FNST_trad_2h

mrovejaxd/FNST_trad_2h

FNST_trad_2h is a machine learning model from mrovejaxd. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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

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Hugging Face

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At a glance

Model type: bert
Access: Public
Created: Sep 29, 2024
Updated: Sep 30, 2024
SHA: e8d36d58

Base models

dccuchile/bert-base-spanish-wwm-cased

Model card

From the Hugging Face model README

FNST_trad_2h

This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3483
Accuracy: 0.6981
F1: 0.6890

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.9472	0.32	500	0.8488	0.6418	0.6296
0.8362	0.64	1000	0.7882	0.6641	0.6492
0.7964	0.96	1500	0.7619	0.6821	0.6745
0.6268	1.28	2000	0.7757	0.6909	0.6829
0.6134	1.6	2500	0.7786	0.6857	0.6757
0.6236	1.92	3000	0.7758	0.6830	0.6713
0.456	2.24	3500	0.9266	0.6779	0.6688
0.4198	2.56	4000	0.8362	0.6859	0.6796
0.4334	2.88	4500	0.8705	0.6916	0.6813
0.3266	3.2	5000	1.0376	0.6934	0.6841
0.2825	3.52	5500	1.0690	0.6940	0.6821
0.294	3.84	6000	1.1104	0.6896	0.6812
0.2493	4.16	6500	1.3248	0.6848	0.6764
0.2085	4.48	7000	1.1945	0.6869	0.6789
0.2202	4.8	7500	1.2104	0.6853	0.6739
0.2011	5.12	8000	1.4272	0.6932	0.6853
0.1724	5.44	8500	1.3933	0.6823	0.6682
0.1873	5.76	9000	1.3483	0.6981	0.6890
0.1791	6.08	9500	1.5552	0.6815	0.6704
0.1527	6.4	10000	1.4202	0.6873	0.6754
0.1631	6.72	10500	1.6333	0.6727	0.6610
0.1635	7.04	11000	1.6169	0.6850	0.6696
0.1341	7.36	11500	1.5840	0.6871	0.6785
0.1513	7.68	12000	1.4788	0.6848	0.6739

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Type: bert
Created: Sep 29, 2024
Updated: Sep 30, 2024