README.md

metadata

license: mit
base_model: xlm-roberta-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: xlm-roberta-base-finetuned-detests24
    results: []

xlm-roberta-base-finetuned-detests24

This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0941
• Accuracy: 0.8151
• F1-score: 0.7439
• Precision: 0.7380
• Recall: 0.7509
• Auc: 0.7509

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
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1-score	Precision	Recall	Auc
0.4432	1.0	153	0.4079	0.8298	0.7158	0.7778	0.6893	0.6893
0.4326	2.0	306	0.5061	0.7447	0.7078	0.7052	0.7840	0.7840
0.2533	3.0	459	0.5227	0.7676	0.7195	0.7070	0.7709	0.7709
0.3354	4.0	612	0.5113	0.8347	0.7689	0.7645	0.7737	0.7737
0.2157	5.0	765	0.8228	0.8020	0.7484	0.7321	0.7830	0.7830
0.1815	6.0	918	0.9407	0.8036	0.7528	0.7359	0.7917	0.7917
0.0829	7.0	1071	0.9539	0.8363	0.7648	0.7676	0.7621	0.7621
0.1077	8.0	1224	0.9649	0.8200	0.7501	0.7445	0.7566	0.7566
0.0473	9.0	1377	1.0557	0.8200	0.7439	0.7439	0.7439	0.7439
0.0632	10.0	1530	1.0941	0.8151	0.7439	0.7380	0.7509	0.7509

Framework versions

• Transformers 4.37.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.1