| license: mit | |
| base_model: xlm-roberta-base | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - udpos28 | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| model-index: | |
| - name: 1a5e2b8e | |
| results: | |
| - task: | |
| name: Token Classification | |
| type: token-classification | |
| dataset: | |
| name: udpos28 | |
| type: udpos28 | |
| config: te | |
| split: validation | |
| args: te | |
| metrics: | |
| - name: Precision | |
| type: precision | |
| value: 0.894336015358501 | |
| - name: Recall | |
| type: recall | |
| value: 0.8576779328683283 | |
| - name: F1 | |
| type: f1 | |
| value: 0.8680916339670367 | |
| - name: Accuracy | |
| type: accuracy | |
| value: 0.947129909365559 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # 1a5e2b8e | |
| This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the udpos28 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.3219 | |
| - Precision: 0.8943 | |
| - Recall: 0.8577 | |
| - F1: 0.8681 | |
| - Accuracy: 0.9471 | |
| ## 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 | |
| - training_steps: 1000 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.0423 | 7.58 | 1000 | 0.3219 | 0.8943 | 0.8577 | 0.8681 | 0.9471 | | |
| ### Framework versions | |
| - Transformers 4.36.0.dev0 | |
| - Pytorch 2.1.0+cu118 | |
| - Datasets 2.15.0 | |
| - Tokenizers 0.15.0 | |