Xlm Mlm 17 1280

🚀 xlm-mlm-17-1280

The XLM model trained on text in 17 languages, using a masked language modeling (MLM) objective.

🚀 Quick Start

To get started with the xlm-mlm-17-1280 model, more information is needed. You can refer to the ipython notebook in the associated GitHub repo for examples.

✨ Features

• Multilingual Support: Trained on text in 17 languages, enabling cross - lingual tasks.
• Masked Language Modeling: Can be used for masked language modeling tasks.

📦 Installation

No installation steps were provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples were provided in the original document, so this section is skipped.

📚 Documentation

Model Details

xlm-mlm-17-1280 is the XLM model proposed in Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau. It is a transformer pretrained using a masked language modeling (MLM) objective and trained on text in 17 languages.

Uses

Direct Use

The model is a language model and can be used for masked language modeling.

Downstream Use

To learn more about this task and potential downstream uses, see the Hugging Face fill mask docs and the Hugging Face Multilingual Models for Inference docs. Also see the associated paper.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

⚠️ Important Note

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Training

This model is the XLM model trained on text in 17 languages. The preprocessing included tokenization and byte - pair - encoding. See the GitHub repo and the associated paper for further details on the training data and training procedure.

Conneau et al. (2020) report that this model has 16 layers, 1280 hidden states, 16 attention heads, and the dimension of the feed - forward layer is 1520. The vocabulary size is 200k and the total number of parameters is 570M (see Table 7).

Evaluation

Testing Data, Factors & Metrics

The model developers evaluated the model on the XNLI cross - lingual classification task (see the XNLI data card for more details on XNLI) using the metric of test accuracy. See the GitHub Repo for further details on the testing data, factors and metrics.

Results

For xlm-mlm-17-1280, the test accuracy on the XNLI cross - lingual classification task in English (en), Spanish (es), German (de), Arabic (ar), and Chinese (zh):

See the GitHub repo for further details.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Technical Specifications

Conneau et al. (2020) report that this model has 16 layers, 1280 hidden states, 16 attention heads, and the dimension of the feed - forward layer is 1520. The vocabulary size is 200k and the total number of parameters is 570M (see Table 7).

Citation

BibTeX:

@article{lample2019cross,
  title={Cross-lingual language model pretraining},
  author={Lample, Guillaume and Conneau, Alexis},
  journal={arXiv preprint arXiv:1901.07291},
  year={2019}
}

APA:

• Lample, G., & Conneau, A. (2019). Cross - lingual language model pretraining. arXiv preprint arXiv:1901.07291.

Model Card Authors

This model card was written by the team at Hugging Face.