--- license: mit base_model: xlm-roberta-large tags: - generated_from_trainer datasets: - masakhaner2 metrics: - f1 model-index: - name: xlm-roberta-large-finetuned-wolof results: - task: name: Token Classification type: token-classification dataset: name: masakhaner2 type: masakhaner2 config: wol split: validation args: wol metrics: - name: F1 type: f1 value: 0.8361858190709046 --- # xlm-roberta-large-finetuned-wolof This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the masakhaner2 dataset. It achieves the following results on the evaluation set: - Loss: 0.3771 - F1: 0.8362 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.7475 | 1.0 | 739 | 0.4053 | 0.6989 | | 0.3252 | 2.0 | 1478 | 0.3251 | 0.6653 | | 0.1983 | 3.0 | 2217 | 0.3703 | 0.8234 | | 0.1139 | 4.0 | 2956 | 0.3170 | 0.8299 | | 0.052 | 5.0 | 3695 | 0.3771 | 0.8362 | ### Framework versions - Transformers 4.33.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3