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asdc/
XLM_Temporal_Expression_Normalization

Name: XLM_Temporal_Expression_Normalization
Rating: 2.5 (15 reviews)

generated_from_trainer

autotrain_compatible

endpoints_compatible

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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

asdc/XLM_Temporal_Expression_Normalization