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# Our cross-lingual example checkpoint.


## Model description

The model is initialized from our monolingual model, and is trained on parallel data (205000 steps) <-> AllNLI (2600 steps), going back and forth for three rounds. This model is the last round checkpoint. We recommend using it with A100 GPU, aligning with training.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 128
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2600


### Framework versions

- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.14.7.dev0
- Tokenizers 0.15.1

{}
transformers pytorch pixel text-classification autotrain_compatible endpoints_compatible region:us