---
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