aroot / eng-fra-wsample.32a

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30-day runs: 4
Model's Last Updated: July 06 2023
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Introduction of eng-fra-wsample.32a

Model Details of eng-fra-wsample.32a

eng-fra-wsample.32a

This model is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1237
  • Bleu: 32.9191
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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3
  • mixed_precision_training: Native AMP
Training results
Framework versions
  • Transformers 4.26.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3

Runs of aroot eng-fra-wsample.32a on huggingface.co

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More Information About eng-fra-wsample.32a huggingface.co Model

eng-fra-wsample.32a huggingface.co

eng-fra-wsample.32a huggingface.co is an AI model on huggingface.co that provides eng-fra-wsample.32a's model effect (), which can be used instantly with this aroot eng-fra-wsample.32a model. huggingface.co supports a free trial of the eng-fra-wsample.32a model, and also provides paid use of the eng-fra-wsample.32a. Support call eng-fra-wsample.32a model through api, including Node.js, Python, http.

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eng-fra-wsample.32a install

eng-fra-wsample.32a is an open source model from GitHub that offers a free installation service, and any user can find eng-fra-wsample.32a on GitHub to install. At the same time, huggingface.co provides the effect of eng-fra-wsample.32a install, users can directly use eng-fra-wsample.32a installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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