xlm_r_base-finetuned_after_mrp-clean-rain-9 – AI Model by haturusinghe | AlphaNeural AI

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haturusinghe/
xlm_r_base-finetuned_after_mrp-clean-rain-9

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text-classification

generated_from_trainer

FacebookAI/xlm-roberta-base

autotrain_compatible

endpoints_compatible

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xlm_r_base-finetuned_after_mrp-clean-rain-9

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

Loss: 0.4542
Precision 0: 0.8565
Precision 1: 0.8077
Recall 0: 0.8721
Recall 1: 0.7862
F1 0: 0.8642
F1 1: 0.7968
Precision Weighted: 0.8367
Recall Weighted: 0.8372
F1 Weighted: 0.8368
Accuracy: 0.8372
F1 Macro: 0.8305

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
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision 0	Precision 1	Recall 0	Recall 1	F1 0	F1 1	Precision Weighted	Recall Weighted	F1 Weighted	Accuracy	F1 Macro
0.4924	1.0	469	0.4688	0.8112	0.7616	0.8478	0.7113	0.8291	0.7356	0.7911	0.7924	0.7911	0.7924	0.7824
0.3778	2.0	938	0.3937	0.8510	0.7896	0.8579	0.7803	0.8545	0.7849	0.8261	0.8264	0.8262	0.8264	0.8197
0.3903	3.0	1407	0.3670	0.8570	0.7978	0.8633	0.7892	0.8601	0.7935	0.8329	0.8332	0.8331	0.8332	0.8268
0.2322	4.0	1876	0.3956	0.8445	0.8281	0.8923	0.7596	0.8677	0.7924	0.8379	0.8384	0.8371	0.8384	0.8301
0.3181	5.0	2345	0.4542	0.8565	0.8077	0.8721	0.7862	0.8642	0.7968	0.8367	0.8372	0.8368	0.8372	0.8305

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1