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: distilbert
Created: Sep 8, 2024
Updated: Sep 8, 2024