| --- |
| license: mit |
| base_model: xlm-roberta-large |
| tags: |
| - generated_from_trainer |
| datasets: |
| - uner_all |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: uner_all |
| results: |
| - task: |
| name: Token Classification |
| type: token-classification |
| dataset: |
| name: uner_all |
| type: uner_all |
| config: default |
| split: None |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.8566170026292725 |
| - name: Recall |
| type: recall |
| value: 0.8522846180676665 |
| - name: F1 |
| type: f1 |
| value: 0.8544453186467348 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9842612991521463 |
| --- |
| |
| <!-- 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. --> |
|
|
| # uner_all |
| |
| This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the uner_all dataset. |
| The uner_all dataset combines all training datasets in UNER. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1180 |
| - Precision: 0.8566 |
| - Recall: 0.8523 |
| - F1: 0.8544 |
| - Accuracy: 0.9843 |
| |
| ## 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: 3e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 5.0 |
| |
| ### Training results |
| |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.31.0 |
| - Pytorch 1.10.1+cu113 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
