2404v3 – AI Model by adriansanz | AlphaNeural AI

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adriansanz/
2404v3

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text-classification

generated_from_trainer

projecte-aina/roberta-base-ca-v2-cased-te

autotrain_compatible

endpoints_compatible

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This model is a fine-tuned version of projecte-aina/roberta-base-ca-v2-cased-te on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6453
Accuracy: 0.8309
Precision: 0.8362
Recall: 0.8309
F1: 0.8302
Ratio: 0.5631

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: 10
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 20
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.06
lr_scheduler_warmup_steps: 4
num_epochs: 1
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Ratio
0.5106	0.1626	10	0.6599	0.8289	0.8500	0.8289	0.8262	0.3772
0.5228	0.3252	20	0.5642	0.8517	0.8517	0.8517	0.8517	0.5020
0.5035	0.4878	30	0.5669	0.8544	0.8554	0.8544	0.8543	0.4725
0.4325	0.6504	40	0.6077	0.8403	0.8442	0.8403	0.8398	0.4463
0.4902	0.8130	50	0.6391	0.8322	0.8369	0.8322	0.8316	0.5591
0.4774	0.9756	60	0.6453	0.8309	0.8362	0.8309	0.8302	0.5631

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1