FULL-12epoch-XLMRoBERTa-finetuned-CEFR_ner-60000news

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: 0.0687
• Accuracy: 0.3222
• Precision: 0.6358
• Recall: 0.8475
• F1: 0.6074

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.087	1.0	1563	0.0917	0.3188	0.7203	0.8273	0.6562
0.08	2.0	3126	0.0747	0.3204	0.7147	0.8331	0.6569
0.0666	3.0	4689	0.0691	0.3211	0.7195	0.8376	0.6624
0.0583	4.0	6252	0.0667	0.3213	0.6889	0.8419	0.6433
0.0514	5.0	7815	0.0650	0.3216	0.7043	0.8433	0.6543
0.0463	6.0	9378	0.0642	0.3219	0.6780	0.8444	0.6362
0.0421	7.0	10941	0.0635	0.3220	0.6759	0.8458	0.6354
0.0385	8.0	12504	0.0644	0.3220	0.6330	0.8470	0.6066
0.0358	9.0	14067	0.0670	0.3221	0.6368	0.8467	0.6068
0.0331	10.0	15630	0.0676	0.3222	0.6442	0.8468	0.6130
0.0309	11.0	17193	0.0680	0.3222	0.6377	0.8472	0.6092
0.0298	12.0	18756	0.0687	0.3222	0.6358	0.8475	0.6074

Framework versions

• Transformers 4.41.2
• Pytorch 2.2.1
• Datasets 2.19.2
• Tokenizers 0.19.1