xlm_r_base-finetuned_after_mrp-v2-different-deluge-11 – AI Model by haturusinghe | AlphaNeural AI

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haturusinghe/
xlm_r_base-finetuned_after_mrp-v2-different-deluge-11

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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-v2-different-deluge-11

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: 0.4623
Precision 0: 0.8620
Precision 1: 0.8034
Recall 0: 0.8667
Recall 1: 0.7970
F1 0: 0.8643
F1 1: 0.8002
Precision Weighted: 0.8382
Recall Weighted: 0.8384
F1 Weighted: 0.8383
F1 Macro: 0.8323

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	F1 Macro
0.5617	1.0	469	0.4641	0.7967	0.8424	0.9158	0.6581	0.8521	0.7389	0.8153	0.8112	0.8062	0.7955
0.3872	2.0	938	0.4605	0.8990	0.6649	0.6949	0.8857	0.7839	0.7596	0.8039	0.7724	0.7740	0.7718
0.3888	3.0	1407	0.3794	0.8290	0.8341	0.9010	0.7281	0.8635	0.7775	0.8311	0.8308	0.8286	0.8205
0.2986	4.0	1876	0.3995	0.8507	0.8186	0.8828	0.7734	0.8665	0.7953	0.8377	0.8384	0.8376	0.8309
0.2518	5.0	2345	0.4623	0.8620	0.8034	0.8667	0.7970	0.8643	0.8002	0.8382	0.8384	0.8383	0.8323

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1