| | --- |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: XLM_temporal_expression_normalization |
| | results: [] |
| | language: |
| | - es |
| | - en |
| | - it |
| | - fr |
| | - eu |
| | --- |
| | |
| | # XLM_normalization_BEST_MODEL |
| | |
| | This model was trained over the XLM-Large model for temporal expression normalization as a result of the paper "A Novel Methodology for Enhancing |
| | Cross-Language and Domain Adaptability in Temporal Expression Normalization" |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | This model requires from extra post-processing. The proper code can be found at "https://github.com/asdc-s5/Temporal-expression-normalization-with-fill-mask" |
| | |
| | ## Training and evaluation data |
| | |
| | All the information about training, evaluation and benchmarking can be found in the paper "A Novel Methodology for Enhancing |
| | Cross-Language and Domain Adaptability in Temporal Expression Normalization" |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 8e-05 |
| | - train_batch_size: 20 |
| | - eval_batch_size: 20 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 3 |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.1+cu121 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
